Muscle Activation Source Model-based sEMG Signal Decomposition and Recognition of Interface Rotation

Muscle activation signals are measured from the skin surface as surface electromyography (EMG) signals that contain information on human intentions; therefore, they are widely used in various robotics applications owing to their usability. However, selective muscle activation extraction is difficult because of the complexity of muscle structures. This study investigated muscle activation source model-based sEMG signal decomposition that considers the anatomical factors of muscle structures. The main advantage of the proposed model-based signal decomposition is that sEMG interface rotation can be recognized by comparing source parameters identified before and after rotation. To assess the performance of the proposed model-based decomposition method, hand motion estimation and rotation recognition were conducted. Additionally, two-dimensional simultaneous control was conducted with an inertial measurement unit to verify the usability of the proposed model. The results indicate that the proposed model decomposes an sEMG signal based on motion with good performance and demonstrate feasibility of motion estimation independent of sEMG interface rotation.