BezelGlide: Interacting with Graphs on Smartwatches with Minimal Screen Occlusion
Bibliographic record
Abstract
We present BezelGlide, a novel suite of bezel interaction techniques, designed to minimize screen occlusion and ‘fat finger’ effects, when interacting with common graphs on smartwatches. To explore the design of BezelGlide, we conducted two user studies. First, we quantified the amount of screen occlusion experienced when interacting with the smartwatch bezel. Next, we designed two techniques that involve gliding the finger along the smartwatch bezel for graph interaction. Full BezelGlide (FBG) and Partial BezelGlide (PBG), use the full or a portion of the bezel, respectively, to reduce screen occlusion while scanning a line chart for data. In the common value detection task, we find that PBG outperforms FBG and Shift, a touchscreen occlusion-free technique, both quantitatively and subjectively, also while mobile. We finally illustrate the generzability potential of PBG to interact with common graph types making it a valuable interaction technique for smartwatch users.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".