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Supervised heart rate tracking using wrist-type photoplethysmographic (PPG) signals during physical exercise without simultaneous acceleration signals

Essalat, M ; Sharif University of Technology | 2017

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  1. Type of Document: Article
  2. DOI: 10.1109/GlobalSIP.2016.7906025
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2017
  4. Abstract:
  5. 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. © 2016 IEEE
  6. Keywords:
  7. Heart Rate Monitoring ; Motion Artifact Reduction ; Neural Network ; Photoplethysmograph (PPG) ; Simultaneous Acceleration Signals ; Heart ; Neural networks ; Signal reconstruction ; Sports ; Benchmark datasets ; Heart-rate monitoring ; Mean absolute error ; Motion artifact reduction ; Neural network (nn) ; Photoplethysmograph ; Photoplethysmographic signals ; Simultaneous acceleration ; Patient monitoring
  8. Source: 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016, 7 December 2016 through 9 December 2016 ; 2017 , Pages 1166-1170 ; 9781509045457 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/7906025