Somatotopic non-invasive proprioceptive feedback strategy for prosthetic hands: a preliminary study
Bibliographic record
Abstract
Abstract Objective. Robotic hand prosthesis users often identify the lack of physiological feedback as a major obstacle to seamless integration. Both the low controllability and high cognitive load required to operate these devices generally lead to their rejection. Consequently, experts highlight sensory feedback as a critical missing features of commercial prostheses. Providing feedback that promotes the integration of artificial limbs is often sought through a biomimetic paradigm, limited by the current technological landscape and the absence of neural embodiment in users. As a result, some researchers are now turning to bio-inspired approaches, choosing to repurpose existing neural structures and focusing on underlying neurocognitive mechanisms that promote the integration of artificial inputs. Approach. Taking a bio-inspired approach, this paper describes the first implementation of a somatotopic, non-invasive proprioceptive feedback strategy for hand prosthesis users, developed using a standard sensory restoration architecture, i.e. pre-processing, encoding and stimulation. The main hypothesis investigated is whether a novel use of transcutaneous electrical stimulation can be leveraged to deliver proprioceptive information of the hand to the user. Main results. The potential of the proposed strategy was highlighted via experimental validation in conveying specific finger apertures and grasp types related to single and multiple degrees of freedom. Six participants were able to identify apertures conveyed by median and ulnar nerve stimulation with an accuracy of 96.5% ± 2.3% and a response time of 0.91 s ± 0.08 s, as well as grasp types conveyed from concurrent median and ulnar nerve stimulation with an accuracy of 88.3% ± 1.2% and a response time of 0.44 s ± 0.27 s through 5 sets of 10 trials. Significance. These results demonstrate the relevance of a somatotopic proprioception feedback strategy for users of prosthetic hands, and the architecture presented in this case study allows for future optimization of the various sub-components.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.003 | 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".