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Improved identification of the human shoulder kinematics with muscle biological filters

par Draye, Jean-Philippe ;Chéron, Guy ;Pavisic, Davor;Libert, Gaëtan
Référence Lecture notes in computer science, 1211, page (417-428)
Publication Publié, 1997

Article révisé par les pairs

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Résumé :

In this paper, we introduce new refinements to the approach based on dynamic recurrent neural networks (DRNN) to identify, in humans, the relationship between the muscle electromyographic (EMG) activity and the arm kinematics during the drawing of the figure eight using an extended arm. This method of identification allows to clearly interpret the role of each muscle in any particular movement. We show here that the quality and the speed of the complex identification process can be improved by applying some treatments to the input signals (i.e. raw EMG signals). These treatments, applied on raw EMG signals, help to get signals that are better reflections of muscle forces which are the real actuators of the movements.