Control of a Robot Arm by Electromyogram -Recognition of Arm Motion by Neural Network
Masafumi Uchida, Hideto Ide
College of Science and Engineering, Aoyama Gakuin University, 6-16-1 Chitosedai, Setagaya-ku, Tokyo, 157 Japan
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. Interval-Valued data is a method express an attribute as a dot in the multi-dimension. For example, the attribute is constant and is changing. EMG were measured under following conditions; (1) closing hand, (2) opening 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.