  State-of-the-art motorized hand prostheses are endowed with actuators able to
provide independent and proportional control of as many as six degrees of
freedom (DOFs). The control signals are derived from residual electromyographic
(EMG) activity, recorded concurrently from relevant forearm muscles.
Nevertheless, the functional mapping between forearm EMG activity and hand
kinematics is only known with limited accuracy. Therefore, no robust method
exists for the reliable computation of control signals for the independent and
proportional actuation of more than two DOFs. A common approach to deal with
this limitation is to pre-program the prostheses for the execution of a
restricted number of behaviors (e.g., pinching, grasping, and wrist rotation)
that are activated by the detection of specific EMG activation patterns.
However, this approach severely limits the range of activities users can
perform with the prostheses during their daily living. In this work, we
introduce a novel method, based on a long short-term memory (LSTM) network, to
continuously map forearm EMG activity onto hand kinematics. Critically, unlike
previous work, which often focuses on simple and highly controlled motor tasks,
we tested our method on a dataset of activities of daily living (ADLs): the
KIN-MUS UJI dataset. To the best of our knowledge, ours is the first reported
work on the prediction of hand kinematics that uses this challenging dataset.
Remarkably, we show that our network is able to generalize to novel untrained
ADLs. Our results suggest that the presented method is suitable for the
generation of control signals for the independent and proportional actuation of
the multiple DOFs of state-of-the-art hand prostheses.
