Research on Fitness Movement Monitoring System Based on Internet of Things

Abstract

A multiuser motion-monitoring system based on MEMS is proposed for fitness movement, it is used to monitor the three important parameters of movement type, movement times, and movement cycle in the body movement and supports the simultaneous use of multiple users. The specific content of the method: (1) In terms of system design, a motion-monitoring system framework based on the Internet of things is proposed considering the motion-monitoring scene oriented to intelligent fitness. (2) In the aspect of algorithm, the relevant research of motion pattern recognition and cycle calculation method is carried out. For action pattern recognition, SVM-based algorithm to adapt to different computing capabilities of the scene is applied. (3) Experiments on 7 kinds of actions show that the proposed deep neural network has a good learning effect on small datasets, the recognition accuracy of the proposed deep neural network reaches 97.61%, and the recognition accuracy of SVM also reaches over 96%. In the 50 times of operation cycle calculation experiments, the frequency statistics algorithm has reached 100% of the calculation accuracy, and the calculation results of the operation cycle are close to the real value, which proves the validity of the method of cycle calculation. The experiment proves that the zero-crossing detection and wavelet analysis methods have a good overall effect and can accurately count and calculate the period when the number of actions is more, improve fitness efficiency, and provide guarantee for human health.