Utilizing Machine Learning Algorithms to Predict Sports Injuries by Analyzing Athlete Activity and Sensor Data, a Brief Review

As the incidence of sports injuries rises among both adolescents and professional athletes, there is a growing emphasis on research aimed at preventing such injuries. Machine learning techniques have been utilized to examine various dimensions of sports injuries, encompassing both intrinsic and extrinsic risk factors. However, certain areas remain underexplored, including the psychological impacts, the role of extrinsic factors, and the potential of action recognition through video analysis and wearable technology. This article overviews recent advances in machine learning (ML) techniques for sports injury prediction and prevention. A literature review was conducted on some original research papers published between ۲۰۲۱ and ۲۰۲۴, focusing on algorithms like K-Nearest Neighbor, K-means, decision tree, random forest, gradient boosting, and neural networks.

کلیدواژه ها:

Machine Learning ، Sport Injury ، Artificial Neural Network.