DocumentCode :
3316557
Title :
Performance comparison of ICA algorithms for Isometric Hand gesture identification using Surface EMG
Author :
Naik, Ganesh R. ; Kumar, Dinesh K. ; Weghorn, Hans
Author_Institution :
RMIT Univ., Melbourne
fYear :
2007
fDate :
3-6 Dec. 2007
Firstpage :
613
Lastpage :
618
Abstract :
There is an urgent need for developing a robust technique that can identify small and subtle hand and other body movements with applications in health, rehabilitation and defence. Surface electromyogram (sEMG) is a measure of the electrical activity of the muscles and a measure of the strength of muscle contraction. While this may be a good measure of the actions and gestures, this is unable to identify small variations in the muscle activity, especially when there are number of simultaneously active muscles. Independent component analysis (ICA) is a statistical based source separation technique that has been shown to be suitable for the decomposition of signals such as sEMG and been shown to improve the ability of sEMG to identify small variations in muscle activity. ICA algorithms using multivariate statistical data analysis technique have been successfully used for signal extraction and source separation in the field of biomedical and statistical signal processing. Recent research has resulted in the development of number of different ICA technique. While there are some researchers who have compared their techniques with the existing methods for audio examples, there is no comparison of performance between ICA algorithms for biosignal applications such as surface electromyography (sEMG) applications. With ICA being the feasible method for source separation and decomposition of biosignals, it is important to compare the different techniques and determine the most suitable method for the applications. This paper has studied the performance of four ICA algorithms (FastICA, JADE, Infomax and TDSEP) for decomposition of sEMG to identify subtle hand gestures. Comparing several ICA algorithms, it is observed that an algorithm based on temporal decorrelation method (TDSEP) which is based on the second order statistics gives the best performance.
Keywords :
bioelectric phenomena; biomechanics; electromyography; gesture recognition; independent component analysis; medical signal detection; medical signal processing; neurophysiology; source separation; FastICA algorithm; ICA algorithm; Infomax algorithm; JADE algorithm; TDSEP algorithm; biomedical processing; biosignal decomposition; hand movements; independent component analysis; isometric hand gesture identification; multivariate statistical data analysis technique; muscle contraction strength; muscle electrical activity; second-order statistics; signal extraction; statistical based source separation technique; statistical signal processing; surface EMG; temporal decorrelation method; Biomedical measurements; Data analysis; Electric variables measurement; Electromyography; Independent component analysis; Muscles; Robustness; Signal processing; Signal processing algorithms; Source separation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Sensors, Sensor Networks and Information, 2007. ISSNIP 2007. 3rd International Conference on
Conference_Location :
Melbourne, Qld.
Print_ISBN :
978-1-4244-1501-4
Electronic_ISBN :
978-1-4244-1502-1
Type :
conf
DOI :
10.1109/ISSNIP.2007.4496913
Filename :
4496913
Link To Document :
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