Dynamic Monitoring Method of Physical Training Intensity Based on Wearable Sensors

Abstract

Physical training data are greatly affected by the human movement state, and the current monitoring method has the problem of low accuracy. A dynamic monitoring method of physical training intensity is designed based on the wearable sensor. The wearable sensor network is established, the quaternion is transformed into the corresponding Euler angle, and the measurement of the sensor is transformed into the human body coordinate system. In the process of human motion reconstruction, the human motion state parameters are estimated based on the wearable sensor data, the parameter eigenvalues are extracted, and the decision tree classification algorithm is used to identify the physical training state. According to the classification results, the dynamic monitoring model of three-dimensional energy training intensity is established, and the numerical index of training intensity is obtained. In terms of various physical training items, the average monitoring accuracy of the dynamic monitoring method of physical training intensity based on the wearable sensor is 97.12%, which is 3.70%, 4.59%, and 5.04% higher than the methods based on reflected interframe image, median average filtering algorithm, and Internet of Things technology, respectively, so as to realize the accurate monitoring of sports state.