Effect Measurement for Human Motion Rehabilitation Training Using Improved Deep Reinforcement Learning and IoT Networks

Abstract

Measuring the effect of human motion rehabilitation training is important to help persons develop motion rehabilitation training plans. The current human motion rehabilitation training effect measurement algorithm has the problems of too large gap between the smoothness of the motion speed curve and the reality, high key frame extraction error rate, low measurement accuracy, long measurement time, and low satisfaction. Therefore, this paper proposes a human motion rehabilitation training effect measurement algorithm using improved deep reinforcement learning and Internet of Things (IoT) networks using IoT network technology to collect human motion rehabilitation training videos. The key frames of the human motion rehabilitation video data are extracted according to the interframe distance, and the metalearning method is used to improve the deep reinforcement learning network, and the obtained key frames are input into the improved deep reinforcement learning network to obtain the motion speed curve of human motion rehabilitation training. The smoothness of the motion velocity curve is calculated, and high smoothness indicates a good human motion rehabilitation training effect, while low smoothness indicates a poor human motion rehabilitation training effect, so as to complete the measure of human motion rehabilitation training effect. The results show that the smoothness of the motion speed curve of the proposed algorithm is closer to reality, the average error rate of key frame extraction is 1.45%, the measurement accuracy of rehabilitation training is more than 90%, the measurement time is controlled below 2.1 s, and the maximum user satisfaction is 93.1, which shows that the practical application of the algorithm is good.