Authors:
Hanna Sigurdson
1
;
2
and
Jonathan H. Chan
2
;
3
Affiliations:
1
Engineering Science, University of Toronto, Toronto, Canada
;
2
Innovative Cognitive Computing (IC2), King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
;
3
School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
Keyword(s):
Machine Learning, Sports Injury, Intrinsic Factors, Extrinsic Factors, Sports Injury Risk Factors, Artificial Intelligence, Literature Review, Sports Psychology.
Abstract:
As sports injuries increase in frequency in adolescents, and injuries in professional athletes create a detrimental impact on the sports industry, research surrounding preventing sports injuries becomes more prevalent. The mechanism for sports injury is well defined and includes intrinsic (age, psychology etc.) and extrinsic risk factors (weather, training load etc.), and the inciting event. With the rise of machine learning (ML), a variety of ML techniques have been applied to various sports injury aspects. The purpose of this work is to assess the current applications of ML to sports injury and identify areas of growth by a systematic analysis of applications to each injury element: intrinsic factors, extrinsic factors, and the inciting event. Current underdeveloped areas are identified as: psychological effect, use of extrinsic factors, analysis of the inciting event, and application of the action recognition ability of videos and wearable technology. Future technical applications
in these underdeveloped areas should be undergone to expand on and improve sports injury prevention technology.
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