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Using sample entropy for automated sign language recognition on sEMG and accelerometer data.

Authors
Type
Published Article
Journal
Medical & Biological Engineering & Computing
1741-0444
Publisher
Springer-Verlag
Publication Date
Volume
48
Issue
3
Pages
255–267
Identifiers
DOI: 10.1007/s11517-009-0557-6
PMID: 19943194
Source
Medline
License
Unknown

Abstract

Communication using sign language (SL) provides alternative means for information transmission among the deaf. Automated gesture recognition involved in SL, however, could further expand this communication channel to the world of hearers. In this study, data from five-channel surface electromyogram and three-dimensional accelerometer from signers' dominant hand were subjected to a feature extraction process. The latter consisted of sample entropy (SampEn)-based analysis, whereas time-frequency feature (TFF) analysis was also performed as a baseline method for the automated recognition of 60-word lexicon Greek SL (GSL) isolated signs. Experimental results have shown a 66 and 92% mean classification accuracy threshold using TFF and SampEn, respectively. These results justify the superiority of SampEn against conventional methods, such as TFF, to provide with high recognition hit-ratios, combined with feature vector dimension reduction, toward a fast and reliable automated GSL gesture recognition.

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