Multiple Hand Gesture Recognition Based on Surface EMG Signal

For realizing a multi-DOF myoelectric control system with a minimal number of sensors, research work on the recognition of twenty-four hand gestures based on two-channel surface EMG signal measured from human forearm muscles has been carried out. Third-order AR model coefficients, Mean Absolute Value and Mean Absolute Value ratio of the sEMG signal segments were used as features and the recognition of gestures was performed with a linear Bayesian classifier. Our experimental results show that the proposed two sensors setup and the sEMG signal processing and recognition methods are well suited for distinguishing hand gestures consisting of various wrist motions and single finger extension.