End-user customization of affective tactile messages: A qualitative examination of tool parameters
Bibliographic record
Abstract
Vibrotactile (VT) signals are found today in many everyday electronic devices (e.g., notification of cellphone messages or calls); but it remains a challenge to design engaging, understandable vibrations to accommodate a broad range of preferences. Here, we examine customization as a way to leverage the affective qualities of vibrations and satisfy diverse tastes; specifically, the desirability and composition of VT customization tools for end-users. A review of existing design and customization tools (haptic and otherwise) yielded five parameters in which such tools can vary: 1) size of design space, 2) granularity of control, 3) provided design framework, 4) facilitated parameter(s), and 5) clarity of design alternatives. We varied these parameters within low-fidelity prototypes of three customization tools, modeled in some respects on existing popular examples. Results of a Wizard-of-Oz study confirm users' general interest in customizing everyday VT signals. Although common in consumer devices, choosing from a list of presets was the least preferred, whereas an option allowing users to balance VT design control with convenience was favored. We report users' opinion of the three tools, and link our findings to the five characterizing parameters for customization tools that we have proposed.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".