PMID- 35231971
OWN - NLM
STAT- MEDLINE
DCOM- 20220303
LR  - 20230701
IS  - 1001-5515 (Print)
IS  - 1001-5515 (Linking)
VI  - 39
IP  - 1
DP  - 2022 Feb 25
TI  - [Research on gait recognition and prediction based on optimized machine learning 
      algorithm].
PG  - 103-111
LID - 10.7507/1001-5515.202106072 [doi]
AB  - 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.
FAU - Gao, Jingwei
AU  - Gao J
AD  - Key Laboratory of Modern Measurement and Control Technology, Ministry of 
      Education Beijing Information Science and Technology University, Beijing 100192, 
      P. R. China.
FAU - Ma, Chao
AU  - Ma C
AD  - Key Laboratory of Modern Measurement and Control Technology, Ministry of 
      Education Beijing Information Science and Technology University, Beijing 100192, 
      P. R. China.
FAU - Su, Hong
AU  - Su H
AD  - Key Laboratory of Modern Measurement and Control Technology, Ministry of 
      Education Beijing Information Science and Technology University, Beijing 100192, 
      P. R. China.
FAU - Wang, Shaohong
AU  - Wang S
AD  - Key Laboratory of Modern Measurement and Control Technology, Ministry of 
      Education Beijing Information Science and Technology University, Beijing 100192, 
      P. R. China.
FAU - Xu, Xiaoli
AU  - Xu X
AD  - Key Laboratory of Modern Measurement and Control Technology, Ministry of 
      Education Beijing Information Science and Technology University, Beijing 100192, 
      P. R. China.
FAU - Yao, Jie
AU  - Yao J
AD  - School of Biological Science and Medical Engineering, Beihang University, Beijing 
      100191, P. R. China.
LA  - chi
PT  - Journal Article
PL  - China
TA  - Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
JT  - Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = 
      Shengwu yixue gongchengxue zazhi
JID - 9426398
SB  - IM
MH  - *Algorithms
MH  - Gait
MH  - Humans
MH  - Machine Learning
MH  - *Neural Networks, Computer
MH  - Walking
PMC - PMC9927734
OTO - NOTNLM
OT  - Gait prediction
OT  - Gated recurrent unit
OT  - Immune particle swarm algorithm
OT  - Neural network
COIS- 利益冲突声明：本文全体作者均声明不存在利益冲突。
EDAT- 2022/03/02 06:00
MHDA- 2022/03/04 06:00
PMCR- 2022/02/25
CRDT- 2022/03/01 20:20
PHST- 2022/03/01 20:20 [entrez]
PHST- 2022/03/02 06:00 [pubmed]
PHST- 2022/03/04 06:00 [medline]
PHST- 2022/02/25 00:00 [pmc-release]
AID - swyxgcxzz-39-1-103 [pii]
AID - 10.7507/1001-5515.202106072 [doi]
PST - ppublish
SO  - Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2022 Feb 25;39(1):103-111. doi: 
      10.7507/1001-5515.202106072.