A combined deep neural network and semi-supervised clustering method for sports injury risk prediction

Abstract

Sports injury risk prediction plays an important role in evaluating the stability of the body in the process of sports. Based on deep neural network and semi-supervised clustering theory, this paper constructs a sports injury risk prediction model, and designs a recognition algorithm based on feature sequence classification combined with Bayesian filtering, which solves the problems of false alarms and missed alarms caused by relying on local spatial attribute features. The FMS (functional movement screen) kit was used to test seven movements (squat, hurdle step, straight leg squat, shoulder flexibility, active straight leg lift, trunk stability push-up, and body rotation) of 29 athletes. In the simulation process, Pareto analysis method was used to evaluate the overall risk of athletes' after-school physical exercise, and the FMS score, receiver operating characteristic (ROC) curve and Youden index were calculated. The results showed that the best cut-off points of the total score of FMS for athletes as a whole, male athletes and female athletes were 15.5, 15.5 and 16.5, respectively, which effectively verified the generalization performance of the above sports prediction model.

Keywords

• Deep neural network
• Risk prediction
• Semi-supervised clustering
• Sports injury