Paper Info
Title
Multi modal gesture identification for HCI using surface EMG
Abstract
Gesture and Speech comprise the most important modalities of human interaction. There has been a considerable amount of research attempts at incorporating these modalities for natural HCI. This involves challenge ranging from the low level signal processing of multi-modal input to the high level interpretation of natural speech and gesture in HCI. This paper proposes novel methods to recognize the hand gestures and unvoiced utterances using surface Electromyogram (sEMG) signals originating from different muscles. The focus of this work is to establish a simple, yet robust system that can be integrated to identify subtle complex hand gestures and unvoiced speech commands for control of prosthesis and other computer assisted devices. The proposed multi-modal system is able to identify the hand gestures and silent utterances using Independent Component Analysis (ICA) and Integral RMS (IRMS) of sEMG respectively. Training of the sEMG features was done using a designed ANN architecture and the results reported with overall recognition accuracy of 90.33%.
Year
DOI
Venue
2008
10.1145/1457199.1457219
MindTrek08
Keywords
Field
DocType
surface emg,semg feature,proposed multi-modal system,robust system,multi modal gesture identification,low level signal processing,subtle complex hand gesture,multi-modal input,hand gesture,natural speech,natural hci,high level interpretation,independent component analysis,human interaction,signal processing
Modalities,Signal processing,Computer vision,Computer science,Gesture,Human interaction,Speech recognition,Ranging,Independent component analysis,Artificial intelligence,Modal
Conference
Citations 
PageRank 
References 
3
0.46
5
Authors
3
Name
Order
Citations
PageRank
Ganesh R. Naik129825.37
Dinesh K. Kumar2839.17
Sridhar P. Arjunan3141.72