Open AccessPersonal‐specific gait recognition based on latent orthogonal feature space
Author(s) -
Zhou Quan,
Shan Jianhua,
Fang Bin,
Zhang Shixin,
Sun Fuchun,
Ding Wenlong,
Wang Chengyin,
Zhang Qin
Publication year - 2021
Publication title -
cognitive computation and systems
Language(s) - English
Resource type - Journals
ISSN - 2517-7567
DOI - 10.1049/ccs2.12007
Subject(s) - exoskeleton , computer science , field (mathematics) , artificial intelligence , feature (linguistics) , motion (physics) , gait , simulation , physical medicine and rehabilitation , mathematics , medicine , linguistics , philosophy , pure mathematics
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.