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

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.