NDLI: Multi run ICA and surface EMG based signal processing system for recognising hand gestures

Content Provider	IEEE Xplore Digital Library
Author	Naik, G.R. Kumar, D.K. Palaniswami, M.
Copyright Year	2008
Description	Author affiliation: Sch. of Electr. & Comput. Eng., RMIT Univ., Melbourne, VIC (Naik, G.R.; Kumar, D.K.)
Abstract	Hand gesture identification is a complex problem, where more number of muscles will be involved even for a simple hand movement. Surface electromyography (sEMG) is an indicator of muscle activity and related to body movement and posture. In the recent past sEMG had been used with various statistical signal processing technique to identify different hand gestures, but since the hand actions require simultaneous muscle contractions reliability issues exist. Recently Blind source separation (BSS) techniques like Independent Component Analysis (ICA) had been used to tackle this problem. In this paper, a novel method is proposed to enhance the performance of ICA of sEMG by decomposing the signal into components originating from different muscles. First, we use FastICA algorithm to generate random mixing matrix, and the best mixing matrix is chosen based on the highest Signal to interference ratio(SIR) of mixing matrix. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The proposed model-based approach is able to overcome the ambiguity problems (order and magnitude problem) of BSS methods by selecting an apriori mixing matrix based on known hand muscle anatomy. Testing was conducted using several single shot experiments conducted with seven subjects. The results indicate that the system is able to classify six different hand gestures with 99% accuracy.
Starting Page	700
Ending Page	705
File Size	179507
Page Count	6
File Format	PDF
ISBN	9781424423576
DOI	10.1109/CIT.2008.4594760
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-08
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wrist Fingers Artificial neural networks Muscles Electromyography Skin Classification algorithms
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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