https://devpost.com/software/open-rehaib
Capturing behavioral deficits in athletes who have suffered a traumatic brain injury (TBI) with pose estimation can be important for several reasons.
First, TBI can result in a range of cognitive and behavioral changes, such as problems with memory, attention, and mood. These changes can affect an athlete's ability to perform at their best, as well as their overall quality of life. By using pose estimation to track changes in an athlete's behavior over time, it may be possible to identify specific deficits that are causing problems and target interventions to address them.
Second, TBI can have long-term effects on an athlete's health and well-being. By monitoring an athlete's behavior and identifying any changes that may be due to TBI, it may be possible to take steps to prevent or mitigate these effects and improve the athlete's overall prognosis.
Finally, pose estimation can be a non-invasive and objective way to assess behavioral changes in athletes who have suffered a TBI. This can be especially useful in situations where it may be difficult to rely on self-report or subjective measures of behavior.
Overall, capturing behavioral deficits in athletes who have suffered a TBI with pose estimation can help to identify specific problems, track changes over time, and inform interventions and support strategies to help athletes recover and return to their best.
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Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017). Realtime multi-person 2D pose estimation using part affinity fields. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.143
Inayat, S., S. Singh, A. Ghasroddashti, Qandeel, P. Egodage, I. Q. Whishaw and M. H. Mohajerani (2019). "A toolbox for automated video analysis of rodents engaged in string-pulling: Phenotyping motor behavior of mice for sensory, whole-body and bimanual skilled hand function." bioRxiv: 2019.2012.2018.881342. https://doi.org/10.1101/2019.12.18.881342
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