Measurement and evaluation of linear motion parameters of ice and snow athletes based on acceleration sensors

With the popularisation and development of ice and snow sports, the measurement and evaluation of athletes' sports parameters are of great significance in improving their competitive level and avoiding sports injuries. This article proposes a linear motion parameter measurement and evaluation method for ice and snow athletes based on acceleration sensors. This method collects real-time acceleration data during exercise by wearing acceleration sensors, and uses data fusion and filtering techniques to extract linear acceleration and velocity changes of athletes. Furthermore, the Kalman filtering algorithm is used to optimise the noisy data in order to improve its accuracy and stability, and used support vector machine (SVM) algorithm to classify and evaluate the athletes' motion state. The experimental results show that this method can accurately measure the linear motion parameters of ice and snow athletes, efficiently evaluate their sports status.