Characterisation and Control of a Woven Biomimetic Actuator for Wearable Neurorehabilitative Devices
Bibliographic record
Abstract
Twisted coiled actuators (TCAs) are a type of soft actuator made from polymer fibres such as nylon sewing thread. As they provide motion in a compact, lightweight, and flexible package, they provide a solution to the actuation of wearable mechatronic devices for motion assistance. Their limitation is that they provide low total force, requiring them to actuate in parallel with multiple units. Previous literature has shown that the force and stroke production can be improved by incorporating them into fabric meshes. A fabric mesh could also improve the contraction efficiency, strain rate, and user comfort. Therefore, this study focused on measuring these performance metrics for a set of TCAs embedded into a woven fabric mesh. The experimental results show that the stroke of the actuators scaled linearly with the number of activated TCAs, achieving a maximum applied force of 11.28 N, a maximum stroke of 12.23%, and an efficiency of 1.8%. Additionally, two control methods were developed and evaluated, resulting in low overshoot and steady-state error. These results indicate that the designed actuators are viable for use in wearable mechatronic devices, since they can scale to meet different requirements, while being able to be accurately controlled with minimal additional components.
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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 | 0.001 |
| 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".