Multimodal Sensing and Interaction for a Robotic Hand Orthosis
This addresses the challenge of user operation for stroke survivors in rehabilitation, but it is incremental as it builds on existing sensing methods.
The paper tackled the problem of enabling effective, intuitive, and robust controls for wearable robotic hand orthoses in stroke rehabilitation by introducing a multimodal sensing and interaction paradigm, showing it can enable tasks for stroke survivors with different impairment patterns.
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.