Wearable sensors are an important tool in the study of head acceleration events and head impact injuries in sporting and military activities. Recent advances in sensor technology have improved our understanding of head kinematics during on-field activities; however, proper utilization and interpretation of data from wearable devices requires careful implementation of best practices. The objective of this paper is to summarize minimum requirements and best practices for on-field deployment of wearable devices for the measurement of head acceleration events in vivo to ensure data evaluated are representative of real events and limitations are accurately defined. Best practices covered in this document include the definition of a verified head acceleration event, data windowing, video verification, advanced post-processing techniques, and on-field logistics, as determined through review of the literature and expert opinion. Careful use of best practices, with accurate acknowledgement of limitations, will allow research teams to ensure data evaluated is representative of real events, will improve the robustness of head acceleration event exposure studies, and generally improve the quality and validity of research into head impact injuries.
Associate Editor Stefan M Duma oversaw the review of this article.
A shortcoming of using environmental sensors for the surveillance of potentially concussive events is substantial uncertainty regarding whether the event was caused by head acceleration (“head impacts”) or sensor motion (with no head acceleration). The goal of the present study is to develop a machine learning model to classify environmental sensor data obtained in the field and evaluate the performance of the model against the performance of the proprietary classification algorithm used by the environmental sensor. Data were collected from Soldiers attending sparring sessions conducted under a U.S. Army Combatives School course. Data from one sparring session were used to train a decision tree classification algorithm to identify good and bad signals. Data from the remaining sparring sessions were kept as an external validation set. The performance of the proprietary algorithm used by the sensor was also compared to the trained algorithm performance. The trained decision tree was able to correctly classify 95% of events for internal cross-validation and 88% of events for the external validation set. Comparatively, the proprietary algorithm was only able to correctly classify 61% of the events. In general, the trained algorithm was better able to predict when a signal was good or bad compared to the proprietary algorithm. The present study shows it is possible to train a decision tree algorithm using environmental sensor data collected in the field.
Wearable devices are increasingly used to measure real-world head impacts and study brain injury mechanisms. These devices must undergo validation testing to ensure they provide reliable and accurate information for head impact sensing, and controlled laboratory testing should be the first step of validation. Past validation studies have applied varying methodologies, and some devices have been deployed for on-field use without validation. This paper presents best practices recommendations for validating wearable head kinematic devices in the laboratory, with the goal of standardizing validation test methods and data reporting. Key considerations, recommended approaches, and specific considerations were developed for four main aspects of laboratory validation, including surrogate selection, test conditions, data collection, and data analysis. Recommendations were generated by a group with expertise in head kinematic sensing and laboratory validation methods and reviewed by a larger group to achieve consensus on best practices. We recommend that these best practices are followed by manufacturers, users, and reviewers to conduct and/or review laboratory validation of wearable devices, which is a minimum initial step prior to on-field validation and deployment. We anticipate that the best practices recommendations will lead to more rigorous validation of wearable head kinematic devices and higher accuracy in head impact data, which can subsequently advance brain injury research and management.
Introduction The U.S. Army conducts airborne operations in order to insert soldiers into combat. Military airborne operations are physically demanding activities with a unique loading environment compared with normal duties. A significant amount of research surrounding airborne operations has focused on assessing the incidence and type of associated injuries as well as the potential risk factors for injuries. During parachute opening shock and other high-acceleration events (e.g., fixed wing flight or vehicle crashes), the neck may be vulnerable to injury if inertial loads overcome the voluntary muscular control of the cervical spine and soft tissue structures. A recent epidemiological survey of sport skydivers showed that the neck, shoulders, and back were the most frequently reported sites of musculoskeletal pain. In addition, the survey indicated that wing loading (a measure of the jumper’s weight divided by the size of the parachute canopy) was a potential contributing factor for developing musculoskeletal pain. Recently, there have been efforts to measure the severity of parachute opening shock as an additional potential risk factor for injury; however, no studies have measured both head and body accelerations and no studies have measured head or body angular rate during parachute opening shock. The purpose of this study was to measure and characterize the accelerations and angular rates of both the head and body during parachute opening shock as well as investigate potential factors contributing to higher severity opening shock, which may link to the development of musculoskeletal pain or injury. Materials and Methods Data were collected from the U.S. Army Parachute Team, The Golden Knights, under an approved Medical Research and Material Command Institutional Review Board protocol. Subjects were instrumented with a helmet- and body-mounted sensor package, which included three angular rate sensors and three single-axis accelerometers each. Data were collected at 2,500 samples per second. Kruskal-Wallis tests were used to determine if helmet-mounted equipment (e.g., cameras), neck length, neck circumference, or wing loading (the ratio of jump weight to the size of the main parachute canopy) affected the accelerations or angular rates of the head or body. Results A total of 54 jumps conducted by 19 experienced free-fall jumpers were analyzed. For the head, the mean (± SD) resultant accelerations and angular rates were 5.8 (± 1.6) g and 255.9 (± 74.2) degrees per second (deg/s), respectively. For the body, the resultant accelerations and angular rates were 4.3 (± 1.5) g and 181.3 (± 61.2) deg/s, respectively. A wing loading above 1.4 pounds per square foot (lb/ft2) was found to have a significant effect on head (P = .001) and body (P = .001) resultant acceleration as well as body angular rate about the Y-axis (P = .001). Conclusions There is evidence to suggest that wing loading has an influence on individual head and body resultant accelerations. However, no significant effects were found for the other variables (e.g., neck length and circumference, helmet-mounted equipment, etc.). Future research should focus on identifying additional factors that result in changes in accelerations and angular rates of the head and body during parachute opening shock events.
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