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PPG based heart rate (HR) monitoring has recently attracted much attentionwith the advent of wearable devices such as smart watches and smart bands.However, due to severe motion artifacts (MA) caused by wristband stumbles, PPGbased HR monitoring is a challenging problem in scenarios where the subjectperforms intensive physical exercises. This work proposes a novel approach tothe problem based on supervised learning by Neural Network (NN). By simulationson the benchmark datasets [1], we achieve acceptable estimation accuracy andimproved run time in comparison with the literature. A major contribution ofthis work is that it alleviates the need to use simultaneous accelerationsignals. The simulation results show that although the proposed method does notprocess the simultaneous acceleration signals, it still achieves the acceptableMean Absolute Error (MAE) of 1.39 Beats Per Minute (BPM) on the benchmark dataset.

Supervised Heart Rate Tracking using Wrist-Type Photoplethysmographic  (PPG) Signals during Physical Exercise without Simultaneous Acceleration  Signals