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A decision fusion pattern classification architecture for human-robotic interface

A decision fusion pattern classification architecture for human-robotic interface
A decision fusion pattern classification architecture for human-robotic interface
A complete signal processing strategy is presented to detect and precisely recognize tongue movement by monitoring changes in airflow that occur in the ear canal. Tongue movements within the human oral cavity create unique, subtle pressure signals in the ear that can be processed to produce command signals in response to that movement. The strategy developed for the human machine interface architecture includes energy-based signal detection and segmentation to extract ear pressure signals due to tongue movements, signal normalization to decrease the trial-to-trial variations in the signals, and pairwise cross-correlation signal averaging to obtain accurate estimates from ensembles of pressure signals. A new decision fusion classification algorithm is formulated to assign the pressure signals to their respective tongue-movement classes. The complete strategy of signal detection and segmentation, estimation, and classification is tested on 4 tongue movementsof 4 subjects. Through extensive experiments, it is demonstrated that the ear pressure signals due to the tongue movements are distinct and that the 4 pressure signals can be classified with over 96% classification accuracies across the 4 subjects using the decision fusion classification algorithm.
3610-3617
IEEE
Vaidyanathan, R.
f062a7b1-fc7e-4227-9e1b-ca0b61330237
Kotta, S.
00280ec9-f913-4409-a6e0-b120d7f0c6a3
Gupta, L.
d2b4415e-e315-431e-8587-fe6d85560485
West, J.
a54f2efc-9fcf-458d-90a8-fc11b3142fd4
Vaidyanathan, R.
f062a7b1-fc7e-4227-9e1b-ca0b61330237
Kotta, S.
00280ec9-f913-4409-a6e0-b120d7f0c6a3
Gupta, L.
d2b4415e-e315-431e-8587-fe6d85560485
West, J.
a54f2efc-9fcf-458d-90a8-fc11b3142fd4

Vaidyanathan, R., Kotta, S., Gupta, L. and West, J. (2006) A decision fusion pattern classification architecture for human-robotic interface. In Proceedings of the 2006 IEEE International Conference on Robotics and Automation, ICRA 2006. IEEE. pp. 3610-3617 .

Record type: Conference or Workshop Item (Paper)

Abstract

A complete signal processing strategy is presented to detect and precisely recognize tongue movement by monitoring changes in airflow that occur in the ear canal. Tongue movements within the human oral cavity create unique, subtle pressure signals in the ear that can be processed to produce command signals in response to that movement. The strategy developed for the human machine interface architecture includes energy-based signal detection and segmentation to extract ear pressure signals due to tongue movements, signal normalization to decrease the trial-to-trial variations in the signals, and pairwise cross-correlation signal averaging to obtain accurate estimates from ensembles of pressure signals. A new decision fusion classification algorithm is formulated to assign the pressure signals to their respective tongue-movement classes. The complete strategy of signal detection and segmentation, estimation, and classification is tested on 4 tongue movementsof 4 subjects. Through extensive experiments, it is demonstrated that the ear pressure signals due to the tongue movements are distinct and that the 4 pressure signals can be classified with over 96% classification accuracies across the 4 subjects using the decision fusion classification algorithm.

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More information

Published date: 2006
Additional Information: ISSN 1050-4729
Venue - Dates: 2006 IEEE International Conference on Robotics and Automation, ICRA 2006, Orlando, USA, 2006-05-15 - 2006-05-19

Identifiers

Local EPrints ID: 43529
URI: http://eprints.soton.ac.uk/id/eprint/43529
PURE UUID: cdd3a4df-23e5-430b-9913-8cd57004b0e8

Catalogue record

Date deposited: 12 Feb 2007
Last modified: 05 Mar 2024 17:38

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Contributors

Author: R. Vaidyanathan
Author: S. Kotta
Author: L. Gupta
Author: J. West

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