NDLI: A linear model for simultaneously and proportionally estimating wrist kinematics from emg during mirrored bilateral movements

Content Provider	IEEE Xplore Digital Library
Author	Lizhi Pan Xinjun Sheng Dingguo Zhang Xiangyang Zhu
Copyright Year	2013
Description	Author affiliation: State Key Lab. of Mech. Syst. & Vibration, Shanghai Jiao Tong Univ., Shanghai, China (Lizhi Pan; Xinjun Sheng; Dingguo Zhang; Xiangyang Zhu)
Abstract	This paper presents a linear model for simultaneous and proportional estimation of the two degree-of-freedoms (DOFs) wrist angle positions with surface electromyography (EMG). A 5th order state-space model was used to estimate wrist kinematics from 4-channel surface EMG signals of the contralateral forearm during mirrored bilateral movements without motion constraints. The EMG signal from each of the three limbed normal subjects was collected along with each angle position in two DOFs from both of the arms, with motion parameters tested including the radial/ulnar deviation and flexion/extension of the wrist. The estimation performance was in the range 0.787-0.885 $(R^{2}$ index) for the two DOFs in three limbed normal subjects. The results show that wrist kinematics can be estimated in 2 DOFs by state-space models with relative high accuracy compared with the results reported previously. The method proposed, as requiring only kinematics measured from the contralateral wrist, is potentially available for a unilateral amputee in simultaneous and proportional control of DOFs in powered upper limb prostheses.
Starting Page	4593
Ending Page	4596
File Size	541685
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6610570
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wrist Electromyography State-space methods Estimation Kinematics Muscles Electrodes
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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