Research on sports injury risk assessment and rehabilitation strategy based on big data analysis
Abstract
By collecting athletes’ basic information, exercise habits, historical injury records and sports performance data, this study constructs a random forest (RF) model to assess the risk of sports injuries. The model can effectively deal with high-dimensional data and capture nonlinear relationships, and has strong generalization ability. The study also defines a risk assessment index (RAI) to visually represent the risk level of athletes’ sports injuries. In addition, this study identified the specific rehabilitation needs of patients with different injury types and degrees through association rule mining technology and cluster analysis, and made a personalized rehabilitation plan. In particular, biomechanical data, such as joint stability and muscle strength balance, are also included in this study to more accurately assess the risk of sports injury and guide rehabilitation training. Through comparative experiments, the results show that personalized rehabilitation plan based on big data analysis can significantly shorten the rehabilitation cycle and improve the quality of rehabilitation and patient satisfaction. The results of this study not only provide scientific sports guidance and rehabilitation suggestions for athletes and fitness enthusiasts, but also provide decision support for sports coaches, rehabilitation teachers and other professionals, which promotes the development of theory and practice in the field of sports injury prevention and rehabilitation.
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