Bibliographic record
Abstract
We investigate, model, and design interaction techniques for hand occlusion with direct pen input. Our focus on occlusion follows from a qualitative and quantitative study of direct pen usability with a conventional graphical user interface (GUI). This study reveals overarching problems relating to poor precision, ergonomics, cognitive differences, limited input, and problems resulting from occlusion. To investigate occlusion more closely, we conduct three formal experiments to examine its area and shape, its affect on performance, and compensatory postures. We find that the shape of the occluded area varies across participants with some common characteristics. Our results provide evidence that occlusion affects target selection performance: especially for continuous tasks or when the goal is initially hidden. We observe how users contort their wrist posture during a simultaneous monitoring task, and show this can increase task time. Based on these investigations, we develop a five parameter geometric model to represent the shape of the occluded area and extend this to a user configurable, real-time version. To evaluate our model, we introduce a novel analytic testing methodology using optimization for geometric fitting and precision-recall statistics for comparison; as well as conducting a user study. To address problems with occlusion, we introduce the notion of occlusion-aware interfaces: techniques which can use our configurable model to track currently occluded regions and then counteract potential problems and/or utilize the occluded area. As a case study, we present the Occlusion-Aware Viewer: an interaction technique which displays otherwise missed previews and status messages in a non-occluded area. Within this thesis we also present a number of methodology contributions for quantitative and qualitative study design, multi-faceted study logging using synchronized video, qualitative analysis, image-based analysis, task visualization, optimization-based analytical testing, and user interface image processing.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".