ICA as pattern recognition technique for gesture identification: A study using bio-signal

Ganesh R. Naik, Dinesh Kumar, Sridhar Arjunan

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In recent times there is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer assisted devices. Surface Electromyogram (sEMG) is a non-invasive measure of the muscle activities but is not reliable because there are multiple simultaneously active muscles. This research first establishes the conditions for the applicability of Independent Component Analysis (ICA) pattern recognition techniques for sEMG. Shortcomings related to order and magnitude ambiguity have been identified and a mitigation strategy has been developed by using a set of unmixing matrix and neural network weight matrix corresponding to the specific user. The experimental results demonstrate a marked improvement in the accuracy. The other advantages of this system are that it is suitable for real time operations and it is easy to train by a lay user.

Original languageEnglish
Title of host publicationImage Processing
Subtitle of host publicationConcepts, Methodologies, Tools, and Applications
Editors Information Resources Management Association
Place of PublicationHershey, PA
PublisherIGI Global
Chapter27
Pages530-549
Number of pages20
Volume2-3
ISBN (Electronic)9781466639959
ISBN (Print)1466639946, 9781466639942
DOIs
Publication statusPublished - 31 May 2013
Externally publishedYes

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