""" End-to-end tests for healthcare fall detection scenario. Tests complete workflow from CSI data collection to fall alert generation. """ import pytest import asyncio import numpy as np from datetime import datetime, timedelta from typing import Dict, Any, List, Optional from unittest.mock import AsyncMock, MagicMock, patch import json from dataclasses import dataclass from enum import Enum class AlertSeverity(Enum): """Alert severity levels.""" LOW = "low" MEDIUM = "medium" HIGH = "high" CRITICAL = "critical" @dataclass class HealthcareAlert: """Healthcare alert data structure.""" alert_id: str timestamp: datetime alert_type: str severity: AlertSeverity patient_id: str location: str confidence: float description: str metadata: Dict[str, Any] class MockPatientMonitor: """Mock patient monitoring system.""" def __init__(self, patient_id: str, room_id: str): self.patient_id = patient_id self.room_id = room_id self.is_monitoring = False self.baseline_activity = None self.activity_history = [] self.alerts_generated = [] self.fall_detection_enabled = True self.sensitivity_level = "medium" async def start_monitoring(self) -> bool: """Start patient monitoring.""" if self.is_monitoring: return False self.is_monitoring = True return True async def stop_monitoring(self) -> bool: """Stop patient monitoring.""" if not self.is_monitoring: return False self.is_monitoring = False return True async def process_pose_data(self, pose_data: Dict[str, Any]) -> Optional[HealthcareAlert]: """Process pose data and detect potential issues.""" if not self.is_monitoring: return None # Extract activity metrics activity_metrics = self._extract_activity_metrics(pose_data) self.activity_history.append(activity_metrics) # Keep only recent history if len(self.activity_history) > 100: self.activity_history = self.activity_history[-100:] # Detect anomalies alert = await self._detect_anomalies(activity_metrics, pose_data) if alert: self.alerts_generated.append(alert) return alert def _extract_activity_metrics(self, pose_data: Dict[str, Any]) -> Dict[str, Any]: """Extract activity metrics from pose data.""" persons = pose_data.get("persons", []) if not persons: return { "person_count": 0, "activity_level": 0.0, "posture": "unknown", "movement_speed": 0.0, "stability_score": 1.0 } # Analyze first person (primary patient) person = persons[0] # Extract posture from activity field or bounding box analysis posture = person.get("activity", "standing") # If no activity specified, analyze bounding box for fall detection if posture == "standing" and "bounding_box" in person: bbox = person["bounding_box"] width = bbox.get("width", 80) height = bbox.get("height", 180) # Fall detection: if width > height, likely fallen if width > height * 1.5: posture = "fallen" # Calculate activity metrics based on posture if posture == "fallen": activity_level = 0.1 movement_speed = 0.0 stability_score = 0.2 elif posture == "walking": activity_level = 0.8 movement_speed = 1.5 stability_score = 0.7 elif posture == "sitting": activity_level = 0.3 movement_speed = 0.1 stability_score = 0.9 else: # standing or other activity_level = 0.5 movement_speed = 0.2 stability_score = 0.8 return { "person_count": len(persons), "activity_level": activity_level, "posture": posture, "movement_speed": movement_speed, "stability_score": stability_score, "confidence": person.get("confidence", 0.0) } async def _detect_anomalies(self, current_metrics: Dict[str, Any], pose_data: Dict[str, Any]) -> Optional[HealthcareAlert]: """Detect health-related anomalies.""" # Fall detection if current_metrics["posture"] == "fallen": return await self._generate_fall_alert(current_metrics, pose_data) # Prolonged inactivity detection if len(self.activity_history) >= 10: recent_activity = [m["activity_level"] for m in self.activity_history[-10:]] avg_activity = np.mean(recent_activity) if avg_activity < 0.1: # Very low activity return await self._generate_inactivity_alert(current_metrics, pose_data) # Unusual movement patterns if current_metrics["stability_score"] < 0.4: return await self._generate_instability_alert(current_metrics, pose_data) return None async def _generate_fall_alert(self, metrics: Dict[str, Any], pose_data: Dict[str, Any]) -> HealthcareAlert: """Generate fall detection alert.""" return HealthcareAlert( alert_id=f"fall_{self.patient_id}_{int(datetime.utcnow().timestamp())}", timestamp=datetime.utcnow(), alert_type="fall_detected", severity=AlertSeverity.CRITICAL, patient_id=self.patient_id, location=self.room_id, confidence=metrics["confidence"], description=f"Fall detected for patient {self.patient_id} in {self.room_id}", metadata={ "posture": metrics["posture"], "stability_score": metrics["stability_score"], "pose_data": pose_data } ) async def _generate_inactivity_alert(self, metrics: Dict[str, Any], pose_data: Dict[str, Any]) -> HealthcareAlert: """Generate prolonged inactivity alert.""" return HealthcareAlert( alert_id=f"inactivity_{self.patient_id}_{int(datetime.utcnow().timestamp())}", timestamp=datetime.utcnow(), alert_type="prolonged_inactivity", severity=AlertSeverity.MEDIUM, patient_id=self.patient_id, location=self.room_id, confidence=metrics["confidence"], description=f"Prolonged inactivity detected for patient {self.patient_id}", metadata={ "activity_level": metrics["activity_level"], "duration_minutes": 10, "pose_data": pose_data } ) async def _generate_instability_alert(self, metrics: Dict[str, Any], pose_data: Dict[str, Any]) -> HealthcareAlert: """Generate movement instability alert.""" return HealthcareAlert( alert_id=f"instability_{self.patient_id}_{int(datetime.utcnow().timestamp())}", timestamp=datetime.utcnow(), alert_type="movement_instability", severity=AlertSeverity.HIGH, patient_id=self.patient_id, location=self.room_id, confidence=metrics["confidence"], description=f"Movement instability detected for patient {self.patient_id}", metadata={ "stability_score": metrics["stability_score"], "movement_speed": metrics["movement_speed"], "pose_data": pose_data } ) def get_monitoring_stats(self) -> Dict[str, Any]: """Get monitoring statistics.""" return { "patient_id": self.patient_id, "room_id": self.room_id, "is_monitoring": self.is_monitoring, "total_alerts": len(self.alerts_generated), "alert_types": { alert.alert_type: len([a for a in self.alerts_generated if a.alert_type == alert.alert_type]) for alert in self.alerts_generated }, "activity_samples": len(self.activity_history), "fall_detection_enabled": self.fall_detection_enabled } class MockHealthcareNotificationSystem: """Mock healthcare notification system.""" def __init__(self): self.notifications_sent = [] self.notification_channels = { "nurse_station": True, "mobile_app": True, "email": True, "sms": False } self.escalation_rules = { AlertSeverity.CRITICAL: ["nurse_station", "mobile_app", "sms"], AlertSeverity.HIGH: ["nurse_station", "mobile_app"], AlertSeverity.MEDIUM: ["nurse_station"], AlertSeverity.LOW: ["mobile_app"] } async def send_alert_notification(self, alert: HealthcareAlert) -> Dict[str, bool]: """Send alert notification through appropriate channels.""" channels_to_notify = self.escalation_rules.get(alert.severity, ["nurse_station"]) results = {} for channel in channels_to_notify: if self.notification_channels.get(channel, False): success = await self._send_to_channel(channel, alert) results[channel] = success if success: self.notifications_sent.append({ "alert_id": alert.alert_id, "channel": channel, "timestamp": datetime.utcnow(), "severity": alert.severity.value }) return results async def _send_to_channel(self, channel: str, alert: HealthcareAlert) -> bool: """Send notification to specific channel.""" # Simulate network delay await asyncio.sleep(0.01) # Simulate occasional failures if np.random.random() < 0.05: # 5% failure rate return False return True def get_notification_stats(self) -> Dict[str, Any]: """Get notification statistics.""" return { "total_notifications": len(self.notifications_sent), "notifications_by_channel": { channel: len([n for n in self.notifications_sent if n["channel"] == channel]) for channel in self.notification_channels.keys() }, "notifications_by_severity": { severity.value: len([n for n in self.notifications_sent if n["severity"] == severity.value]) for severity in AlertSeverity } } class TestHealthcareFallDetection: """Test healthcare fall detection workflow.""" @pytest.fixture def patient_monitor(self): """Create patient monitor.""" return MockPatientMonitor("patient_001", "room_101") @pytest.fixture def notification_system(self): """Create notification system.""" return MockHealthcareNotificationSystem() @pytest.fixture def fall_pose_data(self): """Create pose data indicating a fall.""" return { "persons": [ { "person_id": "patient_001", "confidence": 0.92, "bounding_box": {"x": 200, "y": 400, "width": 150, "height": 80}, # Horizontal position "activity": "fallen", "keypoints": [[x, y, 0.8] for x, y in zip(range(17), range(17))] } ], "zone_summary": {"room_101": 1}, "timestamp": datetime.utcnow().isoformat() } @pytest.fixture def normal_pose_data(self): """Create normal pose data.""" return { "persons": [ { "person_id": "patient_001", "confidence": 0.88, "bounding_box": {"x": 200, "y": 150, "width": 80, "height": 180}, "activity": "standing", "keypoints": [[x, y, 0.9] for x, y in zip(range(17), range(17))] } ], "zone_summary": {"room_101": 1}, "timestamp": datetime.utcnow().isoformat() } @pytest.mark.asyncio async def test_fall_detection_workflow_should_fail_initially(self, patient_monitor, notification_system, fall_pose_data): """Test fall detection workflow - should fail initially.""" # Start monitoring result = await patient_monitor.start_monitoring() # This will fail initially assert result is True assert patient_monitor.is_monitoring is True # Process fall pose data alert = await patient_monitor.process_pose_data(fall_pose_data) # Should generate fall alert assert alert is not None assert alert.alert_type == "fall_detected" assert alert.severity == AlertSeverity.CRITICAL assert alert.patient_id == "patient_001" # Send notification notification_results = await notification_system.send_alert_notification(alert) # Should notify appropriate channels assert len(notification_results) > 0 assert any(notification_results.values()) # At least one channel should succeed # Check statistics monitor_stats = patient_monitor.get_monitoring_stats() assert monitor_stats["total_alerts"] == 1 notification_stats = notification_system.get_notification_stats() assert notification_stats["total_notifications"] > 0 @pytest.mark.asyncio async def test_normal_activity_monitoring_should_fail_initially(self, patient_monitor, normal_pose_data): """Test normal activity monitoring - should fail initially.""" await patient_monitor.start_monitoring() # Process multiple normal pose data samples alerts_generated = [] for i in range(10): alert = await patient_monitor.process_pose_data(normal_pose_data) if alert: alerts_generated.append(alert) # This will fail initially # Should not generate alerts for normal activity assert len(alerts_generated) == 0 # Should have activity history stats = patient_monitor.get_monitoring_stats() assert stats["activity_samples"] == 10 assert stats["is_monitoring"] is True @pytest.mark.asyncio async def test_prolonged_inactivity_detection_should_fail_initially(self, patient_monitor): """Test prolonged inactivity detection - should fail initially.""" await patient_monitor.start_monitoring() # Simulate prolonged inactivity inactive_pose_data = { "persons": [], # No person detected "zone_summary": {"room_101": 0}, "timestamp": datetime.utcnow().isoformat() } alerts_generated = [] # Process multiple inactive samples for i in range(15): alert = await patient_monitor.process_pose_data(inactive_pose_data) if alert: alerts_generated.append(alert) # This will fail initially # Should generate inactivity alert after sufficient samples inactivity_alerts = [a for a in alerts_generated if a.alert_type == "prolonged_inactivity"] assert len(inactivity_alerts) > 0 # Check alert properties alert = inactivity_alerts[0] assert alert.severity == AlertSeverity.MEDIUM assert alert.patient_id == "patient_001" @pytest.mark.asyncio async def test_movement_instability_detection_should_fail_initially(self, patient_monitor): """Test movement instability detection - should fail initially.""" await patient_monitor.start_monitoring() # Simulate unstable movement unstable_pose_data = { "persons": [ { "person_id": "patient_001", "confidence": 0.65, # Lower confidence indicates instability "bounding_box": {"x": 200, "y": 150, "width": 80, "height": 180}, "activity": "walking", "keypoints": [[x, y, 0.5] for x, y in zip(range(17), range(17))] # Low keypoint confidence } ], "zone_summary": {"room_101": 1}, "timestamp": datetime.utcnow().isoformat() } # Process unstable pose data alert = await patient_monitor.process_pose_data(unstable_pose_data) # This will fail initially # May generate instability alert based on stability score if alert and alert.alert_type == "movement_instability": assert alert.severity == AlertSeverity.HIGH assert alert.patient_id == "patient_001" assert "stability_score" in alert.metadata class TestHealthcareMultiPatientMonitoring: """Test multi-patient monitoring scenarios.""" @pytest.fixture def multi_patient_setup(self): """Create multi-patient monitoring setup.""" patients = { "patient_001": MockPatientMonitor("patient_001", "room_101"), "patient_002": MockPatientMonitor("patient_002", "room_102"), "patient_003": MockPatientMonitor("patient_003", "room_103") } notification_system = MockHealthcareNotificationSystem() return patients, notification_system @pytest.mark.asyncio async def test_concurrent_patient_monitoring_should_fail_initially(self, multi_patient_setup): """Test concurrent patient monitoring - should fail initially.""" patients, notification_system = multi_patient_setup # Start monitoring for all patients start_results = [] for patient_id, monitor in patients.items(): result = await monitor.start_monitoring() start_results.append(result) # This will fail initially assert all(start_results) assert all(monitor.is_monitoring for monitor in patients.values()) # Simulate concurrent pose data processing pose_data_samples = [ { "persons": [ { "person_id": patient_id, "confidence": 0.85, "bounding_box": {"x": 200, "y": 150, "width": 80, "height": 180}, "activity": "standing" } ], "zone_summary": {f"room_{101 + i}": 1}, "timestamp": datetime.utcnow().isoformat() } for i, patient_id in enumerate(patients.keys()) ] # Process data for all patients concurrently tasks = [] for (patient_id, monitor), pose_data in zip(patients.items(), pose_data_samples): task = asyncio.create_task(monitor.process_pose_data(pose_data)) tasks.append(task) alerts = await asyncio.gather(*tasks) # Check results assert len(alerts) == len(patients) # Get statistics for all patients all_stats = {} for patient_id, monitor in patients.items(): all_stats[patient_id] = monitor.get_monitoring_stats() assert len(all_stats) == 3 assert all(stats["is_monitoring"] for stats in all_stats.values()) @pytest.mark.asyncio async def test_alert_prioritization_should_fail_initially(self, multi_patient_setup): """Test alert prioritization across patients - should fail initially.""" patients, notification_system = multi_patient_setup # Start monitoring for monitor in patients.values(): await monitor.start_monitoring() # Generate different severity alerts alert_scenarios = [ ("patient_001", "fall_detected", AlertSeverity.CRITICAL), ("patient_002", "prolonged_inactivity", AlertSeverity.MEDIUM), ("patient_003", "movement_instability", AlertSeverity.HIGH) ] generated_alerts = [] for patient_id, alert_type, expected_severity in alert_scenarios: # Create appropriate pose data for each scenario if alert_type == "fall_detected": pose_data = { "persons": [{"person_id": patient_id, "confidence": 0.9, "activity": "fallen"}], "zone_summary": {f"room_{patients[patient_id].room_id}": 1} } else: pose_data = { "persons": [{"person_id": patient_id, "confidence": 0.7, "activity": "standing"}], "zone_summary": {f"room_{patients[patient_id].room_id}": 1} } alert = await patients[patient_id].process_pose_data(pose_data) if alert: generated_alerts.append(alert) # This will fail initially # Should have generated alerts assert len(generated_alerts) > 0 # Send notifications for all alerts notification_tasks = [ notification_system.send_alert_notification(alert) for alert in generated_alerts ] notification_results = await asyncio.gather(*notification_tasks) # Check notification prioritization notification_stats = notification_system.get_notification_stats() assert notification_stats["total_notifications"] > 0 # Critical alerts should use more channels critical_notifications = [ n for n in notification_system.notifications_sent if n["severity"] == "critical" ] if critical_notifications: # Critical alerts should be sent to multiple channels critical_channels = set(n["channel"] for n in critical_notifications) assert len(critical_channels) >= 1 class TestHealthcareSystemIntegration: """Test healthcare system integration scenarios.""" @pytest.mark.asyncio async def test_end_to_end_healthcare_workflow_should_fail_initially(self): """Test complete end-to-end healthcare workflow - should fail initially.""" # Setup complete healthcare monitoring system class HealthcareMonitoringSystem: def __init__(self): self.patient_monitors = {} self.notification_system = MockHealthcareNotificationSystem() self.alert_history = [] self.system_status = "operational" async def add_patient(self, patient_id: str, room_id: str) -> bool: """Add patient to monitoring system.""" if patient_id in self.patient_monitors: return False monitor = MockPatientMonitor(patient_id, room_id) self.patient_monitors[patient_id] = monitor return await monitor.start_monitoring() async def process_pose_update(self, room_id: str, pose_data: Dict[str, Any]) -> List[HealthcareAlert]: """Process pose update for room.""" alerts = [] # Find patients in this room room_patients = [ (patient_id, monitor) for patient_id, monitor in self.patient_monitors.items() if monitor.room_id == room_id ] for patient_id, monitor in room_patients: alert = await monitor.process_pose_data(pose_data) if alert: alerts.append(alert) self.alert_history.append(alert) # Send notification await self.notification_system.send_alert_notification(alert) return alerts def get_system_status(self) -> Dict[str, Any]: """Get overall system status.""" return { "system_status": self.system_status, "total_patients": len(self.patient_monitors), "active_monitors": sum(1 for m in self.patient_monitors.values() if m.is_monitoring), "total_alerts": len(self.alert_history), "notification_stats": self.notification_system.get_notification_stats() } healthcare_system = HealthcareMonitoringSystem() # Add patients to system patients = [ ("patient_001", "room_101"), ("patient_002", "room_102"), ("patient_003", "room_103") ] for patient_id, room_id in patients: result = await healthcare_system.add_patient(patient_id, room_id) assert result is True # Simulate pose data updates for different rooms pose_updates = [ ("room_101", { "persons": [{"person_id": "patient_001", "confidence": 0.9, "activity": "fallen"}], "zone_summary": {"room_101": 1} }), ("room_102", { "persons": [{"person_id": "patient_002", "confidence": 0.8, "activity": "standing"}], "zone_summary": {"room_102": 1} }), ("room_103", { "persons": [], # No person detected "zone_summary": {"room_103": 0} }) ] all_alerts = [] for room_id, pose_data in pose_updates: alerts = await healthcare_system.process_pose_update(room_id, pose_data) all_alerts.extend(alerts) # This will fail initially # Should have processed all updates assert len(pose_updates) == 3 # Check system status system_status = healthcare_system.get_system_status() assert system_status["total_patients"] == 3 assert system_status["active_monitors"] == 3 assert system_status["system_status"] == "operational" # Should have generated some alerts if all_alerts: assert len(all_alerts) > 0 assert system_status["total_alerts"] > 0 @pytest.mark.asyncio async def test_healthcare_system_resilience_should_fail_initially(self): """Test healthcare system resilience - should fail initially.""" patient_monitor = MockPatientMonitor("patient_001", "room_101") notification_system = MockHealthcareNotificationSystem() await patient_monitor.start_monitoring() # Simulate system stress with rapid pose updates rapid_updates = 50 alerts_generated = [] for i in range(rapid_updates): # Alternate between normal and concerning pose data if i % 10 == 0: # Every 10th update is concerning pose_data = { "persons": [{"person_id": "patient_001", "confidence": 0.9, "activity": "fallen"}], "zone_summary": {"room_101": 1} } else: pose_data = { "persons": [{"person_id": "patient_001", "confidence": 0.85, "activity": "standing"}], "zone_summary": {"room_101": 1} } alert = await patient_monitor.process_pose_data(pose_data) if alert: alerts_generated.append(alert) await notification_system.send_alert_notification(alert) # This will fail initially # System should handle rapid updates gracefully stats = patient_monitor.get_monitoring_stats() assert stats["activity_samples"] == rapid_updates assert stats["is_monitoring"] is True # Should have generated some alerts but not excessive assert len(alerts_generated) <= rapid_updates / 5 # At most 20% alert rate notification_stats = notification_system.get_notification_stats() assert notification_stats["total_notifications"] >= len(alerts_generated)