Laser-Induced Graphene/PDMS Soft Pressure Sensors for Detecting Low-Pressure Variations in Sports Garments
Bibliographic record
Abstract
In this study, we present a novel soft pressure sensor array capable of reliably capturing low-pressure variations exerted by sports garments on the human body. The sensors were fabricated using the laser-induced graphene (LIG) polydimethylsiloxane (PDMS) as the elastomeric substrate and encapsulation material. A scalable fabrication method combining ultraviolet laser processing and PDMS micro-molding was developed to enable a precise, cost-effective sensor manufacturing. To enhance measurement sensitivity, a rigid cover supported by soft Eco-flex frames was integrated into the design, effectively concentrating distributed garment pressure at the sensor's center. This modification resulted in a fourfold increase in sensitivity, achieving a maximum value of$3.2 \times 10^{-3}$kP a −1 and enabling detection of pressures as low as 1 kPa. The sensor's performance was evaluated through step-hold and cyclic compression tests, as well as application trials simulating the pressure of a sports bra strap on a curved surface. Results demonstrated that the sensor array could distinguish between slow tension and dynamic chafing of the bra, with output closely matching that of commercial pressure sensors, albeit at much lower fabrication cost and higher sensitivity. These findings suggest that the LIG-based sensor array is a promising, scalable solution for pressure mapping during human garment trials as well as pressure-sensitive manikin studies, offering valuable insights for future wear design and comfort optimization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".