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REVIEW 3 major objections 5 minor 91 references

Contactless pulse rate assessment: Results and insights for application in driving simulator

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A webcam-based video pipeline can estimate a driver's pulse rate in a driving simulator to within about 5 beats per minute of a wrist-worn reference sensor, the paper claims.

desk verdict Honest rPPG application paper, but the headline accuracy number is circular; the age-group finding is the more robust result. read the letter →

arxiv 2505.01299 v4 pith:CUXA5AMT submitted 2025-05-02 eess.IV eess.SP

{DJ}or{j̣e D. Nešković , Kristina Stojmenova Pečečnik , Jaka Sodnik , Nadica Miljković This is my paper · ORCID
classification eess.IVeess.SP
keywords remotephotoplethysmographyEulerianvideomagnificationpulserateestimationdrivingsimulatorcontactlessdrivermonitoringmotionartifactsfacialskincolorvariationEmpaticaE4
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that remote photoplethysmography (rPPG)—reading pulse from subtle facial color changes in ordinary webcam video—is feasible inside a motion-based driving simulator. On recordings from 65 drivers, the authors' video pipeline, with Eulerian video magnification (EVM) and a linear correction applied afterward, reached a mean absolute error of 5.04 bpm and a root mean squared error of 6.38 bpm against the Empatica E4 wrist sensor. The same pipeline also detected a statistically significant pulse-rate difference between younger and older drivers, matching the reference sensor. The claim matters because contactless monitoring could replace wrist-worn devices in driving-simulator studies and eventually support in-car fatigue or stress monitoring, all from a low-cost camera.

What carries the argument

The load-bearing machinery is the Signal of Change in Light Intensity (SCLI), extracted as the first principal component of the mean red, green, and blue pixel values over a face-without-eyes region from every video frame, followed by a modified Pan–Tompkins algorithm for peak detection. Eulerian video magnification (EVM)—an algorithm that amplifies tiny temporal color changes in video—is applied as an optional preprocessing step. A linear correction $y = a x + b$ (with $a = 0.96$, $b = -74.01$ for the EVM case) is then applied to video-derived pulse-rate values to remove a systematic deviation that grows with pulse rate; this correction is grounded in the observation that the Empatica E4 itself shows a similar linear bias against an ECG-based reference in independent data [66].

What would settle it

Run the published pipeline unchanged—same face detector, same 433 ms moving average for the after-EVM case, same linear correction $y = 0.96x - 74.01$—on a fresh set of driving-simulator videos with simultaneous Empatica E4 recordings, and compute MAE on those held-out sequences. If the error is close to the uncorrected 10.55 bpm rather than 5.04 bpm, the correction was an in-sample artifact; if MAE stays near 5 bpm, the central accuracy claim generalizes.

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Extended reading notes

Core claim

The paper's central claim is that a complete video-only pipeline—face detection, facial-skin region-of-interest extraction, optional EVM, PCA-based signal extraction, and a modified Pan–Tompkins peak detector—can turn ordinary webcam footage of a person driving a simulator into a usable pulse-rate estimate. With EVM and a linear correction fitted to their data, the pipeline's mean absolute error against the Empatica E4 wristband was 5.04 ± 0.37 bpm (RMSE 6.38 ± 0.51 bpm); without EVM the MAE was 6.48 ± 0.41 bpm. The paper also claims that the video-derived pulse rate preserves a meaningful physiological signal: it separates younger drivers (mean 80.99 bpm) from older drivers (mean 73.12 bpm) with statistical significance, as the reference sensor does. Cross-correlation between the video signal and the wrist blood-volume-pulse waveform is very low (0.08–0.09), so the claim is about heart-rate estimation at the segment level, not waveform fidelity.

Load-bearing premise

The reported 5.04 bpm error assumes that the linear correction parameters and the moving-average window width, both chosen to minimize error on the same recordings, will also reduce error on new recordings rather than merely fitting noise.

Editorial extensions

If this is right

  • A single low-cost webcam can replace a wrist-worn PPG sensor for group-level pulse-rate monitoring in driving-simulator studies, such as comparing younger and older drivers.
  • EVM's accuracy gain is small relative to its cost—about 20 extra seconds per 30-second sequence—so practical quasi-real-time deployments may skip EVM and still retain most of the benefit.
  • The linear-bias correction transfers conceptually to other reference sensors: because the wrist sensor itself overestimates pulse rate at higher rates, some of the raw error in video-versus-reference comparisons is sensor bias rather than video error.
  • Video-derived pulse rate can support age-related driver workload and stress studies, provided per-individual errors are tolerated.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the correction generalizes, the same calibration approach could be used to cross-calibrate webcam rPPG against any wrist-worn PPG sensor, letting laboratories pool data collected with different wearables.
  • The low waveform correlation coexisting with usable beat-rate estimates suggests that segment-averaged pulse rate, not pulse waveform, is what this method reliably delivers; a testable extension is whether heart-rate variability features derived from the same signal also survive the motion-heavy setting.
  • A natural next experiment is to compare the video pipeline directly against ECG (not wrist PPG) in the same simulator; the paper's use of the Faros 360 data points toward this, and it would separate correction of sensor bias from correction of video error.
  • For driver monitoring, the method's practical ceiling may be set by head motion and lighting; the authors' own improvement list—standardized lighting, explicit skin segmentation, and higher-resolution cameras—gives a concrete checklist for pushing MAE below the roughly 2 bpm best cases they observed.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The manuscript presents a contactless pulse rate (PR) estimation pipeline for driving-simulator scenarios. The pipeline uses YOLO-based face detection, eye exclusion to form a facial skin ROI, optional Eulerian Video Magnification (EVM), PCA-based extraction of a light-intensity signal, and a modified Pan–Tompkins peak detector with a moving-average smoothing step. The authors compare video-derived PR against simultaneously recorded Empatica E4 reference values, report MAE/RMSE before and after a linear correction, test whether EVM improves accuracy, evaluate the method on two public datasets, and compare younger versus older drivers. The headline result is an MAE of 5.04 bpm and RMSE of 6.38 bpm after EVM and linear correction, with the claim that this demonstrates feasibility of rPPG-based PR monitoring in driving simulators. The paper also includes a time-complexity analysis of EVM and an independent assessment of Empatica E4 bias using Faros 360 data.

Significance. If the reported accuracy were out-of-sample, the study would be a useful feasibility demonstration for contactless driver monitoring in a realistic, retrospective driving-simulator corpus. The manuscript has several strengths: it uses a relatively large participant sample (79 recordings from 65 participants) with a wide PR range, makes the dataset publicly available on Zenodo, explicitly compares EVM and non-EVM processing, provides execution-time measurements, and uses an independent Empatica E4 versus Faros 360 dataset to assess reference-device bias. The age-group difference in PR is supported even before the linear correction, which is a meaningful result. However, the central quantitative accuracy claim currently rests on parameters selected on the same data used to report the errors, so the numbers should be interpreted as in-sample optima until cross-validation or an external correction is reported numerically.

major comments (3)
  1. [Section 2.5, Section 2.7, Figure 5] The headline accuracy numbers (MAE 5.04 ± 0.37 bpm, RMSE 6.38 ± 0.51 bpm for A.EVM after correction) are computed on the same data used to choose two sets of parameters. Section 2.5 states that the moving-average window width is selected per overlapping sequence to minimize MAE against the Empatica E4 reference and the per-sequence optima are then averaged; Section 2.7 fits the linear correction parameters a and b to the same B.EVM/A.EVM differences that are subsequently corrected and reported in Figure 5. No cross-validation, leave-one-subject-out split, or nested tuning is described. The 6.48-to-5.04 bpm improvement therefore reflects a fitted optimum on the evaluation data, not a predictive accuracy. The paper should provide out-of-sample estimates, for example participant-level cross-validation for both the window width and the linear correction, or re-label the headline numbers as in-sample and report the external-correction results as the predictive estimate.
  2. [Section 3, Table 2] The age-group finding is not an artifact of the correction: the pre-correction p-values (B.EVM p=0.04, A.EVM p=0.01) are already significant and the reference data show p<0.001. However, the p.f. and p.f.f. columns and the Cliff's delta values (0.29 to 0.38) are computed after applying the in-sample linear correction, so these strengthened results are entangled with fitted parameters. The paper should report effect sizes for the uncorrected data with confidence intervals and treat the post-correction values as supporting, not primary, evidence.
  3. [Section 2.7, Figure 5 (bottom panel)] The external correction derived from Medarević et al. [66] is the only genuinely out-of-sample correction in the paper, yet the manuscript only states that 'errors decrease' and gives no numeric MAE, RMSE, AAE, SAE, or ARE for this correction. Because the external fit parameters (a=0.32, b=-30.42) differ substantially from the in-sample fits, the resulting error values are important for judging generalization; please report them explicitly.
minor comments (5)
  1. [Equation (4)] The definition of ARE contains a double summation with N in both the inner and outer sums; this is malformed and should be corrected.
  2. [Figure 5 caption] The text refers to upper, middle, and bottom panels, but the caption mentions only upper and lower panels; align the caption with the three-panel layout.
  3. [Discussion, reference [1]] The Discussion refers to 'Renne et al.'; the cited author is Renner et al. Please correct the name.
  4. [Abstract and Table 3] The abstract states that EVM adds 'about 20 s for 30 s sequence', while Table 3 reports 23.26 ± 0.86 s for single-core execution and 16.61 ± 1.62 s for four-core execution on a 30-s video; please clarify which configuration the abstract figure refers to.
  5. [Table 1] The row 'Our approach applied to the second dataset presented in [61]' leaves the MAE and RMSE cells empty; either fill them in or state explicitly why they are unavailable.

Circularity Check

2 steps flagged · score 6.0 of 10

The headline MAE of 5.04 bpm is an in-sample optimum: both the §2.5 moving-average window width and the §2.7 linear correction are fit against the Empatica E4 reference used for scoring, with no held-out split.

  1. fitted input called prediction [Section 2.5, Extraction of Light Changes and Peak Detection]
    "The search for the moving average window width is conducted with the widths that varied from 33 ms to 1 s with the step of 33 ms (corresponds to averaging from one to 30 samples because the sampling frequency is 30 Hz) [51]. Suitable values of the window width are determined for each overlapping video sequence to minimize the mean absolute error (MAE) in comparison with the reference average PR values from the Empatica E4 sensor in the corresponding sequence. All suitable values of the moving average window widths are then averaged to obtain a unique parameter applicable to all videos."

    The smoothing window used in the modified Pan-Tompkins peak detector is selected per sequence by minimizing MAE against the Empatica E4 reference, then averaged into a single width. The same reference is later used to report the post-smoothing MAE/RMSE. This makes the smoothing parameter part of an in-sample fit to the evaluation target, so the reported error is not an independent measure of predictive accuracy. No cross-validation or held-out split is described, and the subsequent linear correction in §2.7 compounds the in-sample character of the reported 5.04 bpm result.

  2. fitted input called prediction [Section 2.7, Additional Processing of Extracted Pulse Rates]
    "Therefore, we decide to design a linear fit (showcased on the left-hand panel graphs in Figure 2). Based on this, we correct the extracted faults by subtracting the linear fit values from the calculated differences between the reference and extracted PR (linear fit correction). ... In our case, a simple linear fit correction is applied, with parameters a and b in the basic linear equation y = a ∗ x + b, which are 0.94 and −69.41 for B.EVM as well as 0.96 and −74.01 for A.EVM, respectively, effectively reducing observed deviations."

    The linear correction is estimated from the residual differences between video-derived PR and Empatica E4 PR on the same recordings whose corrected errors are then reported in Figure 5 and Table 1. Subtracting a residual fit from the data used to estimate it cannot increase the mean error on those same data, so the drop from 6.48 bpm (B.EVM) to 5.04 bpm (A.EVM) is an in-sample improvement by construction, not an out-of-sample prediction. Because the paper reports no validation split, the headline MAE/RMSE values are fitted optima rather than held-out accuracies.

full rationale

The rPPG pipeline itself is not circular: face/skin detection, PCA-based SCLI extraction, and modified Pan-Tomkins peak detection are implemented independently, and the paper additionally evaluates the pipeline on public datasets (Table 1) and uses the Medarevic et al. [66] Empatica E4 vs. Faros 360 comparison as an external anchor. The age-group difference is also present before correction (B.EVM p=0.04, A.EVM p=0.01 in Table 2), giving the group-level feasibility claim independent content. However, the headline accuracy figure is partially circular: §2.5 tunes the moving-average window width by minimizing MAE against the Empatica E4 reference on each sequence, and §2.7 fits a linear correction y = a*x + b to the residual differences between video PR and the same Empatica E4 reference, then reports the corrected errors (MAE 5.04 bpm, RMSE 6.38 bpm) on that same data. The corrected error is therefore an in-sample optimum, and the EVM-vs-no-EVM comparison (6.48 vs 5.04 bpm) is entangled with correction parameters fit separately for each condition. Score 6 rather than higher because the underlying signal-extraction chain and the age-group/group-level findings are not themselves derived from the fitted parameters, and the paper includes some genuinely external evaluations.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

Most of the pipeline uses standard, well-known components. The load-bearing choices are the use of Empatica E4 as ground truth, the assumption that the first PCA component of RGB means is a cardiac signal, and the in-sample fitted linear correction and smoothing window. No new physical entities are introduced.

free parameters (4)
  • Moving average window width for peak detection = 400 ms B.EVM, 433 ms A.EVM
    Chosen per sequence to minimize MAE against Empatica E4 reference, then averaged (Section 2.5).
  • Prominence threshold = 0.15
    Empirically selected for peak detection; stated in Section 2.5.
  • EVM magnification factor = 20
    Empirically selected because factor 120 caused pixel saturation (Section 2.4).
  • Linear fit correction parameters a and b = B.EVM: a=0.94, b=-69.41; A.EVM: a=0.96, b=-74.01
    Fit to the same video-vs-Empatica errors that are then reported as corrected (Section 2.7).
assumptions (4)
  • domain assumption Empatica E4 PR and IBI values are treated as ground truth for evaluation.
    The paper documents the device's known motion artifacts and bias (Sections 2.1, 2.7) but still uses it as the reference for all error metrics.
  • domain assumption The first principal component of the mean R, G, B pixel values reflects cardiac blood-volume changes.
    Section 2.5 assumes PCA isolates pulse color changes; the low reported correlation with BVP (0.08-0.09) suggests this assumption is fragile.
  • domain assumption EVM without phase-based motion processing is applicable to the recorded head movements.
    Section 2.4 states that phase-based motion processing was skipped because large head movements cause blurring; this choice may leave uncorrected motion artifacts.
  • ad hoc to paper The device-difference error follows a stable linear trend that can be corrected by a global linear fit.
    Section 2.7 fits a line to the error on the same data and applies it back to those data; no cross-validation supports the trend's stability.

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Pith. "Pith review of Contactless pulse rate assessment: Results and insights for application in driving simulator." pith.science (2026). https://pith.science/paper/CUXA5AMT

@misc{pith2026250501299,
  author       = {Pith},
  title        = {Pith review of: Contactless pulse rate assessment: Results and insights for application in driving simulator},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CUXA5AMT}},
  note         = {Machine review of arXiv:2505.01299}
}
read the original abstract

Remote photoplethysmography (rPPG) offers a promising solution for non-contact driver monitoring by detecting subtle blood flow-induced facial color changes from video. However, motion artifacts in dynamic driving environments remain key challenges. This study presents an rPPG framework that combines signal processing techniques before and after applying Eulerian Video Magnification (EVM) for pulse rate (PR) estimation in driving simulators. While not novel, the approach offers insights into the efficiency of the EVM method and its time complexity. We compare results of the proposed rPPG approach against reference Empatica E4 data and also compare it with existing achievements from the literature. Additionally, the possible bias of the Empatica E4 is further assessed using an independent dataset with both the Empatica E4 and the Faros 360 measurements. EVM slightly improves PR estimation, reducing the mean absolute error (MAE) from 6.48 bpm to 5.04 bpm (the lowest MAE (~2 bpm) was achieved under strict conditions) with an additional time required for EVM of about 20 s for 30 s sequence. Furthermore, statistically significant differences are identified between younger and older drivers in both reference and rPPG data. Our findings demonstrate the feasibility of using rPPG-based PR monitoring, encouraging further research in driving simulations.

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.