Non-Contact NIR PPG Sensing through Large Sequence Signal Regression
Timothy Hanley Dara Golden Robyn Maxwell Ashkan Parsi Joseph Lemley
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.