Analysis of Embroidered Strain Sensors in the Presence of Dynamic Forces
Bibliographic record
Abstract
It is estimated that at least 66% of stroke survivors will present some condition that impair their ability to perform well during their activities of daily living. This is the reason why stroke survivors engage in rehabilitation therapies to improve their quality of life. To aid in their recovery process, robot-assisted technologies in the form of soft wearable devices have been proposed as a complementary method to traditional rehabilitation sessions. Within these soft devices, great attention has been placed on their sensing mechanisms. Among the different sensing modalities used to provide feedback to the soft wearable mechatronic device, force sensing stands out due to them being easier to integrate within the wearable system. However, one flaw with these sensors is that most of the times they are used as pressure sensors due to their nonstretchable characteristics. Therefore, in this study, a novel stretchable Kirigami-based force sensor is presented. This force sensor was created by embroidering a silver-plated conductive onto an elastic band (EB). To test the performance of the sensor in terms of sensitivity, linearity, hysteresis, and repeatability, three sensor samples were fabricated and stretched at a slow, medium, and high speed, while force data were being collected. After these tests, the sensor showed high repeatability, an average${R} ^{{2}}$of 0.9782, an average 10.27% hysteresis, and an average 0.03-V/N gauge factor. These results show that embroidered force sensors have the potential to be used to detect interaction forces within soft wearable robot-assisted therapies.
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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 | 0.001 | 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".