Title :
Multichannel surface electromyography classification based on muscular synergy
Author :
López, Natalia M. ; Orosco, Eugenio ; di Sciascio, F.
Author_Institution :
Gabinete de Tecnol. Medica, Univ. Nac. de San Juan, San Juan, Argentina
fDate :
Aug. 31 2010-Sept. 4 2010
Abstract :
With the aim to control a multiple degrees of freedom electromechanical devices, e.g., assistive robots, powered wheelchair, etc., this paper proposes a real-time multichannel surface electromyography classification scheme based on the coordination or synergies between a functional group of muscles: biceps brachii, triceps brachii, pronator teres, and brachioradialis. The muscular synergy is evaluated by the analysis of a multivariate function, composed by the four corresponding neuromuscular activation functions, and the cross-correlation matrix of muscular force estimated through the root mean square (RMS) value of sEMG amplitude. The resulting features from the training set were used to train an artificial neural network with classification accuracy up of 90%.
Keywords :
electromyography; medical signal processing; neural nets; neurophysiology; signal classification; artificial neural network; biceps brachii; classification scheme; cross-correlation matrix; multiple degrees of freedom electromechanical devices; muscular force; muscular synergy; neuromuscular activation functions; real-time multichannel surface electromyography; root mean square value; triceps brachii; Accuracy; Biological system modeling; Electromyography; Feature extraction; Force; Neuromuscular; Adult; Algorithms; Arm; Electromyography; Female; Humans; Male; Movement; Muscle Contraction; Muscle, Skeletal; Postural Balance;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
Conference_Location :
Buenos Aires
Print_ISBN :
978-1-4244-4123-5
DOI :
10.1109/IEMBS.2010.5626679