Recognition of facial movements and hand gestures using surface. Electromyogram (sEMG) for HCI based applications

Poosapadi Arjunan, S and Kumar, D 2007, 'Recognition of facial movements and hand gestures using surface. Electromyogram (sEMG) for HCI based applications', in M. Bottema, A. Maeder, N. Redding and A. van den Hengel (ed.) Conference of the APRS on Digital Image Computing Techniques and Applications, Glenelg, Australia, 3-5 December 2007, pp. 1-6.


Document type: Conference Paper
Collection: Conference Papers

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Title Recognition of facial movements and hand gestures using surface. Electromyogram (sEMG) for HCI based applications
Author(s) Poosapadi Arjunan, S
Kumar, D
Year 2007
Conference name Conference of the APRS on Digital Image Computing Techniques and Applications (DICTA 2007)
Conference location Glenelg, Australia
Conference dates 3-5 December 2007
Proceedings title Conference of the APRS on Digital Image Computing Techniques and Applications
Editor(s) M. Bottema
A. Maeder
N. Redding
A. van den Hengel
Publisher IEEE
Place of publication Piscataway, USA
Start page 1
End page 6
Total pages 6
Abstract This research reports the recognition of facial movements during unvoiced speech and the identification of hand gestures using surface Electromyogram (sEMG). The paper proposes two different methods for identifying facial movements and hand gestures, which can be useful for providing simple commands and control to computer, an important application of HCI. Experimental results demonstrate that the features of sEMG recordings are suitable for characterising the muscle activation during unvoiced speech and subtle gestures. The scatter plots from the two methods demonstrate the separation of data for each corresponding vowel and each hand gesture. The results indicate that there is small inter-experimental variation but there are large intersubject variations. This inter-subject variation may be attributable to anatomical differences and different speed and style of speaking for the different subjects. The proposed system provides better results when is trained and tested by individual user. The possible applications of this research include giving simple commands to computer for disabled, developing prosthetic hands, use of classifying sEMG for HCI based systems.
Subjects Biomedical Engineering not elsewhere classified
Keyword(s) EMG
HCI
DOI - identifier 10.1109/DICTA.2007.4426768
Copyright notice © 2007 IEEE
ISBN 0-7695-3067-2
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