Dual-Modality Haptic Feedback Improves Dexterous Task Execution With Virtual EMG-Controlled Gripper
Bibliographic record
Abstract
Individuals with upper-extremity limb difference 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 participants without limb difference to perform the task in four conditions: no feedback, vibration feedback of incipient slip, squeezing feedback of grip force, and dual (vibration + squeezing) feedback of incipient slip and grip force. Results suggest that receiving any haptic feedback is better than receiving none, however, dual-modality feedback is far superior to either single-modality feedback approach in terms of preventing the object from breaking or dropping. Control with dual-modality feedback was also seen as more intuitive than with either of the single-modality feedback approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".