A Semi-automatic Control Method for Myoelectric Prosthetic Hand based on Image Information of Objects

In recent years, prosthetic hands for amputees have seen rapid progress while myoelectric prosthetic control is facing a big challenge. It is difficult to control a multi-DOF hand only with electromyographic (EMG) signals to achieve complicated motions and natural and effortless operations, especially in the scenario of practical applications. This study proposes a semi-automatic myoelectric control method combining EMG signals with a vision-based object classifier to control a prosthetic hand. Information of target objects, such as shape features, dimensions, and postures can be obtained from images and then utilized to generate control commands for motors, in conjunction with user's muscle activities via EMG signals. EMG patterns are recognized to represent user's intension of motions. In the meanwhile, EMG power levels are used to modulate motor speed in a proportional manner. Prosthetic hand control experiments have been conducted to verify the proposed method.