The use of self organizing maps to evaluate myoelectric signals.
This paper presents a study of the use and accuracy of self-organizing maps (SOM) in classifying myoelectric signal properties. Myoelectric signals were obtained and classified for four upper-limb movements (elbow flexion, elbow extension, wrist pronation and wrist supination) and their force category. This was done for isolated actions as well as for multiple action sequences. The success of the developed SOM ranged from 92%-97% when determining the motion, from 81%-87% in determining the force category, and from 59%-96% in determining sequences of motions. These successes are encouraging for the continued development of this technique for use in controlling real-time complex motions in prosthetic devices.
Patterson
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PE
College of Engineering, Iowa State University, Ames, IA 50011, USA.
Anderson
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M
D000200:Action Potentials; D000293:Adolescent; D000328:Adult; D001132:Arm; D001186:Artificial Limbs; D006801:Humans; D008297:Male; D009068:Movement; D009119:Muscle Contraction; D018482:Muscle, Skeletal / Q000502:physiology*; D016571:Neural Networks, Computer