Assistance level quantification-based human-robot interaction space reshaping for rehabilitation training

Frontiers in Neurorobotics

Neuroscience

Xi Yu¹  Hongchen He²  Peng Chen³  Qi Lu⁴  Shan Gong⁴  Xiangyun Li⁵  Kang Li⁵ 

[1] Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China;Department of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, China;Department of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, China;School of Rehabilitation Sciences, West China School of Medicine, Sichuan University, Chengdu, China;Key Laboratory of Rehabilitation Medicine in Sichuan Province, West China Hospital, Sichuan University, Chengdu, China;School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China;Sichuan University-Pittsburgh Institute, Sichuan University, Chengdu, China;West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China;Med-X Center for Informatics, Sichuan University, Chengdu, China;

关键词: human-robot interaction;    rehabilitation training;    AAN;    assistance level quantification;    interaction space reshaping;    EMG;   

DOI  :  10.3389/fnbot.2023.1161007

 received in 2023-02-07, accepted in 2023-04-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

Stroke has become a major disease that seriously threatens human health due to its high incidence and disability rates. Most patients undergo upper limb motor dysfunction after stroke, which significantly impairs the ability of stroke survivors in their activities of daily living (ADL). Robots provide an optional solution for stroke rehabilitation by attending therapy in the hospital and the community, however, the rehabilitation robot still has difficulty in providing needed assistance interactively like human clinicians in conventional therapy. For safe and rehabilitation training, a human-robot interaction space reshaping method was proposed based on the recovery states of patients. According to different recovery states, we designed seven experimental protocols suitable for distinguishing rehabilitation training sessions. To achieve assist-as-needed (AAN) control, a PSO-SVM classification model and an LSTM-KF regression model were introduced to recognize the motor ability of patients with electromyography (EMG) and kinematic data, and a region controller for interaction space shaping was studied. Ten groups of offline and online experiments and corresponding data processing were conducted, and the machine learning and AAN control results were presented, which ensured the effective and the safe upper limb rehabilitation training. To discuss the human-robot interaction in different training stages and sessions, we defined a quantified assistance level index that characterizes the rehabilitation needs by considering the engagement of the patients and had the potential to apply in clinical upper limb rehabilitation training.

【 授权许可】

Unknown   
Copyright © 2023 Li, Lu, Chen, Gong, Yu, He and Li.