Reciprocal Learning of Intent Inferral with Augmented Visual Feedback for Stroke

TL;DR

This paper introduces reciprocal learning, a bidirectional approach combining machine learning and human adaptation with visual feedback to improve intent prediction in stroke rehabilitation using biosignals.

Contribution

It proposes a novel reciprocal learning paradigm that iteratively updates models and guides human adaptation via augmented visual feedback in intent inferral tasks.

Findings

Improved intent prediction performance in some stroke subjects.

Subjects learned to produce more distinguishable muscle signals.

Reciprocal learning did not negatively affect other subjects.

Abstract

Intent inferral, the process by which a robotic device predicts a user's intent from biosignals, offers an effective and intuitive way to control wearable robots. Classical intent inferral methods treat biosignal inputs as unidirectional ground truths for training machine learning models, where the internal state of the model is not directly observable by the user. In this work, we propose reciprocal learning, a bidirectional paradigm that facilitates human adaptation to an intent inferral classifier. Our paradigm consists of iterative, interwoven stages that alternate between updating machine learning models and guiding human adaptation with the use of augmented visual feedback. We demonstrate this paradigm in the context of controlling a robotic hand orthosis for stroke, where the device predicts open, close, and relax intents from electromyographic (EMG) signals and provides appropriate assistance. We use LED progress-bar displays to communicate to the user the predicted probabilities for open and close intents by the classifier. Our experiments with stroke subjects show reciprocal learning improving performance in a subset of subjects (two out of five) without negatively impacting performance on the others. We hypothesize that, during reciprocal learning, subjects can learn to reproduce more distinguishable muscle activation patterns and generate more separable biosignals.