Sign language recognition using intrinsic-mode sample entropy on sEMG and accelerometer data

Sign language forms a communication channel among the deaf; however, automated gesture recognition could further expand their communication with the hearers. In this work, data from five-channel surface electromyogram and 3-D accelerometer from the signer's dominant hand were analyzed using intrinsic-mode entropy (IMEn) for the automated recognition of Greek sign language (GSL) isolated signs. Discriminant analysis was used to identify the effective scales of the intrinsic-mode functions and the window length for the calculation of the IMEn that contributes to the efficient classification of the GSL signs. Experimental results from the IMEn analysis applied to GSL signs corresponding to 60-word lexicon repeated ten times by three native signers have shown more than 93% mean classification accuracy using IMEn as the only source of the classification feature set. This provides a promising bed-set toward the automated GSL gesture recognition.