Electroactive polymeric sensors in hand prostheses: Bending response of an ionic polymer metal composite
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it