Thermal Imaging for Contactless Cardiorespiratory and Sudomotor Response Monitoring

TL;DR

This study demonstrates that thermal infrared video can be used as a contactless sensing modality to estimate electrodermal activity, heart rate, and breathing rate for improved human-machine interface safety in industrial settings.

Contribution

It introduces a novel signal-processing pipeline for thermal video analysis to estimate physiological signals relevant to operator state monitoring.

Findings

Thermal video achieves a mean absolute correlation of 0.40 with palm EDA.

Breathing rate estimation has a mean absolute error of 3.1 bpm.

Heart rate estimation has a mean absolute error of 13.8 bpm, limited by camera frame rate.

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.