  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 a recurrent neural network 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 by comparing the overall range of muscle activity and thus
dynamically evaluates similarities between both signals. The model achieves a
remarkable precision for previously trained motion while new motions that were
not trained before still have high accuracy. 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
exploited to improve the rehabilitation of neuromuscular diseases with
myoelectric prostheses and functional electric stimulation.
