Electroactive polymeric sensors in hand prostheses: Bending response of an ionic polymer metal composite
Bibliographic record
Abstract
In stark contrast to the inspiring functionality of the natural hand, limitations of current upper limb prostheses stemming from marginal feedback control, challenges of mechanical design, and lack of sensory capacity, are well-established. This paper provides a critical review of current sensory systems and the potential of a selection of electroactive polymers for sensory applications in hand prostheses. Candidate electroactive polymers are reviewed in terms of their relevant advantages and disadvantages, together with their current implementation in related applications. Empirical analysis of one of the most novel electroactive polymers, ionic polymer metal composites (IPMC), was conducted to demonstrate its potential for prosthetic applications. With linear responses within the operating range typical of hand prostheses, bending angles, and bending rates were accurately measured with 4.4+/-2.5 and 4.8+/-3.5% error, respectively, using the IPMC sensors. With these comparable error rates to traditional resistive bend sensors and a wide range of sensitivities and responses, electroactive polymers offer a promising alternative to more traditional sensory approaches. Their potential role in prosthetics is further heightened by their flexible and formable structure, and their ability to act as both sensors and actuators.
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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 | 0.000 |
| 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) | 0.001 | 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".