Exploiting Spatial Redundancy of Image Sensor for Motion Robust rPPG

Abstract

Abstract-Remote photoplethysmography (rPPG) techniques can measure cardiac activity by detecting pulse-induced colour variations on human skin using an RGB camera. State-of-theart rPPG methods are sensitive to subject body motions (e.g., motion-induced colour distortions). This study proposes a novel framework to improve the motion robustness of rPPG. The basic idea of this work originates from the observation that a camera can simultaneously sample multiple skin regions in parallel, and each of them can be treated as an independent sensor for pulse measurement. The spatial-redundancy of an image sensor can thus be exploited to distinguish the pulsesignal from motion-induced noise. To this end, the pixel-based rPPG sensors are constructed to estimate a robust pulse-signal using motion-compensated pixel-to-pixel pulse extraction, spatial pruning, and temporal filtering. The evaluation of this strategy is not based on a full clinical trial, but on 36 challenging benchmark videos consisting of subjects that differ in gender, skin-types and performed motion-categories. Experimental results show that the proposed method improves the SNR of the state-of-the-art rPPG technique from 3.34dB to 6.76dB, and the agreement (±1.96σ) with instantaneous reference pulse-rate from 55% to 80% correct. ANOVA with post-hoc comparison shows that the improvement on motion robustness is significant. The rPPG method developed in this study has a performance that is very close to that of the contact-based sensor under realistic situations, while its computational efficiency allows real-time processing on an off-the-shelf computer.

Unsupervised Subject Detection via Remote PPG

“…In 2013, de Haan et al introduced a Chrominance-based rPPG method to define the pulse as a linear combination of RGB channels under a standardized skintone assumption [3], which is one of the most accurate rPPG methods in dealing with realistic challenges (e.g., various skin-tones). More recently, Wang et al proposed a complete framework to significantly improve the motion robustness of rPPG [8], which profits from the spatially redundant pixels of a camera sensor. Nevertheless, all these rPPG methods rely on a pre-defined skin area (e.g., face) for pulse extraction.…”

Feasibility of Contactless Pulse Rate Monitoring of Neonates using Google Glass

“…Considering the realistic challenge of body motion for contactless pulse rate monitoring, we implement a motion robust remote photoplethysmographic (rPPG) algorithm proposed by Wang et al [2], which achieves the state-of-the-art performance in motion robustness. Essentially, it exploits the spatial redundancy of a camera sensor to create a statistical pulse-signal that is immune to motion noise.…”

“…Similarly, the photoplethysmographic signal from the pulse oximeter is transformed to the frequency domain using FFT (8s window) to compute the reference pulse rate for comparison. The detailed background on the algorithm is found in [2].…”

“…It has been proven that sensory exposure in the NICU environment causes adverse neurodevelopmental issues for the neonates [1]. Contactless camera-based algorithms [2] are becoming popular as a solution to this intrusive monitoring challenge. However, the investigations into the feasibility of contactless monitoring neonates in a clinical setting are limited.…”

Validation of Computer Models for Evaluating the Efficacy of Cognitive Stimulation Therapy

“…In particular, wearable devices of photoplethysmographic sensors have been pro-gressively developed [16,17], making the PPG technology more atractive for its applications in telemedicine and e-health. Recent reports indicate that the use of PPG is rapidly attracting attention from the biomedical research community and industry, because it can be practically utilized to measure cardiac activity with a color camera [18].…”