A feasibility study on the use of anthropometric variables to make muscle–computer interface more practical

High classification accuracy has been achieved for muscle–computer interfaces (MCIs) based on surface electromyography (EMG) recognition in many recent works with an increasing number of discriminated movements. However, there are many limitations to use these interfaces in the real-world contexts....

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Bibliographic Details
Published in:Engineering applications of artificial intelligence 2013-08, Vol.26 (7), p.1681-1688
Main Authors: Phinyomark, Angkoon, Quaine, Franck, Charbonnier, Sylvie, Serviere, Christine, Tarpin-Bernard, Franck, Laurillau, Yann
Format: Article
Language:English
Subjects:
Biomechanics
Circumference
Electromyography (EMG)
Engineering Sciences
Feature extraction
Gesture recognition
Hand motion
Mechanics
Muscle size
Physics
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Summary:High classification accuracy has been achieved for muscle–computer interfaces (MCIs) based on surface electromyography (EMG) recognition in many recent works with an increasing number of discriminated movements. However, there are many limitations to use these interfaces in the real-world contexts. One of the major problems is compatibility. Designing and training the classification EMG system for a particular individual user is needed in order to reach high accuracy. If the system can calibrate itself automatically/semi-automatically, the development of standard interfaces that are compatible with almost any user could be possible. Twelve anthropometric variables, a measurement of body dimensions, have been proposed and used to calibrate the system in two different ways: a weighting factor for a classifier and a normalizing value for EMG features. The experimental results showed that a number of relationships between anthropometric variables and EMG time-domain features from upper-limb muscles and movements are statistically strong (average r=0.71−0.80) and significant (p
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2013.01.004