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

Calvin Kuo, Lyndia Wu, Jesus Loza, Daniel Senif, Scott C. Anderson, David B. Camarillo
doi: https://doi.org/10.1101/235432
Calvin Kuo
1Department of Mechanical Engineering, Stanford University, Stanford CA
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  • For correspondence: calvink@stanford.edu
Lyndia Wu
2Department of Bioengineering, Stanford University, Stanford CA
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Jesus Loza
1Department of Mechanical Engineering, Stanford University, Stanford CA
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Daniel Senif
3Stanford Sports Medicine, Stanford University, Stanford CA
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Scott C. Anderson
3Stanford Sports Medicine, Stanford University, Stanford CA
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David B. Camarillo
1Department of Mechanical Engineering, Stanford University, Stanford CA
2Department of Bioengineering, Stanford University, Stanford CA
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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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted March 19, 2018.
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Comparison of video-based and sensor-based head impact exposure
Calvin Kuo, Lyndia Wu, Jesus Loza, Daniel Senif, Scott C. Anderson, David B. Camarillo
bioRxiv 235432; doi: https://doi.org/10.1101/235432
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Comparison of video-based and sensor-based head impact exposure
Calvin Kuo, Lyndia Wu, Jesus Loza, Daniel Senif, Scott C. Anderson, David B. Camarillo
bioRxiv 235432; doi: https://doi.org/10.1101/235432

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