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.

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

Schena E.;Silvestri S.;Massaroni C.
2022-01-01

Abstract

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.
Cardiorespiratory; Continuous monitoring; Heart rate estimation; Multi‐ROI approach; Remote monitoring; Respiratory rate estimation; Smartphone’s built‐in camera; Unobtrusive monitoring; Computers, Handheld; Heart Rate; Humans; Monitoring, Physiologic; Signal Processing, Computer-Assisted; Electrocardiography; Respiratory Rate
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12610/69408
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