Research on gait recognition and prediction based on optimized machine learning algorithm

• Published in: Sheng wu yi xue gong cheng xue za zhi Vol. 39; no. 1; p. 103
• Main Authors: Gao, Jingwei, Ma, Chao, Su, Hong, Wang, Shaohong, Xu, Xiaoli, Yao, Jie
• Format: Journal Article
• Language: Chinese
• Published: China 25.02.2022
• Subjects: Algorithms; Gait; Humans; Machine Learning; Neural Networks, Computer; Walking; Gated recurrent unit; Neural network; Immune particle swarm algorithm; Gait prediction
• ISSN: 1001-5515
• DOI: 10.7507/1001-5515.202106072

Aiming at the problems of individual differences in the asynchrony process of human lower limbs and random changes in stride during walking, this paper proposes a method for gait recognition and prediction using motion posture signals. The research adopts an optimized gated recurrent unit (GRU) network algorithm based on immune particle swarm optimization (IPSO) to establish a network model that takes human body posture change data as the input, and the posture change data and accuracy of the next stage as the output, to realize the prediction of human body posture changes. This paper first clearly outlines the process of IPSO's optimization of the GRU algorithm. It collects human body posture change data of multiple subjects performing flat-land walking, squatting, and sitting leg flexion and extension movements. Then, through comparative analysis of IPSO optimized recurrent neural network (RNN), long short-term memory (LSTM) network, GRU network classification and prediction, the effectiveness of the built