Electromyographic signal integrated robot hand control for massage therapy applications

This paper presents an electromyographic (EMG) signal integrated multi-finger robot hand control for massage applications. This research explores the feasible application of multi-finger robot hands except for the use as prostheses and grasping applications. The forearm EMG of a person who is massaged by the human hands is recorded and analyzed statistically. First, the root mean square (RMS) of the raw data is computed as the discrimination between normal and contracted states of the muscle. Then the EMG signal at contracted state is further divided into painful and comfortable groups based on the impulse factor which is defined to estimate the sharpness of waveform variations. As a consequence, two discriminative values of the EMG signal are generated to distinguish painful and comfortable feelings. Based on the relationship between the human feeling and the massage force, we get an appropriate range of input commands of the robot hand for massage applications. A grasp-kneading massage is performed on the human shoulder to verify the proposed process. As a result, an effective and comfortable massage using the multi-finger robot hand is realized.