An Adaptive Filter Based Motion Artifact Cancellation Technique Using Multi-Wavelength PPG for Accurate HR Estimation

Area of Science:

• Biomedical Engineering
• Signal Processing
• Wearable Technology

Background:

• Photoplethysmography (PPG) is widely used for heart rate (HR) estimation.
• Motion artifacts (MA) are a significant challenge, degrading PPG signal quality and HR accuracy.
• Existing methods often struggle to effectively remove MA without compromising signal integrity.

Purpose of the Study:

• To develop and validate a novel MA cancellation technique for accurate PPG-based HR estimation.
• To leverage the distinct characteristics of dual-wavelength PPG signals for MA removal.
• To implement an efficient adaptive filtering approach suitable for resource-constrained hardware.

Main Methods:

• Utilized two closely placed red and green LEDs to acquire dual-wavelength PPG signals.
• Exploited high MA correlation and different AC/DC ratios between the two PPG signals.
• Employed a sign-sign least mean square (SS-LMS) adaptive filter for MA cancellation, preserving AC components.

Main Results:

• The proposed MA cancellation technique significantly reduced the mean absolute error (MAE) in HR estimation.
• MAE decreased from 9.83 bpm to 1.48 bpm on average across walking, running, and squatting movements.
• The SS-LMS adaptive filter demonstrated minimal hardware resource requirements.

Conclusions:

• The dual-wavelength PPG-based MA cancellation method offers a robust solution for accurate HR monitoring during physical activity.
• The SS-LMS algorithm provides an efficient and effective approach for real-time MA removal.
• This technique holds promise for improving the reliability of wearable PPG-based health devices.