SafeWatch
Bibliographic record
Abstract
Driving with distraction or losing alertness increases the risk of the traffic accident. The emerging Internet of Things (IoT) systems for smart driving hold the promise of significantly reducing road accidents. In particular, detecting the unsafe hand motions and warning the driver using smart sensors can improve the driver's self-alertness and the driving skill. However, due to the impact of the vehicle's movement and the significant variation across different driving environments, detecting the position of the driver's hand is challenging. This paper presents SafeWatch -- a system that employs commodity smartwatches and smartphones to detect the driver's unsafe behaviors in a real-time manner. SafeWatch infers driver's hand motions based on several important features such as the posture of the driver's forearm and the vibration of the smartwatch. SafeWatch employs a novel adaptive training algorithm which keeps updating the training dataset at runtime based on inferred hand positions in certain driving conditions. The evaluation with 75 real driving trips from 6 subjects shows that SafeWatch achieves over 97.0% recall and precision rates in detecting of the unsafe hand positions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".