Contactless Cardiac Pulse Monitoring Using Event Cameras

Joseph Lemley; Mohamed Moustafa; Peter Corcoran

REVIEW 3 major objections 6 minor 39 references

Contactless Cardiac Pulse Monitoring Using Event Cameras

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Event-camera face recordings preserve cardiac pulse well enough for contactless heart-rate monitoring.

desk verdict First face-based event-camera pulse extraction with a CNN, and the code release is real; but the headline 120 FPS advantage over RGB rests on ten subjects and one split, so treat the comparison as preliminary. read the letter →

arxiv 2505.09529 v2 pith:AIVT7BBR submitted 2025-05-14 cs.CV cs.ETcs.LGeess.IV

Mohamed Moustafa , Joseph Lemley , Peter Corcoran This is my paper · ORCID

classification cs.CVcs.ETcs.LGeess.IV

keywords eventcamerasneuromorphicvisionremotephotoplethysmographyheartrateestimationconvolutionalneuralnetworkstemporalshiftattentionECGsignalpreprocessingcontactlesshealthmonitoring

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

The paper sets out to show that an event camera pointed at a person's face captures enough of the cardiac pulse to estimate heart rate without contact. It trains a supervised CNN on 2D event frames built from asynchronous brightness-change events, using a processed ECG signal as the target, and reports that event-frame models match or beat an RGB-video baseline when the event frames are generated at 60 or 120 FPS. The best event model reaches 2.13 bpm RMSE, below the RGB baseline's 2.92. If the result holds, event cameras, which use little power and have high dynamic range, become a plausible low-power sensor for remote health monitoring.

What carries the argument

The machinery is the event frame: events, each carrying timestamp, coordinates, and polarity, are binned into non-overlapping windows of fixed duration and summed by polarity into a single-channel 2D image, making the asynchronous event stream compatible with frame-based network architectures. A modified TS-CAN, a two-branch convolutional attention network with temporal shift modules, takes the same single-channel event frame into both branches and learns to output a normalized signal difference that post-processing integrates into a pulse waveform. On the label side, the ECG signal is inverted to emphasize s-wave peaks, smoothed, bandpass filtered, clipped, resampled to the frame rate, and differentiated, so the supervision target is designed to align with the blood-pulse timing the event stream is assumed to encode.

What would settle it

Simultaneously record an event camera, a synchronized RGB camera, and both ECG and fingertip SpO2 from the same subjects; train the same model with ECG-derived labels, then compare its predicted pulse peaks against SpO2 pulse peaks beat by beat. If the event-frame predictions do not track SpO2 timing within a fraction of a cardiac cycle while the RGB model does, the claim that cardiac information is preserved in event streams would be falsified.

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Extended reading notes

Core claim

The paper claims that physiological cardiac information in the facial region is effectively preserved within the event stream, and that a simple 2D event-frame representation lets a standard convolutional network recover it. On its local dataset, the event-frame model at 120 FPS reaches RMSE 2.13 bpm, MAE 1.58 bpm, and Pearson correlation 0.97, outperforming the 30 FPS RGB model (RMSE 2.92) and the 60 FPS event model (RMSE 2.54). The 30 FPS event model is slightly worse than RGB but still within the accepted error range for remote photoplethysmography. The authors also report that an event-count-only method from prior work fails on face data, with RMSE above 25 bpm, while their spatial event-frame method does not.

Load-bearing premise

The paper's central claim rests on the assumption that the processed ECG signal used as the training label tracks the timing and shape of the optical blood-volume pulse that the event camera sees in the face; if that mapping is wrong, the reported errors measure something other than cardiac information in the event stream.

Editorial extensions

If this is right

Contactless pulse monitoring can run on event-camera hardware, which offers microsecond-level latency and lower power than frame-based video capture.
Existing frame-based remote-PPG network architectures can be reused for event data simply by feeding them temporally binned event frames, lowering the barrier to neuromorphic health sensing.
Increasing the event-frame sampling rate from 30 to 120 FPS improves heart-rate accuracy in the reported experiments, so shorter temporal bins appear to be preferable for this task.
The failure of event-count-only methods on face data suggests that spatial information is necessary for face-based event pulse estimation.
The achieved RMSE values fall within the accepted threshold of 5 bpm for remote-PPG methods, so the accuracy is in the clinically relevant range.

Reading between the lines

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

If the finding generalizes to natural head motion, event cameras could be built into vehicle cabins or wearables without some of the illumination constraints that affect RGB remote-PPG, because event cameras have high dynamic range.
A direct test of the label assumption would be to compare the model's predicted pulse peaks against a simultaneous fingertip SpO2 waveform, whose timing reflects blood arrival in the extremities rather than electrical heart activity.
Synthetic generation of event streams from public RGB remote-PPG datasets could be used to scale training data and to test whether the model learns the same optical blood-volume signal that RGB methods exploit.
Beat-by-beat waveform comparison, not just heart-rate error, would clarify whether event frames preserve pulse shape or only periodicity.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this is the first face-based event-camera pulse extraction with an end-to-end CNN, and the code and weights are public. The narrow claim is plausible: event frames from a face contain enough periodic cardiac information for a model to estimate heart rate. The headline comparison, 120 FPS events at 2.13 bpm RMSE vs RGB at 2.92, is not statistically established.

What's new and good: the paper does something no one has done before, and it does it straightforwardly. It bins events into frames, feeds them to a standard TS-CAN, and shows heart rate estimation works at all. It also shows that increasing event frame rate from 30 to 60 to 120 FPS monotonically improves results, which is a useful empirical finding. The authors adapt the ECG label preprocessing explicitly and transparently, and they implement the prior event-count method from [26] on their own data and report it fails badly; that is a useful negative result. Code and weights are on GitHub, so the method itself is reproducible up to the proprietary dataset.

Soft spots: the evaluation is the load-bearing weak point. Everything rests on 10 test subjects, one random split, and one training run per condition. Per-subject RMSE in Table 4 is highly heterogeneous: the RGB model is perfect on five subjects and over 5 bpm on three; the 120 FPS events model is perfect on two and over 5 bpm on one. With that variance, the 2.13 vs 2.92 difference can flip based on one or two people. No confidence intervals, paired tests, or seed averaging are reported. The paper should not say the results prove that cardiac information is preserved or that event cameras are a viable alternative to RGB. What they support is a feasibility demonstration. The label-proxy concern is real but secondary: since heart rate is read from the FFT peak of a post-processed output, the exact waveform of the inverted ECG mostly cancels; the important thing is whether the event stream carries a periodic signal at cardiac frequency, and that is what needs more subjects. Also, stationary subjects only and proprietary data, both acknowledged.

Who it's for: people working on event-based vision or r-PPG who want to know whether event cameras are worth exploring for vitals. It is a first-demonstration paper, not a benchmark.

Recommendation: send it to review, but expect major revision. The authors need to add multiple splits or bootstrapping, report intervals, or scale to a public dataset, and temper the language. It deserves a serious referee because the problem is new and the code release is real.

Referee Report

3 major / 6 minor

Summary. This paper investigates whether event cameras can capture cardiac pulse information from face recordings. The authors collect a proprietary dataset of 64 subjects with simultaneous event, RGB, and ECG data, represent event streams as 2D event frames, and train a modified TS-CAN to predict a processed ECG signal. They compare models trained on RGB frames and on event frames at 30, 60, and 120 FPS, evaluating heart rate via FFT of the post-processed prediction. Reported RMSEs are 2.92 bpm for RGB 30 FPS, and 3.32, 2.54, and 2.13 bpm for event frames at 30, 60, and 120 FPS, respectively. The abstract and conclusion interpret the 120 FPS event result as outperforming the RGB baseline and supporting event cameras as a viable low-power contactless cardiac monitoring sensor.

Significance. If the result holds, this is a useful first demonstration: it extends event-camera vitals monitoring from wrist and chest to the face, uses end-to-end learning rather than hand-crafted event-count features, and provides code and trained weights for reproducibility. The design choices (held-out subject evaluation, no subject exclusion, standard r-PPG error metrics) are appropriate for a feasibility study. The main limitation is statistical: the headline comparison rests on 10 test subjects, one split, and one training run per configuration, with highly variable per-subject errors.

major comments (3)

[Section 4.4, Tables 3 and 4] The claim that the 120 FPS event model outperforms the RGB baseline (RMSE 2.13 vs. 2.92 bpm) is not statistically established. The evaluation uses a single random split (44/10/10 subjects), one training run per configuration, and a test set of only 10 subjects. Per-subject RMSE in Table 4 varies from 0.00 to 5.05 for the 120 FPS event model, so the aggregate difference could be dominated by one or two subjects. Please report confidence intervals, a paired significance test over subjects, and ideally multiple splits or seeds before drawing the comparative conclusion in the abstract.
[Section 3.2] The label pre-processing inverts the ECG to emphasize s-wave peaks and then applies smoothing, band-pass filtering, clipping, resampling, and differentiation, but no evidence is given that the resulting signal tracks the facial blood-volume pulse observed by the event camera. Since heart rate is derived from the FFT peak of the post-processed output, waveform phase details may be less critical, but the claim that the model extracts 'physiological cardiac information' would be considerably stronger if the processed ECG were validated against an SpO2 or standard r-PPG reference, or if an ablation without the inversion step were reported.
[Section 5, Conclusion] The manuscript concludes that event cameras are a viable low-power alternative to RGB for remote heart rate monitoring, but the current evidence supports a narrower feasibility claim: on a stationary, proprietary 10-subject test set, a 120 FPS event-frame model achieves competitive aggregate RMSE. Given the acknowledged lack of motion scenarios, the small test set, and the single-split evaluation, I recommend softening the conclusion to a feasibility demonstration until broader evaluation is available.

minor comments (6)

[Section 3.1.1] Equation (1) defines Wj with an ambiguous set-builder notation; please clarify that i and k are the first and last event indices in the window and write the set condition explicitly.
[Section 3.1.1] The pre-processing description says frames are normalized between -8 and 8 and then multiplied by 255 and cast to 8-bit unsigned integers; negative values cannot be represented in this conversion, so please describe the actual normalization and typecasting procedure.
[Figure 3] The caption says 'A 10-second sample is inverted and smoothed' while the text says the figure shows a 1-second sample; these statements are inconsistent.
[Section 4.3] The paper states that validation loss was used to select optimal weights, but no validation metrics or selected epoch numbers are reported; please include them.
[Section 5, Table 5] The implementation details of the event-count baseline from [26] (filter order, cutoff frequencies, FFT windowing) are not provided, which limits reproducibility of the comparison.
[Section 4.1] The sentence about some subjects providing an additional minute of break data is vague; clarify how recording durations varied across the 64 subjects.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the event-to-pulse mapping is learned from independent ECG labels and evaluated on held-out subjects.

full rationale

The paper's derivation chain is empirical rather than definitional: event frames are generated from raw event streams, a modified TS-CAN is trained with MSE against an ECG-derived label signal, and heart rate is then computed from the post-processed model output via FFT. The labels come from an independent ECG sensor, not from the event stream or from the model's own output, so the learned mapping is tested rather than assumed. The ECG preprocessing in Section 3.2 (inversion, smoothing, bandpass filtering, clipping, resampling, and differentiation) is a modeling assumption about label suitability, but it does not encode the event-frame content or the final heart-rate value, so it is not circular. No fitted parameter is renamed as a prediction, no uniqueness theorem is imported from the authors' prior work, and the self-citations (e.g., Refs. [2], [22], [24]) serve as literature context or external comparison baselines rather than load-bearing justification for the central claim. The limitations acknowledged in Sections 4.4 and 5 (proprietary data, stationary subjects, small test set, single split) are concerns about statistical strength and external validity, not about circularity. The honest finding is therefore a non-finding on circularity, with score 0.

Assumptions & free parameters 8 free parameters · 4 assumptions · 0 invented entities

The central claim is empirical and depends on several hand-chosen preprocessing and training settings, plus domain assumptions about ECG label validity, fixed face cropping, and event frame fidelity. No new physical entities are introduced. The largest unstated burden is the assumption that the inverted and filtered ECG label behaves like the optical pulse signal in the face.

free parameters (8)

Event frame window period L = 33333 us (30 FPS), 16666 us (60 FPS), 8333 us (120 FPS)
Hand-chosen temporal bin width for event frames; it defines the frame rate and directly controls the central comparison between 30, 60, and 120 FPS event models.
Event downsampling factor df = 5
Hand-chosen downsampling factor reducing 1280x720 event coordinates to 144x144 event frames; no ablation is reported.
RGB face crop and downsample size = 400x400 crop, then 144x144
Fixed-index cropping and downsampling for the RGB baseline; hand-chosen without reported sensitivity analysis.
ECG Savitzky-Golay smoothing window = 101 samples, quadratic fit
Hand-chosen smoothing parameters for the 1 kHz ECG signal used to generate training labels.
ECG bandpass filter cutoffs = 0.0015 and 0.0048 normalized (0.75 Hz and 2.5 Hz)
Hand-chosen first-order Butterworth cutoffs based on standard heart rate frequency limits.
ECG clipping percentiles = Top and bottom 1% clipped
Hand-chosen outlier clipping applied to the processed ECG label signal.
Event frame normalization range = [-8, 8], then multiplied by 255 and cast to uint8
Chosen as 'best-practice for visual clarity and noise reduction'; the cast from signed floating point to unsigned integer is not fully specified.
Training hyperparameters = learning rate 18e-5 or 9e-5; frame depth 10, 20, or 40; batch size 8; 30 epochs
Hand-chosen settings. Frame depth is scaled with frame rate, and learning rate is halved for the 60 and 120 FPS experiments, so the frame-rate comparison is not a clean single-variable change.

assumptions (4)

standard math FFT-based heart rate extraction from a bandpass-filtered signal identifies the dominant cardiac frequency.
Used in the post-processing pipeline in Section 3.3 and in the comparison with [26]; this is a standard assumption in rPPG signal processing.
domain assumption The processed and inverted ECG signal is a valid proxy for the blood-volume pulse signal visible in the face.
Section 3.2 states the ECG is inverted to emphasize s-wave peaks and filtered to resemble SpO2 signals. The paper does not independently validate that this processed ECG label matches the optical pulse signal in timing or shape.
domain assumption A fixed-index face crop captures the face without per-frame tracking, which is adequate for the recorded stationary subjects.
Section 3.1.1 applies fixed cropping directly to event coordinates, and Section 4.1 describes subjects sitting still with eyes open or closed. This assumption would break under head motion.
domain assumption Accumulating event polarities into 2D frames preserves enough spatial-temporal structure for a CNN to extract cardiac information.
This premise is the core modeling choice behind the event frame representation in Section 3.1.1. The experimental results support it on this dataset, but the paper does not compare event frames with other event representations.

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Cite this review

Pith. "Pith review of Contactless Cardiac Pulse Monitoring Using Event Cameras." pith.science (2026). https://pith.science/paper/AIVT7BBR

@misc{pith2026250509529,
  author       = {Pith},
  title        = {Pith review of: Contactless Cardiac Pulse Monitoring Using Event Cameras},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AIVT7BBR}},
  note         = {Machine review of arXiv:2505.09529}
}

read the original abstract

Time event cameras are a novel technology for recording scene information at extremely low latency and with low power consumption. Event cameras output a stream of events that encapsulate pixel-level light intensity changes within the scene, capturing information with a higher dynamic range and temporal resolution than traditional cameras. This study investigates the contact-free reconstruction of an individual's cardiac pulse signal from time event recording of their face using a supervised convolutional neural network (CNN) model. An end-to-end model is trained to extract the cardiac signal from a two-dimensional representation of the event stream, with model performance evaluated based on the accuracy of the calculated heart rate. The experimental results confirm that physiological cardiac information in the facial region is effectively preserved within the event stream, showcasing the potential of this novel sensor for remote heart rate monitoring. The model trained on event frames achieves a root mean square error (RMSE) of 3.32 beats per minute (bpm) compared to the RMSE of 2.92 bpm achieved by the baseline model trained on standard camera frames. Furthermore, models trained on event frames generated at 60 and 120 FPS outperformed the 30 FPS standard camera results, achieving an RMSE of 2.54 and 2.13 bpm, respectively.

Figures

Figures reproduced from arXiv: 2505.09529 by the authors.

**Figure 2.** Comparison between ECG and SpO2 data acquired during the same period at a sampling rate of 1 kHz. The [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Illustration of pre-processing steps applied to the ECG signal to obtain the signal used to label frames. A [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: The R and S waves on a standard ECG peak. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: Diagram of the modified TS-CAN trained on event frames. As opposed to the original TS-CAN model where [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: Sample output from the different r-PPG models covering a ten-second duration. Both the model output [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

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