NDLI: A preliminary investigation of the effect of force variation for myoelectric control of hand prosthesis

Content Provider	IEEE Xplore Digital Library
Author	Al-Timemy, A.H. Bugmann, G. Escudero, J. Outram, N.
Copyright Year	2013
Description	Author affiliation: Signal Process. & Multimedia Commun. (SPMC), Plymouth Univ., Plymouth, UK (Escudero, J.; Outram, N.) \|\| Centre for Robot. & Neural Syst., Plymouth Univ., Plymouth, UK (Bugmann, G.) \|\| Centre for Robot. & Neural Syst. (CRNS), Plymouth Univ., Plymouth, UK (Al-Timemy, A.H.)
Abstract	The myoelectric control of prostheses has been an important area of research for the past 40 years. Significant advances have been achieved with Pattern Recognition (PR) systems regarding the number of movements to be classified with high accuracy. However, practical robustness still needs further research. This paper focuses on investigating the effect of the change in force levels by transradial amputee persons on the performance of PR systems. Two below-elbow amputee persons participated in the study. Three levels of forces (low, medium, and high) were recorded for different hand grips with the help of visual feedback from the Electromyography (EMG) signals. Results showed that changing the force level degraded the performance of the myoelectric control system by up to 60% with 12 EMG channels for 4 hand grips and a rest position. We investigated different EMG feature sets in combination with a Linear Discriminant Analysis (LDA) classifier. The performance was slightly better with Time Domain (TD) features compared to Auto Regression (AR) coefficients and Root Mean Square (RMS) features. Finally, the error of the classification was considerably reduced to approximately 17% when the PR system was trained with all force levels.
Starting Page	5758
Ending Page	5761
File Size	530436
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6610859
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	Force Training Electromyography Electrodes Error analysis Visualization
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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