Robust grip force estimation under electric feedback using muscle stiffness and electromyography for powered prosthetic hand

Powered prosthetic hands are becoming increasingly functional through sensory feedback. However, when using electrical stimulation as sensory feedback for electromyographic (EMG) prosthetics, stimulation artifacts may cause EMG data noise. Electrical stimulation and EMG measurements are therefore performed using time-division methods in rehabilitation facilities. Under time-division methods, EMG levels cannot be acquired at the stimulation time. Highly functional prosthetic hands that can estimate grip force, however, use advanced signal processing and require detailed EMG information. EMG measuring cycle expansion may make grip force estimation unstable. We therefore developed a grip force estimation system using muscle stiffness and EMG as the estimation source signals. The estimation system consists of a muscle stiffness sensor, an EMG sensor and an estimation algorithm. We chose a tray holding task for the system evaluation. A weight is dropped on the tray and subjects are expected to control the tray's attitude. Grip force, EMG, and muscle stiffness are measured, and the measured and estimated grip forces are compared. The proposed algorithm estimates grip force with an error of just 18[N], which is 30% smaller than in EMG-only methods. The system response time is lower than human mechanical reaction time, validating the effectiveness of the proposed method.