Personal‐specific gait recognition based on latent orthogonal feature space

Cognitive Computation and Systems

Chengyin Wang¹  Shixin Zhang¹  Wenlong Ding¹  Quan Zhou¹  Jianhua Shan¹  Fuchun Sun²  Bin Fang²  Qin Zhang³ 

[1] AnHui Province Key Laboratory of Special Heavy Load Robot Anhui University of Technology Ma Anshan China;Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China;Huazhong University of Science and Technology China;

关键词: gait analysis;    feature extraction;    medical robotics;    patient rehabilitation;    recurrent neural nets;    wearable robots;   

DOI  :  10.1049/ccs2.12007

来源: DOAJ

【 摘 要 】

Abstract Exoskeleton has been applied in the field of medical rehabilitation and assistance. However, there are still some problems in the interaction between human and exoskeleton, such as time delay, the existence of certain constraints on the human body, and the movement in time is hard to follow. A human motion pattern recognition model based on the long short‐term memory (LSTM) is proposed, which can recognise the state of the human body. Meanwhile, the orthogonalisation method is integrated to make personal‐specific disentangling, and it can effectively improve the generalisation ability of different groups of people, so as to improve the effective follower ability of the exoskeleton. Compared with some other traditional methods, this model has better performance and stronger generalisation ability, which has certain significance in the field of exoskeleton algorithm.

【 授权许可】

Unknown