Application of big data model based on computer action recognition and force measurement system in the mechanism and prevention of lower limb injuries in tennis players

This article uses a computer system to collect relevant data and constructs a big data model to explore the mechanism and prevention of lower limb injuries in tennis players. Methods 12 male tennis students from XX University were selected. The kinematics and dynamics parameters of the joints of the supporting leg (right side) and lower limb during the course of running in different directions were synchronously collected by the infrared high-speed camera system (Qualysis) and the force measuring platform system (Kistler). A big data model was established, and a total of 3 effective data were obtained, and the data were compared and analyzed by statistical methods. All subjects participated voluntarily and gave full informed consent to the trial process. Results In the course of changing direction running of tennis, the range of knee flexion and extension of the supporting leg of the subjects wearing tennis shoes was significantly greater than that of the subjects wearing running shoes (P < 0.05); The maximum varus angle and turnover range of the ankle joint of the supporting leg of the subjects wearing tennis shoes were significantly lower than those wearing running shoes, and the pedal extension range of the knee joint flexion angle was significantly higher than those wearing running shoes (P < 0.05, P < 0.05, P < 0.05). Conclusions Tennis shoes and running shoes have a good cushioning effect on the lower limbs; Compared with running shoes, wearing tennis shoes can reduce the lateral excessive turnover of the ankle joint of the supporting leg, which may reduce the risk of injury; Compared with running shoes, wearing tennis shoes can increase the pedal extension range of knee and ankle, which is conducive to rapid directional movement.