Feel-a-bump: Haptic feedback for foot-based angular menu selection
Bibliographic record
Abstract
Although diverse foot-based applications have been explored, foot-based menu selection is underexplored given its potential for low-fatigue secondary control input. Here, we are investigating whether the effect of adding haptic modalities can achieve higher performance in a menu selection task. We study the effect of auditory or vibrotactile feedback on selection performance in radial menus consisting of three, six and nine items. We compared no feedback to one auditory and two vibrotactile clicks, one across the foot, one localized to the movement direction. All feedback modalities allowed for rapid completion of menu selections and, while audio was generally preferred and our results suggest a superiority over haptics, the latter are still helpful in increasing selection accuracy. However, we argue that the difference is such that haptics could still be used with comparable performance in noisy environments or by users with auditory disabilities. Finally, we use an analysis of the number of attempts required to select the correct position, coupled with the number of errors, to make design recommendations for foot-based menus.
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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 | 0.000 |
| 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 | 0.001 |
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 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".