From Motion to Muscle
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Voluntary human motion is the product of muscle activity that results from upstream motion planning of the motor cortical areas. We show that muscle activity can be artificially generated based on motion features such as position, velocity, and acceleration. For this purpose, we specifically develop an approach based on recurrent neural network that is trained in a supervised learning session; additional neural network architectures are considered and evaluated. The performance is evaluated by a new score called the zero-line score. The latter adaptively rescales the loss function of the generated signal for all channels comparing the overall range of muscle activity and thus dynamically evaluates similarities between both signals. The model achieves remarkable precision for previously trained movements and maintains significantly high precision for new movements that have not been previously trained. Further, these models are trained on multiple subjects and thus are able to generalize across individuals. In addition, we distinguish between a general model that has been trained on several subjects, a subject-specific model, and a specific pre-trained model that uses the general model as a basis and is adapted to a specific subject afterward. The subject-specific generation of muscle activity can be further used to improve the rehabilitation of neuromuscular diseases with myoelectric prostheses and functional electric stimulation.
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