Improving the performance of hand posture classification by perimeter sensor with sEMG

This paper reports a work for improving the performance of the hand posture classifier by using perimeter of the human forearm. Misclassification occurs due to four factors: residual deformation of the muscle after the muscle activation, sensor locations, error from the voltage regulator and the tra...

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Bibliographic Details
Published in2013 IEEE International Conference on Mechatronics and Automation pp. 819 - 824
Main Authors Hwiyong Choi, Sangyoon Lee
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2013
Subjects
Calibration
Force
hand posture classification
least square regression
Muscles
perimeter change of the forearm
Regulators
sEMG
Strain
Training data
Voltage control
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Summary:This paper reports a work for improving the performance of the hand posture classifier by using perimeter of the human forearm. Misclassification occurs due to four factors: residual deformation of the muscle after the muscle activation, sensor locations, error from the voltage regulator and the transition state during posture change. In order to reduce the effect of the factors, a sensor location which gives low residual muscle deformation was selected and one channel of sEMG was employed. The least square regression was used for removing the transition state. The proposed method was verified through simulation with pre-acquired data sets.
ISBN:1467355577
9781467355575
ISSN:2152-7431
2152-744X
DOI:10.1109/ICMA.2013.6618021