Padded Helmet Shell Covers in American Football: A Comprehensive Laboratory Evaluation with Preliminary On-Field Findings.
Annals of biomedical engineering
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
Albino Xenopus laevis tadpoles prefer dark environments compared to wild type.
Tadpoles display preferences for different environments but the sensory modalities that govern these choices are not well understood. Here, we examined light preferences and associated sensory mechanisms of albino and wild-type Xenopus laevis tadpoles. We found that albino tadpoles spent more time in darker environments compared to the wild type, although they showed no differences in overall activity. This preference persisted when the tadpoles had their optic nerve severed or pineal glands removed, suggesting these sensory systems alone are not necessary for phototaxis. These experiments were conducted by an undergraduate laboratory course, highlighting how X. laevis tadpole behavior assays in a classroom setting can reveal new insights into animal behavior.
View details for DOI 10.17912/micropub.biology.000750
View details for PubMedID 36824381
View details for PubMedCentralID PMC9941856
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
Physics-Informed Machine Learning Improves Detection of Head Impacts.
Annals of biomedical engineering
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
Time Window of Head Impact Kinematics Measurement for Calculation of Brain Strain and Strain Rate in American Football.
Annals of biomedical engineering
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