Title :
A dynamic neural network identification of electromyography and arm trajectory relationship during complex movements
Author :
Cheron, Guy ; Draye, Jean Philippe ; Bourgeios, Marc ; Libert, Gaëtan
Author_Institution :
Lab. of Biomech., Univ. Libre de Bruxelles, Belgium
fDate :
5/1/1996 12:00:00 AM
Abstract :
The authors propose a new approach based on dynamic recurrent neural networks (DRNN) to identify, in human, the relationship between the muscle electromyographic (EMG) activity and the arm kinematics during the drawing of the figure eight using an extended arm. After learning, the DRNN simulations showed the efficiency of the model. The authors demonstrated its generalization ability to draw unlearned movements. They developed a test of its physiological plausibility by computing the error velocity vectors when small artificial lesions in the EMG signals were created. These lesion experiments demonstrated that the DRNN has identified the preferential direction of the physiological action of the studied muscles. The network also identified neural constraints such as the covariation between geometrical and kinematics parameters of the movement. This suggests that the information of raw EMG signals is largely representative of the kinematics stored in the central motor pattern. Moreover, the DRNN approach will allow one to dissociate the feedforward command (central motor pattern) and the feedback effects from muscles, skin and joints.
Keywords :
biomechanics; electromyography; kinematics; medical signal processing; physiological models; recurrent neural nets; arm kinematics; arm trajectory; central motor pattern; complex movements; dynamic neural network identification; dynamic recurrent neural networks; error velocity vectors; extended arm; feedback effects; feedforward command; joints; physiological action; preferential direction; raw EMG signals; skin; small artificial lesions; Computational modeling; Electromyography; Humans; Kinematics; Lesions; Muscles; Neural networks; Recurrent neural networks; Skin; Testing; Adult; Arm; Biomechanics; Electrodes; Electromyography; Humans; Male; Models, Neurological; Movement; Nerve Net;
Journal_Title :
Biomedical Engineering, IEEE Transactions on