Non-Contact NIR PPG Sensing through Large Sequence Signal Regression
This work addresses non-contact heart rate monitoring for applications like driver or patient monitoring, but it is incremental as it applies a novel method to an existing problem with specific improvements.
The paper tackled the problem of non-contact photoplethysmography (PPG) sensing from Near Infra-Red (NIR) video, which overcomes limitations of RGB video in varying light or dark conditions, and achieved a Mean Average Error of 0.99 bpm using a Convolution Attention Network architecture.
Non-Contact sensing is an emerging technology with applications across many industries from driver monitoring in vehicles to patient monitoring in healthcare. Current state-of-the-art implementations focus on RGB video, but this struggles in varying/noisy light conditions and is almost completely unfeasible in the dark. Near Infra-Red (NIR) video, however, does not suffer from these constraints. This paper aims to demonstrate the effectiveness of an alternative Convolution Attention Network (CAN) architecture, to regress photoplethysmography (PPG) signal from a sequence of NIR frames. A combination of two publicly available datasets, which is split into train and test sets, is used for training the CAN. This combined dataset is augmented to reduce overfitting to the 'normal' 60 - 80 bpm heart rate range by providing the full range of heart rates along with corresponding videos for each subject. This CAN, when implemented over video cropped to the subject's head, achieved a Mean Average Error (MAE) of just 0.99 bpm, proving its effectiveness on NIR video and the architecture's feasibility to regress an accurate signal output.