Evaluation of surface EMG features for the recognition of American Sign Language gestures

Vasiliki E. Kosmidou, Leontios J. Hadjileontiadis, Stavros M. Panas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

37 Scopus citations

Abstract

In this work, analysis of the surface electromyogram (sEMG) signal is proposed for the recognition of American Sign Language (ASL) gestures. To this purpose, sixteen features are extracted from the sEMG signal acquired from the user's forearm, and evaluated by the Mahalanobis distance criterion. Discriminant analysis is used to reduce the number of features used in the classification of the signed ASL gestures. The proposed features are tested against noise resulting in a further reduced set of features, which are evaluated for their discriminant ability. The classification results reveal that 97.7% of the inspected ASL gestures were correctly recognized using sEMG-based features, providing a promising solution to the automatic ASL gesture recognition problem.

Original languageBritish English
Title of host publication28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Pages6197-6200
Number of pages4
DOIs
StatePublished - 2006
Event28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States
Duration: 30 Aug 20063 Sep 2006

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ISSN (Print)0589-1019

Conference

Conference28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Country/TerritoryUnited States
CityNew York, NY
Period30/08/063/09/06

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