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

Vasiliki E. Kosmidou, Leontios I. Hadjileontiadis

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


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.

Original languageBritish English
Pages (from-to)255-267
Number of pages13
JournalMedical and Biological Engineering and Computing
Issue number3
StatePublished - Mar 2010


  • Gesture recognition
  • Sample entropy
  • SEMG
  • Sign language recognition
  • Three-dimensional accelerometer


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