TY - GEN
T1 - Multi run ICA and surface EMG based signal processing system for recognising hand gestures
AU - Naik, Ganesh R.
AU - Kumar, Dinesh K.
AU - Palaniswami, Marimuthu
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=52049127225&partnerID=8YFLogxK
U2 - 10.1109/CIT.2008.4594760
DO - 10.1109/CIT.2008.4594760
M3 - Conference contribution
AN - SCOPUS:52049127225
SN - 9781424423583
T3 - Proceedings - 2008 IEEE 8th International Conference on Computer and Information Technology, CIT 2008
SP - 700
EP - 705
BT - 2008 8th IEEE International Conference on Computer and Information Technology (CIT)
PB - Institute of Electrical and Electronics Engineers
CY - Sydney, NSW
T2 - 2008 IEEE 8th International Conference on Computer and Information Technology, CIT 2008
Y2 - 8 July 2008 through 11 July 2008
ER -