Recognition of Finger Motion with sEMG and Gyrosensor Signals

A novel method to infer the finger flexing motions of various arm postures is proposed. From the gyroscope signal, the authors recognized forearm posture using K-means clustering method. Then finger motion inferred. For finger motion inference, Gaussian model of information entropy and maximum likelibood method was utilized. Experimentally it is obtained that the average recognition rate with the forearm posture inference is much higher than those without the inference by 30.7%.