Research on gait recognition and prediction based on optimized machine learning algorithm.
10.7507/1001-5515.202106072
- Author:
Jingwei GAO
1
;
Chao MA
1
;
Hong SU
1
;
Shaohong WANG
1
;
Xiaoli XU
1
;
Jie YAO
2
Author Information
1. Key Laboratory of Modern Measurement and Control Technology, Ministry of Education Beijing Information Science and Technology University, Beijing 100192, P. R. China.
2. School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, P. R. China.
- Publication Type:Journal Article
- Keywords:
Gait prediction;
Gated recurrent unit;
Immune particle swarm algorithm;
Neural network
- MeSH:
Algorithms;
Gait;
Humans;
Machine Learning;
Neural Networks, Computer;
Walking
- From:
Journal of Biomedical Engineering
2022;39(1):103-111
- CountryChina
- Language:Chinese
-
Abstract:
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 model is verified. The test results show that the optimized algorithm can better predict the changes in human posture. Among them, the root mean square error (RMSE) of flat-land walking and squatting can reach the accuracy of 10 -3, and the RMSE of sitting leg flexion and extension can reach the accuracy of 10 -2. The R 2 value of various actions can reach above 0.966. The above research results show that the optimized algorithm can be applied to realize human gait movement evaluation and gait trend prediction in rehabilitation treatment, as well as in the design of artificial limbs and lower limb rehabilitation equipment, which provide a reference for future research to improve patients' limb function, activity level, and life independence ability.
