Susceptibility to periodic vibrotactile guidance of human cadence
Bibliographic record
Abstract
In this paper we introduce a new guidance method that employs periodic vibrotactile cues to help users walk at a desired speed. We also explore walker's susceptibility to periodic vibrotactile guidance (PVG): specifically, adjustments of their stride frequency in response to cues that are clearly perceived; and finally, how long users can maintain their stride frequency after the guidance cue stops. While wearing a vibrotactile display on one wrist, each participant was given five vibrotactile tempos, logarithmically spaced across the participant's walking frequency range. We compared realtime stride frequency with cue tempo under conditions that varied cue tempo and presence / absence. Our results suggest that most individuals (here, 13 of 15) can synchronize their cadence with a vibrotactile cue with 95% accuracy (mean error, all participants: -1.5%, SD = 8.1) for a guidance tempo within their physical ability. Once a tempo was matched, walkers could maintain it for at least 30 seconds after the cue was turned off, showing promise for intermittent guidance as a solution to stimulus adaptation and annoyance. This finding informs design of spatiotemporal guidance systems, by showing how the informationally narrow but nevertheless underused haptic channel may have utility in guiding pedestrians' speed, without a need to learn abstracted signals, and through a continuous control system.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".