The Influence of Flexural Stiffness on the Performance and Preference of Bendable Stylus Interfaces
Bibliographic record
Abstract
Flexible sensing styluses deliver additional degrees of input for pen-based interaction, yet no research on bendable styluses has looked at the influence of stiffness on performance or its integration with creative digital applications.We conducted two experiments that evaluated the influence of flexural stiffness on users' performance.To address this, we designed HyperBrush a flexible sensing stylus with interchangeable flexible components.We assessed the performance of different flexibilities on a bend menu technique.While bend input in styluses can perform similarly to pressure input, researchers only have measured stationary input.We furthered this discussion by evaluating performance of simultaneous bend and X-Y pen movement.We conducted a third experiment that investigated how HyperBrush can be a beneficial tool to support users' creativity for digital drawing.We concluded that different flexibilities can pose their own unique advantages analogous to an artist's assortment of paintbrushes.For these challenging past two years, I would like to thank my supervisors Dr. Audrey Girouard and Dr. Thomas Pietrzak for providing their endless guidance and support.If it wasn't for their exceptional experience, and knowledge, I would have not been able to reach this milestone today.I would also like to give tribute to everyone from the Creative Interaction Labs.I am grateful for being part of a diverse lab culture with driven, enthusiastic, friendly, and welcoming students who shared great interest in their own research and as well as my own.I owe it to Alex Eady for influencing me to peruse a master's degree
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".