SEMG for identifying hand gestures using ICA

Ganesh R. Naik; Dinesh K. Kumar; Vijay Pal Singh; M. Palaniswami

SEMG for identifying hand gestures using ICA

Ganesh R. Naik, Dinesh K. Kumar, Vijay Pal Singh, M. Palaniswami

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Citations (Scopus)

Abstract

There is an urgent need for establishing a simple yet robust systemthat can be used to identify hand actions and gestures for machine and computer control. Researchers have reported the use of multi-channel electromyogram (EMG) to determine the hand actions and gestures. The limitation of the earlier works is that the systems are suitable for gross actions, and when there is one prime-mover muscle involved. This paper reports overcoming the difficulty by using independent component analysis to separate muscle activity from different muscles and classified using backpropogation neural networks. The system is tested and found to be effective in classifying EMG.

Original language	English
Title of host publication	Proceedings of the 2nd International Workshop on Biosignal Processing and Classification (BPC), in Conjunction with ICINCO 2006
Pages	61-67
Number of pages	7
Publication status	Published - 2006
Externally published	Yes
Event	2nd International Workshop on Biosignal Processing and Classification, BPC 2006, in Conjunction with ICINCO 2006 - Setubal, Portugal Duration: 1 Aug 2006 → 5 Aug 2006

Conference

Conference	2nd International Workshop on Biosignal Processing and Classification, BPC 2006, in Conjunction with ICINCO 2006
Country/Territory	Portugal
City	Setubal
Period	1/08/06 → 5/08/06

Cite this

@inproceedings{f046aa423d86470ebb7a640a0f72bf54,

title = "SEMG for identifying hand gestures using ICA",

abstract = "There is an urgent need for establishing a simple yet robust systemthat can be used to identify hand actions and gestures for machine and computer control. Researchers have reported the use of multi-channel electromyogram (EMG) to determine the hand actions and gestures. The limitation of the earlier works is that the systems are suitable for gross actions, and when there is one prime-mover muscle involved. This paper reports overcoming the difficulty by using independent component analysis to separate muscle activity from different muscles and classified using backpropogation neural networks. The system is tested and found to be effective in classifying EMG.",

author = "Naik, {Ganesh R.} and Kumar, {Dinesh K.} and Singh, {Vijay Pal} and M. Palaniswami",

year = "2006",

language = "English",

isbn = "9728865678",

pages = "61--67",

booktitle = "Proceedings of the 2nd International Workshop on Biosignal Processing and Classification (BPC), in Conjunction with ICINCO 2006",

note = "2nd International Workshop on Biosignal Processing and Classification, BPC 2006, in Conjunction with ICINCO 2006 ; Conference date: 01-08-2006 Through 05-08-2006",

}

Naik, GR, Kumar, DK, Singh, VP & Palaniswami, M 2006, SEMG for identifying hand gestures using ICA. in Proceedings of the 2nd International Workshop on Biosignal Processing and Classification (BPC), in Conjunction with ICINCO 2006. pp. 61-67, 2nd International Workshop on Biosignal Processing and Classification, BPC 2006, in Conjunction with ICINCO 2006, Setubal, Portugal, 1/08/06.

TY - GEN

T1 - SEMG for identifying hand gestures using ICA

AU - Naik, Ganesh R.

AU - Kumar, Dinesh K.

AU - Singh, Vijay Pal

AU - Palaniswami, M.

PY - 2006

Y1 - 2006

N2 - There is an urgent need for establishing a simple yet robust systemthat can be used to identify hand actions and gestures for machine and computer control. Researchers have reported the use of multi-channel electromyogram (EMG) to determine the hand actions and gestures. The limitation of the earlier works is that the systems are suitable for gross actions, and when there is one prime-mover muscle involved. This paper reports overcoming the difficulty by using independent component analysis to separate muscle activity from different muscles and classified using backpropogation neural networks. The system is tested and found to be effective in classifying EMG.

AB - There is an urgent need for establishing a simple yet robust systemthat can be used to identify hand actions and gestures for machine and computer control. Researchers have reported the use of multi-channel electromyogram (EMG) to determine the hand actions and gestures. The limitation of the earlier works is that the systems are suitable for gross actions, and when there is one prime-mover muscle involved. This paper reports overcoming the difficulty by using independent component analysis to separate muscle activity from different muscles and classified using backpropogation neural networks. The system is tested and found to be effective in classifying EMG.

UR - http://www.scopus.com/inward/record.url?scp=44649163725&partnerID=8YFLogxK

UR - http://www.icinco.org/Abstracts/2006/ICINCO_2006_Abstracts.htm

M3 - Conference contribution

AN - SCOPUS:44649163725

SN - 9728865678

SN - 9789728865672

SP - 61

EP - 67

BT - Proceedings of the 2nd International Workshop on Biosignal Processing and Classification (BPC), in Conjunction with ICINCO 2006

T2 - 2nd International Workshop on Biosignal Processing and Classification, BPC 2006, in Conjunction with ICINCO 2006

Y2 - 1 August 2006 through 5 August 2006

ER -

SEMG for identifying hand gestures using ICA

Abstract

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