Noise-Robust Heart Rate Estimation Algorithm from Photoplethysmography Signal with Low Computational Complexity

Area of Science:

• Biomedical Engineering
• Signal Processing
• Wearable Technology

Background:

• Photoplethysmography (PPG) signals are widely used for heart rate (HR) estimation.
• Motion artifacts significantly degrade PPG signal quality, challenging accurate HR estimation, especially during physical activity.
• Existing algorithms often struggle with noise robustness and computational efficiency for real-time applications.

Purpose of the Study:

• To develop a novel, noise-robust algorithm for estimating heart rate (HR) from wrist-type PPG signals.
• To enhance the reliability of HR monitoring during physical activities with significant motion.
• To achieve low computational complexity for practical implementation in wearable devices.

Main Methods:

• The proposed algorithm integrates a preprocessing block, a motion artifact reduction block, and a frequency tracking block.
• Utilized wrist-type PPG signals from 12 subjects.
• Evaluated algorithm performance on data collected during treadmill exercise.
• Compared the algorithm against existing HR estimation methods.

Main Results:

• The algorithm demonstrated significant robustness against motion noise.
• Achieved low computational complexity, making it suitable for resource-constrained devices.
• Performance validation confirmed its effectiveness in reducing motion artifact impact on HR estimation.
• Comparative analysis indicated competitive or superior performance against existing algorithms.

Conclusions:

• The developed algorithm provides a robust and computationally efficient solution for HR estimation using wrist PPG.
• It offers a reliable method for continuous HR monitoring during exercise and other motion-intensive activities.
• The findings support the potential of this algorithm for integration into next-generation wearable health devices.