Adaptive Semi-Supervised Intent Inferral to Control a Powered Hand Orthosis for Stroke

TL;DR

This paper introduces a semi-supervised learning approach to improve the robustness and reduce user training burden in controlling a powered hand orthosis for stroke patients, adapting to signal changes during use.

Contribution

It presents the first application of semi-supervised learning for orthotic control, specifically a disagreement-based algorithm for handling concept drift with multimodal sensing.

Findings

The algorithm effectively adapts to intrasession signal drift using unlabeled data.

It reduces the training burden on users during device calibration.

Two subjects successfully performed a functional pick-and-handover task.

Abstract

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.