Tomohiro Nishikawa

Power assistance for human elbow motion support using minimal EMG signals with admittance control

In this paper, different from other researches, we developed an admittance control model with EMG signal to control our developed power assistance device for human elbow motion support. A two degree of freedom power assistance device is developed, and a simple but effective EMG signal processing method is proposed. Further, an admittance control model using EMG signal is introduced to generate speed command based on the EMG signal to perform power assist task. It can easily and arbitrarily implement different power assist ratio with different parameters in admittance model. Experimental results show validity of the proposed approach.

Construction of real-time BMI control system based on motor imagery

Recently, a lot of BMIs (Brain-Machine Interfaces) using EEG (electroencephalogram) signals are developed to control external devices such as prostheses and robots. In this paper, in order to develop a new BMI for rehabilitation and/or power support, four different tasks corresponding to different brain excitation degrees are designed. Their EEG spectra are analyzed with short-time FFT, and their features of mu and beta rhythms corresponding to the different tasks are extracted. Finally, one-joint robot arm is controlled by the extracted features, and the proposed approach is confirmed.