Non-Contact Detection of Vital Signs Based on Improved Adaptive EEMD Algorithm (July 2022)

Area of Science:

• Biomedical Engineering
• Signal Processing
• Radar Technology

Background:

• Non-contact vital sign detection offers enhanced comfort for monitoring respiratory and heartbeat signals.
• Frequency Modulated Continuous Wave (FMCW) radar is utilized for extracting these vital signs.
• Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) are signal processing techniques for data analysis.

Purpose of the Study:

• To develop an improved non-contact vital sign detection method using FMCW radar.
• To address challenges in signal decomposition and noise interference in public environments.
• To enhance the accuracy of extracting respiratory and heartbeat signals.

Main Methods:

• A static clutter filtering method was proposed to eliminate ambient clutter in radar data.
• An improved EEMD method was developed, replacing Gaussian noise with symmetrical alpha stable distribution.
• The enhanced EEMD was applied to separate respiratory and heartbeat signals from FMCW radar echo data.

Main Results:

• The static clutter filtering effectively identified periodic moving targets.
• The improved EEMD method demonstrated superior separation of heartbeat, respiration, and their harmonics.
• Accurate heart rate estimation was achieved within a detection range of 0.5m to 2.5m.

Conclusions:

• The proposed static clutter filtering and improved EEMD offer an effective approach for non-contact vital sign monitoring.
• The stable alpha noise-based EEMD enhances the robustness and accuracy of vital sign extraction.
• This technology shows promise for comfortable and precise remote monitoring of physiological signals.