Contactless and Wearable Multisensor Physiological Dataset for Stress Monitoring (HR/RR)

This dataset supplements Giguère et al. (2025; DOI: 10.5281/zenodo.15330493) by adding an additional source of contactless physiological data and by providing fused multisensor recordings. It consists of a subset of 43 participants drawn from the original cohort of 125, with data collected between April 17 and June 24, 2025. Participants included in this dataset belong to the time stressor group described in Giguère et al. (2025). In this condition, stress was induced by increasing task event frequency and reducing available response time. Group membership was encoded through participant identifiers; all included records follow the P2XX identifier structure. This Zenodo release includes 43 participants with identifiers ranging from P200 to P242. Due to a lack of RealSense camera physiological estimates for P237, fused physiology files cover 42 participants. The dataset contains fused physiological signals derived from four sensing modalities: Zephyr BioHarness (Biopac): Chest-worn sensor providing Heart Rate (HR) and Respiration Rate (RR). Intel® RealSense™ Depth Camera D455: Contactless sensor processed offline using a proprietary, depth-based physiological estimation algorithm developed in-house, providing HR and RR estimates. Millimeter wave (mmWave) radar (Texas Instruments IWR1443): Providing HR and RR estimates. Fossil Gen 5 smartwatch (model D10F1): Providing HR estimates. Furthermore, the dataset provides the raw data files from all the sensors from which the fused data files were obtained using the code available in https://github.com/labcodot/contactless-cardiorespiratory-benchmarking This work was made possible thanks to the technical support of the Canadian Space Agency (CSA). Financial support was provided by Mitacs throught the Mitacs Accelerate program (grant no. IT39574), Defence Research and Development Canada (DRDC), and research grants awarded to S. Tremblay (National Sciences and Engineering Research Council of Canada, no. RGPIN-2022-04852) and to A. Marois (Fonds de recherche du Québec - Nature et technologies, no. 342553). Inquiries about the dataset can be directed to D. Benesch ([e-mail]), and S. Tremblay ([e-mail]).