  PPG based heart rate (HR) monitoring has recently attracted much attention
with the advent of wearable devices such as smart watches and smart bands.
However, due to severe motion artifacts (MA) caused by wristband stumbles, PPG
based HR monitoring is a challenging problem in scenarios where the subject
performs intensive physical exercises. This work proposes a novel approach to
the problem based on supervised learning by Neural Network (NN). By simulations
on the benchmark datasets [1], we achieve acceptable estimation accuracy and
improved run time in comparison with the literature. A major contribution of
this work is that it alleviates the need to use simultaneous acceleration
signals. The simulation results show that although the proposed method does not
process the simultaneous acceleration signals, it still achieves the acceptable
Mean Absolute Error (MAE) of 1.39 Beats Per Minute (BPM) on the benchmark data
set.
