A Piezoresistive Tactile Sensor Array for Touchscreen Panels
Bibliographic record
Abstract
Touchscreen panels (TSPs), as human-machine interfaces, have been ubiquitously adopted in our daily life, especially in consumer electronics and numerous industrial applications. However, the lack of sensing the force information in conventional TSPs draws compromises in gesture strategies and user experiences, leading to complex gestures, multi-level menus, waiting and so on. This paper investigated a piezoresistive tactile sensor array, which sensed the force amplitude of a touch event with the location information using four tactile cells. The development of the sensor array prototype has been discussed, including the design, fabrication, packaging, and tests. Each sensor in the array was tested, demonstrating the sensitivity of 0.31 mV/mN·V in the normal direction. The responses of the sensor array to a 30-mN normal force at various locations along two diagonal lines have been tested, gathering high agreements with numerical solutions. The approach for quantifying the force and location information using a lookup table based on the least square method has been discussed by the probe tests with a 50-mN force in the normal direction on the sensor array. The sensor array showed the capability to achieve the location resolution of 2 mm with tested forces ranging from 0.01 to 0.25 N. The prototype of concept shed light on reducing the number of tactile cells for touchscreen applications. Further numerical analysis indicates the sensor array has the scalability for potential applications, in which a larger area of detection is needed without increasing the number of tactile cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".