Robust Heart Rate Variability Measurement from Facial Videos

Ismoil Odinaev; Kwan Long Wong; Jing Wei Chin; Raghav Goyal; Tsz Tai Chan; Richard H Y So

doi:10.3390/bioengineering10070851

Robust Heart Rate Variability Measurement from Facial Videos

Bioengineering (Basel). 2023 Jul 18;10(7):851. doi: 10.3390/bioengineering10070851.

Authors

Ismoil Odinaev¹, Kwan Long Wong^{1

2}, Jing Wei Chin¹, Raghav Goyal^{1

3}, Tsz Tai Chan¹, Richard H Y So^{1

2}

Affiliations

¹ PanopticAI Ltd., Hong Kong, China.
² Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China.
³ Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China.

Abstract

Remote Photoplethysmography (rPPG) is a contactless method that enables the detection of various physiological signals from facial videos. rPPG utilizes a digital camera to detect subtle changes in skin color to measure vital signs such as heart rate variability (HRV), an important biomarker related to the autonomous nervous system. This paper presents a novel contactless HRV extraction algorithm, WaveHRV, based on the Wavelet Scattering Transform technique, followed by adaptive bandpass filtering and inter-beat-interval (IBI) analysis. Furthermore, a novel method is introduced to preprocess noisy contact-based PPG signals. WaveHRV is bench-marked against existing algorithms and public datasets. Our results show that WaveHRV is promising and achieves the lowest mean absolute error (MAE) of 10.5 ms and 6.15 ms for RMSSD and SDNN on the UBFCrPPG dataset.

Keywords: Baevsky stress index; RMSSD; SDNN; heart rate variability; remote photoplethysmography; wavelet scattering transform.

Grants and funding

This research is partially funded by TSSSU/HKUST/21/10, IsP/320/21 under the Innovation and Technology Commission, and PF18-19412 under the Research Grants Council of Hong Kong.