Radar Echo-Based Human Respiration and Heartbeat Detection Using FFT

Radar echo-based human respiration and heartbeat detection is a non-contact monitoring method that utilizes radar technology to capture vital signs. By analyzing micro-motion characteristics in radar reflection signals, frequency information of respiration and heartbeat can be extracted.

Technical Principle Electromagnetic waves emitted by the radar reflect back when encountering the human body. Since respiration and heartbeat cause tiny chest movements, these micro-motions induce slight phase changes in the echo signals. Analyzing these phase changes enables detection of respiration and heartbeat frequencies.

Implementation Approach Signal Acquisition: Radar receivers capture echo signals reflected from the human body, typically requiring sufficiently high sampling rates (e.g., above 100Hz) to capture high-frequency components of heartbeats. Implementation involves configuring ADC parameters and signal conditioning circuits. Preprocessing: Filter signals (e.g., bandpass filtering) to remove noise and clutter, preserving useful signals in the respiration (0.1-0.5Hz) and heartbeat (0.8-2Hz) frequency bands. Code implementation might use Butterworth filters with scipy.signal.butter() for digital filtering. FFT Spectrum Analysis: Apply Fast Fourier Transform (FFT) to convert time-domain signals to frequency domain, extracting peak frequencies of respiration and heartbeat. Implementation typically uses numpy.fft.fft() with proper windowing functions (e.g., Hanning window) to reduce spectral leakage. Feature Extraction: Identify harmonic components of respiration and heartbeat from the spectrum, calculating respective frequencies and amplitudes through peak detection algorithms. Python code could utilize scipy.signal.find_peaks() with prominence thresholding for robust peak identification.

Challenges and Optimization Motion Interference: Random body movements (e.g., limb swaying) affect signal quality. Adaptive filtering (LMS/RLS algorithms) or machine learning methods (SVM/neural networks) can suppress interference. Multi-Target Resolution: When multiple human targets exist, combine spatial beamforming or MIMO radar techniques to separate signals. Implementation may require array signal processing using MUSIC or Capon algorithms. Real-time Requirements: For embedded systems, optimize FFT computation efficiency using ARM CMSIS-DSP libraries or implement sliding window Short-Time Fourier Transform (STFT) with overlap-add methods.

Application Prospects This technology applies to smart home, medical monitoring, and emergency rescue scenarios, particularly suitable for non-contact long-term monitoring such as sleep quality analysis or remote vital signs monitoring for COVID-19 patients.