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| import numpy as np
from scipy.signal import butter, filtfilt, find_peaks
from typing import Tuple, Optional
class MicromovementDetector:
"""
微动检测器
检测儿童/宠物的呼吸和心跳微动信号
用于 CPD（儿童存在检测）
"""
def __init__(self, fs: int = 100):
"""
Args:
fs: 雷达采样率（Hz）
"""
self.fs = fs
self.breath_band = (0.1, 0.7)
self.heart_band = (0.8, 3.0)
self.breath_filter = self._design_bandpass(self.breath_band)
self.heart_filter = self._design_bandpass(self.heart_band)
def _design_bandpass(self, band: Tuple[float, float]) -> Tuple:
"""设计带通滤波器"""
nyq = self.fs / 2
low = band[0] / nyq
high = band[1] / nyq
return butter(4, [low, high], btype='band')
def detect(
self,
radar_signal: np.ndarray,
duration: float = 5.0
) -> dict:
"""
检测微动信号
Args:
radar_signal: 雷达信号（复数，包含相位信息）
duration: 检测窗口时长（秒）
Returns:
{
'has_micromovement': bool,
'breathing_detected': bool,
'heartbeat_detected': bool,
'breathing_rate': float, # 次/分钟
'heartbeat_rate': float, # 次/分钟
'confidence': float
}
"""
phase = np.angle(radar_signal)
phase_unwrapped = np.unwrap(phase)
breath_signal = filtfilt(self.breath_filter[0], self.breath_filter[1], phase_unwrapped)
heart_signal = filtfilt(self.heart_filter[0], self.heart_filter[1], phase_unwrapped)
breathing_detected, breathing_rate = self._detect_periodic(
breath_signal, self.breath_band
)
heartbeat_detected, heartbeat_rate = self._detect_periodic(
heart_signal, self.heart_band
)
has_micromovement = breathing_detected or heartbeat_detected
confidence = self._calculate_confidence(
breath_signal, heart_signal, breathing_detected, heartbeat_detected
)
return {
'has_micromovement': has_micromovement,
'breathing_detected': breathing_detected,
'heartbeat_detected': heartbeat_detected,
'breathing_rate': breathing_rate,
'heartbeat_rate': heartbeat_rate,
'confidence': confidence
}
def _detect_periodic(
self,
signal: np.ndarray,
freq_band: Tuple[float, float]
) -> Tuple[bool, float]:
"""
检测周期性信号
Returns:
(detected, rate) - rate 单位：次/分钟
"""
n = len(signal)
freq = np.fft.rfftfreq(n, 1.0 / self.fs)
fft_mag = np.abs(np.fft.rfft(signal))
mask = (freq >= freq_band[0]) & (freq <= freq_band[1])
if not np.any(mask):
return False, 0.0
freq_in_band = freq[mask]
mag_in_band = fft_mag[mask]
peak_idx = np.argmax(mag_in_band)
peak_freq = freq_in_band[peak_idx]
peak_mag = mag_in_band[peak_idx]
avg_mag = np.mean(mag_in_band)
snr = peak_mag / avg_mag if avg_mag > 0 else 0
detected = snr > 3.0
rate = peak_freq * 60.0
return detected, rate
def _calculate_confidence(
self,
breath_signal: np.ndarray,
heart_signal: np.ndarray,
breathing_detected: bool,
heartbeat_detected: bool
) -> float:
"""计算检测置信度"""
breath_energy = np.var(breath_signal)
heart_energy = np.var(heart_signal)
base_confidence = 0.5
if breathing_detected:
base_confidence += 0.3
if heartbeat_detected:
base_confidence += 0.2
energy_factor = min(breath_energy + heart_energy, 1.0)
confidence = min(base_confidence * (1 + energy_factor), 1.0)
return confidence
if __name__ == "__main__":
detector = MicromovementDetector(fs=100)
t = np.arange(0, 10, 0.01)
breath_component = 0.1 * np.sin(2 * np.pi * 0.3 * t)
heart_component = 0.02 * np.sin(2 * np.pi * 1.5 * t)
noise = 0.01 * np.random.randn(len(t))
phase = breath_component + heart_component + noise
radar_signal = np.exp(1j * phase * 10)
result = detector.detect(radar_signal)
print("微动检测结果:")
print(f" 检测到微动: {result['has_micromovement']}")
print(f" 呼吸检测: {result['breathing_detected']} ({result['breathing_rate']:.1f} 次/分钟)")
print(f" 心跳检测: {result['heartbeat_detected']} ({result['heartbeat_rate']:.1f} 次/分钟)")
print(f" 置信度: {result['confidence']:.2f}")
|