Multi-Bin Breathing Pattern Estimation by Radar Fusion for Enhanced Driver Monitoring
Bibliographic record
Abstract
Monitoring the status of the driver is a crucial aspect of health monitoring inside vehicles as it helps to identify potential health or safety risks that could affect a driver’s ability to operate a vehicle safely. This includes monitoring for fatigue, distraction, or impairment, among other things, which can potentially cause car crashes. Although many solutions for health monitoring in private vehicles have been proposed, the majority of them are inconvenient to use or have the risk of leaking private information. Radars have the potential to address the above drawbacks by their inherent privacy protection and contactless operation in addition to their high accuracy, convenience, affordable price, and resilience to environmental factors. Among many possible radar configurations, millimeter FMCW radars can accurately detect range and monitor displacements that are essential in breathing pattern monitoring. Breathing pattern monitoring is one of the key signatures of the driver’s health. An accurate estimation of the breathing pattern enables the detection of breathing abnormalities, including tachypnea, bradypnea, biot, cheyne–stokes, and apnea. The breathing pattern can be estimated from both the chest and abdomen. For this purpose, we employed two 60 GHz FMCW radars. The proposed algorithm is capable of detecting the mentioned breathing abnormalities through breathing rate (BR) estimation and breath-hold period detection. In addition, the proposed method in this paper estimates BR based on the multiple range bins. We conducted a study on the human radar geometry problem inside a vehicle to determine the accurate number of range bins for BR estimation. The experimental results demonstrate a maximum BR error of 1.9 breaths per minute using the proposed multi-bin technique. In addition, the dual radar fusion system can detect breath-hold periods with minimal false detections.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".