Application of wearable motion tracking devices in training, monitoring, and evaluation

The current wearable motion tracking devices have significant differences in heart rate monitoring, inaccurate calorie tracking, and measurement errors in exercise speed and distance.Based on this, this paper optimises the design of wearable motion tracking devices.Firstly, this paper establishes a real-time data mining model for sports training wearable devices using fuzzy algorithms, and determines the heterogeneity of sports training data.Then, this paper constructs a feature extraction model and processes the data using thresholding and Savitzky Golay filtering.Subsequently, this paper elaborates on the calibration method of sensors in wearable motion tracking devices, and finally tests the application of the device in training monitoring and evaluation.The research results indicate that the heart rate of student 11 measured by the device in this paper is 84 beats per minute under normal conditions and 123 beats per minute under high-intensity exercise.