Control of a Robot Arm by Electromyogram -Recognition of Arm Motion by Neural Network | Zendy

Masafumi Uchida | Zendy; Hideto Ide | Zendy

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Control of a Robot Arm by Electromyogram -Recognition of Arm Motion by Neural Network

Author(s) -

Masafumi Uchida,

Hideto Ide

Publication year - 1996

Publication title -

journal of robotics and mechatronics

Language(s) - English

Resource type - Journals

eISSN - 1883-8049

pISSN - 0915-3942

DOI - 10.20965/jrm.1996.p0372

Subject(s) - wrist , closing (real estate) , artificial neural network , computer science , artificial intelligence , bending , electromyography , simulation , control theory (sociology) , physical medicine and rehabilitation , engineering , control (management) , structural engineering , medicine , anatomy , political science , law

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.

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