MUSIC-based Non-contact Heart Rate Estimation with Adaptive Window Size Setting

Area of Science:

• Biomedical Engineering
• Signal Processing
• Wearable Technology

Background:

• Continuous Heart Rate (HR) monitoring is crucial for daily stress estimation.
• Doppler sensors offer a non-contact approach for HR estimation.
• Previous MUSIC-based methods struggled with HR fluctuations causing inaccurate peak detection.

Purpose of the Study:

• To develop an adaptive windowing technique for MUSIC-based HR estimation.
• To enhance the accuracy of stress index calculation (CVI, CSI) through improved HR monitoring.
• To address limitations of fixed window sizes in dynamic HR conditions.

Main Methods:

• Utilized the MUSIC (MUltiple SIgnal Classification) algorithm for HR estimation.
• Implemented an adaptive windowing strategy that dynamically adjusts window size.
• Shortened the time window iteratively when multiple peaks appeared in the MUSIC spectrum.

Main Results:

• The proposed adaptive window method significantly improved HR estimation accuracy.
• Outperformed previous MUSIC-based methods and other existing techniques.
• Demonstrated superior performance in estimating stress indexes like Cardiac Vagal Index (CVI) and Cardiac Sympathetic Index (CSI).

Conclusions:

• Adaptive windowing is effective for robust HR estimation using Doppler sensors and MUSIC algorithm.
• This method enhances the reliability of non-contact stress monitoring in real-world scenarios.
• The improved accuracy in HR and stress index estimation has implications for personalized health management.