2020/10/02 by Mahmoud Essalat, Mahdi Boloursaz Mashhadi, Essalat, Mahmoud +3
Engineering · Mathematics · Medicine · #Acceleration #Accelerometer #Artificial intelligence #Artificial neural network #Benchmark (surveying) #Computer science #Computer vision #Embedded system #FOS: Electrical engineering #Heart Rate Variability and Autonomic Control #Hemodynamic Monitoring and Therapy #Mathematics #Mean absolute error #Mean squared error #Non-Invasive Vital Sign Monitoring #Pattern recognition (psychology) #Photoplethysmogram #Process (computing) #SIGNAL (programming language) #Signal Processing (eess.SP) #Statistics #Wearable computer #Wearable technology #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2010.00769
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/10/02 · openalex publication_date 2020/10/02 · arxiv updated 2020/10/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
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.