Applying the Cumulative Fatigue Model to Interaction on Large, Multi-Touch Displays
Bibliographic record
Abstract
Large touch displays have long been studied in the lab, and are beginning to see widespread deployment in public spaces. However, a common limitation is fatigue -- often called 'gorilla arm' -- that prevents users from working with large displays for extended periods of time. A first step towards addressing fatigue is quantifying it, and while methods have been developed to quantify users' fatigue in mid-air interactions, there remains little understanding of fatigue on touch-based interfaces. To address this gap, we evaluated the accuracy of Jang et al.'s mid-air Cumulative Fatigue model for touch interaction tasks on a large display. We found that their model underestimates subjective fatigue for multi-touch interaction, but can provide accurate estimates through fine-tuning of model parameters. We discuss the implications of this finding, and the need to further develop tools to evaluate fatigue on large, multi-touch displays.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".