Personal‐specific gait recognition based on latent orthogonal feature space. Issue 1 (22nd February 2021)

• Record Type: Journal Article
• Authors: Zhou, Quan; Shan, Jianhua; Fang, Bin; Zhang, Shixin; Sun, Fuchun; Ding, Wenlong; Wang, Chengyin; Zhang, Qin
• Is Part Of: Cognitive computation and systems. Volume 3:Issue 1(2021)
• Page Start: 61
• Page End: 69
• Publication Date: 2021-02-22
• Subjects: gait analysis -- feature extraction -- medical robotics -- patient rehabilitation -- recurrent neural nets -- wearable robots -- human‐robot interaction -- medical signal processing; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs2.12007
• Languages: English
• ISSNs: 2517-7567

Abstract

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.