Fabrication and Evaluation of Woven and Crocheted Strain Sensors for Soft Rehabilitation Robots
Bibliographic record
Abstract
Recent advancements in technology have made possible the use of soft wearable mechatronic devices for musculoskeletal rehabilitation. Soft sensors are crucial components of these devices, as they are used for user data collection and device control. However, current fabrication methods are expensive, complex, and not available to those in low resource communities. To address this issue, this paper presents advances in low-cost textile sensors created using either crocheting or weaving. The performance of four different types of sensors was evaluated in terms of working range, linearity, hysteresis, sensitivity, and repeatability. The results showed that a fully crocheted sensor made with elastic and silver-plated conductive thread performed the best. This sensor demonstrated high repeatability, an excellent working range (49.76%), and relatively good hysteresis ($20.12 \pm 19$) and linearity ($R^{2}$ of $0.7741 \pm 0.03$). Overall, these results indicate that soft textile strain sensors created using simpler techniques, such as crocheting, have the potential to be used in applications requiring tracking of human motion, meaning that they could be used as integral components of soft wearable robots. This low-cost approach could make soft wearable rehabilitation technologies more accessible and affordable, paving the way for broader implementation and improved patient outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".