Bibliographic record
Abstract
Recent advances in touch sensing technologies have made it possible to interact with computers in a device-free manner, allowing for arguably more natural and intuitive input using multiple hands and fingers. Unfortunately, existing multi-point touch-sensitive devices have a number of sensor limitations which restrict the types of manipulations that can be performed. Additionally, while many well-studied techniques from the bimanual interaction literature are applicable to these emerging multi-point devices, there remain many unanswered questions as to how multiple fingers from a single hand can best be utilized on these touch-sensitive surfaces. This dissertation attempts to address some of these open issues. We first develop the Visual Touchpad, a low-cost vision-based input device that allows for detecting multiple hands and fingertips over a constrained planar surface. Unlike existing multi-point devices, the Visual Touchpad extracts a reliable 2D image of the entire hand that can be used to extract more detailed information about the fingers such as labels, orientation, and hover. We then design and implement three systems that leverage the capabilities of the Visual Touchpad to explore how multiple fingers could be used in real-world interface scenarios. Next we propose and experimentally validate a fluid interaction style that uses the thumb and index finger of a single hand in an asymmetric-dependent manner to control bi-digit widgets, where the index finger performs the primary and more frequent 2D tasks and the thumb performs secondary and less frequent tasks to support the index finger's manipulations. We then investigate the impact of visual feedback on the perception of finger span when using bi-digit widgets to merge command selection and direct manipulation. Results suggest that users are capable of selecting from up to 4 discrete commands with the thumb without any visual feedback, which allows us to design a set of more advanced bidigit widgets that facilitate smooth transitioning from novice to expert usage.
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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 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".