Getting Your Hands Dirty Outside the Lab: A Practical Primer for Conducting Wearable Vibrotactile Haptics Research
Bibliographic record
Abstract
As haptics have become an ingrained part of our wearable experience, particularly through phones, smartwatches, and fitness trackers, significant research effort has been conducted to find new ways of using wearable haptics to convey information, especially while we are on-the-go. In this paper, instead of focusing on aspects of haptic information design, such as tacton encoding methods, actuators, and technical fabrication of devices, we address the more general recurring issues and "gotchas" that arise when moving from core haptic perceptual studies and in-lab wearable experiments to real world testing of wearable vibrotactile haptic systems. We summarize key issues for practitioners to take into account when designing and carrying out in-the-wild wearable haptic user studies, as well as for user studies in a lab environment that seek to simulate real-world conditions. We include not only examples from published work and commercial sources, but also hard-won illustrative examples derived from issues and failures from our own haptic studies. By providing a broad-based, accessible overview of recurring issues, we expect that both novice and experienced haptic researchers will find suggestions that will improve their own mobile wearable haptic studies.
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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 | 0.039 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".