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This study presents a novel method for associating features of the surface electromyogram (EMG) recorded from one upper limb to the force produced by the contralateral limb. Bilateral-mirrored contractions from ten able-bodied subjects were recorded along with isometric forces in multiple degrees of freedom (DOF) from the right wrist. An artificial neural network was trained to provide force estimation...
The paper proposes a method to estimate wrist kinematics from surface EMG signals for proportional and simultaneous control of multiple degrees of freedom (DOFs). The approach is based on the concurrent detection of surface EMG signals from forearm muscles and hand kinematics of both limbs during mirrored bilateral movements in free space which involve the simultaneous activation of wrist flexion/extension,...
A generative model for the surface EMG is presented. The model is built on the assumption that motor units in synergistic muscle share neural drives from spinal level, which correspond to the activation of different degrees of freedom (DOF) of natural movements, and are embedded within surface EMG. An artificial neural network (ANN) is developed to extract these drives simultaneously from the multi-channel...
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