Intrinsic mode entropy: an enhanced classification means for automated Greek Sign Language gesture recognition

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 three-dimensional accelerometer and five-channel surface electromyogram of the user's dominant forearm are analyzed using intrinsic mode entropy (IMEn) for the automated recognition of Greek Sign Language (GSL) gestures. IMEn was estimated for various window lengths and evaluated by the Mahalanobis distance criterion. 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 correct classification of the GSL gestures. Experimental results from the IMEn analysis of GSL gestures corresponding to ten words have shown 100% classification accuracy using IMEn as the only classification feature. This provides a promising bed-set towards the automated GSL gesture recognition.