Comparison of video-based and sensor-based head impact exposure

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 s...

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Bibliographic Details
Published in:PloS one 2018-06, Vol.13 (6), p.e0199238-e0199238
Main Authors: Kuo, Calvin, Wu, Lyndia, Loza, Jesus, Senif, Daniel, Anderson, Scott C, Camarillo, David B
Format: Article
Language:English
Subjects:
Acceleration
Angular position
Athletic Injuries - physiopathology
Athletic Injuries - prevention & control
Bioengineering
Biology and Life Sciences
Biomechanical Phenomena
Biosensing Techniques - instrumentation
College football
Comparative analysis
Computer & video games
Concussion
Confidence intervals
Craniocerebral Trauma - physiopathology
Craniocerebral Trauma - prevention & control
Datasets
Diagnosis
Exposure
Football
Football - injuries
Head - physiopathology
Head injuries
Humans
Impact prediction
Impact tests
Kinematics
Mechanical engineering
Medicine
Medicine and Health Sciences
Motion
Mouthguards
Neurodegeneration
Physical Sciences
Protective equipment
Sensors
Sports injuries
Sports medicine
Traumatic brain injury
Wearable Electronic Devices
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Summary: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
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0199238