Design and verification of a human–robot interaction system for upper limb exoskeleton rehabilitation

This paper presents the design of a motion intent recognition system, based on an altitude signal sensor, to improve the human–robot interaction performance of upper limb exoskeleton robots during rehabilitation training. A modified adaptive Kalman filter combined with clipping filtering is proposed...

Full description

Saved in:
Bibliographic Details
Published in:Medical engineering & physics 2020-05, Vol.79, p.19-25
Main Authors: Wendong, Wang, Hanhao, Li, Menghan, Xiao, Yang, Chu, Xiaoqing, Yuan, Xing, Ming, Bing, Zhang
Format: Article
Language:English
Subjects:
Exoskeleton robot
Humans
Human–robot interaction
Mixed filter
Motion prediction
Movement
Rehabilitation - instrumentation
Rehabilitation - methods
Robotics
Upper Extremity - physiology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents the design of a motion intent recognition system, based on an altitude signal sensor, to improve the human–robot interaction performance of upper limb exoskeleton robots during rehabilitation training. A modified adaptive Kalman filter combined with clipping filtering is proposed for the control system to mitigate the noise and time delay of the collected signal. The clipping filtering method was used to filter the accidental error and avoid the safety problem caused by a mistrigger. A modified adaptive Kalman filter was used to account for the sudden change of the motion state during rehabilitation training. The results show that the intent recognition system designed herein can accurately recognize the human–robot interaction information, and estimate the intent of human motion in time. Therefore, it can be concluded that the designed system effectively follows the predicted motion intent with the proposed method, which is a significant improvement for human–robot interaction control of upper limb extremity rehabilitation robots.
ISSN:1350-4533
1873-4030
DOI:10.1016/j.medengphy.2020.01.016