Welcome to the InfoSci Platform
ICA as Pattern Recognition Technique for Gesture Identification: A Study Using Bio-Signal

ICA as Pattern Recognition Technique for Gesture Identification: A Study Using Bio-Signal

Ganesh Naik, Dinesh Kant Kumar, Sridhar Arjunan
Copyright: © 2013 |Pages: 20
ISBN13: 9781466639942|ISBN10: 1466639946|EISBN13: 9781466639959
DOI: 10.4018/978-1-4666-3994-2.ch027
Cite Chapter Cite Chapter

MLA

Naik, Ganesh, et al. "ICA as Pattern Recognition Technique for Gesture Identification: A Study Using Bio-Signal." Image Processing: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2013, pp. 530-549. https://doi.org/10.4018/978-1-4666-3994-2.ch027

APA

Naik, G., Kumar, D. K., & Arjunan, S. (2013). ICA as Pattern Recognition Technique for Gesture Identification: A Study Using Bio-Signal. In I. Management Association (Ed.), Image Processing: Concepts, Methodologies, Tools, and Applications (pp. 530-549). IGI Global. https://doi.org/10.4018/978-1-4666-3994-2.ch027

Chicago

Naik, Ganesh, Dinesh Kant Kumar, and Sridhar Arjunan. "ICA as Pattern Recognition Technique for Gesture Identification: A Study Using Bio-Signal." In Image Processing: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 530-549. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3994-2.ch027

Export Reference

Mendeley
Favorite

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.