Open AccessElectromyographic Grasp Recognition for a Five Fingered Robotic Hand
Author(s) -
Nayan M. Kakoty,
Mantoo Kaiborta,
Shyamanta M. Hazarika
Publication year - 2012
Publication title -
international journal of robotics and automation (ijra)/iaes international journal of robotics and automation
Language(s) - English
Resource type - Journals
eISSN - 2722-2586
pISSN - 2089-4856
DOI - 10.11591/ijra.v2i1.pp1-10
Subject(s) - grasp , artificial intelligence , pattern recognition (psychology) , computer science , support vector machine , robot , kernel (algebra) , electromyography , speech recognition , computer vision , mathematics , psychology , neuroscience , programming language , combinatorics
This paper presents classification of grasp types based on surface electromyographic signals. Classification is through radial basis function kernel support vector machine using sum of wavelet decomposition coefficients of the EMG signals. In a study involving six subjects, we achieved an average recognition rate of 86%. The electromyographic grasp recognition together with a 8-bit microcontroller has been employed to control a five fingered robotic hand to emulate six grasp types used during 70% daily living activities.