Restoring the human touch: Prosthetics imbued with haptics give their wearers fine motor control and a sense of connection
Bibliographic record
Abstract
Wearing a blindfold and noise-canceling headphones, Igor Spetic gropes for the bowl in front of him, reaches into it, and picks up a cherry by its stem. He uses his left hand, which is his own flesh and blood. His right hand, though, is a plastic and metal prosthetic, the consequence of an industrial accident. Spetic is a volunteer in our research at the Louis Stokes Cleveland Veterans Affairs Medical Center, and he has been using this "myoelectric" device for years, controlling it by flexing the muscles in his right arm. The prosthetic, typical of those used by amputees, provides only crude control. As we watch, Spetic grabs the cherry between his prosthetic thumb and forefinger so that he can pull off the stem. Instead, the fruit bursts between his fingers. ¶ Next, my colleagues and I turn on the haptic system that we and our partners have been developing at the Functional Neural Interface Lab at Case Western Reserve University, also in Cleveland. Previously, surgeons J. Robert Anderson and Michael Keith had implanted electrodes in Speticis right forearm, which now make contact with three nerves at 20 locations. Stimulating different nerve fibers produces realistic sensations that Spetic perceives as coming from his missing hand: When we stimulate one spot, he feels a touch on his right palm; another spot produces sensation in his thumb, and so on.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".