Improving Haptic Feedback on Wearable Devices through Accelerometer Measurements
Bibliographic record
Abstract
Many variables have been shown to impact whether a vibration stimulus will be perceived. We present a user study that takes into account not only previously investigated predictors such as vibration intensity and duration along with the age of the person receiving the stimulus, but also the amount of motion, as measured by an accelerometer, at the site of vibration immediately preceding the stimulus. This is a more specific measure than in previous studies showing an effect on perception due to gross conditions such as walking. We show that a logistic regression model including prior acceleration is significantly better at predicting vibration perception than a model including only vibration intensity, duration and participant age. In addition to the overall regression, we discuss individual participant differences and measures of classification performance for real-world applications. Our expectation is that haptic interface designers will be able to use such results to design better vibrations that are perceivable under the user's current activity conditions, without being annoyingly loud or jarring, eventually approaching ``perceptually equivalent' feedback independent of motion.
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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 | 0.009 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".