Design and fabrication of auxetic stretchable force sensor for hand rehabilitation
Bibliographic record
Abstract
Using a melt electrospinning technique, stretchable force sensors were designed for use in an application of hand rehabilitation. The main purpose of this study was to verify that the use of auxetic sensors improved hand rehabilitation practices when compared to their absence. For this study, novel stretchable poly ( -caprolactone) (PCL) force sensors were fabricated into the following formations: auxetic microfiber sheets (AMSs), auxetic solid sheets (ASSs), microfiber sheets (MSs), and solid sheets (SSs). A femtosecond laser device was used to make an auxetic structure in the MSs and SSs. Subsequently, these sensors were coated with gold particles to make them conductive for the electrical current resistance assays. Through the cycles of applied stress and strain, auxetic structures were able to retain their original shape once these forces have been dissipated. This stretchable sensor could potentially measure applied external loads, resistance, and strain and could also be attachable to a desired substrate. In order to verify the workability and practicality of our designed sensors, we have attempted to use the sensors on a human hand. The AMS sensor had the highest sensitivity on measuring force and resistance among the four types of sensors. To our knowledge, this is the first study to form a stretchable force sensor using a melt electrospinning technique.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".