Multi-ROI Spectral Approach for the Continuous Remote Cardio-Respiratory Monitoring from Mobile Device Built-In Cameras.

Heart rate (HR) and respiratory rate (fR) can be estimated by processing videos framing the upper body and face regions without any physical contact with the subject. This paper proposed a technique for continuously monitoring HR and fR via a multi-ROI approach based on the spectral analysis of RGB video frames recorded with a mobile device (i.e., a smartphone's camera). The respiratory signal was estimated by the motion of the chest, whereas the cardiac signal was retrieved from the pulsatile activity at the level of right and left cheeks and forehead. Videos were recorded from 18 healthy volunteers in four sessions with different user-camera distances (i.e., 0.5 m and 1.0 m) and illumination conditions (i.e., natural and artificial light). For HR estimation, three approaches were investigated based on single or multi-ROI approaches. A commercially available multiparametric device was used to record reference respiratory signals and electrocardiogram (ECG). The results demonstrated that the multi-ROI approach outperforms the single-ROI approach providing temporal trends of both the vital parameters comparable to those provided by the reference, with a mean absolute error (MAE) consistently below 1 breaths·min-1 for fR in all the scenarios, and a MAE between 0.7 bpm and 6 bpm for HR estimation, whose values increase at higher distances.

AND-rPPG: A novel denoising-rPPG network for improving remote heart rate estimation.

Heart rate (HR) estimation is an essential physiological parameter in the field of biomedical imaging. Remote Photoplethysmography (r-PPG) is a pathbreaking development in this field wherein the PPG signal is extracted from non-contact face videos. In the COVID-19 pandemic, rPPG plays a vital role for doctors and patients to perform telehealthcare. Existing rPPG methods provide incorrect HR estimation when face video contains facial deformations induced by facial expression. These methods process the entire face and utilize the same knowledge to mitigate different noises. It limits the performance of these methods because different facial expressions induce different noise characteristics depending on the facial region. Another limitation is that these methods neglect the facial expression for denoising even though it is the prominent noise source in temporal signals. These issues are mitigated in this paper by proposing a novel HR estimation method AND-rPPG, that is, A Novel Denoising-rPPG. We initiate the utilization of Action Units (AUs) for denoising temporal signals. Our denoising network models the temporal signals better than sequential architectures and mitigate the AUs-based (or face expression-based) noises effectively. The experiments performed on publicly available datasets reveal that our proposed method outperforms state-of-the-art HR estimation methods, and our denoising model can be easily integrated with existing methods to improve their HR estimation.