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

TL;DR

This paper presents a supervised neural network approach for wrist-based PPG heart rate monitoring during exercise that does not require acceleration signals, achieving accurate results despite motion artifacts.

Contribution

It introduces a novel neural network method that estimates heart rate from PPG signals alone, eliminating the need for simultaneous acceleration data during physical activity.

Findings

Achieves a Mean Absolute Error of 1.39 BPM on benchmark datasets.

Improves runtime efficiency compared to existing methods.

Effectively handles motion artifacts without acceleration signals.

Abstract

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.