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Heart Rate Estimation from Wrist PPG Signal During Intense Physical Exercise

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Abstract

In recent years, the wearable reflectance-type photoplethysmography (PPG) sensor has gained unprecedented attention for real-time heart rate monitoring due to its noninvasive and inexpensive nature. However, during intensive physical exercises, motion artifacts (MAs) must be eliminated from the PPG signals to estimate the heart rate accurately. Most previous works focus on the accelerometer or gyroscopic signals to overcome this issue. In this work, we proposed a novel method—VADAF (variational mode decomposition with adaptive filtering) to reduce the average absolute error (AAE) of accelerometer and gyroscopic signals. Our results are compared with the DFDF (direct finding the dominant frequency) method, where the AAE of accelerometer and gyroscopic signals are found as 10.03 bpm and 4.88 bpm, respectively. Conversely, using the proposed VADAF method, the Average Absolute Error is measured as 2.67 bpm and 2.84, respectively. The MATLAB code and the dataset for reproducing the result are available at https://github.com/KamrulHasan1743/ppg_journal. For the future, this research proposes switching between the accelerometer and gyroscope based on different situations to improve the overall efficiency of PPG-based heart rate (HR) estimation.

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Funding

The authors have no relevant financial or non-financial interests to disclose.

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Authors and Affiliations

  1. Department of EEE, CUET, Chittagong, 4349, Bangladesh

    Kamrul Hasan, Mehdi Hasan Chowdhury, Naqib Sad Pathan & Quazi Delwar Hossain

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  1. Kamrul Hasan
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  2. Mehdi Hasan Chowdhury
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  3. Naqib Sad Pathan
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  4. Quazi Delwar Hossain
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Contributions

KH Conceptualization, Formal analysis, Methodology, Visualization, Writing—original draft. MHC Formal analysis, Methodology, Writing—original draft NSP Methodology, Writing—original draft. QDH Supervision, Writing—review and editing.

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Correspondence to Mehdi Hasan Chowdhury.

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Hasan, K., Chowdhury, M.H., Pathan, N.S. et al. Heart Rate Estimation from Wrist PPG Signal During Intense Physical Exercise. SN COMPUT. SCI. 4, 684 (2023). https://doi.org/10.1007/s42979-023-02173-6

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