1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
|
import numpy as np
from scipy.signal import butter, filtfilt, find_peaks
class RadarVitalSignsDetector:
"""
60GHz雷达生命体征检测
基于TI AWRL6432参考实现
"""
def __init__(self, config: dict = None):
self.breathing_freq_range = (0.1, 0.5)
self.heartbeat_freq_range = (0.8, 2.0)
self fft_size = 256
self.range_bins = 64
if config:
for key, value in config.items():
setattr(self, key, value)
def extract_vital_signs(self, radar_data: np.ndarray) -> dict:
"""
从雷达数据提取生命体征
Args:
radar_data: 雷达ADC数据 shape=(frames, rx, samples)
Returns:
vital_signs: {
'breathing_rate': Hz,
'heartbeat_rate': Hz,
'presence_detected': bool,
'confidence': float
}
"""
range_fft = self._range_fft(radar_data)
target_bin = self._find_target_bin(range_fft)
time_series = range_fft[:, target_bin]
phase_signal = np.angle(time_series)
phase_signal = np.unwrap(phase_signal)
breathing_signal = self._bandpass_filter(
phase_signal,
self.breathing_freq_range[0],
self.breathing_freq_range[1],
fs=30
)
heartbeat_signal = self._bandpass_filter(
phase_signal,
self.heartbeat_freq_range[0],
self.heartbeat_freq_range[1],
fs=30
)
breathing_rate = self._estimate_frequency(breathing_signal, fs=30)
heartbeat_rate = self._estimate_frequency(heartbeat_signal, fs=30)
presence = self._check_presence(breathing_signal, heartbeat_signal)
confidence = self._calculate_confidence(breathing_signal, heartbeat_signal)
return {
'breathing_rate': breathing_rate,
'heartbeat_rate': heartbeat_rate,
'presence_detected': presence,
'confidence': confidence,
'target_distance': target_bin * 5
}
def _range_fft(self, data: np.ndarray) -> np.ndarray:
"""距离FFT"""
fft_result = np.fft.fft(data, n=self.fft_size, axis=-1)
return np.abs(fft_result[:, :, :self.range_bins])
def _find_target_bin(self, range_fft: np.ndarray) -> int:
"""找到目标距离bin"""
energy = np.sum(range_fft, axis=0)
max_bin = np.argmax(np.sum(energy, axis=0))
return max_bin
def _bandpass_filter(self, signal: np.ndarray,
lowcut: float, highcut: float,
fs: int) -> np.ndarray:
"""带通滤波"""
nyq = 0.5 * fs
low = lowcut / nyq
high = highcut / nyq
b, a = butter(4, [low, high], btype='band')
return filtfilt(b, a, signal)
def _estimate_frequency(self, signal: np.ndarray, fs: int) -> float:
"""估计主频率"""
fft_result = np.fft.fft(signal)
freqs = np.fft.fftfreq(len(signal), 1/fs)
positive_freqs = freqs[:len(freqs)//2]
magnitude = np.abs(fft_result[:len(fft_result)//2])
peak_idx = np.argmax(magnitude)
return positive_freqs[peak_idx]
def _check_presence(self, breathing: np.ndarray,
heartbeat: np.ndarray) -> bool:
"""判断是否有生命体征"""
breathing_energy = np.var(breathing)
heartbeat_energy = np.var(heartbeat)
return breathing_energy > 0.001 or heartbeat_energy > 0.0005
def _calculate_confidence(self, breathing: np.ndarray,
heartbeat: np.ndarray) -> float:
"""计算检测置信度"""
breathing_peaks = find_peaks(breathing, distance=15)[0]
heartbeat_peaks = find_peaks(heartbeat, distance=10)[0]
if len(breathing_peaks) > 2:
intervals = np.diff(breathing_peaks)
regularity = 1 - np.std(intervals) / np.mean(intervals)
breathing_score = max(0, regularity) * 0.5
else:
breathing_score = 0
if len(heartbeat_peaks) > 3:
intervals = np.diff(heartbeat_peaks)
regularity = 1 - np.std(intervals) / np.mean(intervals)
heartbeat_score = max(0, regularity) * 0.5
else:
heartbeat_score = 0
return breathing_score + heartbeat_score
if __name__ == "__main__":
np.random.seed(42)
frames = 300
samples = 256
rx = 3
breathing_signal = 0.05 * np.sin(2 * np.pi * 0.3 * np.arange(frames) / 30)
heartbeat_signal = 0.01 * np.sin(2 * np.pi * 1.2 * np.arange(frames) / 30)
phase_signal = breathing_signal + heartbeat_signal + np.random.normal(0, 0.005, frames)
radar_data = np.zeros((frames, rx, samples), dtype=np.float32)
for i in range(frames):
radar_data[i, :, 20] = np.exp(1j * phase_signal[i])
detector = RadarVitalSignsDetector()
result = detector.extract_vital_signs(radar_data)
print("=== 生命体征检测结果 ===")
print(f"呼吸频率: {result['breathing_rate']*60:.1f} 次/分钟")
print(f"心跳频率: {result['heartbeat_rate']*60:.1f} 次/分钟")
print(f"检测置信度: {result['confidence']:.2f}")
print(f"目标距离: {result['target_distance']} cm")
print(f"存在生命体征: {result['presence_detected']}")
|