Exploring and Understanding Unintended Touch during Direct Pen Interaction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The user experience on tablets that support both touch and styli is less than ideal, due in large part to the problem of unintended touch or palm rejection . Devices are often unable to distinguish between intended touch (i.e., interaction on the screen intended for action) and unintended touch (i.e., incidental interaction from the palm, forearm, or fingers). This often results in stray ink strokes and accidental navigation, frustrating users. We present a data collection experiment where participants performed inking tasks, and where natural tablet and stylus behaviors were observed and analyzed from both digitizer and behavioral perspectives. An analysis and comparison of novel and existing unintended touch algorithms revealed that the use of stylus information can greatly reduce unintended touch. Our analysis also revealed many natural stylus behaviors that influence unintended touch, underscoring the importance of application and ecosystem demands, and providing many avenues for future research and technological advancement.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| 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 it