  In order to provide therapy in a functional context, controls for wearable
robotic orthoses need to be robust and intuitive. We have previously introduced
an intuitive, user-driven, EMG-based method to operate a robotic hand orthosis,
but the process of training a control that is robust to concept drift (changes
in the input signal) places a substantial burden on the user. In this paper, we
explore semi-supervised learning as a paradigm for controlling a powered hand
orthosis for stroke subjects. To the best of our knowledge, this is the first
use of semi-supervised learning for an orthotic application. Specifically, we
propose a disagreement-based semi-supervision algorithm for handling
intrasession concept drift based on multimodal ipsilateral sensing. We evaluate
the performance of our algorithm on data collected from five stroke subjects.
Our results show that the proposed algorithm helps the device adapt to
intrasession drift using unlabeled data and reduces the training burden placed
on the user. We also validate the feasibility of our proposed algorithm with a
functional task; in these experiments, two subjects successfully completed
multiple instances of a pick-and-handover task.
