Evaluating the Measurement of Driver Heart and Breathing Rates from a Sensor-Equipped Steering Wheel using Spectrotemporal Signal Processing
Bibliographic record
Abstract
Driver's status and behaviours such as inattention, drunk driving, or sleeping while driving play important roles in approximately half of all automobile crashes. For this reason, the last decade has seen an emergence of non-intrusive driver status monitoring systems with the ultimate goal of reducing the number of such accidents. From the different number of proposed methods, the use of the physiological signals, specifically the electrocardiogram (ECG), has shown useful. The acquisition of ECG signals during driving, however, presents a challenge due to movement artifacts, such as car and driver motion, and a good contact of the sensing electrodes, e.g., embedded on the driver seat. In this paper, we evaluate the ECG signals acquired from electrodes placed on the steering wheel under three aspects: (i) quality of the acquired signals; (ii) their usability to estimate an average and an instantaneous heart rate, and (iii) their usability to estimate the driver's breathing rate via innovative spectrotemporal processing of the acquired signals. Experimental results show that ECG signals obtained from the steering wheel have quality inline with that obtained from a benchmark chest ECG device, allow for both average and instantaneous heart rate to be measured, as well as breathing rate to be extracted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".