Multimodal Sensing and Interaction for a Robotic Hand Orthosis

TL;DR

This paper presents a multimodal sensing approach for a robotic hand orthosis, combining EMG, finger bend, and contact sensors to improve control for diverse impairment patterns in stroke rehabilitation.

Contribution

It introduces a novel multimodal sensing and interaction paradigm that enhances control robustness and effectiveness for users with varied impairments.

Findings

Multimodal sensors enable better task performance for stroke survivors.

The approach improves control robustness across different impairment patterns.

Demonstrated feasibility of multimodal interaction in a proof-of-concept device.

Abstract

Wearable robotic hand rehabilitation devices can allow greater freedom and flexibility than their workstation-like counterparts. However, the field is generally lacking effective methods by which the user can operate the device: such controls must be effective, intuitive, and robust to the wide range of possible impairment patterns. Even when focusing on a specific condition, such as stroke, the variety of encountered upper limb impairment patterns means that a single sensing modality, such as electromyography (EMG), might not be sufficient to enable controls for a broad range of users. To address this significant gap, we introduce a multimodal sensing and interaction paradigm for an active hand orthosis. In our proof-of-concept implementation, EMG is complemented by other sensing modalities, such as finger bend and contact pressure sensors. We propose multimodal interaction methods that utilize this sensory data as input, and show they can enable tasks for stroke survivors who exhibit different impairment patterns. We believe that robotic hand orthoses developed as multimodal sensory platforms with help address some of the key challenges in physical interaction with the user.