David Camarillo
Associate Professor of Bioengineering and, by courtesy, of Neurosurgery and of Mechanical Engineering
Web page: http://www.camlab.stanford.edu
Bio
David B. Camarillo is Associate Professor of Bioengineering, (by courtesy) Mechanical Engineering and Neurosurgery at Stanford University. Dr. Camarillo holds a B.S.E in Mechanical and Aerospace Engineering from Princeton University, a Ph.D. in Mechanical Engineering from Stanford University and completed postdoctoral fellowships in Biophysics at the UCSF and Biodesign Innovation at Stanford. Dr. Camarillo worked in the surgical robotics industry at Intuitive Surgical and Hansen Medical, before launching his laboratory at Stanford in 2012. His current research focuses on precision human measurement for multiple clinical and physiological areas including the brain, heart, lungs, and reproductive system. Dr. Camarillo has been awarded the Hellman Fellowship, the Office of Naval Research Young Investigator Program award, among other honors including multiple best paper awards in brain injury and robotic surgery. His research has been funded by the NIH, NSF, DoD, as well as corporations and private philanthropy. His lab’s research has been featured on NPR, the New York Times, The Washington Post, Science News, ESPN, and TED.com as well as other media outlets aimed at education of the public.
Academic Appointments
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Associate Professor, Bioengineering
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Associate Professor (By courtesy), Neurosurgery
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Associate Professor (By courtesy), Mechanical Engineering
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Member, Bio-X
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Member, Wu Tsai Neurosciences Institute
Boards, Advisory Committees, Professional Organizations
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Member, Wu Tsai Human Performance Alliance (2021 - Present)
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Member, Stanford Center for Precision Mental Health & Wellness (PMHW) (2021 - Present)
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Invited Session Organizer, World Congress of Biomechanics (2018 - 2018)
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Co-Chair, NIH-NINDS Common Data Elements Working Committee on Sensors (2017 - Present)
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Member, Scientific Advisory Committee for National Operating Committee on Standards in Athletic Equipment (NOCSAE) (2017 - Present)
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Member, American Society of Mechanical Engineers (ASME) Bioengineering Division Diversity Committee (2017 - Present)
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Organizing Committee, NIH/DoD sensors workshop at National Neurotrauma Symposium (2017 - 2017)
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Member, International Society of Biomechanics (2017 - Present)
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Member, American Society of Reproductive Medicine (ASRM) (2015 - 2018)
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Member, Biomedical Engineering Society (BMES) (2014 - Present)
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Invited Session Organizer, World Congress of Biomechanics (2014 - 2014)
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Member, National Neurotrauma Society (NNS) (2014 - Present)
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Member, American Society of Mechanical Engineers (ASME) (2012 - Present)
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Member, Program in Biodesign (2012 - Present)
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Member, Institute of Electrical and Electronics Engineers (IEEE) (2005 - Present)
Professional Education
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PhD, Stanford University, Mechanical Engineering (2008)
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MS, Stanford University, Mechanical Engineering (2003)
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BSE, Princeton, Mechanical and Aerospace Engineering (2001)
Clinical Trials
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Correlation Between Oocyte and Embryo Mechanical Properties on Embryo Development and Clinical Pregnancy After In Vitro Fertilization
Not Recruiting
The purpose of this study is to determine whether oocyte and embryo mechanical properties measured during in vitro fertilization can predict embryo development outcomes and clinical pregnancy.
Stanford is currently not accepting patients for this trial. For more information, please contact Valerie Baker, MD, 650-723-3861.
2024-25 Courses
- Bioengineering Departmental Research Colloquium
BIOE 393 (Aut) - Introduction to Biomechanics and Mechanobiology
BIOE 282, ME 283 (Spr) -
Independent Studies (4)
- Bioengineering Problems and Experimental Investigation
BIOE 191 (Aut, Win, Spr) - Directed Investigation
BIOE 392 (Aut, Win, Spr) - Directed Study
BIOE 391 (Aut, Win, Spr) - Ph.D. Research Rotation
ME 398 (Aut, Spr)
- Bioengineering Problems and Experimental Investigation
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Prior Year Courses
2023-24 Courses
- Bioengineering Departmental Research Colloquium
BIOE 393 (Aut) - Introduction to Biomechanics and Mechanobiology
BIOE 282, ME 283 (Win) - Promoting Effective and Equitable Teaching in Bioengineering
BIOE 296 (Spr)
2022-23 Courses
- Introduction to Biomechanics and Mechanobiology
BIOE 282, ME 283 (Spr) - Promoting Effective and Equitable Teaching in Bioengineering
BIOE 296 (Spr)
2021-22 Courses
- Needs Finding in Healthcare
BIOE 10SC (Sum)
- Bioengineering Departmental Research Colloquium
Stanford Advisees
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Postdoctoral Faculty Sponsor
Vahidullah Tac -
Doctoral Dissertation Advisor (AC)
Jessica Towns -
Doctoral (Program)
Rong Chi, Michelle Joyce, Megan Martin, Wally Niu, Jack Rao
All Publications
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AI-Based Denoising of Head Impact Kinematics Measurements With Convolutional Neural Network for Traumatic Brain Injury Prediction
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2024; 71 (9): 2759-2770
Abstract
Wearable devices are developed to measure head impact kinematics but are intrinsically noisy because of the imperfect interface with human bodies. This study aimed to improve the head impact kinematics measurements obtained from instrumented mouthguards using deep learning to enhance traumatic brain injury (TBI) risk monitoring.We developed one-dimensional convolutional neural network (1D-CNN) models to denoise mouthguard kinematics measurements for tri-axial linear acceleration and tri-axial angular velocity from 163 laboratory dummy head impacts. The performance of the denoising models was evaluated on three levels: kinematics, brain injury criteria, and tissue-level strain and strain rate. Additionally, we performed a blind test on an on-field dataset of 118 college football impacts and a test on 413 post-mortem human subject (PMHS) impacts.On the dummy head impacts, the denoised kinematics showed better correlation with reference kinematics, with relative reductions of 36% for pointwise root mean squared error and 56% for peak absolute error. Absolute errors in six brain injury criteria were reduced by a mean of 82%. For maximum principal strain and maximum principal strain rate, the mean error reduction was 35% and 69%, respectively. On the PMHS impacts, similar denoising effects were observed and the peak kinematics after denoising were more accurate (relative error reduction for 10% noisiest impacts was 75.6%).The 1D-CNN denoising models effectively reduced errors in mouthguard-derived kinematics measurements on dummy and PMHS impacts.This study provides a novel approach for denoising head kinematics measurements in dummy and PMHS impacts, which can be further validated on more real-human kinematics data before real-world applications.
View details for DOI 10.1109/TBME.2024.3392537
View details for Web of Science ID 001297719500013
View details for PubMedID 38683703
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Brain Deformation Estimation With Transfer Learning for Head Impact Datasets Across Impact Types
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2024; 71 (6): 1853-1863
Abstract
The machine-learning head model (MLHM) to accelerate the calculation of brain strain and strain rate, which are the predictors for traumatic brain injury (TBI), but the model accuracy was found to decrease sharply when the training/test datasets were from different head impacts types (i.e., car crash, college football), which limits the applicability of MLHMs to different types of head impacts and sports. Particularly, small sizes of target dataset for specific impact types with tens of impacts may not be enough to train an accurate impact-type-specific MLHM.To overcome this, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR).The strategies were tested on American football (338), mixed martial arts (457), reconstructed car crash (48) and reconstructed American football (36) and we found that the MLHMs developed with transfer learning are significantly more accurate in estimating MPS and MPSR than other models, with a mean absolute error (MAE) smaller than 0.03 in predicting MPS and smaller than [Formula: see text] in predicting MPSR on all target impact datasets. High performance in concussion detection was observed based on the MPS and MPSR estimated by the transfer-learning-based models.The MLHMs can be applied to various head impact types for rapidly and accurately calculating brain strain and strain rate.This study enables developing MLHMs for the head impact type with limited availability of data, and will accelerate the applications of MLHMs.
View details for DOI 10.1109/TBME.2024.3354192
View details for Web of Science ID 001230139500007
View details for PubMedID 38224520
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Development and evaluation of a usable blastocyst predictive model using the biomechanical properties of human oocytes.
PloS one
2024; 19 (5): e0299602
Abstract
The purposes of this study were to determine whether biomechanical properties of mature oocytes could predict usable blastocyst formation better than morphological information or maternal factors, and to demonstrate the safety of the aspiration measurement procedure used to determine the biomechanical properties of oocytes.A prospective split cohort study was conducted with patients from two IVF clinics who underwent in vitro fertilization. Each patient's oocytes were randomly divided into a measurement group and a control group. The aspiration depth into a micropipette was measured, and the biomechanical properties were derived. Oocyte fertilization, day 3 morphology, and blastocyst development were observed and compared between measured and unmeasured cohorts. A predictive classifier was trained to predict usable blastocyst formation and compared to the predictions of four experienced embryologists.68 patients and their corresponding 1252 oocytes were included in the study. In the safety analyses, there was no significant difference between the cohorts for fertilization, while the day 3 and 5 embryo development were not negatively affected. Four embryologists predicted usable blastocyst development based on oocyte morphology with an average accuracy of 44% while the predictive classifier achieved an accuracy of 71%. Retaining the variables necessary for normal fertilization, only data from successfully fertilized oocytes were used, resulting in a classifier an accuracy of 81%.To date, there is no standard guideline or technique to aid in the selection of oocytes that have a higher likelihood of developing into usable blastocysts, which are chosen for transfer or vitrification. This study provides a comprehensive workflow of extracting biomechanical properties and building a predictive classifier using these properties to predict mature oocytes' developmental potential. The classifier has greater accuracy in predicting the formation of usable blastocysts than the predictions provided by morphological information or maternal factors. The measurement procedure did not negatively affect embryo culture outcomes. While further analysis is necessary, this study shows the potential of using biomechanical properties of oocytes to predict embryo developmental outcomes.
View details for DOI 10.1371/journal.pone.0299602
View details for PubMedID 38696439
View details for PubMedCentralID PMC11065297
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Adaptive Machine Learning Head Model Across Different Head Impact Types Using Unsupervised Domain Adaptation and Generative Adversarial Networks
IEEE SENSORS JOURNAL
2024; 24 (5): 7097-7106
View details for DOI 10.1109/JSEN.2023.3349213
View details for Web of Science ID 001280052100001
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A wearable hydraulic shock absorber with efficient energy dissipation
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
2024; 270
View details for DOI 10.1016/j.ijmecsci.2024.109097
View details for Web of Science ID 001188127600001
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Differences In Strain Distribution Across Brain Regions In Non-concussive Collegiate Football Head Impacts
LIPPINCOTT WILLIAMS & WILKINS. 2023: 416
View details for Web of Science ID 001158156601290
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Microstructural Alterations in Tract Development in College Football and Volleyball Players: A Longitudinal Diffusion MRI Study.
Neurology
2023
Abstract
BACKGROUND AND OBJECTIVES: Repeated impacts in high-contact sports like American football can affect the brain's microstructure, which can be studied using diffusion MRI. Most imaging studies are cross-sectional, do not include low-contact players as controls, or lack advanced tract-specific microstructural metrics. We aimed to investigate longitudinal changes in high-contact collegiate athletes compared to low-contact controls using advanced diffusion MRI and automated fiber quantification.METHODS: We examined brain microstructure in high-contact (football) and low-contact (volleyball) collegiate athletes with up to 4 years of follow-up. Inclusion criteria included university and team enrollment. Exclusion criteria included history of neurosurgery, severe brain injury, major neurologic or substance abuse disorder. We investigated diffusion metrics along the length of tracts using nested linear mixed-effects models to ascertain the acute and chronic effects of sub-concussive and concussive impacts, and associations between diffusion changes with clinical, behavioral, and sports-related measures.RESULTS: Forty-nine football and twenty-four volleyball players (271 total scans) were included. Football players had significantly divergent trajectories in multiple microstructural metrics and tracts. Longitudinal increases in fractional anisotropy and axonal water fraction, and decreases in radial/mean diffusivity and orientation dispersion index, were present in volleyball but absent in football players (all findings |T-statistic|> 3.5, p-value < .0001). This pattern was present in the callosum forceps minor, superior longitudinal fasciculus, thalamic radiation, and cingulum hippocampus. Longitudinal differences were more prominent and observed in more tracts in concussed football players (n=24, |T|> 3.6, p < .0001). An analysis of immediate-post concussion scans (n=12) demonstrated a transient localized increase in axial diffusivity, mean/radial kurtosis in the uncinate and cingulum hippocampus (|T| > 3.7, p < .0001). Finally, within football players, those with high position-based impact risk demonstrated increased intra-cellular volume fraction longitudinally (T = 3.6, p < .0001).DISCUSSION: The observed longitudinal changes seen in football, and especially concussed athletes, could reveal diminished myelination, altered axonal calibers, or depressed pruning processes leading to a static, non-decreasing axonal dispersion. This prospective longitudinal study demonstrates divergent tract-specific trajectories of brain microstructure, possibly reflecting a concussive and repeated sub-concussive impact-related alteration of white matter development in football athletes.
View details for DOI 10.1212/WNL.0000000000207543
View details for PubMedID 37479529
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Longitudinal alterations of cerebral blood flow in high-contact sports.
Annals of neurology
2023
Abstract
Repetitive head trauma is common in high-contact sports. Cerebral blood flow (CBF) can measure changes in brain perfusion that could indicate injury. Longitudinal studies with a control group are necessary to account for interindividual and developmental effects. We investigated whether exposure to head impacts causes longitudinal CBF changes.We prospectively studied 63 American football (high-contact cohort) and 34 volleyball (low-contact controls) male collegiate athletes, tracking CBF using 3D-pseudo-continuous arterial-spin-labeling (ASL) MRI for up to four years. Regional relative CBF (rCBF, normalized to cerebellar CBF) was computed after co-registering to T1-weighted images. A linear-mixed-effects model assessed the relationship of rCBF to sport, time, and their interaction. Within football players, we modeled rCBF against position-based head impact risk and baseline SCAT (Standardized Concussion Assessment Tool) score. Additionally, we evaluated early (1-5 days) and delayed (3-6 months) post-concussion rCBF changes (in-study concussion).Supratentorial gray matter rCBF declined in football compared to volleyball (sport-time interaction p=0.012), with a strong effect in the parietal lobe (p=0.002). Football players with higher position-based impact-risk had lower occipital rCBF over time (interaction p=0.005), while players with lower baseline SCAT score (worse performance) had relatively decreased rCBF in the cingulate-insula over time (interaction effect: p=0.007). Both cohorts showed a left-right rCBF asymmetry that decreased over time. Football players with an in-study concussion exhibited an early increase in occipital lobe rCBF (p=0.0166).These results suggest head impacts may result in an early increase in rCBF, but cumulatively a long-term decrease in rCBF. This article is protected by copyright. All rights reserved.
View details for DOI 10.1002/ana.26718
View details for PubMedID 37306544
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Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptation.
ArXiv
2023
Abstract
Machine learning head models (MLHMs) are developed to estimate brain deformation for early detection of traumatic brain injury (TBI). However, the overfitting to simulated impacts and the lack of generalizability caused by distributional shift of different head impact datasets hinders the broad clinical applications of current MLHMs. We propose brain deformation estimators that integrates unsupervised domain adaptation with a deep neural network to predict whole-brain maximum principal strain (MPS) and MPS rate (MPSR). With 12,780 simulated head impacts, we performed unsupervised domain adaptation on on-field head impacts from 302 college football (CF) impacts and 457 mixed martial arts (MMA) impacts using domain regularized component analysis (DRCA) and cycle-GAN-based methods. The new model improved the MPS/MPSR estimation accuracy, with the DRCA method significantly outperforming other domain adaptation methods in prediction accuracy (p<0.001): MPS RMSE: 0.027 (CF) and 0.037 (MMA); MPSR RMSE: 7.159 (CF) and 13.022 (MMA). On another two hold-out testsets with 195 college football impacts and 260 boxing impacts, the DRCA model significantly outperformed the baseline model without domain adaptation in MPS and MPSR estimation accuracy (p<0.001). The DRCA domain adaptation reduces the MPS/MPSR estimation error to be well below TBI thresholds, enabling accurate brain deformation estimation to detect TBI in future clinical applications.
View details for PubMedID 37332565
View details for PubMedCentralID PMC10274939
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Padded Helmet Shell Covers in American Football: A Comprehensive Laboratory Evaluation with Preliminary On-Field Findings.
Annals of biomedical engineering
2023
Abstract
Protective headgear effects measured in the laboratory may not always translate to the field. In this study, we evaluated the impact attenuation capabilities of a commercially available padded helmet shell cover in the laboratory andon the field. In the laboratory, we evaluated the padded helmet shell cover's efficacy in attenuating impact magnitude across six impact locations and three impact velocities when equipped to three different helmet models. In a preliminary on-field investigation, we used instrumented mouthguards to monitor head impact magnitude in collegiate linebackers during practice sessions while not wearing the padded helmet shell covers (i.e., bare helmets) for one season and whilst wearing the padded helmet shell covers for another season. The addition of the padded helmet shell cover was effective in attenuating the magnitude of angular head accelerations and two brain injury risk metrics (DAMAGE, HARM) across most laboratory impact conditions, but did not significantly attenuate linear head accelerations for all helmets. Overall, HARM values were reduced in laboratory impact tests by an average of 25% at 3.5m/s (range: 9.7 to 39.6%), 18% at 5.5m/s (range: -5.5 to 40.5%), and 10% at 7.4m/s (range: -6.0 to 31.0%). However, on the field, no significant differences in any measure of head impact magnitude were observed between the bare helmet impacts and padded helmet impacts. Further laboratory tests were conducted to evaluate the ability of the padded helmet shell cover to maintain its performance after exposure to repeated, successive impacts and across a range of temperatures. This research provides a detailed assessment of padded helmet shell covers and supports the continuation of in vivo helmet research to validate laboratory testing results.
View details for DOI 10.1007/s10439-023-03169-2
View details for PubMedID 36917295
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Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics.
Journal of sport and health science
2023
Abstract
Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo, and the characteristics of different types of impacts are not well studied. We investigated the spectral characteristics of different head impact types with kinematics classification.Data was analyzed from 3262 head impacts from lab reconstruction, American football, mixed martial arts, and publicly available car crash data. A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football, car crash, mixed martial arts). To test the classifier robustness, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards. Finally, with the classifier, type-specific, nearest-neighbor regression models were built for brain strain.The classifier reached a median accuracy of 96% over 1000 random partitions of training and test sets. The most important features in the classification included both low-frequency and high-frequency features, both linear acceleration features and angular velocity features. Different head impact types had different distributions of spectral densities in low- and high-frequency ranges (e.g., the spectral densities of MMA impacts were higher in the high-frequency range than in the low-frequency range). The type-specific regression showed a generally higher R2-value than baseline models without classification.The machine-learning-based classifier enables a better understanding of the impact kinematics spectral density in different sports, and it can be applied to evaluate the quality of impact-simulation systems and on-field data augmentation.
View details for DOI 10.1016/j.jshs.2023.03.003
View details for PubMedID 36921692
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Correction: Identifying Factors Associated with Head Impact Kinematics and Brain Strain in High School American Football via Instrumented Mouthguards.
Annals of biomedical engineering
2023
View details for DOI 10.1007/s10439-022-03132-7
View details for PubMedID 36593307
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Finite element evaluation of an American football helmet featuring liquid shock absorbers for protecting against concussive and subconcussive head impacts.
Frontiers in bioengineering and biotechnology
2023; 11: 1160387
Abstract
Introduction: Concern has grown over the potential long-term effects of repeated head impacts and concussions in American football. Recent advances in impact engineering have yielded the development of soft, collapsible, liquid shock absorbers, which have demonstrated the ability to dramatically attenuate impact forces relative to existing helmet shock absorbers. Methods: To further explore how liquid shock absorbers can improve the efficacy of an American football helmet, we developed and optimized a finite element (FE) helmet model including 21 liquid shock absorbers spread out throughout the helmet. Using FE models of an anthropomorphic test headform and linear impactor, a previously published impact test protocol representative of concussive National Football League impacts (six impact locations, three velocities) was performed on the liquid FE helmet model and four existing FE helmet models. We also evaluated the helmets at three lower impact velocities representative of subconcussive football impacts. Head kinematics were recorded for each impact and used to compute the Head Acceleration Response Metric (HARM), a metric factoring in both linear and angular head kinematics and used to evaluate helmet performance. The head kinematics were also input to a FE model of the head and brain to calculate the resulting brain strain from each impact. Results: The liquid helmet model yielded the lowest value of HARM at 33 of the 36 impact conditions, offering an average 33.0% (range: -37.5% to 56.0%) and 32.0% (range: -2.2% to 50.5%) reduction over the existing helmet models at each impact condition in the subconcussive and concussive tests, respectively. The liquid helmet had a Helmet Performance Score (calculated using a summation of HARM values weighted based on injury incidence data) of 0.71, compared to scores ranging from 1.07 - 1.21 from the other four FE helmet models. Resulting brain strains were also lower in the liquid helmet. Discussion: The results of this study demonstrate the promising ability of liquid shock absorbers to improve helmet safety performance and encourage the development of physical prototypes of helmets featuring this technology. The implications of the observed reductions on brain injury risk are discussed.
View details for DOI 10.3389/fbioe.2023.1160387
View details for PubMedID 37362208
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Using an Accelerated Undergraduate Needs Finding Course to Build Skills, Inspire Confidence, and Promote Interest in Health Technology Innovation.
Biomedical engineering education
2023; 3 (2): 319-329
Abstract
Many undergraduate educational experiences in biomedical design lack clinical immersion-based needs finding training for students. Convinced of the merits of this type of training for undergraduates, but unable to offer a quarter-long course due to faculty and administrative constraints, we developed an accelerated block-plan course, during which students were dedicated solely to our class for 3 weeks. The course focused on the earliest stages of the health technology innovation process-conducting effective clinical observations and performing comprehensive need research and screening. We grounded the course in experiential learning theory (with hands-on, collaborative, and immersive experiences) and constructivist learning theory (where students integrated prior knowledge with new material on need-driven innovation). This paper describes the design of this intensive block-plan course and the teaching methods intended to support the achievement of five learning objectives. We used pre- and post-course surveys to gather self-reported data about the effect of the course on student learning. Despite the accelerated format, we saw statistically significant gains for all but one sub-measure across the learning objectives. Our experience supports key benefits of the block-plan model, and the results indicate that specific course design choices were effective in achieving positive learning outcomes. These design decisions include (1) opportunities for students to practice observations before entering the clinical setting; (2) a framework for the curriculum that reinforced important concepts iteratively throughout the program; (3) balanced coverage of preparation, clinical immersion, and need research; (4) extensive faculty and peer coaching; and (5) providing hands-on prototyping opportunities while staying focused on need characterization rather than solution development. Based on our experience, we expect that this model is replicable across institutions with limited bandwidth to support clinical immersion opportunities.
View details for DOI 10.1007/s43683-023-00109-3
View details for PubMedID 37575216
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Laboratory And On-field Testing Of A Commercially Available Padded Helmet Cover
LIPPINCOTT WILLIAMS & WILKINS. 2022: 45
View details for Web of Science ID 000888056600127
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Piecewise Multivariate Linearity Between Kinematic Features and Cumulative Strain Damage Measure (CSDM) Across Different Types of Head Impacts.
Annals of biomedical engineering
2022
Abstract
In a previous study, we found that the relationship between brain strain and kinematic features cannot be described by a generalized linear model across different types of head impacts. In this study, we investigate if such a linear relationship exists when partitioning head impacts using a data-driven approach. We applied the K-means clustering method to partition 3161 impacts from various sources including simulation, college football, mixed martial arts, and car crashes. We found piecewise multivariate linearity between the cumulative strain damage (CSDM; assessed at the threshold of 0.15) and head kinematic features. Compared with the linear regression models without partition and the partition according to the types of head impacts, K-means-based data-driven partition showed significantly higher CSDM regression accuracy, which suggested the presence of piecewise multivariate linearity across types of head impacts. Additionally, we compared the piecewise linearity with the partitions based on individual features used in clustering. We found that the partition with maximum angular acceleration magnitude at 4706 rad/s2 led to the highest piecewise linearity. This study may contribute to an improved method for the rapid prediction of CSDM in the future.
View details for DOI 10.1007/s10439-022-03020-0
View details for PubMedID 35922726
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A REAL-TIME SYSTEM TO MONITOR BRAIN STRAIN TO DETECT DANGEROUS HEAD IMPACTS
MARY ANN LIEBERT, INC. 2022: A22
View details for Web of Science ID 000840122300077
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Find the spatial co-variation of brain deformation with principal component analysis.
IEEE transactions on bio-medical engineering
2022; PP
Abstract
Strain and strain rate are effective traumatic brain injury metrics. In finite element (FE) head model, thousands of elements were used to represent the spatial distribution of these metrics. Owing that these metrics are resulted from brain inertia, their spatial distribution can be represented in more concise pattern. Since head kinematic features and brain deformation vary largely across head impact types, we apply principal component analysis (PCA) to find the spatial co-variation of injury metrics (maximum principal strain (MPS), MPS rate (MPSR) and MPS ×MPSR) in four impact types: simulation, football, mixed martial arts and car crashes, and use the PCA to find patterns in these metrics and improve the machine learning head model (MLHM).We applied PCA to decompose the injury metrics for all impacts in each impact type, and investigate the spatial co-variation using the first principal component (PC1). Furthermore, we developed a MLHM to predict PC1 and then inverse-transform to predict for all brain elements. The accuracy, the model complexity and the size of training dataset of PCA-MLHM are compared with previous MLHM.PC1 explained >80% variance on the datasets. Based on PC1 coefficients, the corpus callosum and midbrain exhibit high variance on all datasets. Finally, the PCA-MLHM reduced model parameters by 74% with a similar MPS estimation accuracy.The brain injury metric in a dataset can be decomposed into mean components and PC1 with high explained variance.The spatial co-variation analysis enables better interpretation of the patterns in brain injury metrics. It also improves the efficiency of MLHM.
View details for DOI 10.1109/TBME.2022.3163230
View details for PubMedID 35349430
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Physics-Informed Machine Learning Improves Detection of Head Impacts.
Annals of biomedical engineering
2022
Abstract
In this work we present a new physics-informed machine learning model that can be used to analyze kinematic data from an instrumented mouthguard and detect impacts to the head. Monitoring player impacts is vitally important to understanding and protecting from injuries like concussion. Typically, to analyze this data, a combination of video analysis and sensor data is used to ascertain the recorded events are true impacts and not false positives. In fact, due to the nature of using wearable devices in sports, false positives vastly outnumber the true positives. Yet, manual video analysis is time-consuming. This imbalance leads traditional machine learning approaches to exhibit poor performance in both detecting true positives and preventing false negatives. Here, we show that by simulating head impacts numerically using a standard Finite Element head-neck model, a large dataset of synthetic impacts can be created to augment the gathered, verified, impact data from mouthguards. This combined physics-informed machine learning impact detector reported improved performance on test datasets compared to traditional impact detectors with negative predictive value and positive predictive values of 88 and 87% respectively. Consequently, this model reported the best results to date for an impact detection algorithm for American football, achieving an F1 score of 0.95. In addition, this physics-informed machine learning impact detector was able to accurately detect true and false impacts from a test dataset at a rate of 90% and 100% relative to a purely manual video analysis workflow. Saving over 12 h of manual video analysis for a modest dataset, at an overall accuracy of 92%, these results indicate that this model could be used in place of, or alongside, traditional video analysis to allow for larger scale and more efficient impact detection in sports such as American Football.
View details for DOI 10.1007/s10439-022-02911-6
View details for PubMedID 35303171
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The Presence of the Temporal Horn Exacerbates the Vulnerability of Hippocampus During Head Impacts.
Frontiers in bioengineering and biotechnology
2022; 10: 754344
Abstract
Hippocampal injury is common in traumatic brain injury (TBI) patients, but the underlying pathogenesis remains elusive. In this study, we hypothesize that the presence of the adjacent fluid-containing temporal horn exacerbates the biomechanical vulnerability of the hippocampus. Two finite element models of the human head were used to investigate this hypothesis, one with and one without the temporal horn, and both including a detailed hippocampal subfield delineation. A fluid-structure interaction coupling approach was used to simulate the brain-ventricle interface, in which the intraventricular cerebrospinal fluid was represented by an arbitrary Lagrangian-Eulerian multi-material formation to account for its fluid behavior. By comparing the response of these two models under identical loadings, the model that included the temporal horn predicted increased magnitudes of strain and strain rate in the hippocampus with respect to its counterpart without the temporal horn. This specifically affected cornu ammonis (CA) 1 (CA1), CA2/3, hippocampal tail, subiculum, and the adjacent amygdala and ventral diencephalon. These computational results suggest that the presence of the temporal horn exacerbate the vulnerability of the hippocampus, highlighting the mechanobiological dependency of the hippocampus on the temporal horn.
View details for DOI 10.3389/fbioe.2022.754344
View details for PubMedID 35392406
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A low-cost, highly functional, emergency use ventilator for the COVID-19 crisis.
PloS one
2022; 17 (3): e0266173
Abstract
Respiratory failure complicates most critically ill patients with COVID-19 and is characterized by heterogeneous pulmonary parenchymal involvement, profound hypoxemia and pulmonary vascular injury. The high incidence of COVID-19 related respiratory failure has exposed critical shortages in the supply of mechanical ventilators, and providers with the necessary skills to treat. Traditional mass-produced ventilators rely on an internal compressor and mixer to moderate and control the gas mixture delivered to a patient. However, the current emergency has energized the pursuit of alternative designs, enabling greater flexibility in supply chain, manufacturing, storage, and maintenance considerations. To achieve this, we hypothesized that using the medical gasses and flow interruption strategy would allow for a high performance, low cost, functional ventilator. A low-cost ventilator designed and built-in accordance with the Emergency Use guidance from the US Food and Drug Administration (FDA) is presented wherein pressurized medical grade gases enter the ventilator and time limited flow interruption determines the ventilator rate and tidal volume. This simple strategy obviates the need for many components needed in traditional ventilators, thereby dramatically shortening the time from storage to clinical deployment, increasing reliability, while still providing life-saving ventilatory support. The overall design philosophy and its applicability in this new crisis is described, followed by both bench top and animal testing results used to confirm the precision, safety and reliability of this low cost and novel approach to mechanical ventilation. The ventilator meets and exceeds the critical requirements included in the FDA emergency use guidelines. The ventilator has received emergency use authorization from the FDA.
View details for DOI 10.1371/journal.pone.0266173
View details for PubMedID 35353851
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Translational models of mild traumatic brain injury tissue biomechanics
Current Opinion in Biomedical Engineering
2022; 24
View details for DOI 10.1016/j.cobme.2022.100422
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Towards a comprehensive delineation of white matter tract-related deformation.
Journal of neurotrauma
2021
Abstract
Finite element (FE) models of the human head are valuable instruments to explore the mechanobiological pathway from external loading, localized brain response, and resultant injury risks. The injury predictability of these models depends on the use of effective criteria as injury predictors. The FE-derived normal deformation along white matter (WM) fiber tracts (i.e., tract-oriented strain) has recently been suggested as an appropriate predictor for axonal injury. However, the tract-oriented strain only represents a partial depiction of the WM fiber tract deformation. A comprehensive delineation of tract-related deformation may improve the injury predictability of the FE head model by delivering new tract-related criteria as injury predictors. Thus, the present study performed a theoretical strain analysis to comprehensively characterize the WM fiber tract deformation by relating the strain tensor of the WM element to its embedded fiber tract. Three new tract-related strains with exact analytical solutions were proposed, measuring the normal deformation perpendicular to the fiber tracts (i.e., tract-perpendicular strain), and shear deformation along and perpendicular to the fiber tracts (i.e., axial-shear strain and lateral-shear strain, respectively). The injury predictability of these three newly-proposed strain peaks along with the previously-used tract-oriented strain peak and maximum principal strain (MPS) were evaluated by simulating 151 impacts with known outcome (concussion or non-concussion). The results preliminarily showed that four tract-related strain peaks exhibited superior performance than MPS in discriminating concussion and non-concussion cases. This study presents a comprehensive quantification of WM tract-related deformation and advocates the use of orientation-dependent strains as criteria for injury prediction, which may ultimately contribute to an advanced mechanobiological understanding and enhanced computational predictability of brain injury.
View details for DOI 10.1089/neu.2021.0195
View details for PubMedID 34617451
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Identifying Factors Associated with Head Impact Kinematics and Brain Strain in High School American Football via Instrumented Mouthguards.
Annals of biomedical engineering
2021
Abstract
Repeated head impact exposure and concussions are common in American football. Identifying the factors associated with high magnitude impacts aids in informing sport policy changes, improvements to protective equipment, and better understanding of the brain's response to mechanical loading. Recently, the Stanford Instrumented Mouthguard (MiG2.0) has seen several improvements in its accuracy in measuring head kinematics and its ability to correctly differentiate between true head impact events and false positives. Using this device, the present study sought to identify factors (e.g., player position, helmet model, direction of head acceleration, etc.) that are associated with head impact kinematics and brain strain in high school American football athletes. 116 athletes were monitored over a total of 888 athlete exposures. 602 total impacts were captured and verified by the MiG2.0's validated impact detection algorithm. Peak values of linear acceleration, angular velocity, and angular acceleration were obtained from the mouthguard kinematics. The kinematics were also entered into a previously developed finite element model of the human brain to compute the 95th percentile maximum principal strain. Overall, impacts were (mean ± SD) 34.0 ± 24.3 g for peak linear acceleration, 22.2 ± 15.4 rad/s for peak angular velocity, 2979.4 ± 3030.4 rad/s2 for peak angular acceleration, and 0.262 ± 0.241 for 95th percentile maximum principal strain. Statistical analyses revealed that impacts resulting in Forward head accelerations had higher magnitudes of peak kinematics and brain strain than Lateral or Rearward impacts and that athletes in skill positions sustained impacts of greater magnitude than athletes in line positions. 95th percentile maximum principal strain was significantly lower in the observed cohort of high school football athletes than previous reports of collegiate football athletes. No differences in impact magnitude were observed in athletes with or without previous concussion history, in athletes wearing different helmet models, or in junior varsity or varsity athletes. This study presents novel information on head acceleration events and their resulting brain strain in high school American football from our advanced, validated method of measuring head kinematics via instrumented mouthguard technology.
View details for DOI 10.1007/s10439-021-02853-5
View details for PubMedID 34549342
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Neuroradiologic Evaluation of MRI in High-Contact Sports.
Frontiers in neurology
2021; 12: 701948
Abstract
Background and Purpose: Athletes participating in high-contact sports experience repeated head trauma. Anatomical findings, such as a cavum septum pellucidum, prominent CSF spaces, and hippocampal volume reductions, have been observed in cases of mild traumatic brain injury. The extent to which these neuroanatomical findings are associated with high-contact sports is unknown. The purpose of this study was to determine whether there are subtle neuroanatomic differences between athletes participating in high-contact sports compared to low-contact athletic controls. Materials and Methods: We performed longitudinal structural brain MRI scans in 63 football (high-contact) and 34 volleyball (low-contact control) male collegiate athletes with up to 4 years of follow-up, evaluating a total of 315 MRI scans. Board-certified neuroradiologists performed semi-quantitative visual analysis of neuroanatomic findings, including: cavum septum pellucidum type and size, extent of perivascular spaces, prominence of CSF spaces, white matter hyperintensities, arterial spin labeling perfusion asymmetries, fractional anisotropy holes, and hippocampal size. Results: At baseline, cavum septum pellucidum length was greater in football compared to volleyball controls (p = 0.02). All other comparisons were statistically equivalent after multiple comparison correction. Within football at baseline, the following trends that did not survive multiple comparison correction were observed: more years of prior football exposure exhibited a trend toward more perivascular spaces (p = 0.03 uncorrected), and lower baseline Standardized Concussion Assessment Tool scores toward more perivascular spaces (p = 0.02 uncorrected) and a smaller right hippocampal size (p = 0.02 uncorrected). Conclusion: Head impacts in high-contact sport (football) athletes may be associated with increased cavum septum pellucidum length compared to low-contact sport (volleyball) athletic controls. Other investigated neuroradiology metrics were generally equivalent between sports.
View details for DOI 10.3389/fneur.2021.701948
View details for PubMedID 34456852
View details for PubMedCentralID PMC8385770
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Neuroradiologic Evaluation of MRI in High-Contact Sports
FRONTIERS IN NEUROLOGY
2021; 12
View details for DOI 10.3389/fneur.2021.701948
View details for Web of Science ID 000685118200006
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Identifying Risk Factors For Head Impact Exposure In High School Football Using A Validated Instrumented Mouthguard
LIPPINCOTT WILLIAMS & WILKINS. 2021: 148
View details for Web of Science ID 000693128400457
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Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation.
Annals of biomedical engineering
2021
Abstract
Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of kinematics on the head, finite element (FE) models and various brain injury criteria based on different factors of these kinematics have been developed, but the contribution of different kinematic factors has not been comprehensively analyzed across different types of head impacts in a data-driven manner. To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in (1) the derivative order (angular velocity, angular acceleration, angular jerk), (2) the direction and (3) the power (e.g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events. Ordinary least squares regressions were built from kinematics factors to the 95% maximum principal strain (MPS95), and we compared zero-order correlation coefficients, structure coefficients, commonality analysis, and dominance analysis. The angular acceleration, the magnitude and the first power factors showed the highest predictive power for the majority of impacts including laboratory impacts, American football impacts, with few exceptions (angular velocity for MMA and NASCAR impacts). The predictive power of rotational kinematics about three directions (x: posterior-to-anterior, y: left-to-right, z: superior-to-inferior) of kinematics varied with different sports and types of head impacts.
View details for DOI 10.1007/s10439-021-02813-z
View details for PubMedID 34244908
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Time Window of Head Impact Kinematics Measurement for Calculation of Brain Strain and Strain Rate in American Football.
Annals of biomedical engineering
2021
Abstract
Wearable devices have been shown to effectively measure the head's movement during impacts in sports like American football. When a head impact occurs, the device is triggered to collect and save the kinematic measurements during a predefined time window. Then, based on the collected kinematics, finite element (FE) head models can calculate brain strain and strain rate, which are used to evaluate the risk of mild traumatic brain injury. To find a time window that can provide a sufficient duration of kinematics for FE analysis, we investigated 118 on-field video-confirmed football head impacts collected by the Stanford Instrumented Mouthguard. The simulation results based on the kinematics truncated to a shorter time window were compared with the original to determine the minimum time window needed for football. Because the individual differences in brain geometry influence these calculations, we included six representative brain geometries and found that larger brains need a longer time window of kinematics for accurate calculation. Among the different sizes of brains, a pre-trigger time of 40ms and a post-trigger time of 70ms were found to yield calculations of brain strain and strain rate that were not significantly different from calculations using the original 200ms time window recorded by the mouthguard. Therefore, approximately 110ms is recommended for complete modeling of impacts for football.
View details for DOI 10.1007/s10439-021-02821-z
View details for PubMedID 34231091
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Collapsible fluid-filled fabric shock absorber with constant force
JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES
2021
View details for DOI 10.1177/1045389X211023578
View details for Web of Science ID 000682131700001
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A new open-access platform for measuring and sharing mTBI data.
Scientific reports
2021; 11 (1): 7501
Abstract
Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the use of wearable sensors and neurological testing. However, as the number of institutions operating these studies grows, there is a growing need for a platform to share these data to facilitate our understanding of concussion mechanisms and aid in the development of suitable diagnostic tools. To that end, this paper puts forth two contributions: (1) a centralized, open-access platform for storing and sharing head impact data, in collaboration with the Federal Interagency Traumatic Brain Injury Research informatics system (FITBIR), and (2) a deep learning impact detection algorithm (MiGNet) to differentiate between true head impacts and false positives for the previously biomechanically validated instrumented mouthguard sensor (MiG2.0), all of which easily interfaces with FITBIR. We report 96% accuracy using MiGNet, based on a neural network model, improving on previous work based on Support Vector Machines achieving 91% accuracy, on an out of sample dataset of high school and collegiate football head impacts. The integrated MiG2.0 and FITBIR system serve as a collaborative research tool to be disseminated across multiple institutions towards creating a standardized dataset for furthering the knowledge of concussion biomechanics.
View details for DOI 10.1038/s41598-021-87085-2
View details for PubMedID 33820939
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Correction to: Validation and Comparison of Instrumented Mouthguards for Measuring Head Kinematics and Assessing Brain Deformation in Football Impacts.
Annals of biomedical engineering
2021; 49 (3): 1119-1120
View details for DOI 10.1007/s10439-020-02701-y
View details for PubMedID 33725223
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Validation and Comparison of Instrumented Mouthguards for Measuring Head Kinematics and Assessing Brain Deformation in Football Impacts (vol 48, pg 2580, 2020)
ANNALS OF BIOMEDICAL ENGINEERING
2021
View details for DOI 10.1007/s10439-020-02701-y
View details for Web of Science ID 000613969900002
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White matter tract-oriented deformation is dependent on real-time axonal fiber orientation.
Journal of neurotrauma
2021
Abstract
Traumatic axonal injury (TAI) is a critical public health issue with its pathogenesis remaining largely elusive. Finite element (FE) head models are promising tools to bridge the gap between mechanical insult, localized brain response, and resultant injury. In particular, the FE-derived deformation along the direction of white matter (WM) tracts (i.e., tract-oriented strain) has been shown to be an appropriate predictor for TAI. However, the evolution of fiber orientation in time during the impact and its potential influence on the tract-oriented strain remains unknown. To address this question, the present study leveraged an embedded element approach to track real-time fiber orientation during impacts. A new scheme to calculate the tract-oriented strain was proposed by projecting the strain tensors from pre-computed simulations along the temporal fiber direction instead of its static counterpart directly obtained from diffuse tensor imaging. The results revealed that incorporating the real-time fiber orientation not only altered the direction but also amplified the magnitude of the tract-oriented strain, resulting in a generally more extended distribution and a larger volume ratio of WM exposed to high deformation along fiber tracts. These effects were exacerbated with the impact severities characterized by the acceleration magnitudes. Results of this study provide insights into how best to incorporate fiber orientation in head injury models and derive the WM tract-oriented deformation from computational simulations, which is important for furthering our understanding of the underlying mechanisms of TAI.
View details for DOI 10.1089/neu.2020.7412
View details for PubMedID 33446060
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The relationship between brain injury criteria and brain strain across different types of head impacts can be different
Journal of Royal Society Interface
2021; 18 (20210260)
View details for DOI 10.1098/rsif.2021.0260
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Rapid Estimation of Entire Brain Strain Using Deep Learning Models
IEEE Transactions on Biomedical Engineering
2021: 11
Abstract
Many recent studies suggest that brain deformation resulting from head impacts are linked to the corresponding clinical outcome, such as mild traumatic brain injury (mTBI). Even if several finite element (FE) head models have been developed and validated to calculate brain deformation based on impact kinematics, the clinical application of these FE head models is limited due to the time-consuming nature of FE simulations. This work aims to accelerate the brain deformation calculation and thus improve the potential for clinical applications.We propose a deep learning head model with a five-layer deep neural network and feature engineering, and trained and tested the model on 2511 head impacts from a combination of head model simulations and on-field college football and mixed martial arts impacts.The proposed deep learning head model can calculate the maximum principal strain (Green Lagrange) for every element in the entire brain in less than 0.001 s with an average root mean squared error of 0.022 and a standard deviation of 0.001 over twenty repeats with random data partition and model initialization.Trained and tested using the dataset of 2511 head impacts, this model can be applied to various sports in the calculation of brain strain with accuracy, and its applicability can even further be extended by incorporating data from other types of head impacts.In addition to the potential clinical application in real-time brain deformation monitoring, this model will help researchers estimate the brain strain from a large number of head impacts more efficiently than using FE models.
View details for DOI 10.1109/TBME.2021.3073380
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A Computational Study of Liquid Shock Absorption for Prevention of Traumatic Brain Injury.
Journal of biomechanical engineering
2020
Abstract
Mild traumatic brain injury (mTBI), more colloquially known as concussion, is common in contact sports such as American football, leading to increased scrutiny of head protective gear. Standardized laboratory impact testing, such as the yearly NFL helmet test, is used to rank the protective performance of football helmets, motivating new technologies to improve the safety of helmets relative to existing equipment. In this work, we hypothesized that a helmet which transmits a nearly constant minimum force will result in a reduced risk of mTBI. To evaluate the plausibility of this hypothesis, we first show that the optimal force transmitted to the head, in a reduced order model of the brain, is in fact a constant force profile. To simulate the effects of a constant force within a helmet, we conceptualize a fluid-based shock absorber system for use within a football helmet. We integrate this system within a computational helmet model and simulate its performance on the standard NFL helmet test impact conditions. The simulated helmet is compared with other helmet designs with different technologies. Computer simulations of head impacts with liquid shock absorption predict that, at the highest impact speed (9.3 m/s), the average brain tissue strain is reduced by 27.6% ± 9.3 compared to existing helmet padding when tested on the NFL helmet protocol. This simulation-based study puts forth a target benchmark for the future design of physical manifestations of this technology.
View details for DOI 10.1115/1.4049155
View details for PubMedID 33210108
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Variable area, constant force shock absorption motivated by traumatic brain injury prevention
SMART MATERIALS AND STRUCTURES
2020; 29 (8)
View details for DOI 10.1088/1361-665X/ab905f
View details for Web of Science ID 000551722800001
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Analysis of head acceleration events in collegiate-level American football: A combination of qualitative video analysis and in-vivo head kinematic measurement.
Journal of biomechanics
2020; 110: 109969
Abstract
The contact nature of American football has made head acceleration exposure a concern. We aimed to quantify the head kinematics associated with direct helmet contact and inertial head loading events in collegiate-level American football. A cohort of collegiate-level players were equipped with instrumented mouthguards synchronised with time-stamped multiple camera-view video footage of matches and practice. Video-verified contact events were identified as direct helmet contact or inertial head loading events and categorised as blocking, blocked, tackling, tackled or ground contact. Linear mixed-effects models were utilised to compare peak head kinematics between contact event categories. The timestamp-based cross-verification of the video analysis and instrumented mouthguard approach resulted in 200 and 328 direct helmet contact and inertial head loading cases, respectively. Median linear acceleration, angular acceleration and angular velocity for inertial head loading cases was greater than direct helmet contact events by 8% (p=0.007), 55% (p<0.001) and 4% (p=0.007), respectively. Median head kinematics for all contact event categories appeared similar with no pairwise comparison resulting in statistical significance (p>0.05). The study highlights the potential of combining qualitative video analysis with in-vivo head kinematics measurements. The findings suggest that a number of direct helmet contact events sustained in American football are of lower magnitude to what is sustained during regular play (i.e. from inertial head loading). Additionally, the findings illustrate the importance of including all contact events, including direct helmet contact and inertial head loading cases, when assessing head acceleration exposure and player load during a season of American football.
View details for DOI 10.1016/j.jbiomech.2020.109969
View details for PubMedID 32827770
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Characterizing head impact exposure in youth female soccer with a custom-instrumented mouthpiece
RESEARCH IN SPORTS MEDICINE
2020; 28 (1): 55–71
View details for DOI 10.1080/15438627.2019.1590833
View details for Web of Science ID 000508883700005
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Validation and Comparison of Instrumented Mouthguards for Measuring Head Kinematics and Assessing Brain Deformation in Football Impacts.
Annals of biomedical engineering
2020
Abstract
Because of the rigid coupling between the upper dentition and the skull, instrumented mouthguards have been shown to be a viable way of measuring head impact kinematics for assisting in understanding the underlying biomechanics of concussions. This has led various companies and institutions to further develop instrumented mouthguards. However, their use as a research tool for understanding concussive impacts makes quantification of their accuracy critical, especially given the conflicting results from various recent studies. Here we present a study that uses a pneumatic impactor to deliver impacts characteristic to football to a Hybrid III headform, in order to validate and compare five of the most commonly used instrumented mouthguards. We found that all tested mouthguards gave accurate measurements for the peak angular acceleration, the peak angular velocity, brain injury criteria values (mean average errors < 13, 8, 13%, respectively), and the mouthguards with long enough sampling time windows are suitable for a convolutional neural network-based brain model to calculate the brain strain (mean average errors < 9%). Finally, we found that the accuracy of the measurement varies with the impact locations yet is not sensitive to the impact velocity for the most part.
View details for DOI 10.1007/s10439-020-02629-3
View details for PubMedID 32989591
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MR elastography frequency-dependent and independent parameters demonstrate accelerated decrease of brain stiffness in elder subjects.
European radiology
2020
Abstract
To analyze the mechanical properties in different regions of the brain in healthy adults in a wide age range: 26 to 76 years old.We used a multifrequency magnetic resonance elastography (MRE) protocol to analyze the effect of age on frequency-dependent (storage and loss moduli, G' and G″, respectively) and frequency-independent parameters (μ1, μ2, and η, as determined by a standard linear solid model) of the cerebral parenchyma, cortical gray matter (GM), white matter (WM), and subcortical GM structures of 46 healthy male and female subjects. The multifrequency behavior of the brain and frequency-independent parameters were analyzed across different age groups.The annual change rate ranged from - 0.32 to - 0.36% for G' and - 0.43 to - 0.55% for G″ for the cerebral parenchyma, cortical GM, and WM. For the subcortical GM, changes in G' ranged from - 0.18 to - 0.23%, and G″ changed - 0.43%. Interestingly, males exhibited decreased elasticity, while females exhibited decreased viscosity with respect to age in some regions of subcortical GM. Significantly decreased values were also found in subjects over 60 years old.Values of G' and G″ at 60 Hz and the frequency-independent μ2 of the caudate, putamen, and thalamus may serve as parameters that characterize the aging effect on the brain. The decrease in brain stiffness accelerates in elderly subjects.• We used a multifrequency MRE protocol to assess changes in the mechanical properties of the brain with age. • Frequency-dependent (storage moduli G' and loss moduli G″) and frequency-independent (μ1, μ2, and η) parameters can bequantitatively measured by our protocol. • The decreased value of viscoelastic properties due to aging varies in different regions of subcortical GM in males and females, and the decrease in brain stiffness is accelerated in elderly subjects over 60 years old.
View details for DOI 10.1007/s00330-020-07054-7
View details for PubMedID 32683552
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Viscoelasticity of children and adolescent brains through MR elastography.
Journal of the mechanical behavior of biomedical materials
2020; 115: 104229
Abstract
Magnetic Resonance Elastography (MRE) is an elasticity imaging technique that allows a safe, fast, and non-invasive evaluation of the mechanical properties of biological tissues in vivo. Since mechanical properties reflect a tissue's composition and arrangement, MRE is a powerful tool for the investigation of the microstructural changes that take place in the brain during childhood and adolescence. The goal of this study was to evaluate the viscoelastic properties of the brain in a population of healthy children and adolescents in order to identify potential age and sex dependencies. We hypothesize that because of myelination, age dependent changes in the mechanical properties of the brain will occur during childhood and adolescence. Our sample consisted of 26 healthy individuals (13 M, 13 F) with age that ranged from 7-17 years (mean: 11.9 years). We performed multifrequency MRE at 40, 60, and 80 Hz actuation frequencies to acquire the complex-valued shear modulus G = G' + iG″ with the fundamental MRE parameters being the storage modulus (G'), the loss modulus (G″), and the magnitude of complex-valued shear modulus (|G|). We fitted a springpot model to these frequency-dependent MRE parameters in order to obtain the parameter α, which is related to tissue's microstructure, and the elasticity parameter k, which was converted to a shear modulus parameter (μ) through viscosity (η). We observed no statistically significant variation in the parameter μ, but a significant increase of the microstructural parameter α of the white matter with increasing age (p < 0.05). Therefore, our MRE results suggest that subtle microstructural changes such as neural tissue's enhanced alignment and geometrical reorganization during childhood and adolescence could result in significant biomechanical changes. In line with previously reported MRE data for adults, we also report significantly higher shear modulus (μ) for female brains when compared to males (p < 0.05). The data presented here can serve as a clinical baseline in the analysis of the pediatric and adolescent brain's viscoelasticity over this age span, as well as extending our understanding of the biomechanics of brain development.
View details for DOI 10.1016/j.jmbbm.2020.104229
View details for PubMedID 33387852
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Longitudinal alteration of cortical thickness and volume in high-impact sports.
NeuroImage
2020: 116864
Abstract
Collegiate football athletes are subject to repeated head impacts. The purpose of this study was to determine whether this exposure can lead to changes in brain structure. This prospective cohort study was conducted with up to 4 years of follow-up on 63 football (high-impact) and 34 volleyball (control) male collegiate athletes with a total of 315 MRI scans (after exclusions: football n=50, volleyball n= 24, total scans=273) using high-resolution structural imaging. Volumetric and cortical thickness estimates were derived using FreeSurfer 5.3's longitudinal pipeline. A linear mixed-effects model assessed the effect of group (football vs. volleyball), time from baseline MRI, and the interaction between group and time. We confirmed an expected developmental decrement in cortical thickness and volume in our cohort (p<0.001). Superimposed on this, total cortical gray matter volume (p = .03) and cortical thickness within the left hemisphere (p=.04) showed a group by time interaction, indicating less age-related volume reduction and thinning in football compared to volleyball athletes. At the regional level, sport by time interactions on thickness and volume were identified in the left orbitofrontal (p=.001), superior temporal (p=.001), and postcentral regions (p< .001). Additional cortical thickness interactions were found in the left temporal pole (p=.003) and cuneus (p=.005). At the regional level, we also found main effects of sport in football athletes characterized by reduced volume in the right hippocampus (p=.003), right superior parietal cortical gray (p<.001) and white matter (p<.001), and increased volume of the left pallidum (p=.002). Within football, cortical thickness was higher with greater years of prior play (left hemisphere p=.013, right hemisphere p=.005), and any history of concussion was associated with less cortical thinning (left hemisphere p=.010, right hemisphere p=.011). Additionally, both position-associated concussion risk (p=.002) and SCAT scores (p=.023) were associated with less of the expected volume decrement of deep gray structures. This prospective longitudinal study comparing football and volleyball athletes shows divergent age-related trajectories of cortical thinning, possibly reflecting an impact-related alteration of normal cortical development. This warrants future research into the underlying mechanisms of impacts to the head on cortical maturation.
View details for DOI 10.1016/j.neuroimage.2020.116864
View details for PubMedID 32360690
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Comment on "Frequency and Magnitude of Game-Related Head Impacts in Male Contact Sports Athletes: A Systematic Review and Meta-Analysis".
Sports medicine (Auckland, N.Z.)
2019
View details for DOI 10.1007/s40279-019-01230-6
View details for PubMedID 31761993
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DYSFUNCTION OF BLOOD BRAIN BARRIER IN CONCUSSIVE HEAD TRAUMA
MARY ANN LIEBERT, INC. 2019: A97–A98
View details for Web of Science ID 000473049600266
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Optimization of a Multifrequency Magnetic Resonance Elastography Protocol for the Human Brain
JOURNAL OF NEUROIMAGING
2019; 29 (4): 440–46
View details for DOI 10.1111/jon.12619
View details for Web of Science ID 000488852500003
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Longitudinal Changes in Hippocampal Subfield Volume Associated with Collegiate Football
JOURNAL OF NEUROTRAUMA
2019
View details for DOI 10.1089/neu.2018.6357
View details for Web of Science ID 000472105100001
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Lateral impacts correlate with falx cerebri displacement and corpus callosum trauma in sports-related concussions
BIOMECHANICS AND MODELING IN MECHANOBIOLOGY
2019; 18 (3): 631–49
View details for DOI 10.1007/s10237-018-01106-0
View details for Web of Science ID 000467394100007
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Passive cervical spine ligaments provide stability during head impacts.
Journal of the Royal Society, Interface
2019; 16 (154): 20190086
Abstract
It has been suggested that neck muscle strength and anticipatory cocontraction can decrease head motions during head impacts. Here, we quantify the relative angular impulse contributions of neck soft tissue to head stabilization using an OpenSim musculoskeletal model with Hill-type muscles and rate-dependent ligaments. We simulated sagittal extension and lateral flexion mild experimental head impacts performed on 10 subjects with relaxed or cocontracted muscles, and median American football head impacts. We estimated angular impulses from active muscle, passive muscle and ligaments during head impact acceleration and deceleration phases. During the acceleration phase, active musculature produced resistive angular impulses that were 30% of the impact angular impulse in experimental impacts with cocontracted muscles. This was reduced below 20% in football impacts. During the deceleration phase, active musculature stabilized the head with 50% of the impact angular impulse in experimental impacts with cocontracted muscles. However, passive ligaments provided greater stabilizing angular impulses in football impacts. The redistribution of stabilizing angular impulses results from ligament and muscle dependence on lengthening rate, where ligaments stiffen substantially compared to active muscle at high lengthening rates. Thus, ligaments provide relatively greater deceleration impulses in these impacts, which limit the effectiveness of muscle strengthening or anticipated activations.
View details for DOI 10.1098/rsif.2019.0086
View details for PubMedID 31138091
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Optimization of a Multifrequency Magnetic Resonance Elastography Protocol for the Human Brain.
Journal of neuroimaging : official journal of the American Society of Neuroimaging
2019
Abstract
BACKGROUND AND PURPOSE: The brain's stiffness measurements from magnetic resonance elastography (MRE) strongly depend on actuation frequencies, which makes cross-study comparisons challenging. We performed a preliminary study to acquire optimal sets of actuation frequencies to accurately obtain rheological parameters for the whole brain (WB), white matter (WM), and gray matter (GM).METHODS: Six healthy volunteers aged between 26 and 72 years old went through MRE with a modified single-shot spin-echo echo planar imaging pulse sequence embedded with motion encoding gradients on a 3T scanner. Frequency-independent brain material properties and best-fit material model were determined from the frequency-dependent brain tissue response data (20 -80 Hz), by comparing four different linear viscoelastic material models (Maxwell, Kelvin-Voigt, Springpot, and Zener). During the material fitting, spatial averaging of complex shear moduli (G*) obtained under single actuation frequency was performed, and then rheological parameters were acquired. Since clinical scan time is limited, a combination of three actuation frequencies that would provide the most accurate approximation and lowest fitting error was determined for WB, WM, and GM by optimizing for the lowest Bayesian information criterion (BIC).RESULTS: BIC scores for the Zener and Springpot models showed these models approximate the multifrequency response of the tissue best. The best-fit frequency combinations for the reference Zener and Springpot models were identified to be 30-60-70 and 30-40-80 Hz, respectively, for the WB.CONCLUSIONS: Optimal sets of actuation frequencies to accurately obtain rheological parameters for WB, WM, and GM were determined from shear moduli measurements obtained via 3-dimensional direct inversion. We believe that our study is a first-step in developing a region-specific multifrequency MRE protocol for the human brain.
View details for PubMedID 31056818
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Dependency of Head Impact Rotation on Head-Neck Positioning and Soft Tissue Forces
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2019; 66 (4): 988–99
Abstract
Humans are susceptible to traumatic brain injuries from rapid head rotations that shear and stretch the brain tissue. Conversely, animals such as woodpeckers intentionally undergo repetitive head impacts without apparent injury. Here, we represent the head as the end effector of a rigid linkage cervical spine model to quantify how head angular accelerations are affected by the linkage positioning (head-neck configuration) and the soft tissue properties (muscles, ligaments, tendons).We developed a two-pivot manipulator model of the human cervical spine with passive torque elements to represent soft tissue forces. Passive torque parameters were fit against five human subjects undergoing mild laboratory head impacts with tensed and relaxed neck muscle activations. With this representation, we compared the effects of the linkage configuration dependent end-effector inertial properties and the soft tissue resistive forces on head impact rotation.Small changes in cervical spine positioning (<5 degrees) can drastically affect the resulting rotational head accelerations (>100%) following an impact by altering the effective end-effector inertia. Comparatively, adjusting the soft tissue torque elements from relaxed to tensed muscle activations had a smaller (<30%) effect on maximum rotational head accelerations. Extending our analysis to a woodpecker rigid linkage model, we postulate that woodpeckers experience relatively minimal head impact rotation due to the configuration of their skeletal anatomy.Cervical spine positioning dictates the head angular acceleration following an impact, rather than the soft tissue torque elements.This analysis quantifies the importance of head positioning prior to impact, and may help us to explain why other species are naturally more resilient to head impacts than humans.
View details for DOI 10.1109/TBME.2018.2866147
View details for Web of Science ID 000462363300009
View details for PubMedID 30130169
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Characterizing head impact exposure in youth female soccer with a custom-instrumented mouthpiece.
Research in sports medicine (Print)
2019: 1–17
Abstract
While many research efforts have focused on head impact exposure in professional soccer, there have been few studies characterizing exposure at the youth level. The aim of this study is to evaluate a new instrumentation approach and collect some of the first head impact exposure data for youth female soccer players. Athletes were instrumented with custom-fit mouthpieces that measure head impacts. Detailed video analysis was conducted to identify characteristics describing impact source (e.g., kick, header, throw). A total of 763 verified head impacts were collected over 23 practices and 8 games from 7 athletes. The median peak linear accelerations, rotational velocities, and rotational accelerations of all impacts were 9.4 g, 4.1 rad/s, and 689 rad/s2, respectively. Pairwise comparisons resulted in statistically significant differences in kinematics by impact source. Headers following a kicked ball had the highest accelerations and velocity when compared to headers from thrown or another header.
View details for PubMedID 30880469
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Lateral impacts correlate with falx cerebri displacement and corpus callosum trauma in sports-related concussions.
Biomechanics and modeling in mechanobiology
2019
Abstract
Corpus callosum trauma has long been implicated in mild traumatic brain injury (mTBI), yet the mechanism by which forces penetrate this structure is unknown. We investigated the hypothesis that coronal and horizontal rotations produce motion of the falx cerebri that damages the corpus callosum. We analyzed previously published head kinematics of 115 sports impacts (2 diagnosed mTBI) measured with instrumented mouthguards and used finite element (FE) simulations to correlate falx displacement with corpus callosum deformation. Peak coronal accelerations were larger in impacts with mTBI (8592rad/s2avg.) than those without (1412rad/s2avg.). From FE simulations, coronal acceleration was strongly correlated with deep lateral motion of the falx center (r=0.85), while horizontal acceleration was correlated with deep lateral motion of the falx periphery (r>0.78). Larger lateral displacement at the falx center and periphery was correlated with higher tract-oriented strains in the corpus callosum body (r=0.91) and genu/splenium (r>0.72), respectively. The relationship between the corpus callosum and falx was unique: removing the falx from the FE model halved peak strains in the corpus callosum from 35% to 17%. Consistent with model results, we found indications of corpus callosum trauma in diffusion tensor imaging of the mTBI athletes. For a measured alteration of consciousness, depressed fractional anisotropy and increased mean diffusivity indicated possible damage to the mid-posterior corpus callosum. Our results suggest that the corpus callosum may be sensitive to coronal and horizontal rotations because they drive lateral motion of a relatively stiff membrane, the falx, in the direction of commissural fibers below.
View details for PubMedID 30859404
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Longitudinal changes in hippocampal subfield volume associated with collegiate football.
Journal of neurotrauma
2019
Abstract
Collegiate football athletes are subject to repeated head impacts that may cause brain injury. The hippocampus is composed of several distinct subfields with possible differential susceptibility to injury. The purpose of this study is to determine whether there are longitudinal changes in hippocampal subfield volume in collegiate football. A prospective cohort study was conducted over a 5-year period tracking 63 football and 34 volleyball male collegiate athletes. Athletes underwent high-resolution structural magnetic resonance imaging, and automated segmentation provided hippocampal subfield volumes. At baseline, football athletes demonstrated a smaller subiculum volume than volleyball athletes (-67.77 mm3, P=.012). A regression analysis performed within football athletes similarly demonstrated a smaller subiculum volume among those at increased concussion risk based on athlete position (P=.001). For the longitudinal analysis, a linear mixed-effects model assessed the interaction between sport and time, revealing a significant decrease in CA1 volume in football athletes without an in-study concussion compared to volleyball athletes (volume difference per year=-35.22 mm3, P=.005). This decrease in CA1 volume over time was significant when football athletes were examined in isolation from volleyball athletes (P=.011). Thus, this prospective longitudinal study showed a decrease in CA1 volume over time in football athletes, in addition to baseline differences that were identified in the downstream subiculum. Hippocampal changes may have important implications for high-contact sports.
View details for PubMedID 31044639
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OffsetNet: Deep Learning for Localization in the Lung using Rendered Images
IEEE. 2019: 5046–52
View details for Web of Science ID 000494942303097
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Orienting Oocytes using Vibrations for In-Vitro Fertilization Procedures
IEEE. 2019: 4837–43
View details for Web of Science ID 000494942303076
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Vulnerable locations on the head to brain injury and implications for helmet design.
Journal of biomechanical engineering
2019
Abstract
In studying traumatic brain injury (TBI), it has been long hypothesized that the head is more vulnerable to injury from impacts in certain directions or locations, as the relationship between impact force and the resulting neurological outcome is complex and can vary significantly between individual cases. Many studies have identified head angular acceleration to be the putative cause of brain trauma, but it is not well understood how impact location can affect the resulting head kinematics and tissue strain. Here, we identify the susceptibility of the head to accelerations and brain strain from normal forces at contact points across the surface of the skull and jaw using a 3-dimensional, 20 degree-of-freedom rigid-body head and cervical spine model. We find that head angular acceleration and brain tissue strain resulting from an input force can vary by orders of magnitude based on impact location on the skull, with the mandible as the most vulnerable region. Conversely, head linear acceleration is not sensitive to contact location. Using these analyses, we present an optimization scheme to distribute helmet padding thickness to minimize angular acceleration, resulting in a reduction of angular acceleration by an estimated 25% at the most vulnerable contact point compared to uniform thickness padding. This work gives intuition behind the relationship between input force and resulting brain injury risk, and presents a framework for developing and evaluating novel head protection gear.
View details for DOI 10.1115/1.4044876
View details for PubMedID 31523753
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Dynamic Blood-Brain Barrier Regulation in Mild Traumatic Brain Injury.
Journal of neurotrauma
2019
Abstract
Whereas the diagnosis of moderate and severe traumatic brain injury (TBI) is readily visible on current medical imaging paradigms (magnetic resonance imaging [MRI] and computed tomography [CT] scanning), a far greater challenge is associated with the diagnosis and subsequent management of mild TBI (mTBI), especially concussion which, by definition, is characterized by a normal CT. To investigate whether the integrity of the blood-brain barrier (BBB) is altered in a high-risk population for concussions, we studied professional mixed martial arts (MMA) fighters and adolescent rugby players. Additionally, we performed the linear regression between the BBB disruption defined by increased gadolinium contrast extravasation on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) on MRI and multiple biomechanical parameters indicating the severity of impacts recorded using instrumented mouthguards in professional MMA fighters. MMA fighters were examined pre-fight for a baseline and again within 120 h post-competitive fight, whereas rugby players were examined pre-season and again post-season or post-match in a subset of cases. DCE-MRI, serological analysis of BBB biomarkers, and an analysis of instrumented mouthguard data, was performed. Here, we provide pilot data that demonstrate disruption of the BBB in both professional MMA fighters and rugby players, dependent on the level of exposure. Our data suggest that biomechanical forces in professional MMA and adolescent rugby can lead to BBB disruption. These changes on imaging may serve as a biomarker of exposure of the brain to repetitive subconcussive forces and mTBI.
View details for DOI 10.1089/neu.2019.6483
View details for PubMedID 31702476
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Multi-directional dynamic model for traumatic brain injury detection.
Journal of neurotrauma
2019
Abstract
Given the worldwide adverse impact of traumatic brain injury (TBI) on the human population, its diagnosis and prediction are of utmost importance. Historically, many studies have focused on associating head kinematics to brain injury risk. Recently, there has been a push towards using computationally expensive finite element (FE) models of the brain to create tissue deformation metrics of brain injury. Here, we develop a new brain injury metric, the Brain Angle Metric (BAM), based on the dynamics of a 3 degree-of-freedom lumped parameter brain model. The brain model is built based on the measured natural frequencies of an FE brain model simulated with live human impact data. We show it can be used to rapidly estimate peak brain strains experienced during head rotational accelerations that cause mild TBI. On our dataset, the simplified model correlates with peak principal FE strain (R2=0.82). Further, coronal and axial brain model displacement correlated with fiber-oriented peak strain in the corpus callosum (R2=0.77). Our proposed injury metric BAM uses the maximum angle predicted by our brain model and is compared against a number of existing rotational and translational kinematic injury metrics on a dataset of head kinematics from 27 clinically diagnosed injuries and 887 non-injuries. We found that BAM performed comparably to peak angular acceleration, translational acceleration, and angular velocity in classifying injury and non-injury events. Metrics which separated time traces into their directional components had improved model deviance to those which combined components into a single time trace magnitude. Our brain model can be used in future work to rapidly approximate the peak strain resulting from mild to moderate head impacts and to quickly assess brain injury risk.
View details for DOI 10.1089/neu.2018.6340
View details for PubMedID 31856650
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Voluntary Head Rotational Velocity and Implications for Brain Injury Risk Metrics
JOURNAL OF NEUROTRAUMA
2019; 36 (7): 1125–35
View details for DOI 10.1089/neu.2016.4758
View details for Web of Science ID 000447011500001
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Head Impact Kinematics Estimation With Network of Inertial Measurement Units.
Journal of biomechanical engineering
2018; 140 (9)
Abstract
Wearable sensors embedded with inertial measurement units have become commonplace for the measurement of head impact biomechanics, but individual systems often suffer from a lack of measurement fidelity. While some researchers have focused on developing highly accurate, single sensor systems, we have taken a parallel approach in investigating optimal estimation techniques with multiple noisy sensors. In this work, we present a sensor network methodology that utilizes multiple skin patch sensors arranged on the head and combines their data to obtain a more accurate estimate than any individual sensor in the network. Our methodology visually localizes subject-specific sensor transformations, and based on rigid body assumptions, applies estimation algorithms to obtain a minimum mean squared error estimate. During mild soccer headers, individual skin patch sensors had over 100% error in peak angular velocity magnitude, angular acceleration magnitude, and linear acceleration magnitude. However, when properly networked using our visual localization and estimation methodology, we obtained kinematic estimates with median errors below 20%. While we demonstrate this methodology with skin patch sensors in mild soccer head impacts, the formulation can be generally applied to any dynamic scenario, such as measurement of cadaver head impact dynamics using arbitrarily placed sensors.
View details for PubMedID 29801166
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Comparison of video-based and sensor-based head impact exposure
PLOS ONE
2018; 13 (6)
View details for DOI 10.1371/journal.pone.e0199238
View details for Web of Science ID 000435541300028
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Spinal constraint modulates head instantaneous center of rotation and dictates head angular motion.
Journal of biomechanics
2018
Abstract
The head is kinematically constrained to the torso through the spine and thus, the spine dictates the amount of output head angular motion expected from an input impact. Here, we investigate the spinal kinematic constraint by analyzing the head instantaneous center of rotation (HICOR) with respect to the torso in head/neck sagittal extension and coronal lateral flexion during mild loads applied to 10 subjects. We found the mean HICOR location was near the C5-C6 intervertebral joint in sagittal extension, and T2-T3 intervertebral joint in coronal lateral flexion. Using the impulse-momentum relationship normalized by subject mass and neck length, we developed a non-dimensional analytical ratio between output angular velocity and input linear impulse as a function of HICOR location. The ratio was 0.65 and 0.50 in sagittal extension and coronal lateral flexion respectively, implying 30% greater angular velocities in sagittal extension given an equivalent impulse. Scaling to subject physiology also predicts larger required impulses given greater subject mass and neck length to achieve equivalent angular velocities, which was observed experimentally. Furthermore, the HICOR has greater motion in sagittal extension than coronal lateral flexion, suggesting the head and spine can be represented with a single inverted pendulum in coronal lateral flexion, but requires a more complex representation in sagittal extension. The upper cervical spine has substantial compliance in sagittal extension, and may be responsible for the complex motion and greater extension angular velocities. In analyzing the HICOR, we can gain intuition regarding the neck's role in dictating head motion during external loading.
View details for PubMedID 29929891
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Voluntary Head Rotational Velocity and Implications for Brain Injury Risk Metrics.
Journal of neurotrauma
2018
Abstract
We investigated whether humans could sustain high head rotational velocities without brain injury. Rotational velocity has long been implicated for predicting concussion risk, and has recently been used to develop the rotational velocity-based Brain Injury Criterion (BrIC). To assess the efficacy of rotational velocity and BrIC for predicting concussion risk, we instrumented 9 male subjects with sensor-laden mouthguards and measured six-degree-of-freedom head accelerations for 27 rapid voluntary head rotations. The fastest rotations produced peak rotational velocities of 12.6 rad/s, 17.4 rad/s, and 25.0 rad/s in the coronal, sagittal, and horizontal planes, respectively. All of these exceeded the corresponding medians from padded sports impacts (8.9 rad/s, 10.7 rad/s, and 8.4 rad/s, respectively), and in the case of sagittal and horizontal rotation, were within 1 standard deviation of published concussion averages. In the horizontal plane, 4 voluntary rotations exceeded the concussive impact median BrIC. The area under the precision-recall curve was lower in BrIC (0.49) than just using horizontal rotational acceleration (0.8), which distinguished concussive and subconcussive motions better. Voluntary motions produced less than 4% max principal strain (MPS) in finite element simulation, 5 times below predictions from dummy impacts used to develop BrIC. Despite having the highest critical velocity in BrIC, coronal rotation produced more tract-oriented strain in the corpus callosum than other planes. Baseline and post-experiment neurological testing revealed no significant deficits. We find that the head can tolerate high-velocity, low-acceleration rotational inputs too slow to produce substantial brain deformation. These findings suggest that the time regime over which angular velocities occur must be carefully considered for concussion prediction.
View details for PubMedID 29848152
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Validation of a Custom Instrumented Retainer Form Factor for Measuring Linear and Angular Head Impact Kinematics
JOURNAL OF BIOMECHANICAL ENGINEERING-TRANSACTIONS OF THE ASME
2018; 140 (5)
View details for DOI 10.1115/1.4039165
View details for Web of Science ID 000428700000011
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Validation of a Custom Instrumented Retainer Form Factor for Measuring Linear and Angular Head Impact Kinematics.
Journal of biomechanical engineering
2018; 140 (5)
Abstract
Head impact exposure in popular contact sports is not well understood, especially in the youth population, despite recent advances in impact-sensing technology which has allowed widespread collection of real-time head impact data. Previous studies indicate that a custom-instrumented mouthpiece is a superior method for collecting accurate head acceleration data. The objective of this study was to evaluate the efficacy of mounting a sensor device inside an acrylic retainer form factor to measure six-degrees-of-freedom (6DOF) head kinematic response. This study compares 6DOF mouthpiece kinematics at the head center of gravity (CG) to kinematics measured by an anthropomorphic test device (ATD). This study found that when instrumentation is mounted in the rigid retainer form factor, there is good coupling with the upper dentition and highly accurate kinematic results compared to the ATD. Peak head kinematics were correlated with r2 > 0.98 for both rotational velocity and linear acceleration and r2 = 0.93 for rotational acceleration. These results indicate that a rigid retainer-based form factor is an accurate and promising method of collecting head impact data. This device can be used to study head impacts in helmeted contact sports such as football, hockey, and lacrosse as well as nonhelmeted sports such as soccer and basketball. Understanding the magnitude and frequency of impacts sustained in various sports using an accurate head impact sensor, such as the one presented in this study, will improve our understanding of head impact exposure and sports-related concussion.
View details for DOI 10.1115/1.4039165
View details for PubMedID 29383374
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Common Data Elements (CDEs) for Biomechanical Devices used in Blunt Head Impact and Blast Exposure Dynamics: The National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH) and Department of Defense (DOD) Version 1.0 Recommendations
LIPPINCOTT WILLIAMS & WILKINS. 2018
View details for Web of Science ID 000453090801011
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Mechanistic Insights into Human Brain Impact Dynamics through Modal Analysis
PHYSICAL REVIEW LETTERS
2018; 120 (13): 138101
Abstract
Although concussion is one of the greatest health challenges today, our physical understanding of the cause of injury is limited. In this Letter, we simulated football head impacts in a finite element model and extracted the most dominant modal behavior of the brain's deformation. We showed that the brain's deformation is most sensitive in low frequency regimes close to 30 Hz, and discovered that for most subconcussive head impacts, the dynamics of brain deformation is dominated by a single global mode. In this Letter, we show the existence of localized modes and multimodal behavior in the brain as a hyperviscoelastic medium. This dynamical phenomenon leads to strain concentration patterns, particularly in deep brain regions, which is consistent with reported concussion pathology.
View details for PubMedID 29694192
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Propagation of errors from skull kinematic measurements to finite element tissue responses
BIOMECHANICS AND MODELING IN MECHANOBIOLOGY
2018; 17 (1): 235–47
Abstract
Real-time quantification of head impacts using wearable sensors is an appealing approach to assess concussion risk. Traditionally, sensors were evaluated for accurately measuring peak resultant skull accelerations and velocities. With growing interest in utilizing model-estimated tissue responses for injury prediction, it is important to evaluate sensor accuracy in estimating tissue response as well. Here, we quantify how sensor kinematic measurement errors can propagate into tissue response errors. Using previous instrumented mouthguard validation datasets, we found that skull kinematic measurement errors in both magnitude and direction lead to errors in tissue response magnitude and distribution. For molar design instrumented mouthguards susceptible to mandible disturbances, 150-400% error in skull kinematic measurements resulted in 100% error in regional peak tissue response. With an improved incisor design mitigating mandible disturbances, errors in skull kinematics were reduced to <50%, and several tissue response errors were reduced to <10%. Applying 30[Formula: see text] rotations to reference kinematic signals to emulate sensor transformation errors yielded below 10% error in regional peak tissue response; however, up to 20% error was observed in peak tissue response for individual finite elements. These findings demonstrate that kinematic resultant errors result in regional peak tissue response errors, while kinematic directionality errors result in tissue response distribution errors. This highlights the need to account for both kinematic magnitude and direction errors and accurately determine transformations between sensors and the skull.
View details for PubMedID 28856485
View details for PubMedCentralID PMC5809213
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Comparison of video-based and sensor-based head impact exposure.
PloS one
2018; 13 (6): e0199238
Abstract
Previous research has sought to quantify head impact exposure using wearable kinematic sensors. However, many sensors suffer from poor accuracy in estimating impact kinematics and count, motivating the need for additional independent impact exposure quantification for comparison. Here, we equipped seven collegiate American football players with instrumented mouthguards, and video recorded practices and games to compare video-based and sensor-based exposure rates and impact location distributions. Over 50 player-hours, we identified 271 helmet contact periods in video, while the instrumented mouthguard sensor recorded 2,032 discrete head impacts. Matching video and mouthguard real-time stamps yielded 193 video-identified helmet contact periods and 217 sensor-recorded impacts. To compare impact locations, we binned matched impacts into frontal, rear, side, oblique, and top locations based on video observations and sensor kinematics. While both video-based and sensor-based methods found similar location distributions, our best method utilizing integrated linear and angular position only correctly predicted 81 of 217 impacts. Finally, based on the activity timeline from video assessment, we also developed a new exposure metric unique to American football quantifying number of cross-verified sensor impacts per player-play. We found significantly higher exposure during games (0.35, 95% CI: 0.29-0.42) than practices (0.20, 95% CI: 0.17-0.23) (p<0.05). In the traditional impacts per player-hour metric, we observed higher exposure during practices (4.7) than games (3.7) due to increased player activity in practices. Thus, our exposure metric accounts for variability in on-field participation. While both video-based and sensor-based exposure datasets have limitations, they can complement one another to provide more confidence in exposure statistics.
View details for PubMedID 29920559
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Detection of American Football Head Impacts Using Biomechanical Features and Support Vector Machine Classification.
Scientific reports
2017; 8 (1): 855
Abstract
Accumulation of head impacts may contribute to acute and long-term brain trauma. Wearable sensors can measure impact exposure, yet current sensors do not have validated impact detection methods for accurate exposure monitoring. Here we demonstrate a head impact detection method that can be implemented on a wearable sensor for detecting field football head impacts. Our method incorporates a support vector machine classifier that uses biomechanical features from the time domain and frequency domain, as well as model predictions of head-neck motions. The classifier was trained and validated using instrumented mouthguard data from collegiate football games and practices, with ground truth data labels established from video review. We found that low frequency power spectral density and wavelet transform features (10~30Hz) were the best performing features. From forward feature selection, fewer than ten features optimized classifier performance, achieving 87.2% sensitivity and 93.2% precision in cross-validation on the collegiate dataset (n=387), and over 90% sensitivity and precision on an independent youth dataset (n=32). Accurate head impact detection is essential for studying and monitoring head impact exposure on the field, and the approach in the current paper may help to improve impact detection performance on wearable sensors.
View details for PubMedID 29321637
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Pilot Findings of Brain Displacements and Deformations during Roller Coaster Rides
JOURNAL OF NEUROTRAUMA
2017; 34 (22): 3198–3205
Abstract
With 300,000,000 riders annually, roller coasters are a popular recreational activity. Although the number of roller coaster injuries is relatively low, the precise effect of roller coaster rides on our brains remains unknown. Here we present the quantitative characterization of brain displacements and deformations during roller coaster rides. For two healthy adult male subjects, we recorded head accelerations during three representative rides, and, for comparison, during running and soccer headers. From the recordings, we simulated brain displacements and deformations using rigid body dynamics and finite element analyses. Our findings show that despite having lower linear accelerations than sports head impacts, roller coasters may lead to brain displacements and strains comparable to mild soccer headers. The peak change in angular velocity on the rides was 9.9 rad/sec, which was higher than the 5.6 rad/sec in soccer headers with ball velocities reaching 7 m/sec. Maximum brain surface displacements of 4.0 mm and maximum principal strains of 7.6% were higher than in running and similar to soccer headers, but below the reported average concussion strain. Brain strain rates during roller coaster rides were similar to those in running, and lower than those in soccer headers. Strikingly, on the same ride and at a similar position, the two subjects experienced significantly different head kinematics and brain deformation. These results indicate that head motion and brain deformation during roller coaster rides are highly sensitive to individual subjects. Although our study suggests that roller coaster rides do not present an immediate risk of acute brain injury, their long-term effects require further longitudinal study.
View details for PubMedID 28683585
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Performance Evaluation of a Pre-computed Brain Response Atlas in Dummy Head Impacts
ANNALS OF BIOMEDICAL ENGINEERING
2017; 45 (10): 2437–50
Abstract
A pre-computed brain response atlas (pcBRA) may have the potential to accelerate the investigation of the biomechanical mechanisms of traumatic brain injury on a large-scale. In this study, we further enhance the technique and evaluate its performance using six degree-of-freedom angular velocity profiles from dummy head impacts. Using the pcBRA to simplify profiles into acceleration-only shapes, sufficiently accurate strain estimates were obtained for impacts with a major dominating velocity peak. However, they were largely under-estimated when substantial deceleration occurred that reversed the direction of the angular velocity. For these impacts, estimation accuracy was substantially improved with a biphasic profile simplification (average correlation coefficient and linear regression slope of 0.92 ± 0.03 and 0.95 ± 0.07 for biphasic shapes, respectively, vs. 0.80 ± 0.06 and 0.80 ± 0.08 for acceleration-only shapes). Peak maximum principal strain (ɛ p) and cumulative strain damage measure (CSDM) from the estimated strains consistently correlated stronger than kinematic metrics with respect to the baseline ɛ p and CSDM from the directly simulated responses, regardless of the brain region, and by a large margin (e.g., correlation of 0.93 vs. 0.75 compared to Brain Injury Criterion (BrIC) for ɛ p in the whole-brain, and 0.91 vs. 0.47 compared to BrIC for CSDM in the corpus callosum). These findings further support the pre-computation technique for accurate, real-time strain estimation, which could be important to accelerate model-based brain injury studies in the future.
View details for PubMedID 28710533
View details for PubMedCentralID PMC5693659
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Modeling and Optimization of Airbag Helmets for Preventing Head Injuries in Bicycling
ANNALS OF BIOMEDICAL ENGINEERING
2017; 45 (4): 1148-1160
Abstract
Bicycling is the leading cause of sports-related traumatic brain injury. Most of the current bike helmets are made of expanded polystyrene (EPS) foam and ultimately designed to prevent blunt trauma, e.g., skull fracture. However, these helmets have limited effectiveness in preventing brain injuries. With the availability of high-rate micro-electrical-mechanical systems sensors and high energy density batteries, a new class of helmets, i.e., expandable helmets, can sense an impending collision and expand to protect the head. By allowing softer liner medium and larger helmet sizes, this novel approach in helmet design provides the opportunity to achieve much lower acceleration levels during collision and may reduce the risk of brain injury. In this study, we first develop theoretical frameworks to investigate impact dynamics of current EPS helmets and airbag helmets-as a form of expandable helmet design. We compared our theoretical models with anthropomorphic test dummy drop test experiments. Peak accelerations obtained from these experiments with airbag helmets achieve up to an 8-fold reduction in the risk of concussion compared to standard EPS helmets. Furthermore, we construct an optimization framework for airbag helmets to minimize concussion and severe head injury risks at different impact velocities, while avoiding excessive deformation and bottoming-out. An optimized airbag helmet with 0.12 m thickness at 72 ± 8 kPa reduces the head injury criterion (HIC) value to 190 ± 25 at 6.2 m/s head impact velocity compared to a HIC of 1300 with a standard EPS helmet. Based on a correlation with previously reported HIC values in the literature, this airbag helmet design substantially reduces the risks of severe head injury up to 9 m/s.
View details for DOI 10.1007/s10439-016-1732-1
View details for Web of Science ID 000398737100026
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Microfluidic analysis of oocyte and embryo biomechanical properties to improve outcomes in assisted reproductive technologies
MOLECULAR HUMAN REPRODUCTION
2017; 23 (4): 235-247
View details for DOI 10.1093/molehr/gaw071
View details for Web of Science ID 000401056300004
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Microfluidic analysis of oocyte and embryo biomechanical properties to improve outcomes in assisted reproductive technologies.
Molecular human reproduction
2016: -?
Abstract
Measurement of oocyte and embryo biomechanical properties has recently emerged as an exciting new approach to obtain a quantitative, objective estimate of developmental potential. However, many traditional methods for probing cell mechanical properties are time consuming, labor intensive and require expensive equipment. Microfluidic technology is currently making its way into many aspects of assisted reproductive technologies (ART), and is particularly well suited to measure embryo biomechanics due to the potential for robust, automated single-cell analysis at a low cost. This review will highlight microfluidic approaches to measure oocyte and embryo mechanics along with their ability to predict developmental potential and find practical application in the clinic. Although these new devices must be extensively validated before they can be integrated into the existing clinical workflow, they could eventually be used to constantly monitor oocyte and embryo developmental progress and enable more optimal decision making in ART.
View details for PubMedID 27932552
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Modeling and Optimization of Airbag Helmets for Preventing Head Injuries in Bicycling.
Annals of biomedical engineering
2016: -?
Abstract
Bicycling is the leading cause of sports-related traumatic brain injury. Most of the current bike helmets are made of expanded polystyrene (EPS) foam and ultimately designed to prevent blunt trauma, e.g., skull fracture. However, these helmets have limited effectiveness in preventing brain injuries. With the availability of high-rate micro-electrical-mechanical systems sensors and high energy density batteries, a new class of helmets, i.e., expandable helmets, can sense an impending collision and expand to protect the head. By allowing softer liner medium and larger helmet sizes, this novel approach in helmet design provides the opportunity to achieve much lower acceleration levels during collision and may reduce the risk of brain injury. In this study, we first develop theoretical frameworks to investigate impact dynamics of current EPS helmets and airbag helmets-as a form of expandable helmet design. We compared our theoretical models with anthropomorphic test dummy drop test experiments. Peak accelerations obtained from these experiments with airbag helmets achieve up to an 8-fold reduction in the risk of concussion compared to standard EPS helmets. Furthermore, we construct an optimization framework for airbag helmets to minimize concussion and severe head injury risks at different impact velocities, while avoiding excessive deformation and bottoming-out. An optimized airbag helmet with 0.12 m thickness at 72 ± 8 kPa reduces the head injury criterion (HIC) value to 190 ± 25 at 6.2 m/s head impact velocity compared to a HIC of 1300 with a standard EPS helmet. Based on a correlation with previously reported HIC values in the literature, this airbag helmet design substantially reduces the risks of severe head injury up to 9 m/s.
View details for PubMedID 27679447
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Bandwidth and sample rate requirements for wearable head impact sensors
JOURNAL OF BIOMECHANICS
2016; 49 (13): 2918-2924
Abstract
Wearable inertial sensors measure human head impact kinematics important to the on-going development and validation of head injury criteria. However, sensor specifications have not been scientifically justified in the context of the anticipated field impact dynamics. The objective of our study is to determine the minimum bandwidth and sample rate required to capture the impact frequency response relevant to injury. We used high-bandwidth head impact data as ground-truth measurements, and investigated the attenuation of various injury criteria at lower bandwidths. Given a 10% attenuation threshold, we determined the minimum bandwidths required to study injury criteria based on skull kinematics and brain deformation in three different model systems: helmeted cadaver (no neck), unhelmeted cadaver (no neck), and helmeted dummy impacts (with neck). We found that higher bandwidths are required for unhelmeted impacts in general and for studying strain rate injury criteria. Minimum gyroscope bandwidths of 300Hz in helmeted sports and 500Hz in unhelmeted sports are necessary to study strain rate based injury criteria. A minimum accelerometer bandwidth of 500Hz in unhelmeted sports is necessary to study most injury criteria. Current devices typically sample at 1000Hz, with gyroscope bandwidths below 200Hz, which are not always sufficient according to these requirements. With hard contact test conditions, the identified requirements may be higher than most soft contacts on the field, but should be satisfied to capture the worst contact, and often higher risk, scenarios relative to the specific sport or activity. Our findings will help establish standard guidelines for sensor choice and design in traumatic brain injury research.
View details for DOI 10.1016/j.jbiomech.2016.07.004
View details for Web of Science ID 000385472300047
View details for PubMedID 27497499
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Effect of the mandible on mouthguard measurements of head kinematics
JOURNAL OF BIOMECHANICS
2016; 49 (9): 1845-1853
Abstract
Wearable sensors are becoming increasingly popular for measuring head motions and detecting head impacts. Many sensors are worn on the skin or in headgear and can suffer from motion artifacts introduced by the compliance of soft tissue or decoupling of headgear from the skull. The instrumented mouthguard is designed to couple directly to the upper dentition, which is made of hard enamel and anchored in a bony socket by stiff ligaments. This gives the mouthguard superior coupling to the skull compared with other systems. However, multiple validation studies have yielded conflicting results with respect to the mouthguard׳s head kinematics measurement accuracy. Here, we demonstrate that imposing different constraints on the mandible (lower jaw) can alter mouthguard kinematic accuracy in dummy headform testing. In addition, post mortem human surrogate tests utilizing the worst-case unconstrained mandible condition yield 40% and 80% normalized root mean square error in angular velocity and angular acceleration respectively. These errors can be modeled using a simple spring-mass system in which the soft mouthguard material near the sensors acts as a spring and the mandible as a mass. However, the mouthguard can be designed to mitigate these disturbances by isolating sensors from mandible loads, improving accuracy to below 15% normalized root mean square error in all kinematic measures. Thus, while current mouthguards would suffer from measurement errors in the worst-case unconstrained mandible condition, future mouthguards should be designed to account for these disturbances and future validation testing should include unconstrained mandibles to ensure proper accuracy.
View details for DOI 10.1016/j.jbiomech.2016.04.017
View details for Web of Science ID 000377731200058
View details for PubMedID 27155744
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In Vivo Evaluation of Wearable Head Impact Sensors
ANNALS OF BIOMEDICAL ENGINEERING
2016; 44 (4): 1234-1245
Abstract
Inertial sensors are commonly used to measure human head motion. Some sensors have been tested with dummy or cadaver experiments with mixed results, and methods to evaluate sensors in vivo are lacking. Here we present an in vivo method using high speed video to test teeth-mounted (mouthguard), soft tissue-mounted (skin patch), and headgear-mounted (skull cap) sensors during 6-13 g sagittal soccer head impacts. Sensor coupling to the skull was quantified by displacement from an ear-canal reference. Mouthguard displacements were within video measurement error (<1 mm), while the skin patch and skull cap displaced up to 4 and 13 mm from the ear-canal reference, respectively. We used the mouthguard, which had the least displacement from skull, as the reference to assess 6-degree-of-freedom skin patch and skull cap measurements. Linear and rotational acceleration magnitudes were over-predicted by both the skin patch (with 120% NRMS error for a(mag), 290% for α(mag)) and the skull cap (320% NRMS error for a(mag), 500% for α(mag)). Such over-predictions were largely due to out-of-plane motion. To model sensor error, we found that in-plane skin patch linear acceleration in the anterior-posterior direction could be modeled by an underdamped viscoelastic system. In summary, the mouthguard showed tighter skull coupling than the other sensor mounting approaches. Furthermore, the in vivo methods presented are valuable for investigating skull acceleration sensor technologies.
View details for DOI 10.1007/s10439-015-1423-3
View details for Web of Science ID 000373741800034
View details for PubMedID 26289941
View details for PubMedCentralID PMC4761340
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Six Degree-of-Freedom Measurements of Human Mild Traumatic Brain Injury (vol 43, pg 1918, 2015)
ANNALS OF BIOMEDICAL ENGINEERING
2016; 44 (3): 828–29
View details for PubMedID 26518413
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Human oocyte developmental potential is predicted by mechanical properties within hours after fertilization.
Nature communications
2016; 7: 10809-?
Abstract
The causes of embryonic arrest during pre-implantation development are poorly understood. Attempts to correlate patterns of oocyte gene expression with successful embryo development have been hampered by the lack of reliable and nondestructive predictors of viability at such an early stage. Here we report that zygote viscoelastic properties can predict blastocyst formation in humans and mice within hours after fertilization, with >90% precision, 95% specificity and 75% sensitivity. We demonstrate that there are significant differences between the transcriptomes of viable and non-viable zygotes, especially in expression of genes important for oocyte maturation. In addition, we show that low-quality oocytes may undergo insufficient cortical granule release and zona-hardening, causing altered mechanics after fertilization. Our results suggest that embryo potential is largely determined by the quality and maturation of the oocyte before fertilization, and can be predicted through a minimally invasive mechanical measurement at the zygote stage.
View details for DOI 10.1038/ncomms10809
View details for PubMedID 26904963
View details for PubMedCentralID PMC4770082
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Human oocyte developmental potential is predicted by mechanical properties within hours after fertilization.
Nature communications
2016; 7: 10809-?
Abstract
The causes of embryonic arrest during pre-implantation development are poorly understood. Attempts to correlate patterns of oocyte gene expression with successful embryo development have been hampered by the lack of reliable and nondestructive predictors of viability at such an early stage. Here we report that zygote viscoelastic properties can predict blastocyst formation in humans and mice within hours after fertilization, with >90% precision, 95% specificity and 75% sensitivity. We demonstrate that there are significant differences between the transcriptomes of viable and non-viable zygotes, especially in expression of genes important for oocyte maturation. In addition, we show that low-quality oocytes may undergo insufficient cortical granule release and zona-hardening, causing altered mechanics after fertilization. Our results suggest that embryo potential is largely determined by the quality and maturation of the oocyte before fertilization, and can be predicted through a minimally invasive mechanical measurement at the zygote stage.
View details for DOI 10.1038/ncomms10809
View details for PubMedID 26904963
View details for PubMedCentralID PMC4770082
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Evaluation of a laboratory model of human head impact biomechanics.
Journal of biomechanics
2015; 48 (12): 3469-3477
Abstract
This work describes methodology for evaluating laboratory models of head impact biomechanics. Using this methodology, we investigated: how closely does twin-wire drop testing model head rotation in American football impacts? Head rotation is believed to cause mild traumatic brain injury (mTBI) but helmet safety standards only model head translations believed to cause severe TBI. It is unknown whether laboratory head impact models in safety standards, like twin-wire drop testing, reproduce six degree-of-freedom (6DOF) head impact biomechanics that may cause mTBI. We compared 6DOF measurements of 421 American football head impacts to twin-wire drop tests at impact sites and velocities weighted to represent typical field exposure. The highest rotational velocities produced by drop testing were the 74th percentile of non-injury field impacts. For a given translational acceleration level, drop testing underestimated field rotational acceleration by 46% and rotational velocity by 72%. Primary rotational acceleration frequencies were much larger in drop tests (~100Hz) than field impacts (~10Hz). Drop testing was physically unable to produce acceleration directions common in field impacts. Initial conditions of a single field impact were highly resolved in stereo high-speed video and reconstructed in a drop test. Reconstruction results reflected aggregate trends of lower amplitude rotational velocity and higher frequency rotational acceleration in drop testing, apparently due to twin-wire constraints and the absence of a neck. These results suggest twin-wire drop testing is limited in modeling head rotation during impact, and motivate continued evaluation of head impact models to ensure helmets are tested under conditions that may cause mTBI.
View details for DOI 10.1016/j.jbiomech.2015.05.034
View details for PubMedID 26117075
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Six Degree-of-Freedom Measurements of Human Mild Traumatic Brain Injury
ANNALS OF BIOMEDICAL ENGINEERING
2015; 43 (8): 1918-1934
Abstract
This preliminary study investigated whether direct measurement of head rotation improves prediction of mild traumatic brain injury (mTBI). Although many studies have implicated rotation as a primary cause of mTBI, regulatory safety standards use 3 degree-of-freedom (3DOF) translation-only kinematic criteria to predict injury. Direct 6DOF measurements of human head rotation (3DOF) and translation (3DOF) have not been previously available to examine whether additional DOFs improve injury prediction. We measured head impacts in American football, boxing, and mixed martial arts using 6DOF instrumented mouthguards, and predicted clinician-diagnosed injury using 12 existing kinematic criteria and 6 existing brain finite element (FE) criteria. Among 513 measured impacts were the first two 6DOF measurements of clinically diagnosed mTBI. For this dataset, 6DOF criteria were the most predictive of injury, more than 3DOF translation-only and 3DOF rotation-only criteria. Peak principal strain in the corpus callosum, a 6DOF FE criteria, was the strongest predictor, followed by two criteria that included rotation measurements, peak rotational acceleration magnitude and Head Impact Power (HIP). These results suggest head rotation measurements may improve injury prediction. However, more 6DOF data is needed to confirm this evaluation of existing injury criteria, and to develop new criteria that considers directional sensitivity to injury.
View details for DOI 10.1007/s10439-014-1212-4
View details for Web of Science ID 000358249800018
View details for PubMedCentralID PMC4478276
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Six Degree-of-Freedom Measurements of Human Mild Traumatic Brain Injury.
Annals of biomedical engineering
2015; 43 (8): 1918-34
Abstract
This preliminary study investigated whether direct measurement of head rotation improves prediction of mild traumatic brain injury (mTBI). Although many studies have implicated rotation as a primary cause of mTBI, regulatory safety standards use 3 degree-of-freedom (3DOF) translation-only kinematic criteria to predict injury. Direct 6DOF measurements of human head rotation (3DOF) and translation (3DOF) have not been previously available to examine whether additional DOFs improve injury prediction. We measured head impacts in American football, boxing, and mixed martial arts using 6DOF instrumented mouthguards, and predicted clinician-diagnosed injury using 12 existing kinematic criteria and 6 existing brain finite element (FE) criteria. Among 513 measured impacts were the first two 6DOF measurements of clinically diagnosed mTBI. For this dataset, 6DOF criteria were the most predictive of injury, more than 3DOF translation-only and 3DOF rotation-only criteria. Peak principal strain in the corpus callosum, a 6DOF FE criteria, was the strongest predictor, followed by two criteria that included rotation measurements, peak rotational acceleration magnitude and Head Impact Power (HIP). These results suggest head rotation measurements may improve injury prediction. However, more 6DOF data is needed to confirm this evaluation of existing injury criteria, and to develop new criteria that considers directional sensitivity to injury.
View details for DOI 10.1007/s10439-014-1212-4
View details for PubMedID 25533767
View details for PubMedCentralID PMC4478276
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Resonance of human brain under head acceleration.
Journal of the Royal Society, Interface / the Royal Society
2015; 12 (108)
Abstract
Although safety standards have reduced fatal head trauma due to single severe head impacts, mild trauma from repeated head exposures may carry risks of long-term chronic changes in the brain's function and structure. To study the physical sensitivities of the brain to mild head impacts, we developed the first dynamic model of the skull-brain based on in vivo MRI data. We showed that the motion of the brain can be described by a rigid-body with constrained kinematics. We further demonstrated that skull-brain dynamics can be approximated by an under-damped system with a low-frequency resonance at around 15 Hz. Furthermore, from our previous field measurements, we found that head motions in a variety of activities, including contact sports, show a primary frequency of less than 20 Hz. This implies that typical head exposures may drive the brain dangerously close to its mechanical resonance and lead to amplified brain-skull relative motions. Our results suggest a possible cause for mild brain trauma, which could occur due to repetitive low-acceleration head oscillations in a variety of recreational and occupational activities.
View details for DOI 10.1098/rsif.2015.0331
View details for PubMedID 26063824
View details for PubMedCentralID PMC4528602
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Resonance of human brain under head acceleration.
Journal of the Royal Society, Interface / the Royal Society
2015; 12 (108)
View details for DOI 10.1098/rsif.2015.0331
View details for PubMedID 26063824
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A Head Impact Detection System Using SVM Classification and Proximity Sensing in an Instrumented Mouthguard
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2014; 61 (11): 2659-2668
View details for DOI 10.1109/TBME.2014.2320153
View details for Web of Science ID 000344082000001
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Model-Less Feedback Control of Continuum Manipulators in Constrained Environments
IEEE TRANSACTIONS ON ROBOTICS
2014; 30 (4): 880-889
View details for DOI 10.1109/TRO.2014.2309194
View details for Web of Science ID 000340451800009
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A Head Impact Detection System Using SVM Classification and Proximity Sensing in an Instrumented Mouthguard.
IEEE transactions on bio-medical engineering
2014
Abstract
Injury from blunt head impacts causes acute neurological deficits and may lead to chronic neurodegeneration. A head impact detection device can serve both as a research tool for studying head injury mechanisms and a clinical tool for real-time trauma screening. The simplest approach is an acceleration thresholding algorithm, which may falsely detect high-acceleration spurious events such as manual manipulation of the device. We designed a head impact detection system that distinguishes head impacts from non-impacts through two subsystems. First, we use infrared proximity sensing to determine if the mouthguard is worn on the teeth to filter out all offteeth events. Second, on-teeth, non-impact events are rejected using a support vector machine classifier trained on frequency domain features of linear acceleration and rotational velocity. The remaining events are classified as head impacts. In a controlled laboratory evaluation, the present system performed substantially better than a 10g acceleration threshold in head impact detection (98% sensitivity, 99.99% specificity, 99% accuracy, and 99.98% precision, compared to 92% sensitivity, 58% specificity, 65% accuracy, and 37% precision). Once adapted for field deployment by training and validation with field data, this system has the potential to effectively detect head trauma in sports, military service, and other high-risk activities.
View details for DOI 10.1109/TBME.2014.2320153
View details for PubMedID 24800918
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Multicellular architecture of malignant breast epithelia influences mechanics.
PloS one
2014; 9 (8)
Abstract
Cell-matrix and cell-cell mechanosensing are important in many cellular processes, particularly for epithelial cells. A crucial question, which remains unexplored, is how the mechanical microenvironment is altered as a result of changes to multicellular tissue structure during cancer progression. In this study, we investigated the influence of the multicellular tissue architecture on mechanical properties of the epithelial component of the mammary acinus. Using creep compression tests on multicellular breast epithelial structures, we found that pre-malignant acini with no lumen (MCF10AT) were significantly stiffer than normal hollow acini (MCF10A) by 60%. This difference depended on structural changes in the pre-malignant acini, as neither single cells nor normal multicellular acini tested before lumen formation exhibited these differences. To understand these differences, we simulated the deformation of the acini with different multicellular architectures and calculated their mechanical properties; our results suggest that lumen filling alone can explain the experimentally observed stiffness increase. We also simulated a single contracting cell in different multicellular architectures and found that lumen filling led to a 20% increase in the "perceived stiffness" of a single contracting cell independent of any changes to matrix mechanics. Our results suggest that lumen filling in carcinogenesis alters the mechanical microenvironment in multicellular epithelial structures, a phenotype that may cause downstream disruptions to mechanosensing.
View details for DOI 10.1371/journal.pone.0101955
View details for PubMedID 25111489
View details for PubMedCentralID PMC4128597
- Model-less Feedback Control of Continuum Manipulators in Constrained Environments IEEE Transactions on Robotics 2014; 30 (4): 880-889
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Outcomes from a Postgraduate Biomedical Technology Innovation Training Program: The First 12 Years of Stanford Biodesign
ANNALS OF BIOMEDICAL ENGINEERING
2013; 41 (9): 1803-1810
Abstract
The Stanford Biodesign Program began in 2001 with a mission of helping to train leaders in biomedical technology innovation. A key feature of the program is a full-time postgraduate fellowship where multidisciplinary teams undergo a process of sourcing clinical needs, inventing solutions and planning for implementation of a business strategy. The program places a priority on needs identification, a formal process of selecting, researching and characterizing needs before beginning the process of inventing. Fellows and students from the program have gone on to careers that emphasize technology innovation across industry and academia. Biodesign trainees have started 26 companies within the program that have raised over $200 million and led to the creation of over 500 new jobs. More importantly, although most of these technologies are still at a very early stage, several projects have received regulatory approval and so far more than 150,000 patients have been treated by technologies invented by our trainees. This paper reviews the initial outcomes of the program and discusses lessons learned and future directions in terms of training priorities.
View details for DOI 10.1007/s10439-013-0761-2
View details for Web of Science ID 000323736800002
View details for PubMedID 23404074
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An Instrumented Mouthguard for Measuring Linear and Angular Head Impact Kinematics in American Football
ANNALS OF BIOMEDICAL ENGINEERING
2013; 41 (9): 1939-1949
Abstract
The purpose of this study was to evaluate a novel instrumented mouthguard as a research device for measuring head impact kinematics. To evaluate kinematic accuracy, laboratory impact testing was performed at sites on the helmet and facemask for determining how closely instrumented mouthguard data matched data from an anthropomorphic test device. Laboratory testing results showed that peak linear acceleration (r (2) = 0.96), peak angular acceleration (r (2) = 0.89), and peak angular velocity (r (2) = 0.98) measurements were highly correlated between the instrumented mouthguard and anthropomorphic test device. Normalized root-mean-square errors for impact time traces were 9.9 ± 4.4% for linear acceleration, 9.7 ± 7.0% for angular acceleration, and 10.4 ± 9.9% for angular velocity. This study demonstrates the potential of an instrumented mouthguard as a research tool for measuring in vivo impacts, which could help uncover the link between head impact kinematics and brain injury in American football.
View details for DOI 10.1007/s10439-013-0801-y
View details for Web of Science ID 000323736800015
View details for PubMedID 23604848
- An Instrumented Mouthguard for Measuring Linear and Angular Head Impact Kinematics in American Football. Annals of Biomedical Engineering 2013; 41 (9): 1939-1949
- Comparing In Vivo Head Impact Kinematics from American Football with Laboratory Drop and Linear Impactors. 2013
- Model-less Feedback Control of Continuum Manipulators in Constrained Environments. IEEE Transactions on Robotic. 2013
- Head Contacts in Collegiate Football Measured with an Instrumented Mouthguard. 2012
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In Vivo Micro-Image Mosaicing
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2011; 58 (1): 159-171
Abstract
Recent advances in optical imaging have led to the development of miniature microscopes that can be brought to the patient for visualizing tissue structures in vivo. These devices have the potential to revolutionize health care by replacing tissue biopsy with in vivo pathology. One of the primary limitations of these microscopes, however, is that the constrained field of view can make image interpretation and navigation difficult. In this paper, we show that image mosaicing can be a powerful tool for widening the field of view and creating image maps of microanatomical structures. First, we present an efficient algorithm for pairwise image mosaicing that can be implemented in real time. Then, we address two of the main challenges associated with image mosaicing in medical applications: cumulative image registration errors and scene deformation. To deal with cumulative errors, we present a global alignment algorithm that draws upon techniques commonly used in probabilistic robotics. To accommodate scene deformation, we present a local alignment algorithm that incorporates deformable surface models into the mosaicing framework. These algorithms are demonstrated on image sequences acquired in vivo with various imaging devices including a hand-held dual-axes confocal microscope, a miniature two-photon microscope, and a commercially available confocal microendoscope.
View details for DOI 10.1109/TBME.2010.2085082
View details for Web of Science ID 000285515500020
View details for PubMedID 20934939
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Configuration Tracking for Continuum Manipulators With Coupled Tendon Drive
IEEE TRANSACTIONS ON ROBOTICS
2009; 25 (4): 798-808
View details for DOI 10.1109/TRO.2009.2022426
View details for Web of Science ID 000268757400004
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Task-Space Control of Continuum Manipulators with Coupled Tendon Drive
11th International Symposium on Experimental Robotics (ISER)
SPRINGER-VERLAG BERLIN. 2009: 271–280
View details for Web of Science ID 000268803300026
- Configuration Tracking for Continuum Manipulators with Coupled Tendon Drive. IEEE Transactions on Robotics 2009; 25 (4): 798-808
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Mechanics Modeling of Tendon-Driven Continuum Manipulators
IEEE TRANSACTIONS ON ROBOTICS
2008; 24 (6): 1262-1273
View details for DOI 10.1109/TRO.2008.2002311
View details for Web of Science ID 000262220900002
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Vision based 3-D shape sensing of flexible manipulators
IEEE International Conference on Robotics and Automation
IEEE. 2008: 2940–2947
View details for Web of Science ID 000258095002034
- Real-Time Image Mosaicing with a Hand-Held Dual-Axis Confocal Microscope. 2008
- Vision Based 3-D Shape Sensing of Flexible Manipulators. 2008
- Task-space Feedback Control of Continuum Manipulators with Coupled Tendon Drive. 2008
- Mechanics Modeling of Tendon Driven Continuum Manipulators. IEEE Transactions on Robotics. 2008; 24 (6): 1262-1273
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Real-time image mosaicing with a hand-held dual-axes confocal microscope
Conference on Endoscopic Microscopy III
SPIE-INT SOC OPTICAL ENGINEERING. 2008
View details for DOI 10.1117/12.764322
View details for Web of Science ID 000255395800007
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Real-Time Image Mosaicing for Medical Applications
15th Conference on Medicine Meets Virtual Reality
I O S PRESS. 2007: 304–309
Abstract
In this paper we describe the development of a robotically-assisted image mosaicing system for medical applications. The processing occurs in real-time due to a fast initial image alignment provided by robotic position sensing. Near-field imaging, defined by relatively large camera motion, requires translations as well as pan and tilt orientations to be measured. To capture these measurements we use 5-d.o.f. sensing along with a hand-eye calibration to account for sensor offset. This sensor-based approach speeds up the mosaicing, eliminates cumulative errors, and readily handles arbitrary camera motions. Our results have produced visually satisfactory mosaics on a dental model but can be extended to other medical images.
View details for Web of Science ID 000270613800069
View details for PubMedID 17377290
- Deformable Image Mosaicing for Optical Biopsy. 2007
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Deformable image mosaicing for optical biopsy
11th IEEE International Conference on Computer Vision
IEEE. 2007: 2212–2219
View details for Web of Science ID 000255099302025
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Robotic technology in surgery: past, present, and future
AMERICAN JOURNAL OF SURGERY
2004; 188 (4A): 2S-15S
Abstract
It has been nearly 20 years since the first appearance of robotics in the operating room. In that time, much progress has been made in integrating robotic technologies with surgical instrumentation, as evidenced by the many thousands of successful robot-assisted cases. However, to build on past success and to fully leverage the potential of surgical robotics in the future, it is essential to maximize a shared understanding and communication among surgeons, engineers, entrepreneurs, and healthcare administrators. This article provides an introduction to medical robotic technologies, develops a possible taxonomy, reviews the evolution of a surgical robot, and discusses future prospects for innovation. Robotic surgery has demonstrated some clear benefits. It remains to be seen where these benefits will outweigh the associated costs over the long term. In the future, surgical robots should be smaller, less expensive, easier to operate, and should seamlessly integrate emerging technologies from a number of different fields. Such advances will enable continued progress in surgical instrumentation and, ultimately, surgical care.
View details for DOI 10.1016/j.amjsung.2004.08.025
View details for Web of Science ID 000224479800003
View details for PubMedID 15476646