  Movement control of artificial limbs has made big advances in recent years.
New sensor and control technology enhanced the functionality and usefulness of
artificial limbs to the point that complex movements, such as grasping, can be
performed to a limited extent. To date, the most successful results were
achieved by applying recurrent neural networks (RNNs). However, in the domain
of artificial hands, experiments so far were limited to non-mobile wrists,
which significantly reduces the functionality of such prostheses. In this
paper, for the first time, we present empirical results on gesture recognition
with both mobile and non-mobile wrists. Furthermore, we demonstrate that
recurrent neural networks with simple recurrent units (SRU) outperform regular
RNNs in both cases in terms of gesture recognition accuracy, on data acquired
by an arm band sensing electromagnetic signals from arm muscles (via surface
electromyography or sEMG). Finally, we show that adding domain adaptation
techniques to continuous gesture recognition with RNN improves the transfer
ability between subjects, where a limb controller trained on data from one
person is used for another person.
