Jennifer Hicks is Deputy Director of the Wu Tsai Human Performance Alliance at Stanford, with a focus on collaborative research projects and programs to advance our understanding of the biological principles underlying human performance. Dr. Hicks also serves as the Director of Research for the Mobilize Center, an NIH Biomedical Technology Resource Center at Stanford University and the Restore Center, an NIH-funded center that brings state-of-the-art engineering tools to rehabilitation scientists. Her research is focused on interfacing biomechanical modeling with statistical and machine learning methods to predict the effects of surgery and other interventions on human movement. She is also using data from mobile phones and other novel sources to understand physical activity and performance. Dr. Hicks helps run the multi-faceted training and outreach programs of the Human Performance Alliance, the Mobilize Center and the Restore Center. In addition, as the Research and Development Manager for the OpenSim software project, she guides the project’s development team and serves as the voice of the software user/researcher.
Current Role at Stanford
Deputy Director, Wu Tsai Human Performance Alliance at Stanford
Director of Research, Mobilize Center
Directory of Research, Restore Center
Research and Development Manager, OpenSim Project
Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation.
Journal of neuroengineering and rehabilitation
2021; 18 (1): 126
Modeling human motor control and predicting how humans will move in novel environments is a grand scientific challenge. Researchers in the fields of biomechanics and motor control have proposed and evaluated motor control models via neuromechanical simulations, which produce physically correct motions of a musculoskeletal model. Typically, researchers have developed control models that encode physiologically plausible motor control hypotheses and compared the resulting simulation behaviors to measurable human motion data. While such plausible control models were able to simulate and explain many basic locomotion behaviors (e.g. walking, running, and climbing stairs), modeling higher layer controls (e.g. processing environment cues, planning long-term motion strategies, and coordinating basic motor skills to navigate in dynamic and complex environments) remains a challenge. Recent advances in deep reinforcement learning lay a foundation for modeling these complex control processes and controlling a diverse repertoire of human movement; however, reinforcement learning has been rarely applied in neuromechanical simulation to model human control. In this paper, we review the current state of neuromechanical simulations, along with the fundamentals of reinforcement learning, as it applies to human locomotion. We also present a scientific competition and accompanying software platform, which we have organized to accelerate the use of reinforcement learning in neuromechanical simulations. This "Learn to Move" competition was an official competition at the NeurIPS conference from 2017 to 2019 and attracted over 1300 teams from around the world. Top teams adapted state-of-the-art deep reinforcement learning techniques and produced motions, such as quick turning and walk-to-stand transitions, that have not been demonstrated before in neuromechanical simulations without utilizing reference motion data. We close with a discussion of future opportunities at the intersection of human movement simulation and reinforcement learning and our plans to extend the Learn to Move competition to further facilitate interdisciplinary collaboration in modeling human motor control for biomechanics and rehabilitation research.
View details for DOI 10.1186/s12984-021-00919-y
View details for PubMedID 34399772
An ecosystem service perspective on urban nature, physical activity, and health.
Proceedings of the National Academy of Sciences of the United States of America
2021; 118 (22)
Nature underpins human well-being in critical ways, especially in health. Nature provides pollination of nutritious crops, purification of drinking water, protection from floods, and climate security, among other well-studied health benefits. A crucial, yet challenging, research frontier is clarifying how nature promotes physical activity for its many mental and physical health benefits, particularly in densely populated cities with scarce and dwindling access to nature. Here we frame this frontier by conceptually developing a spatial decision-support tool that shows where, how, and for whom urban nature promotes physical activity, to inform urban greening efforts and broader health assessments. We synthesize what is known, present a model framework, and detail the model steps and data needs that can yield generalizable spatial models and an effective tool for assessing the urban nature-physical activity relationship. Current knowledge supports an initial model that can distinguish broad trends and enrich urban planning, spatial policy, and public health decisions. New, iterative research and application will reveal the importance of different types of urban nature, the different subpopulations who will benefit from it, and nature's potential contribution to creating more equitable, green, livable cities with active inhabitants.
View details for DOI 10.1073/pnas.2018472118
View details for PubMedID 33990458
Wearable sensors enable personalized predictions of clinical laboratory measurements.
Vital signs, including heart rate and body temperature, are useful in detecting or monitoring medical conditions, but are typically measured in the clinic and require follow-up laboratory testing for more definitive diagnoses. Here we examined whether vital signs as measured by consumer wearable devices (that is, continuously monitored heart rate, body temperature, electrodermal activity and movement) can predict clinical laboratory test results using machine learning models, including random forest and Lasso models. Our results demonstrate that vital sign data collected from wearables give a more consistent and precise depiction of resting heart rate than do measurements taken in the clinic. Vital sign data collected from wearables can also predict several clinical laboratory measurements with lower prediction error than predictions made using clinically obtained vital sign measurements. The length of time over which vital signs are monitored and the proximity of the monitoring period to the date of prediction play a critical role in the performance of the machine learning models. These results demonstrate the value of commercial wearable devices for continuous and longitudinal assessment of physiological measurements that today can be measured only with clinical laboratory tests.
View details for DOI 10.1038/s41591-021-01339-0
View details for PubMedID 34031607
The effects of motor modularity on performance, learning and generalizability in upper-extremity reaching: a computational analysis.
Journal of the Royal Society, Interface
2020; 17 (167): 20200011
It has been hypothesized that the central nervous system simplifies the production of movement by limiting motor commands to a small set of modules known as muscle synergies. Recently, investigators have questioned whether a low-dimensional controller can produce the rich and flexible behaviours seen in everyday movements. To study this issue, we implemented muscle synergies in a biomechanically realistic model of the human upper extremity and performed computational experiments to determine whether synergies introduced task performance deficits, facilitated the learning of movements, and generalized to different movements. We derived sets of synergies from the muscle excitations our dynamic optimizations computed for a nominal task (reaching in a plane). Then we compared the performance and learning rates of a controller that activated all muscles independently to controllers that activated the synergies derived from the nominal reaching task. We found that a controller based on synergies had errors within 1 cm of a full-dimensional controller and achieved faster learning rates (as estimated from computational time to converge). The synergy-based controllers could also accomplish new tasks-such as reaching to targets on a higher or lower plane, and starting from alternative initial poses-with average errors similar to a full-dimensional controller.
View details for DOI 10.1098/rsif.2020.0011
View details for PubMedID 32486950
Pre-operative gastrocnemius lengths in gait predict outcomes following gastrocnemius lengthening surgery in children with cerebral palsy.
2020; 15 (6): e0233706
Equinus deformity is one of the most common gait deformities in children with cerebral palsy. We examined whether estimates of gastrocnemius length in gait could identify limbs likely to have short-term and long-term improvements in ankle kinematics following gastrocnemius lengthening surgery to correct equinus. We retrospectively analyzed data of 891 limbs that underwent a single-event multi-level surgery (SEMLS), and categorized outcomes based on the normalcy of ankle kinematics. Limbs with short gastrocnemius lengths that received a gastrocnemius lengthening surgery as part of a SEMLS (case limbs) were 2.2 times more likely than overtreated limbs (i.e., limbs who did not have short lengths, but still received a lengthening surgery) to have a good surgical outcome at the follow-up gait visit (good outcome rate of 71% vs. 33%). Case limbs were 1.2 times more likely than control limbs (i.e., limbs that had short gastrocnemius lengths but no lengthening surgery) to have a good outcome (71% vs. 59%). Three-fourths of the case limbs with a good outcome at the follow-up gait visit maintained this outcome over time, compared to only one-half of the overtreated limbs. Our results caution against over-prescription of gastrocnemius lengthening surgery and suggest gastrocnemius lengths can be used to identify good surgical candidates.
View details for DOI 10.1371/journal.pone.0233706
View details for PubMedID 32502157
Testing Simulated Assistance Strategies on a Hip-Knee-Ankle Exoskeleton: a Case Study
IEEE. 2020: 700-707
View details for Web of Science ID 000636920600112
OpenSim Moco: Musculoskeletal optimal control.
PLoS computational biology
2020; 16 (12): e1008493
Musculoskeletal simulations are used in many different applications, ranging from the design of wearable robots that interact with humans to the analysis of patients with impaired movement. Here, we introduce OpenSim Moco, a software toolkit for optimizing the motion and control of musculoskeletal models built in the OpenSim modeling and simulation package. OpenSim Moco uses the direct collocation method, which is often faster and can handle more diverse problems than other methods for musculoskeletal simulation. Moco frees researchers from implementing direct collocation themselves-which typically requires extensive technical expertise-and allows them to focus on their scientific questions. The software can handle a wide range of problems that interest biomechanists, including motion tracking, motion prediction, parameter optimization, model fitting, electromyography-driven simulation, and device design. Moco is the first musculoskeletal direct collocation tool to handle kinematic constraints, which enable modeling of kinematic loops (e.g., cycling models) and complex anatomy (e.g., patellar motion). To show the abilities of Moco, we first solved for muscle activity that produced an observed walking motion while minimizing squared muscle excitations and knee joint loading. Next, we predicted how muscle weakness may cause deviations from a normal walking motion. Lastly, we predicted a squat-to-stand motion and optimized the stiffness of an assistive device placed at the knee. We designed Moco to be easy to use, customizable, and extensible, thereby accelerating the use of simulations to understand the movement of humans and other animals.
View details for DOI 10.1371/journal.pcbi.1008493
View details for PubMedID 33370252
Deep neural networks enable quantitative movement analysis using single-camera videos.
2020; 11 (1): 4054
Many neurological and musculoskeletal diseases impair movement, which limits people's function and social participation. Quantitative assessment of motion is critical to medical decision-making but is currently possible only with expensive motion capture systems and highly trained personnel. Here, we present a method for predicting clinically relevant motion parameters from an ordinary video of a patient. Our machine learning models predict parameters include walking speed (r = 0.73), cadence (r = 0.79), knee flexion angle at maximum extension (r = 0.83), and Gait Deviation Index (GDI), a comprehensive metric of gait impairment (r = 0.75). These correlation values approach the theoretical limits for accuracy imposed by natural variability in these metrics within our patient population. Our methods for quantifying gait pathology with commodity cameras increase access to quantitative motion analysis in clinics and at home and enable researchers to conduct large-scale studies of neurological and musculoskeletal disorders.
View details for DOI 10.1038/s41467-020-17807-z
View details for PubMedID 32792511
- Best practices for analyzing large-scale health data from wearables and smartphone apps NPJ DIGITAL MEDICINE 2019; 2
Best practices for analyzing large-scale health data from wearables and smartphone apps.
NPJ digital medicine
2019; 2: 45
Smartphone apps and wearable devices for tracking physical activity and other health behaviors have become popular in recent years and provide a largely untapped source of data about health behaviors in the free-living environment. The data are large in scale, collected at low cost in the "wild", and often recorded in an automatic fashion, providing a powerful complement to traditional surveillance studies and controlled trials. These data are helping to reveal, for example, new insights about environmental and social influences on physical activity. The observational nature of the datasets and collection via commercial devices and apps pose challenges, however, including the potential for measurement, population, and/or selection bias, as well as missing data. In this article, we review insights gleaned from these datasets and propose best practices for addressing the limitations of large-scale data from apps and wearables. Our goal is to enable researchers to effectively harness the data from smartphone apps and wearable devices to better understand what drives physical activity and other health behaviors.
View details for DOI 10.1038/s41746-019-0121-1
View details for PubMedID 31304391
View details for PubMedCentralID PMC6550237
Predicting gait adaptations due to ankle plantarflexor muscle weakness and contracture using physics-based musculoskeletal simulations.
PLoS computational biology
2019; 15 (10): e1006993
Deficits in the ankle plantarflexor muscles, such as weakness and contracture, occur commonly in conditions such as cerebral palsy, stroke, muscular dystrophy, Charcot-Marie-Tooth disease, and sarcopenia. While these deficits likely contribute to observed gait pathologies, determining cause-effect relationships is difficult due to the often co-occurring biomechanical and neural deficits. To elucidate the effects of weakness and contracture, we systematically introduced isolated deficits into a musculoskeletal model and generated simulations of walking to predict gait adaptations due to these deficits. We trained a planar model containing 9 degrees of freedom and 18 musculotendon actuators to walk using a custom optimization framework through which we imposed simple objectives, such as minimizing cost of transport while avoiding falling and injury, and maintaining head stability. We first generated gaits at prescribed speeds between 0.50 m/s and 2.00 m/s that reproduced experimentally observed kinematic, kinetic, and metabolic trends for walking. We then generated a gait at self-selected walking speed; quantitative comparisons between our simulation and experimental data for joint angles, joint moments, and ground reaction forces showed root-mean-squared errors of less than 1.6 standard deviations and normalized cross-correlations above 0.8 except for knee joint moment trajectories. Finally, we applied mild, moderate, and severe levels of muscle weakness or contracture to either the soleus (SOL) or gastrocnemius (GAS) or both of these major plantarflexors (PF) and retrained the model to walk at a self-selected speed. The model was robust to all deficits, finding a stable gait in all cases. Severe PF weakness caused the model to adopt a slower, "heel-walking" gait. Severe contracture of only SOL or both PF yielded similar results: the model adopted a "toe-walking" gait with excessive hip and knee flexion during stance. These results highlight how plantarflexor weakness and contracture may contribute to observed gait patterns.
View details for DOI 10.1371/journal.pcbi.1006993
View details for PubMedID 31589597
- Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities JOURNAL OF BIOMECHANICS 2018; 81: 1–11
Estimating the effect size of surgery to improve walking in children with cerebral palsy from retrospective observational clinical data.
2018; 8 (1): 16344
Single-event multilevel surgery (SEMLS) is a standard treatment approach aimed at improving gait for patients with cerebral palsy, but the effect of this approach compared to natural progression without surgical intervention is unclear. In this study, we used retrospective patient history, physical exam, and three-dimensional gait analysis data from 2,333 limbs to build regression models estimating the effect of SEMLS on gait, while controlling for expected natural progression. Post-hoc classifications using the regression model results identified which limbs would exhibit gait within two standard deviations of typical gait at the follow-up visit with or without a SEMLS with 73% and 77% accuracy, respectively. Using these models, we found that, while surgery was expected to have a positive effect on 93% of limbs compared to natural progression, in only 37% of limbs was this expected effect a clinically meaningful improvement. We identified 26% of the non-surgically treated limbs that may have shown a clinically meaningful improvement in gait had they received surgery. Our models suggest that pre-operative physical therapy focused on improving biomechanical characteristics, such as walking speed and strength, may improve likelihood of positive surgical outcomes. These models are shared with the community to use as an evaluation tool when considering whether or not a patient should undergo a SEMLS.
View details for PubMedID 30397268
- Estimating the effect size of surgery to improve walking in children with cerebral palsy from retrospective observational clinical data SCIENTIFIC REPORTS 2018; 8
Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities.
Journal of biomechanics
Traditional laboratory experiments, rehabilitation clinics, and wearable sensors offer biomechanists a wealth of data on healthy and pathological movement. To harness the power of these data and make research more efficient, modern machine learning techniques are starting to complement traditional statistical tools. This survey summarizes the current usage of machine learning methods in human movement biomechanics and highlights best practices that will enable critical evaluation of the literature. We carried out a PubMed/Medline database search for original research articles that used machine learning to study movement biomechanics in patients with musculoskeletal and neuromuscular diseases. Most studies that met our inclusion criteria focused on classifying pathological movement, predicting risk of developing a disease, estimating the effect of an intervention, or automatically recognizing activities to facilitate out-of-clinic patient monitoring. We found that research studies build and evaluate models inconsistently, which motivated our discussion of best practices. We provide recommendations for training and evaluating machine learning models and discuss the potential of several underutilized approaches, such as deep learning, to generate new knowledge about human movement. We believe that cross-training biomechanists in data science and a cultural shift toward sharing of data and tools are essential to maximize the impact of biomechanics research.
View details for PubMedID 30279002
- OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement PLOS COMPUTATIONAL BIOLOGY 2018; 14 (7)
OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement.
PLoS computational biology
2018; 14 (7): e1006223
Movement is fundamental to human and animal life, emerging through interaction of complex neural, muscular, and skeletal systems. Study of movement draws from and contributes to diverse fields, including biology, neuroscience, mechanics, and robotics. OpenSim unites methods from these fields to create fast and accurate simulations of movement, enabling two fundamental tasks. First, the software can calculate variables that are difficult to measure experimentally, such as the forces generated by muscles and the stretch and recoil of tendons during movement. Second, OpenSim can predict novel movements from models of motor control, such as kinematic adaptations of human gait during loaded or inclined walking. Changes in musculoskeletal dynamics following surgery or due to human-device interaction can also be simulated; these simulations have played a vital role in several applications, including the design of implantable mechanical devices to improve human grasping in individuals with paralysis. OpenSim is an extensible and user-friendly software package built on decades of knowledge about computational modeling and simulation of biomechanical systems. OpenSim's design enables computational scientists to create new state-of-the-art software tools and empowers others to use these tools in research and clinical applications. OpenSim supports a large and growing community of biomechanics and rehabilitation researchers, facilitating exchange of models and simulations for reproducing and extending discoveries. Examples, tutorials, documentation, and an active user forum support this community. The OpenSim software is covered by the Apache License 2.0, which permits its use for any purpose including both nonprofit and commercial applications. The source code is freely and anonymously accessible on GitHub, where the community is welcomed to make contributions. Platform-specific installers of OpenSim include a GUI and are available on simtk.org.
View details for PubMedID 30048444
Perspectives on Sharing Models and Related Resources in Computational Biomechanics Research
JOURNAL OF BIOMECHANICAL ENGINEERING-TRANSACTIONS OF THE ASME
2018; 140 (2)
The role of computational modeling for biomechanics research and related clinical care will be increasingly prominent. The biomechanics community has been developing computational models routinely for exploration of the mechanics and mechanobiology of diverse biological structures. As a result, a large array of models, data, and discipline-specific simulation software has emerged to support endeavors in computational biomechanics. Sharing computational models and related data and simulation software has first become a utilitarian interest, and now, it is a necessity. Exchange of models, in support of knowledge exchange provided by scholarly publishing, has important implications. Specifically, model sharing can facilitate assessment of reproducibility in computational biomechanics and can provide an opportunity for repurposing and reuse, and a venue for medical training. The community's desire to investigate biological and biomechanical phenomena crossing multiple systems, scales, and physical domains, also motivates sharing of modeling resources as blending of models developed by domain experts will be a required step for comprehensive simulation studies as well as the enhancement of their rigor and reproducibility. The goal of this paper is to understand current perspectives in the biomechanics community for the sharing of computational models and related resources. Opinions on opportunities, challenges, and pathways to model sharing, particularly as part of the scholarly publishing workflow, were sought. A group of journal editors and a handful of investigators active in computational biomechanics were approached to collect short opinion pieces as a part of a larger effort of the IEEE EMBS Computational Biology and the Physiome Technical Committee to address model reproducibility through publications. A synthesis of these opinion pieces indicates that the community recognizes the necessity and usefulness of model sharing. There is a strong will to facilitate model sharing, and there are corresponding initiatives by the scientific journals. Outside the publishing enterprise, infrastructure to facilitate model sharing in biomechanics exists, and simulation software developers are interested in accommodating the community's needs for sharing of modeling resources. Encouragement for the use of standardized markups, concerns related to quality assurance, acknowledgement of increased burden, and importance of stewardship of resources are noted. In the short-term, it is advisable that the community builds upon recent strategies and experiments with new pathways for continued demonstration of model sharing, its promotion, and its utility. Nonetheless, the need for a long-term strategy to unify approaches in sharing computational models and related resources is acknowledged. Development of a sustainable platform supported by a culture of open model sharing will likely evolve through continued and inclusive discussions bringing all stakeholders at the table, e.g., by possibly establishing a consortium.
View details for PubMedID 29247253
View details for PubMedCentralID PMC5821103
- Introduction to NIPS 2017 Competition Track NIPS'17 COMPETITION: BUILDING INTELLIGENT SYSTEMS 2018: 1–23
- Learning to Run Challenge: Synthesizing Physiologically Accurate Motion Using Deep Reinforcement Learning NIPS'17 COMPETITION: BUILDING INTELLIGENT SYSTEMS 2018: 101–20
- Learning to Run Challenge Solutions: Adapting Reinforcement Learning Methods for Neuromusculoskeletal Environments NIPS'17 COMPETITION: BUILDING INTELLIGENT SYSTEMS 2018: 121–53
Large-scale physical activity data reveal worldwide activity inequality
2017; 547 (7663): 336-+
To be able to curb the global pandemic of physical inactivity and the associated 5.3 million deaths per year, we need to understand the basic principles that govern physical activity. However, there is a lack of large-scale measurements of physical activity patterns across free-living populations worldwide. Here we leverage the wide usage of smartphones with built-in accelerometry to measure physical activity at the global scale. We study a dataset consisting of 68 million days of physical activity for 717,527 people, giving us a window into activity in 111 countries across the globe. We find inequality in how activity is distributed within countries and that this inequality is a better predictor of obesity prevalence in the population than average activity volume. Reduced activity in females contributes to a large portion of the observed activity inequality. Aspects of the built environment, such as the walkability of a city, are associated with a smaller gender gap in activity and lower activity inequality. In more walkable cities, activity is greater throughout the day and throughout the week, across age, gender, and body mass index (BMI) groups, with the greatest increases in activity found for females. Our findings have implications for global public health policy and urban planning and highlight the role of activity inequality and the built environment in improving physical activity and health.
View details for PubMedID 28693034
View details for PubMedCentralID PMC5774986
Preparatory co-activation of the ankle muscles may prevent ankle inversion injuries.
Journal of biomechanics
2017; 52: 17-23
Ankle inversion sprains are the most frequent acute musculoskeletal injuries occurring in physical activity. Interventions that retrain muscle coordination have helped rehabilitate injured ankles, but it is unclear which muscle coordination strategies, if any, can prevent ankle sprains. The purpose of this study was to determine whether coordinated activity of the ankle muscles could prevent excessive ankle inversion during a simulated landing on a 30° incline. We used a set of musculoskeletal simulations to evaluate the efficacy of two strategies for coordinating the ankle evertor and invertor muscles during simulated landing scenarios: planned co-activation and stretch reflex activation with physiologic latency (60-ms delay). A full-body musculoskeletal model of landing was used to generate simulations of a subject dropping onto an inclined surface with each coordination condition. Within each condition, the intensity of evertor and invertor co-activity or stretch reflexes were varied systematically. The simulations revealed that strong preparatory co-activation of the ankle evertors and invertors prior to ground contact prevented ankle inversion from exceeding injury thresholds by rapidly generating eversion moments after initial contact. Conversely, stretch reflexes were too slow to generate eversion moments before the simulations reached the threshold for inversion injury. These results suggest that training interventions to protect the ankle should focus on stiffening the ankle with muscle co-activation prior to landing. The musculoskeletal models, controllers, software, and simulation results are freely available online at http://simtk.org/home/ankle-sprains, enabling others to reproduce the results and explore new injury scenarios and interventions.
View details for DOI 10.1016/j.jbiomech.2016.11.002
View details for PubMedID 28057351
Simulating ideal assistive devices to reduce the metabolic cost of walking with heavy loads.
2017; 12 (7): e0180320
Wearable robotic devices can restore and enhance mobility. There is growing interest in designing devices that reduce the metabolic cost of walking; however, designers lack guidelines for which joints to assist and when to provide the assistance. To help address this problem, we used musculoskeletal simulation to predict how hypothetical devices affect muscle activity and metabolic cost when walking with heavy loads. We explored 7 massless devices, each providing unrestricted torque at one degree of freedom in one direction (hip abduction, hip flexion, hip extension, knee flexion, knee extension, ankle plantarflexion, or ankle dorsiflexion). We used the Computed Muscle Control algorithm in OpenSim to find device torque profiles that minimized the sum of squared muscle activations while tracking measured kinematics of loaded walking without assistance. We then examined the metabolic savings provided by each device, the corresponding device torque profiles, and the resulting changes in muscle activity. We found that the hip flexion, knee flexion, and hip abduction devices provided greater metabolic savings than the ankle plantarflexion device. The hip abduction device had the greatest ratio of metabolic savings to peak instantaneous positive device power, suggesting that frontal-plane hip assistance may be an efficient way to reduce metabolic cost. Overall, the device torque profiles generally differed from the corresponding net joint moment generated by muscles without assistance, and occasionally exceeded the net joint moment to reduce muscle activity at other degrees of freedom. Many devices affected the activity of muscles elsewhere in the limb; for example, the hip flexion device affected muscles that span the ankle joint. Our results may help experimentalists decide which joint motions to target when building devices and can provide intuition for how devices may interact with the musculoskeletal system. The simulations are freely available online, allowing others to reproduce and extend our work.
View details for PubMedID 28700630
View details for PubMedCentralID PMC5507502
ShortFuse: Biomedical Time Series Representations in the Presence of Structured Information.
Proceedings of machine learning research
2017; 68: 59–74
In healthcare applications, temporal variables that encode movement, health status and longitudinal patient evolution are often accompanied by rich structured information such as demographics, diagnostics and medical exam data. However, current methods do not jointly optimize over structured covariates and time series in the feature extraction process. We present ShortFuse, a method that boosts the accuracy of deep learning models for time series by explicitly modeling temporal interactions and dependencies with structured covariates. ShortFuse introduces hybrid convolutional and LSTM cells that incorporate the covariates via weights that are shared across the temporal domain. ShortFuse outperforms competing models by 3% on two biomedical applications, forecasting osteoarthritis-related cartilage degeneration and predicting surgical outcomes for cerebral palsy patients, matching or exceeding the accuracy of models that use features engineered by domain experts.
View details for PubMedID 30882086
Gait biomechanics in the era of data science.
Journal of biomechanics
Data science has transformed fields such as computer vision and economics. The ability of modern data science methods to extract insights from large, complex, heterogeneous, and noisy datasets is beginning to provide a powerful complement to the traditional approaches of experimental motion capture and biomechanical modeling. The purpose of this article is to provide a perspective on how data science methods can be incorporated into our field to advance our understanding of gait biomechanics and improve treatment planning procedures. We provide examples of how data science approaches have been applied to biomechanical data. We then discuss the challenges that remain for effectively using data science approaches in clinical gait analysis and gait biomechanics research, including the need for new tools, better infrastructure and incentives for sharing data, and education across the disciplines of biomechanics and data science. By addressing these challenges, we can revolutionize treatment planning and biomechanics research by capitalizing on the wealth of knowledge gained by gait researchers over the past decades and the vast, but often siloed, data that are collected in clinical and research laboratories around the world.
View details for DOI 10.1016/j.jbiomech.2016.10.033
View details for PubMedID 27814971
Full-Body Musculoskeletal Model for Muscle-Driven Simulation of Human Gait
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2016; 63 (10): 2068-2079
Musculoskeletal models provide a non-invasive means to study human movement and predict the effects of interventions on gait. Our goal was to create an open-source 3-D musculoskeletal model with high-fidelity representations of the lower limb musculature of healthy young individuals that can be used to generate accurate simulations of gait.Our model includes bony geometry for the full body, 37 degrees of freedom to define joint kinematics, Hill-type models of 80 muscle-tendon units actuating the lower limbs, and 17 ideal torque actuators driving the upper body. The model's musculotendon parameters are derived from previous anatomical measurements of 21 cadaver specimens and magnetic resonance images of 24 young healthy subjects. We tested the model by evaluating its computational time and accuracy of simulations of healthy walking and running.Generating muscle-driven simulations of normal walking and running took approximately 10 minutes on a typical desktop computer. The differences between our muscle-generated and inverse dynamics joint moments were within 3% (RMSE) of the peak inverse dynamics joint moments in both walking and running, and our simulated muscle activity showed qualitative agreement with salient features from experimental electromyography data.These results suggest that our model is suitable for generating muscle-driven simulations of healthy gait. We encourage other researchers to further validate and apply the model to study other motions of the lower extremity.The model is implemented in the open-source software platform OpenSim. The model and data used to create and test the simulations are freely available at https://simtk.org/home/full_body/, allowing others to reproduce these results and create their own simulations.
View details for DOI 10.1109/TBME.2016.2586891
View details for Web of Science ID 000384570200010
View details for PubMedID 27392337
Simulating Ideal Assistive Devices to Reduce the Metabolic Cost of Running
2016; 11 (9)
Tools have been used for millions of years to augment the capabilities of the human body, allowing us to accomplish tasks that would otherwise be difficult or impossible. Powered exoskeletons and other assistive devices are sophisticated modern tools that have restored bipedal locomotion in individuals with paraplegia and have endowed unimpaired individuals with superhuman strength. Despite these successes, designing assistive devices that reduce energy consumption during running remains a substantial challenge, in part because these devices disrupt the dynamics of a complex, finely tuned biological system. Furthermore, designers have hitherto relied primarily on experiments, which cannot report muscle-level energy consumption and are fraught with practical challenges. In this study, we use OpenSim to generate muscle-driven simulations of 10 human subjects running at 2 and 5 m/s. We then add ideal, massless assistive devices to our simulations and examine the predicted changes in muscle recruitment patterns and metabolic power consumption. Our simulations suggest that an assistive device should not necessarily apply the net joint moment generated by muscles during unassisted running, and an assistive device can reduce the activity of muscles that do not cross the assisted joint. Our results corroborate and suggest biomechanical explanations for similar effects observed by experimentalists, and can be used to form hypotheses for future experimental studies. The models, simulations, and software used in this study are freely available at simtk.org and can provide insight into assistive device design that complements experimental approaches.
View details for DOI 10.1371/journal.pone.0163417
View details for Web of Science ID 000383893200115
View details for PubMedID 27656901
View details for PubMedCentralID PMC5033584
Simulation-Based Design for Wearable Robotic Systems: An Optimization Framework for Enhancing a Standing Long Jump
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2016; 63 (5): 894-903
Technologies that augment human performance are the focus of intensive research and development, driven by advances in wearable robotic systems. Success has been limited by the challenge of understanding human-robot interaction. To address this challenge, we developed an optimization framework to synthesize a realistic human standing long jump and used the framework to explore how simulated wearable robotic devices might enhance jump performance.A planar, five-segment, seven-degree-of-freedom model with physiological torque actuators, which have variable torque capacity depending on joint position and velocity, was used to represent human musculoskeletal dynamics. An active augmentation device was modeled as a torque actuator that could apply a single pulse of up to 100 Nm of extension torque. A passive design was modeled as rotational springs about each lower limb joint. Dynamic optimization searched for physiological and device actuation patterns to maximize jump distance.Optimization of the nominal case yielded a 2.27 m jump that captured salient kinematic and kinetic features of human jumps. When the active device was added to the ankle, knee, or hip, jump distance increased to between 2.49 and 2.52 m. Active augmentation of all three joints increased the jump distance to 3.10 m. The passive design increased jump distance to 3.32 m by adding torques of 135, 365, and 297 Nm to the ankle, knee, and hip, respectively.Dynamic optimization can be used to simulate a standing long jump and investigate human-robot interaction.Simulation can aid in the design of performance-enhancing technologies.
View details for DOI 10.1109/TBME.2015.2463077
View details for Web of Science ID 000375001600002
View details for PubMedID 26258930
Stretching Your Energetic Budget: How Tendon Compliance Affects the Metabolic Cost of Running.
2016; 11 (3)
Muscles attach to bones via tendons that stretch and recoil, affecting muscle force generation and metabolic energy consumption. In this study, we investigated the effect of tendon compliance on the metabolic cost of running using a full-body musculoskeletal model with a detailed model of muscle energetics. We performed muscle-driven simulations of running at 2-5 m/s with tendon force-strain curves that produced between 1 and 10% strain when the muscles were developing maximum isometric force. We computed the average metabolic power consumed by each muscle when running at each speed and with each tendon compliance. Average whole-body metabolic power consumption increased as running speed increased, regardless of tendon compliance, and was lowest at each speed when tendon strain reached 2-3% as muscles were developing maximum isometric force. When running at 2 m/s, the soleus muscle consumed less metabolic power at high tendon compliance because the strain of the tendon allowed the muscle fibers to operate nearly isometrically during stance. In contrast, the medial and lateral gastrocnemii consumed less metabolic power at low tendon compliance because less compliant tendons allowed the muscle fibers to operate closer to their optimal lengths during stance. The software and simulations used in this study are freely available at simtk.org and enable examination of muscle energetics with unprecedented detail.
View details for DOI 10.1371/journal.pone.0150378
View details for PubMedID 26930416
View details for PubMedCentralID PMC4773147
The mobilize center: an NIH big data to knowledge center to advance human movement research and improve mobility.
Journal of the American Medical Informatics Association
2015; 22 (6): 1120-1125
Regular physical activity helps prevent heart disease, stroke, diabetes, and other chronic diseases, yet a broad range of conditions impair mobility at great personal and societal cost. Vast amounts of data characterizing human movement are available from research labs, clinics, and millions of smartphones and wearable sensors, but integration and analysis of this large quantity of mobility data are extremely challenging. The authors have established the Mobilize Center (http://mobilize.stanford.edu) to harness these data to improve human mobility and help lay the foundation for using data science methods in biomedicine. The Center is organized around 4 data science research cores: biomechanical modeling, statistical learning, behavioral and social modeling, and integrative modeling. Important biomedical applications, such as osteoarthritis and weight management, will focus the development of new data science methods. By developing these new approaches, sharing data and validated software tools, and training thousands of researchers, the Mobilize Center will transform human movement research.
View details for DOI 10.1093/jamia/ocv071
View details for PubMedID 26272077
View details for PubMedCentralID PMC4639715
Predictive Simulation Generates Human Adaptations during Loaded and Inclined Walking
2015; 10 (4)
Predictive simulation is a powerful approach for analyzing human locomotion. Unlike techniques that track experimental data, predictive simulations synthesize gaits by minimizing a high-level objective such as metabolic energy expenditure while satisfying task requirements like achieving a target velocity. The fidelity of predictive gait simulations has only been systematically evaluated for locomotion data on flat ground. In this study, we construct a predictive simulation framework based on energy minimization and use it to generate normal walking, along with walking with a range of carried loads and up a range of inclines. The simulation is muscle-driven and includes controllers based on muscle force and stretch reflexes and contact state of the legs. We demonstrate how human-like locomotor strategies emerge from adapting the model to a range of environmental changes. Our simulation dynamics not only show good agreement with experimental data for normal walking on flat ground (92% of joint angle trajectories and 78% of joint torque trajectories lie within 1 standard deviation of experimental data), but also reproduce many of the salient changes in joint angles, joint moments, muscle coordination, and metabolic energy expenditure observed in experimental studies of loaded and inclined walking.
View details for DOI 10.1371/journal.pone.0121407
View details for Web of Science ID 000352135600071
View details for PubMedID 25830913
View details for PubMedCentralID PMC4382289
Is My Model Good Enough? Best Practices for Verification and Validation of Musculoskeletal Models and Simulations of Movement
JOURNAL OF BIOMECHANICAL ENGINEERING-TRANSACTIONS OF THE ASME
2015; 137 (2)
Computational modeling and simulation of neuromusculoskeletal (NMS) systems enables researchers and clinicians to study the complex dynamics underlying human and animal movement. NMS models use equations derived from physical laws and biology to help solve challenging real-world problems, from designing prosthetics that maximize running speed to developing exoskeletal devices that enable walking after a stroke. NMS modeling and simulation has proliferated in the biomechanics research community over the past 25 years, but the lack of verification and validation standards remains a major barrier to wider adoption and impact. The goal of this paper is to establish practical guidelines for verification and validation of NMS models and simulations that researchers, clinicians, reviewers, and others can adopt to evaluate the accuracy and credibility of modeling studies. In particular, we review a general process for verification and validation applied to NMS models and simulations, including careful formulation of a research question and methods, traditional verification and validation steps, and documentation and sharing of results for use and testing by other researchers. Modeling the NMS system and simulating its motion involves methods to represent neural control, musculoskeletal geometry, muscle-tendon dynamics, contact forces, and multibody dynamics. For each of these components, we review modeling choices and software verification guidelines; discuss variability, errors, uncertainty, and sensitivity relationships; and provide recommendations for verification and validation by comparing experimental data and testing robustness. We present a series of case studies to illustrate key principles. In closing, we discuss challenges the community must overcome to ensure that modeling and simulation are successfully used to solve the broad spectrum of problems that limit human mobility.
View details for DOI 10.1115/1.4029304
View details for Web of Science ID 000350571400005
View details for PubMedID 25474098
View details for PubMedCentralID PMC4321112
Musculoskeletal modelling deconstructs the paradoxical effects of elastic ankle exoskeletons on plantar-flexor mechanics and energetics during hopping
JOURNAL OF EXPERIMENTAL BIOLOGY
2014; 217 (22): 4018-4028
Experiments have shown that elastic ankle exoskeletons can be used to reduce ankle joint and plantar-flexor muscle loading when hopping in place and, in turn, reduce metabolic energy consumption. However, recent experimental work has shown that such exoskeletons cause less favourable soleus (SO) muscle-tendon mechanics than is observed during normal hopping, which might limit the capacity of the exoskeleton to reduce energy consumption. To directly link plantar-flexor mechanics and energy consumption when hopping in exoskeletons, we used a musculoskeletal model of the human leg and a model of muscle energetics in simulations of muscle-tendon dynamics during hopping with and without elastic ankle exoskeletons. Simulations were driven by experimental electromyograms, joint kinematics and exoskeleton torque taken from previously published data. The data were from seven males who hopped at 2.5 Hz with and without elastic ankle exoskeletons. The energetics model showed that the total rate of metabolic energy consumption by ankle muscles was not significantly reduced by an ankle exoskeleton. This was despite large reductions in plantar-flexor force production (40-50%). The lack of larger metabolic reductions with exoskeletons was attributed to increases in plantar-flexor muscle fibre velocities and a shift to less favourable muscle fibre lengths during active force production. This limited the capacity for plantar-flexors to reduce activation and energy consumption when hopping with exoskeleton assistance.
View details for DOI 10.1242/jeb.107656
View details for Web of Science ID 000344867000016
View details for PubMedID 25278469
View details for PubMedCentralID PMC4229366
Muscle contributions to vertical and fore-aft accelerations are altered in subjects with crouch gait.
Gait & posture
2013; 38 (1): 86-91
The goals of this study were to determine if the muscle contributions to vertical and fore-aft acceleration of the mass center differ between crouch gait and unimpaired gait and if these muscle contributions change with crouch severity. Examining muscle contributions to mass center acceleration provides insight into the roles of individual muscles during gait and can provide guidance for treatment planning. We calculated vertical and fore-aft accelerations using musculoskeletal simulations of typically developing children and children with cerebral palsy and crouch gait. Analysis of these simulations revealed that during unimpaired gait the quadriceps produce large upward and backward accelerations during early stance, whereas the ankle plantarflexors produce large upward and forward accelerations later in stance. In contrast, during crouch gait, the quadriceps and ankle plantarflexors produce large, opposing fore-aft accelerations throughout stance. The quadriceps force required to accelerate the mass center upward was significantly larger in crouch gait than in unimpaired gait and increased with crouch severity. The gluteus medius accelerated the mass center upward during midstance in unimpaired gait; however, during crouch gait the upward acceleration produced by the gluteus medius was significantly reduced. During unimpaired gait the quadriceps and ankle plantarflexors accelerate the mass center at different times, efficiently modulating fore-aft accelerations. However, during crouch gait, the quadriceps and ankle plantarflexors produce fore-aft accelerations at the same time and the opposing fore-aft accelerations generated by these muscles contribute to the inefficiency of crouch gait.
View details for DOI 10.1016/j.gaitpost.2012.10.019
View details for PubMedID 23200083
View details for PubMedCentralID PMC3600387
Can biomechanical variables predict improvement in crouch gait?
GAIT & POSTURE
2011; 34 (2): 197-201
Many patients respond positively to treatments for crouch gait, yet surgical outcomes are inconsistent and unpredictable. In this study, we developed a multivariable regression model to determine if biomechanical variables and other subject characteristics measured during a physical exam and gait analysis can predict which subjects with crouch gait will demonstrate improved knee kinematics on a follow-up gait analysis. We formulated the model and tested its performance by retrospectively analyzing 353 limbs of subjects who walked with crouch gait. The regression model was able to predict which subjects would demonstrate 'Improved' and 'Unimproved' knee kinematics with over 70% accuracy, and was able to explain approximately 49% of the variance in subjects' change in knee flexion between gait analyses. We found that improvement in stance phase knee flexion was positively associated with three variables that were drawn from knowledge about the biomechanical contributors to crouch gait: (i) adequate hamstrings lengths and velocities, possibly achieved via hamstrings lengthening surgery, (ii) normal tibial torsion, possibly achieved via tibial derotation osteotomy, and (iii) sufficient muscle strength.
View details for DOI 10.1016/j.gaitpost.2011.04.009
View details for Web of Science ID 000293429800010
View details for PubMedID 21616666
View details for PubMedCentralID PMC3130107
Muscle contributions to support and progression during single-limb stance in crouch gait
JOURNAL OF BIOMECHANICS
2010; 43 (11): 2099-2105
Pathological movement patterns like crouch gait are characterized by abnormal kinematics and muscle activations that alter how muscles support the body weight during walking. Individual muscles are often the target of interventions to improve crouch gait, yet the roles of individual muscles during crouch gait remain unknown. The goal of this study was to examine how muscles contribute to mass center accelerations and joint angular accelerations during single-limb stance in crouch gait, and compare these contributions to unimpaired gait. Subject-specific dynamic simulations were created for ten children who walked in a mild crouch gait and had no previous surgeries. The simulations were analyzed to determine the acceleration of the mass center and angular accelerations of the hip, knee, and ankle generated by individual muscles. The results of this analysis indicate that children walking in crouch gait have less passive skeletal support of body weight and utilize substantially higher muscle forces to walk than unimpaired individuals. Crouch gait relies on the same muscles as unimpaired gait to accelerate the mass center upward, including the soleus, vasti, gastrocnemius, gluteus medius, rectus femoris, and gluteus maximus. However, during crouch gait, these muscles are active throughout single-limb stance, in contrast to the modulation of muscle forces seen during single-limb stance in an unimpaired gait. Subjects walking in crouch gait rely more on proximal muscles, including the gluteus medius and hamstrings, to accelerate the mass center forward during single-limb stance than subjects with an unimpaired gait.
View details for DOI 10.1016/j.jbiomech.2010.04.003
View details for Web of Science ID 000281534000008
View details for PubMedID 20493489
View details for PubMedCentralID PMC2914221
CROUCH GAIT REPRESENTS A SIMPLIFIED MUSCULAR SUPPORT STRATEGY DURING SINGLE-LIMB STANCE COMPARED TO UNIMPAIRED GAIT
ASME Summer Bioengineering Conference
AMER SOC MECHANICAL ENGINEERS. 2009: 1093–1094
View details for Web of Science ID 000280089000547
Crouched postures reduce the capacity of muscles to extend the hip and knee during the single-limb stance phase of gait
JOURNAL OF BIOMECHANICS
2008; 41 (5): 960-967
Many children with cerebral palsy walk in a crouch gait that progressively worsens over time, decreasing walking efficiency and leading to joint degeneration. This study examined the effect of crouched postures on the capacity of muscles to extend the hip and knee joints and the joint flexions induced by gravity during the single-limb stance phase of gait. We first characterized representative mild, moderate, and severe crouch gait kinematics based on a large group of subjects with cerebral palsy (N=316). We then used a three-dimensional model of the musculoskeletal system and its associated equations of motion to determine the effect of these crouched gait postures on (1) the capacity of individual muscles to extend the hip and knee joints, which we defined as the angular accelerations of the joints, towards extension, that resulted from applying a 1N muscle force to the model, and (2) the angular acceleration of the joints induced by gravity. Our analysis showed that the capacities of almost all the major hip and knee extensors were markedly reduced in a crouched gait posture, with the exception of the hamstrings muscle group, whose extension capacity was maintained in a crouched posture. Crouch gait also increased the flexion accelerations induced by gravity at the hip and knee throughout single-limb stance. These findings help explain the increased energy requirements and progressive nature of crouch gait in patients with cerebral palsy.
View details for DOI 10.1016/j.jbiomech.2008.01.002
View details for Web of Science ID 000254943800005
View details for PubMedID 18291404
View details for PubMedCentralID PMC2443703
The effect of excessive tibial torsion on the capacity of muscles to extend the hip and knee during single-limb stance
GAIT & POSTURE
2007; 26 (4): 546-552
Excessive tibial torsion, a rotational deformity about the long axis of the tibia, is common in patients with cerebral palsy who walk with a crouch gait. Previous research suggests that this deformity may contribute to crouch gait by reducing the capacity of soleus to extend the knee; however, the effects of excess external torsion on the capacity of other muscles to extend the stance limb during walking are unknown. A computer model of the musculoskeletal system was developed to simulate a range of tibial torsion deformities. A dynamic analysis was then performed to determine the effect of these deformities on the capacity of lower limb muscles to extend the hip and knee at body positions corresponding to the single-limb stance phase of a normal gait cycle. Analysis of the model confirmed that excessive external torsion reduces the extension capacity of soleus. In addition, our analysis revealed that several important muscles crossing the hip and knee are also adversely affected by excessive tibial torsion. With a tibial torsion deformity of 30 degrees , the capacities of soleus, posterior gluteus medius, and gluteus maximus to extend both the hip and knee were all reduced by over 10%. Since a tibial torsion deformity reduces the capacity of muscles to extend the hip and knee, it may be a significant contributor to crouch gait, especially when greater than 30 degrees from normal, and thus should be considered by clinicians when making treatment decisions.
View details for DOI 10.1016/j.gaitpost.2006.12.003
View details for Web of Science ID 000250291100011
View details for PubMedID 17229573
View details for PubMedCentralID PMC2443695
Clinical applicability of using spherical fitting to find hip joint centers
GAIT & POSTURE
2005; 22 (2): 138-145
The functional or sphere-fitting method has been proposed as an alternative to the traditional predictive approach to locating hip centers based on inter-ASIS breadth. In the functional approach, the movement of a marker on the thigh is fit to a sphere whose center coincides with the hip joint center. The first objective of this study was to determine the required parameters that allow an accurate application of a sphere-fitting method. The parameters examined in this study included: (1) the range of motion in flexion-extension and abduction-adduction, (2) the specific algorithm used to fit a sphere to the data, (3) the method of placing markers on the thigh, and (4) the type of motion used to generate points, either walking or a standing leg motion (SLM) trial. This objective was addressed with a computer simulation and clinical data. The second objective was to compare the accuracy of the functional method to the traditional predictive approach in a group of nine human subjects. The location of the hip center estimates from both methods were compared to an ultrasound-determined hip center standard, and linear errors and errors along each axis were compared. Results from the computer simulation indicated that an iterative algorithm is needed, with a method using the derivative yielding slightly more accurate results. Clinical results indicated that the functional method with a standing leg motion trial produced significantly smaller errors in hip joint center estimates (1.34 cm) versus the predictive method (2.16 cm). In addition, the range of error across hips was smaller for the functional method. If high joint center accuracy is needed or in populations characterized by obesity or pelvic asymmetries, the subject specificity and independence from anatomical landmarks characteristic of the functional method would likely provide more accurate results.
View details for DOI 10.1016/j.gaitpost.2004.08.004
View details for Web of Science ID 000232198100007
View details for PubMedID 16139749