Can Wearable Devices Facilitate a Driver’s Brake Response Time in a Classic Car-Following Task?
Bibliographic record
Abstract
Effective warnings of potential collision risks are important countermeasures for drowsy and distracted driving. This study explores the possibility of using smart wearable devices to provide vibrotactile warnings. We assessed the effectiveness of a vibrating wearable device as a warning system. Participants performed a classic car-following task in a driving simulator under four conditions: no warning, warnings at the finger, wrist, temple area. When the lead vehicle braked intermittently, warnings would be delivered to the same vibrating device, which was placed at the finger, wrist, or temple area. Results showed that warnings at the finger and the wrist produced shorter brake response time than the no warning condition. Warnings at the temple area did not produce significant benefits in brake response time over the no warning condition. Participants preferred warnings at the finger and the wrist than the temple area. Quicker brake response time for warnings at the finger and wrist area may be explained by the relative sizes of cortex area in the brain which corresponds to the sensory organs, as visualized by the classic Penfield Homunculus. The current study of wearable tactile warnings can inform future designs of warning systems for drivers.
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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 | 0.000 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".