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[Heart rate extraction algorithm based on adaptive heart rate search model].

Ronghao Meng1, Zhuoshi Li1, Helong Yu1

  • 1Jilin Agricultural University, Changchun 130000, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
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Summary
This summary is machine-generated.

This study introduces a novel algorithm for accurate heart rate monitoring during exercise using photoplethysmography (PPG). The self-adaptive model effectively reduces noise interference, improving precision in wearable devices.

Keywords:
Acceleration signalPhotoplethysmographySelf-adaptive heart rate separation modelStrong motion noise

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Wearable Technology

Background:

  • Photoplethysmography (PPG) is a cost-effective, non-invasive method for heart rate measurement, widely adopted in smart wearables.
  • High-intensity physical activity introduces noise into PPG signals, significantly reducing heart rate measurement accuracy during sports.
  • Existing methods struggle to provide precise heart rate data in the presence of motion artifacts.

Purpose of the Study:

  • To develop an advanced heart rate extraction algorithm for improved accuracy in noisy environments.
  • To enhance the reliability of PPG-based heart rate monitoring in wearable devices during physical exertion.
  • To address the limitations of current PPG techniques in accurately capturing heart rate during high-intensity movements.

Main Methods:

  • A self-adaptive heart rate separation model was developed, integrating preprocessed acceleration and PPG signals.
  • Cadence and historical heart rate data were extracted to inform the self-adaptive model.
  • The algorithm narrows the potential heart rate domain to filter out noise and improve precision.

Main Results:

  • The algorithm demonstrated high accuracy on the 2015 IEEE Signal Processing Cup PPG dataset.
  • Average absolute error on training sets was 1.12 bpm (Pearson r=0.996).
  • Average absolute error on testing sets was 3.19 bpm (Pearson r=0.990), indicating robust performance.

Conclusions:

  • The proposed algorithm effectively extracts heart rate information even with significant noise interference.
  • This method shows strong potential for reliable deployment in smart wearable devices for sports applications.
  • The self-adaptive model offers a promising solution for accurate, real-time heart rate monitoring during exercise.