Ultrasound-based sensing models for finger motion classification

Motions of the fingers are complex since hand grasping and manipulation are conducted by spatial and temporal coordination of forearm muscles and tendons. The dominant methods based on surface electromyography (sEMG) could not offer satisfactory solutions for finger motion classification due to its inherent nature of measuring the electrical activity of motor units at the skin’s surface. In order to recognize morphological changes of forearm muscles for accurate hand motion prediction, ultrasound imaging is employed to investigate the feasibility of detecting mechanical deformation of deep muscle compartments in potential clinical applications. In this study, finger motion classification has been represented as subproblems: recognizing the discrete finger motions and predicting the continuous finger angles. Predefined 14 finger motions are presented in both sEMG signals and ultrasound images and captured simultaneously. Linear discriminant analysis classifier shows the ultrasound has better average accuracy (95.88%) than the sEMG (90.14%). On the other hand, the study of predicting the metacarpophalangeal (MCP) joint angle of each finger in non-period movements also confirms that classification method based on ultrasound achieves better results (average correlation 0.89±0.07 and NRMSE 0.15±0.05) than sEMG (0.81±0.09 and 0.19±0.05). The research outcomes evidently demonstrate that the ultrasound can be a feasible solution for muscle-driven machine interface, such as accurate finger motion control of prostheses and wearable robotic devices.