筋電によるロボットアームの制御

抄録

It has been considered that electromyogram (EMG) detected by surface electrodes attached to the subject's arm is useful for controlling a robot arm. Moreover, since the amplitude of EMG is changed by physical strength, it is considered EMG possesses effective informations to assume the grip. However, in order to control the robot arm at will, subject have to train their muscles to consistently generate EMG needed to control the robot arm. EMG pattern recognition system is easy to be influenced by the position slip of electrodes and the artifact. In this study, 1/3 octave-analyzed EMG patterns were classified by neural networks which possess learning ability and deal with Interval-Valued data to cope with the position slip of electrodes. Moreover, the grip were assumed by fussyu inference. Applying their results, the robot arm was controlled to adapt to dynamical arm movement. Interval-Valued data is a method express an attribute as a dot in the multi-dimension. For example, the attribute is not constant and is changing. EMG were measured under folloing conditions; (1) closing hand, (2) openning hand, (3) bending wrist to the bending side, (4) bending wrist to the stretching side, (5) turning wrist to the inside, (6) turning wrist to the outside.
In the experiment of assumption of the grip, EMG generated when subject gripped ‘hand-grip’ were used. Applying their results, the robot arm was controlled. It took a little under 2 minute to begin to move the robot arm since subject began to move his arm. Bending angle was set up 10 degrees at 1 operation Consequently, in point of the rate of recognition, the neural network which deal with real-valued data. Moreover, in order to control the robot arm by assuming the grip using EMG, applying fuzzy inference was useful considerably.