Performance Evaluation of Embroidered Honeycomb Resistive Textile Strain Sensors
Bibliographic record
Abstract
The rise of soft wearable sensors has opened the door for less obtrusive sensing during upper limb rehabilitation. Many studies have proposed different methods of fabrication for these sensors, but the simplest ones include those made using knitting, stitching, or embroidering to create resistive strain sensors. However, the reliability of these sensors is influenced by the amount of contact points of the conductive thread used at any given time. These contact points can suffer from deformations due to forces applied during each stretching cycle, which can affect the sensor response and produce erroneous measurements. These issues can be avoided by creating embroidered sensors with patterns that do not affect the contact points of the stitches. Still, forces applied directly to the conductive thread can cause irreparable damage to the sensor. Therefore, in this study a novel embroidered strain sensor is created using a honeycomb pattern. This pattern has two main purposes: a distribution of the axial forces across the walls of the pattern to protect the conductive thread; and the addition of stretchiness to the embroidered sensor. Sensors created using this pattern were embroidered onto an elastic band and then attached to a strain divider system to increase the stretchability of the sensor further. After 50 stretching cycles, sensors showed good linearity, an average gauge factor of 0.24, an average hysteresis of 36.85% and a 55.56% working range. These results show that the proposed sensor is robust to thread damages, thus making it a viable alternative for strain sensing applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".