Triboelectric Pressure Sensor With Microstructured PDMS for Human Motion and Gait Pattern Monitoring
Bibliographic record
Abstract
This work presents a low-cost, out-of-cleanroom method for fabricating microstructured polydimethylsiloxane (PDMS) films for triboelectric pressure sensors, using a tape mold replication process that eliminates the need for expensive equipment. A triboelectric nanogenerator (TENG)-based pressure sensor is developed with materials, including PDMS, polyimide (kapton) films, and copper electrodes. The TENG-based pressure sensors have been successfully applied to monitor various human motions, such as walking (via insole integration), tactile sensing (via cup integration), foot pressure detection, and tracking movements such as elbow and finger bending, as well as jumping. The flexible sensor demonstrated high linearity ($R^{2} =0.9817$), a quick response time (100 ms), and a reliable loading and unloading rate (10 Hz). The sensor showed stable output across diverse forces and frequencies, which is ideal for flexible and wearable applications. These findings highlight the potential of the proposed TENG fabrication method for applications in wearable pressure sensors, self-powered electronic skin (e-skin) for humanoid robots, and human--machine interaction systems.
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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 | 0.000 |
| 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".