Sensory Feedback of Grasp Security by Direct Neural Stimulation Improves Amputee Prediction of Object Slip
Bibliographic record
Abstract
Background: Prostheses are becoming more advanced and biomimetic with time, providing additional capabilities to their users. However, prosthetic sensation lags far behind its natural limb counterpart, limiting the use of sensory feedback in prosthetic motion planning and execution. Without actionable sensation, prostheses may never meet the functional requirements to match biological performance. Methods: We propose an approach for upper limb prosthetic grasp security feedback, delivered to the wearer through direct nerve stimulation proportional to the likelihood of objects slipping from grasp. This proportional feedback is based on a linear regression of the sensors embedded in a prosthetic hand to predict slip before it occurs. Four participants with transhumeral amputation performed pulling tasks with their prosthetic hand grasping an object at predetermined grip forces, attempting to pull the object with as much force as possible without slip. These trials were performed with two different prediction notification paradigms. Results: At lower grasp forces, where slip was more likely, a strong, single impulse notification of impending slip reduced the incidence of object slip by a median of 32%, but the maximum achieved pull forces did not change. At higher grasp forces, where slip was less likely, the maximum achieved pull forces increased by a median of 19% across participants when provided with a stimulation strength inversely proportional to the grasp security, but slip incidence was unchanged. Conclusions: These results suggest that this approach may be effective in recreating a lost sense of grip stability in the missing limb that can be incorporated into motor planning and ultimately prevent unanticipated object slips.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".