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

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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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Acknowledgments

The authors would like to thank the Greek Sign Language teachers, Mr. Grigoris Petropoulos, Mr. Thanasis Germanidis, and Mrs. Maria Christoforidou for performing the GSL gestures during the acquisition process. The authors are grateful to Mrs. Anastasia Gouvatzi, Director of the Special School for the Deaf, Thessaloniki, for her contribution to the construction of the GSL vocabulary and her help in the data acquisition experiments. The authors would like to thank the General Secretariat for Research and Technology (GSRT), Greek Ministry of Development, for its financial support under the Grant No. 44 (22195/16 Dec. 2005) within the 3rd Community Support Programme-Operational Programme “Information Society 2000-2008 A3-M3.3” (Funding: 75% European Regional Development Fund-25% National Fund).

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  1. Department of Electrical & Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, University Campus, 541 24, Thessaloniki, Greece

    Vasiliki E. Kosmidou & Leontios I. Hadjileontiadis

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  1. Vasiliki E. Kosmidou
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  2. Leontios I. Hadjileontiadis
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Correspondence to Leontios I. Hadjileontiadis.

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Kosmidou, V.E., Hadjileontiadis, L.I. Using sample entropy for automated sign language recognition on sEMG and accelerometer data. Med Biol Eng Comput 48, 255–267 (2010). https://doi.org/10.1007/s11517-009-0557-6

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