Transactions of the Society of Instrument and Control Engineers
Online ISSN : 1883-8189
Print ISSN : 0453-4654
ISSN-L : 0453-4654
Paper
Classifier Update Method Using Semi-supervised Learning for EMG-based Motion Recognition
Shintaro NAKATANINozomu ARAKITakao SATOYasuo KONISHI
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JOURNAL FREE ACCESS

2015 Volume 51 Issue 8 Pages 535-541

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Abstract

In recent years, numerous studies on motion classification using surface electromyography (sEMG) have been conducted to realize a myoelectric hand system. This study considered the practical issues in a motion classification method using sEMG, which will be used to control a myoelectric hand. One such issue is the change in the sEMG characteristics that is caused by a change in the position of the sEMG electrode, owing to the replacement of the electrode. Because such changes influence the performance of a motion classifier, it is generally necessary to be relearn the classifier that the position of the electrode may change. To solve this problem, we propose a new classifier update method using a semi-supervised learning technique. In this method, the data measured under electrode position change is categorized into each motion by a semi-supervised learning technique using the relationship between the unsupervised data and the known categorized supervised data measured at a reference position; the classifier is then recalculated using categorized data. The experimental result shows that the performance of our proposed method is maintained even if the position of the electrode is changed.

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© 2015 The Society of Instrument and Control Engineers
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