Back-of-Device Force Feedback Improves Touchscreen Interaction for Mobile Devices
Why this work is in the frame
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Bibliographic record
Abstract
Touchscreen interaction suffers from occlusion problems as fingers can cover small targets, which makes interacting with such targets challenging. To improve touchscreen interaction accuracy and consequently the selection of small or hidden objects, we introduce a back-of-device force feedback system for smartphones. We introduce a new solution that combines force feedback on the back to enhance touch input on the front screen. The interface includes three actuated pins at the back of a smartphone. All three pins are driven by microservos and can be actuated up to a frequency of 50 Hz and a maximum amplitude of 5 mm. In a first psychophysical user study, we explored the limits of the system. Thereafter, we demonstrate through a performance study that the proposed interface can enhance touchscreen interaction precision, compared to state-of-the-art methods. In particular, the selection of small targets performed remarkably well with force feedback. The study additionally shows that users subjectively felt significantly more accurate with force feedback. Based on the results, we discuss back-to-front feedback design issues and demonstrate potential applications through several prototypical concepts to illustrate where the back-of-device force feedback could be beneficial.
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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 | 0.000 | 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 it