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| import numpy as np
from dataclasses import dataclass
from typing import List
@dataclass
class FatigueEvolutionPhase:
"""疲劳演化的眼动阶段模型"""
name: str
duration_min: int
saccade_freq: float
fixation_duration: float
blink_rate: float
pupil_var: float
class AviationFatigueEvolutionModel:
"""
航空管制员疲劳演化模型
基于Nature Scientific Reports 2026研究：
疲劳在眼动指标中分4个阶段演化
跨行业启示：汽车驾驶疲劳演化模式类似
"""
PHASES = [
FatigueEvolutionPhase(
name="清醒期 (Alert)",
duration_min=30,
saccade_freq=2.5,
fixation_duration=0.3,
blink_rate=15,
pupil_var=0.1,
),
FatigueEvolutionPhase(
name="适应期 (Adaptation)",
duration_min=60,
saccade_freq=2.0,
fixation_duration=0.4,
blink_rate=18,
pupil_var=0.15,
),
FatigueEvolutionPhase(
name="疲劳早期 (Early Fatigue)",
duration_min=45,
saccade_freq=1.3,
fixation_duration=0.6,
blink_rate=25,
pupil_var=0.25,
),
FatigueEvolutionPhase(
name="疲劳晚期 (Late Fatigue)",
duration_min=30,
saccade_freq=0.8,
fixation_duration=0.9,
blink_rate=35,
pupil_var=0.35,
),
]
def detect_phase(self, saccade_freq: float,
fixation_dur: float,
blink_rate: float,
pupil_var: float) -> str:
"""
基于实时眼动指标检测疲劳阶段
Args:
saccade_freq: 扫视频率(次/秒)
fixation_dur: 平均注视时间(秒)
blink_rate: 眨眼频率(次/分)
pupil_var: 瞳孔直径变异
Returns:
phase_name: 疲劳阶段名称
"""
scores = []
for phase in self.PHASES:
dist = (abs(saccade_freq - phase.saccade_freq) / 3.0 +
abs(fixation_dur - phase.fixation_duration) / 1.0 +
abs(blink_rate - phase.blink_rate) / 30.0 +
abs(pupil_var - phase.pupil_var) / 0.5)
scores.append((phase.name, dist))
scores.sort(key=lambda x: x[1])
return scores[0][0]
def get_automotive_mapping(self):
"""
航空→汽车映射
航空管制员的疲劳演化模式
在汽车驾驶中的对应表现
"""
return {
"清醒期": "正常驾驶：活跃扫视，注意力集中",
"适应期": "长途驾驶30-60分钟后：扫视略减，开始习惯化",
"疲劳早期": "连续驾驶1-2小时：注视延长，眨眼增多 ← IMS应在此阶段触发一级警告",
"疲劳晚期": "连续驾驶2小时+：凝视固定，PERCLOS上升 ← IMS应触发二级警告"
}
class MultiModalFatigueDetector:
"""
多模态疲劳检测器
航空研究结论：EEG+ECG融合达88.42%准确率
汽车改造：眼动(非接触) + rPPG(非接触心率)
rPPG = 远程光电容积脉搏波
通过摄像头非接触提取心率，替代ECG
"""
def __init__(self):
self.weights = {
'eye_tracking': 0.35,
'rppg_hrv': 0.25,
'behavior': 0.20,
'context': 0.20,
}
def detect_fatigue(self, eye_features: dict,
rppg_features: dict,
behavior_features: dict,
context_features: dict) -> dict:
"""
多模态融合疲劳检测
Args:
eye_features: 眼动特征(扫视/注视/眨眼/瞳孔)
rppg_features: rPPG心率变异性特征
behavior_features: 驾驶行为特征(方向盘/车道偏离)
context_features: 上下文(驾驶时长/时间段/路况)
Returns:
result: dict with fatigue score, level, recommendation
"""
eye_score = self._eye_fatigue_score(eye_features)
hrv_score = self._hrv_fatigue_score(rppg_features)
behav_score = self._behavior_fatigue_score(behavior_features)
ctx_score = self._context_fatigue_score(context_features)
total = (self.weights['eye_tracking'] * eye_score +
self.weights['rppg_hrv'] * hrv_score +
self.weights['behavior'] * behav_score +
self.weights['context'] * ctx_score)
if total < 0.3:
level = 'normal'
action = 'continue'
elif total < 0.5:
level = 'mild_fatigue'
action = 'suggest_break'
elif total < 0.7:
level = 'moderate_fatigue'
action = 'warning_level_1'
else:
level = 'severe_fatigue'
action = 'warning_level_2'
return {
'fatigue_score': round(total, 3),
'fatigue_level': level,
'action': action,
'sub_scores': {
'eye_tracking': round(eye_score, 3),
'rppg_hrv': round(hrv_score, 3),
'behavior': round(behav_score, 3),
'context': round(ctx_score, 3),
}
}
def _eye_fatigue_score(self, feat: dict) -> float:
score = 0
score += min(feat.get('perclos', 0) / 0.3, 1) * 0.4
blink = feat.get('blink_rate', 15)
score += min(abs(blink - 15) / 25, 1) * 0.2
score += min(max(0, feat.get('fixation_dur', 0.3) - 0.3) / 0.6, 1) * 0.2
sacc = feat.get('saccade_freq', 2.0)
score += min(max(0, 2.0 - sacc) / 1.5, 1) * 0.2
return min(score, 1.0)
def _hrv_fatigue_score(self, feat: dict) -> float:
hrv_norm = feat.get('rmssd_normalized', 1.0)
return max(0, 1 - hrv_norm)
def _behavior_fatigue_score(self, feat: dict) -> float:
score = 0
score += min(feat.get('lane_deviation_rate', 0) / 10, 1) * 0.5
score += min(feat.get('steering_correction_rate', 0) / 20, 1) * 0.5
return score
def _context_fatigue_score(self, feat: dict) -> float:
score = 0
hours = feat.get('driving_hours', 0)
score += min(hours / 4, 1) * 0.5
hour = feat.get('clock_hour', 12)
if hour < 6 or hour > 22:
score += 0.5
return min(score, 1.0)
if __name__ == "__main__":
model = AviationFatigueEvolutionModel()
print("疲劳演化阶段:")
for phase in model.PHASES:
print(f" {phase.name} ({phase.duration_min}min): "
f"扫视={phase.saccade_freq}/s, "
f"注视={phase.fixation_duration}s, "
f"眨眼={phase.blink_rate}/min")
print("\n汽车映射:")
for k, v in model.get_automotive_mapping().items():
print(f" {k} → {v}")
print("\n实时检测测试:")
phase = model.detect_phase(
saccade_freq=1.0,
fixation_dur=0.8,
blink_rate=30,
pupil_var=0.3
)
print(f" 输入: 扫视1.0/s, 注视0.8s, 眨眼30/min, 瞳孔变异0.3")
print(f" 检测阶段: {phase}")
detector = MultiModalFatigueDetector()
result = detector.detect_fatigue(
eye_features={'perclos': 0.25, 'blink_rate': 28,
'fixation_dur': 0.6, 'saccade_freq': 1.2},
rppg_features={'rmssd_normalized': 0.4},
behavior_features={'lane_deviation_rate': 5,
'steering_correction_rate': 12},
context_features={'driving_hours': 2.5, 'clock_hour': 14}
)
print(f"\n多模态融合检测结果:")
print(f" 疲劳评分: {result['fatigue_score']}")
print(f" 等级: {result['fatigue_level']}")
print(f" 建议: {result['action']}")
print(f" 子评分: {result['sub_scores']}")
|
