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arxiv: 2602.12361 · v2 · submitted 2026-02-12 · 💻 cs.CV

Thermal Imaging for Contactless Cardiorespiratory and Sudomotor Response Monitoring

Constantino \'Alvarez Casado , Mohammad Rahman , Sasan Sharifipour , Nhi Nguyen , Manuel Lage Ca\~nellas , Xiaoting Wu , Miguel Bordallo L\'opez This is my paper

Pith reviewed 2026-05-16 02:01 UTC · model grok-4.3

classification 💻 cs.CV
keywords thermal imagingcontactless monitoringelectrodermal activitybreathing rateheart ratefacial regionssudomotor responsehuman-machine interfaces
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The pith

Thermal video of the face can extract sudomotor activity and breathing rate signals for contactless operator monitoring.

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

This paper tests whether thermal infrared video can serve as a contactless way to monitor electrodermal activity, heart rate, and breathing rate by capturing skin temperature changes on the face. It introduces a pipeline that selects and tracks specific facial regions, aggregates the temperature time series, and splits slow sweat-related trends from faster cardiorespiratory oscillations. When evaluated on 31 sessions from a public driver dataset against contact references, the strongest fixed configuration reaches a mean absolute EDA correlation of 0.40, with some individual sessions at 0.89, breathing rate error of 3.1 bpm, and heart rate error of 13.8 bpm. These numbers indicate that thermal cues can supply useful state information for human-machine interfaces where visible cameras face lighting or privacy limits. The results also highlight practical constraints from region choice, signal polarity, latency, and person-to-person differences.

Core claim

The central claim is that thermal video provides usable sudomotor and respiratory signals through a pipeline of facial region tracking, thermal signal aggregation, and frequency-based separation of slow EDA trends from faster cardiorespiratory components, achieving a best fixed EDA mean absolute correlation of 0.40 against palm reference and breathing rate mean absolute error of 3.1 bpm while heart rate remains limited by the 7.5 Hz camera rate.

What carries the argument

Signal-processing pipeline that tracks chosen facial regions, aggregates their thermal time series, separates slow sudomotor trends from faster cardiorespiratory components, and applies orthogonal matrix image transformation for heart rate peak detection.

If this is right

  • Thermal video supplies auxiliary respiratory and sudomotor information for adaptive industrial human-machine interfaces.
  • Breathing rate can be recovered from nasal and cheek thermal signals via spectral peak detection with roughly 3 bpm average error.
  • Heart rate estimation from multiple facial regions remains constrained by the thermal camera's frame rate.
  • ROI selection and polarity handling are critical design choices that determine whether the extracted signals remain usable.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Higher-frame-rate thermal cameras would likely reduce the heart-rate error and make the method more competitive with contact sensors.
  • Subject-specific or session-adaptive ROI selection could lower the observed variability in EDA correlations.
  • The approach could be tested as a privacy-preserving layer in vehicle cabins or factory workstations where RGB imaging is restricted.

Load-bearing premise

Facial thermal signals can be cleanly divided into sudomotor and cardiorespiratory parts with only modest interference from motion, subject differences, or polarity flips.

What would settle it

Re-running the 288 ROI configurations on the same dataset and finding that no configuration exceeds 0.25 mean absolute EDA correlation across all sessions would show the separation is not reliable enough for the claimed utility.

Figures

Figures reproduced from arXiv: 2602.12361 by Constantino \'Alvarez Casado, Manuel Lage Ca\~nellas, Miguel Bordallo L\'opez, Mohammad Rahman, Nhi Nguyen, Sasan Sharifipour, Xiaoting Wu.

Figure 1
Figure 1. Figure 1: Thermal facial video can be converted into ROI temperature traces and [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Pipeline overview. Thermal frames are processed by a face detector to localise landmarks. Six facial ROIs are defined and scalar temperature traces are [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: PCCabs against PEDA for all 48 ROI-method combinations (n = 31). Task-stratified results explain part of this variability. Table II reports the two strongest ROI–method combinations per task, computed over the alpha-sensitivity sweep. Against PEDA, PD is best captured by the right periorbital and forehead signals with exponential MA (PCCabs ≈ 0.58), ND by the nose with median or exponential smoothing (≈ 0.… view at source ↗
Figure 5
Figure 5. Figure 5: PCCabs against PEDA by driving condition (n = 7–8 per task). TABLE V BEST EDA ROI-METHOD PER SUBJECT (MEAN ACROSS SESSIONS - N). Subj. S/A n Best (PP NR) Best (PEDA) T002 F/Y 4 cheeks/eMA 0.44±0.19 nose/med 0.36±0.22 T003 M/Y 4 nose/mAvg 0.35±0.23 cheeks/eMA 0.53±0.16 T005 M/Y 3 foreh./med 0.39±0.09 cheeks/eMA 0.35±0.05 T014 F/Y 4 cheeks/eMA 0.43±0.19 cheeks/eMA 0.39±0.34 T029 F/O 4 nose/mAvg 0.40±0.15 che… view at source ↗
Figure 6
Figure 6. Figure 6: T003-ED: PP NR and PEDA GT (top), nose ROI vs PP NR (middle, r = 0.42), eyes ROI (bottom, r = 0.15). HMI: respiration provides the most stable low-rate physiolog￾ical input, EDA-like trends can support arousal or workload context when ROI quality and polarity are handled, and HR requires higher frame rates, more specific vascular ROIs, or complementary sensing. Demographic and subject-level analyzes indica… view at source ↗
read the original abstract

Human-machine interfaces in industrial automation need sensing modules that monitor operator actions and physiological state. This is important in factories, vehicles, machinery cabins, and human-robot collaboration, where workload, stress, fatigue, or reduced attention can affect safety. RGB monitoring is limited by low light, shadows, and privacy concerns, while thermal infrared imaging captures skin temperature dynamics without visible illumination. This paper studies thermal video as a contactless computer vision modality for estimating electrodermal activity (EDA), heart rate (HR), and breathing rate (BR), with the goal of supporting adaptive human-machine interfaces and operator-state awareness. We propose a signal-processing pipeline that tracks facial regions, aggregates thermal signals, and separates slow sudomotor trends from faster cardiorespiratory components. HR is estimated using orthogonal matrix image transformation (OMIT) across multiple facial regions, while BR is estimated from nasal and cheek thermal signals using spectral peak detection. We characterize 288 ROI-method configurations against contact references with lag-tolerant metrics using 31 sessions from the public SIMULATOR STUDY 1 (SIM1) driver monitoring dataset. The best fixed EDA configuration reaches a mean absolute correlation of $0.40 \pm 0.23$ against palm EDA, with individual sessions reaching $0.89$. BR estimation achieves $3.1 \pm 1.1$\,bpm mean absolute error, while HR estimation yields $13.8 \pm 7.5$\,bpm MAE, limited by the $7.5$\,Hz thermal camera frame rate. The results show that thermal video provides useful respiratory and sudomotor cues, while revealing limitations caused by ROI selection, polarity changes, latency, and subject variability. These findings provide baseline design guidance for thermal computer vision as an auxiliary sensing layer in adaptive industrial HMI systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that facial thermal dynamics encode EDA, HR, and BR signals separable by standard filtering, plus empirical selection of ROI configurations; no new entities are introduced and free parameters are limited to the tested ROI-method choices.

free parameters (1)
  • ROI-method configuration
    288 configurations evaluated; best fixed one selected post-hoc for reported EDA correlation.
axioms (1)
  • domain assumption Facial skin temperature dynamics reflect sudomotor, cardiac, and respiratory activity
    Invoked in the signal-processing pipeline description to justify separation of slow and fast components.

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discussion (0)

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