REVIEW 5 major objections 5 minor 5 references
Towards Generalizable Drowsiness Monitoring with Physiological Sensors: A Preliminary Study
T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash
Pith's one-line read This paper claims that three physiological features—higher heart rate stability, reduced respiratory amplitude, and decreased tonic EDA—are robustly associated with increased drowsiness across four datasets using different inducers and…
desk verdict A useful cross-dataset comparison and an honest preliminary frame, but the headline robust markers and the objective-vs-subjective sensitivity claim are confounded beyond what the analysis can support. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central object is a set of binary logistic regression models with generalized estimating equations for repeated measures, built on 14 physiological features selected from 32 extracted features. Each of the first four models fits one dataset, and the fifth pools all datasets while adding a categorical variable named Drowsy Type that encodes which dataset and inducer each sample came from; post-hoc comparisons on that variable are what support the claims about inducer differences and assessment-method sensitivity.
What would settle it
A leave-one-dataset-out test: train on three datasets and predict the held-out dataset's drowsy labels from the three robust features; if accuracy is at chance or the coefficients flip sign, the claim of robustness fails. Alternatively, collect one study where the same participants experience all four inducers with both subjective and objective labels, and check whether increased heart-rate stability, reduced respiratory amplitude, and decreased tonic EDA still track drowsiness.
Extended reading notes
Core claim
On the paper's own terms, the central discovery is that common autonomic shifts accompany drowsiness regardless of how it is induced or labelled: cardiac activity becomes higher in rate but more stable (higher heart rate with reduced variability), respiratory amplitude drops, and tonic electrodermal activity decreases. These 'robust' features emerge from a pooled logistic regression that also includes a categorical Drowsy Type variable, which was significant and showed that mental fatigue had much lower odds of being labelled drowsy than physical fatigue, low arousal, or sleep deprivation. The paper further claims that objective annotations (e.g., trained raters on facial video) were far more sensitive than subjective KSS ratings, with an odds ratio of 12.16, while cautioning that objective labels may trade sensitivity for specificity.
Load-bearing premise
The paper's cross-dataset claims rest on treating four datasets as interchangeable examples of the same 'drowsiness' construct, even though dataset identity, inducer, assessment method, sensor hardware, and participant population are completely entangled.
Editorial extensions
If this is right
- A monitoring model trained on data from one drowsiness inducer may perform poorly on another, since mental fatigue was far less likely to be labelled drowsy than physical fatigue or low arousal.
- Training sets for drowsiness detection should deliberately include multiple inducers and both subjective and objective labels.
- A small set of three physiological markers could serve as a common foundation for simplified, generalizable detection algorithms.
- Combining subjective and objective labels may balance the high sensitivity of objective ratings against their possible false-positive cost.
- Future naturalistic driving studies should test whether these markers survive real-world motion and measurement noise.
Reading between the lines
- Because Drowsy Type is identical to dataset identity, the pooled analysis cannot actually separate 'different inducers' from 'different hardware, populations, and protocols'; a cleaner design would hold one dimension fixed.
- The 12.16 odds ratio for objective versus subjective labels may partly reflect threshold effects and rater behaviour rather than intrinsic sensitivity; a within-subject study with both labels on the same video would disentangle this.
- If these three markers replicate in naturalistic driving, they would enable camera-free, privacy-preserving drowsiness monitoring using wearable or contact-sensor hardware.
- A direct testable extension would be to extract the same 14 features from a new dataset with a novel inducer and check, before any re-fitting, whether the three robust features predict the drowsy label in the pooled model's direction.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript analyzes four existing datasets of physiological recordings (ECG, EDA, RESP) collected under different drowsiness inducers and assessment methods. The authors preprocess the signals, extract 32 features, and use multicollinearity screening plus QIC-based backward selection to retain 14 features. They fit binary logistic regression models with GEE, separately for each dataset and then on a pooled dataset with an additional categorical variable called 'Drowsy Type' that gives each dataset a distinct ID. From the pooled model and post-hoc comparisons, they claim that (RQ1) different inducers produce different physiological responses, (RQ2) objective assessments have higher sensitivity than subjective ones (OR = 12.16), and (RQ3) increased heart rate stability, reduced respiratory amplitude, and decreased tonic EDA are robustly associated with drowsiness. The paper is positioned as a preliminary, exploratory analysis aimed at informing future generalizable drowsiness monitoring.
Significance. If the central claims were supported, the study would be a useful preliminary contribution toward reconciling conflicting physiological markers of drowsiness across datasets and toward guiding the collection of more generalizable training data. The authors deserve credit for assembling multiple datasets, including a private one, and for explicitly acknowledging that inducers and assessment methods vary across studies. However, the analysis as presented does not support the headline claims: the comparison of assessment methods is confounded with dataset and inducer, the pooled 'robust' features rely on a model that assumes homogeneous effects that the per-dataset models contradict, and the reported statistics contain an internal inconsistency. Because these issues are load-bearing for all three research questions, the contribution is not currently established.
major comments (5)
- [Approach: Statistics Models; Results, Table 7] Second major comment.
- [Results, post-hoc comparison after Model (e)] Third major comment.
- [Results, Table 5] Fourth major comment.
- [Approach: Statistics Models; Results, Tables 3-7] Fifth major comment.
- [Approach: Datasets] Sixth major comment.
minor comments (5)
- [Approach: Datasets] Minor comment.
- [Tables 3-7] Minor comment.
- [Results, Table 5] Minor comment.
- [References] Minor comment.
- [Discussion, RQ2] Minor comment.
Circularity Check
No significant circularity: empirical regression study with no derivation that reduces to its own inputs.
full rationale
This is an empirical study using binary logistic regression on four datasets; there is no mathematical derivation whose conclusion is equivalent to its premises. The 'robust' features are estimated associations from a pooled model, not predictions validated on held-out data, so the same-data feature selection and inference is a statistical overfitting/selection concern rather than a circularity. The Drowsy Type variable in Model (e) operationally equals dataset identity, and the post-hoc objective-vs-subjective comparison is confounded with dataset, inducer, hardware, and population; this threatens the validity of the sensitivity claim but does not make the result circular, because the contrasts are estimated from the model, not assumed by construction. The two self-citations (Ayas et al. 2023; 2024) are background support and private data, respectively, and are not load-bearing for any derived quantity. No equations or definitions reduce to each other; no uniqueness theorem or ansatz is imported from prior work by the same authors. The acknowledged limitations (non-driving physical-fatigue data, simulated environments) further indicate that concerns are about external validity, not circularity.
Assumptions & free parameters
free parameters (3)
- KSS drowsiness threshold =
KSS >= 7
- Multicollinearity correlation threshold =
|r| > 0.8
- QIC-based backward selection criterion =
Quasi-likelihood under Independence Model Criterion
assumptions (4)
- domain assumption Physiological signals from disparate devices can be made comparable by resampling to 100 Hz and applying standard filters.
- domain assumption KSS self-reports and objective facial-video annotations both measure a common latent 'drowsiness' construct.
- domain assumption Fatigue and drowsiness can be treated as interchangeable for the cross-dataset analysis, despite different definitions.
- standard math GEE and logistic regression assumptions hold with small cluster numbers (e.g., 12 participants in Fatigueset).
Cite this review
Pith. "Pith review of Towards Generalizable Drowsiness Monitoring with Physiological Sensors: A Preliminary Study." pith.science (2026). https://pith.science/paper/TR4VAOH7
@misc{pith2026250606360,
author = {Pith},
title = {Pith review of: Towards Generalizable Drowsiness Monitoring with Physiological Sensors: A Preliminary Study},
year = {2026},
howpublished = {\url{https://pith.science/paper/TR4VAOH7}},
note = {Machine review of arXiv:2506.06360}
}
read the original abstract
Accurately detecting drowsiness is vital to driving safety. Among all measures, physiological-signal-based drowsiness monitoring can be more privacy-preserving than a camera-based approach. However, conflicts exist regarding how physiological metrics are associated with different drowsiness labels across datasets. Thus, we analyzed key features from electrocardiograms (ECG), electrodermal activity (EDA), and respiratory (RESP) signals across four datasets, where different drowsiness inducers (such as fatigue and low arousal) and assessment methods (subjective vs. objective) were used. Binary logistic regression models were built to identify the physiological metrics that are associated with drowsiness. Findings indicate that distinct different drowsiness inducers can lead to different physiological responses, and objective assessments were more sensitive than subjective ones in detecting drowsiness. Further, the increased heart rate stability, reduced respiratory amplitude, and decreased tonic EDA are robustly associated with increased drowsiness. The results enhance understanding of drowsiness detection and can inform future generalizable monitoring designs.
Reference graph
Works this paper leans on
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[1]
Towards Generalizable Drowsiness Monitoring with Physiological Sensors: A Preliminary Study Jiyao Wang1, Suzan Ayas2, Jiahao Zhang1, Xiao Wen3, Dengbo He1, Birsen Donmez2 1 The Hong Kong University of Science and Technology (Guangzhou) 2 University of Toronto 3 The Hong Kong University of Science and Technology Accurately detecting drowsiness is vital to ...
work page 2008
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[2]
Descriptive Statistics of the Investigated Datasets. Dataset Date Subject Age (Mean, Min-Max, SD) Gender Collection Device Sampling Frequency Drowsiness Criteria Fatigueset 2021 12 30.75, 21-40, 5.8 9 male; 3 female BioHarness, E4 100 HZ Objective AdVitam 2023 63 23.8, 18-64, 4.8 45 male; 18 female BioPac MP36 1000 HZ Subjective MCDD 2024 42 35.3, 23-53, ...
work page 2021
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[6]
Fatigue Monitoring Using Wearables and AI: Trends, Challenges, and Future Opportunities
were both associated with ECG and EDA indicators. Finally, while the RESP-RMSSD was associated with the drowsiness caused by sleep deprivation, no RESP-related feature was associated with drowsiness induced by mental fatigue. Next, Table 7 further shows that certain features from all three physiological measures were significantly associated with levels of...
work page Pith review arXiv 2024
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[270]
Prigent, G., Apte, S., Paraschiv-Ionescu, A., Besson, C., Gremeaux, V., & Aminian, K. (2022). Concurrent evolution of biomechanical and physiological parameters with running-induced acute fatigue. Frontiers in Physiology, 13, 814172. Sakata, T., Yamauchi, K., Karumi, T., Omi, T., & Sawai, S. (2024). Proposal for Reproducible and Practical Drowsiness Indic...
arXiv 2022
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[2024]
involves 42 drivers who, over 2.5 hours of L3 automated driving, performed three non-driving cognitive tasks (N-back, mental arithmetic, and spatial search); drowsiness ratings were collected every five minutes using the KSS. Finally, Ayas et al. (2024) is private dataset. It observed 27 drivers in a monotonous Level 2 automated driving scenario lasting up...
work page 2024
Reviewed August 7, 2026 · model on record in the stance chip above.
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