Dual-Modality Haptic Feedback Improves Dexterous Task Execution with Virtual EMG-Controlled Gripper
Kezi Li, Jeremy D. Brown

TL;DR
This study demonstrates that dual-modality haptic feedback significantly enhances dexterous task performance in a virtual EMG-controlled grasp task, outperforming single-modality feedback in preventing object breakage or dropping.
Contribution
It introduces and evaluates a dual-modality haptic feedback system for virtual EMG-controlled grasp tasks, showing its superiority over single-modality feedback in dexterity and intuitiveness.
Findings
Dual-modality feedback reduces object breakage and dropping.
Participants found dual feedback more intuitive.
Any feedback is better than none for prosthetic control.
Abstract
Upper-extremity amputees who use myoelectric prostheses currently lack the haptic sensory information needed to perform dexterous activities of daily living. While considerable research has focused on restoring this haptic information, these approaches often rely on single-modality feedback schemes which are necessary but insufficient for the feedforward and feedback control strategies employed by the central nervous system. Multi-modality feedback approaches have been gaining attention in several application domains, however, the utility for myoelectric prosthesis use remains unclear. In this study, we investigated the utility of dual-modality haptic feedback in a virtual EMG-controlled grasp-and-hold task with a brittle object and variable load force. We recruited N=20 non-amputee participants to perform the task in four conditions: no feedback, vibration feedback of incipient slip,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMuscle activation and electromyography studies · Neuroscience and Neural Engineering · EEG and Brain-Computer Interfaces
