A Multi-Axis Tactile Sensor Array for Touchscreen Applications
Bibliographic record
Abstract
Touchscreens have been prevalent in daily life and ubiquitously applied in consuming electronics, industrial control systems, and other applications as human computer interfaces (HCIs), which offers a convenient way for the human to interact with the smart devices. However, the lack of tactile force feedback from these conventional touchscreens draws limitations on the dexterity and intuitiveness of those devices, which results in multi-level menus, waiting, multi-finger gestures, and so on. To enhance and diversify functions of touchscreens, this paper presents a multi-axis tactile sensor array prototype with a unique layered structure, which is capable of sensing both 3-directional tactile force and location information over an area of 60 mm × 60 mm by utilizing only 2 × 2 piezoresistive MEMS force sensors. This paper integrated the sensibility of tangential force and normal force within one system, which sheds light on multi-axis tactile applications and dramatically reduced the number of tactile cells. The sensor array design, fabrication and packaging, and test approaches have been discussed in this paper. Qualitative and quantitative analysis have been conducted to evaluate the performance of the tactile sensor array with tested force range of 0.1 ~ 0.5 N. The results demonstrated the functionality of proposed sensor array, which exhibited promising potential for touchscreen applications.
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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".