AI-Powered HMI & Input

// AI-Powered Input Multiplexing System
import asyncio
import numpy as np
from typing import Dict, List, Optional, Tuple
from collections import deque
from dataclasses import dataclass
from enum import Enum

@dataclass
class InputEvent:
    device_id: str
    event_type: str
    data: Dict
    timestamp: float
    confidence: float
    priority: int

class InputModalityType(Enum):
    GESTURE = "gesture"
    GAZE = "gaze"
    VOICE = "voice"
    TOUCH = "touch"
    BIOMETRIC = "biometric"
    TRADITIONAL = "traditional"
    BCI = "brain_computer"

class IntelligentInputMultiplexer:
    def __init__(self, config: Dict):
        # Input processors voor verschillende modaliteiten
        self.gesture_processor = GestureRecognitionProcessor()
        self.gaze_tracker = EyeTrackingProcessor()
        self.voice_processor = VoiceCommandProcessor()
        self.biometric_processor = BiometricAuthProcessor()

        # AI modules
        self.context_analyzer = ContextAnalysisEngine()
        self.intent_recognizer = IntentRecognitionModel()
        self.predictive_router = PredictiveRoutingEngine()

        # Real-time processing
        self.input_queue = asyncio.Queue(maxsize=1000)
        self.event_buffer = deque(maxlen=50)
        self.active_sessions = {}

        # Learning en adaptation
        self.user_profile = UserProfileManager()
        self.adaptation_engine = AdaptationEngine()

    async def process_input_stream(self):
        """Main input processing loop"""
        while True:
            try:
                # Get next input event
                event = await self.input_queue.get()

                # Context analysis
                context = await self.analyze_context(event)

                # Intent recognition met multi-modal fusion
                intent = await self.recognize_intent(event, context)

                # Predictive routing en priority assignment
                routing_decision = await self.route_input(event, intent, context)

                # Execute input action
                await self.execute_input_action(routing_decision)

                # Update learning models
                await self.update_learning_models(event, intent, routing_decision)

                self.event_buffer.append(event)

            except Exception as e:
                await self.handle_error(e, event)

    async def analyze_context(self, event: InputEvent) -> Dict:
        """Analyze current context voor intelligent input processing"""
        context = {
            'user_state': await self.get_user_state(),
            'system_state': await self.get_system_state(),
            'environmental_factors': await self.get_environmental_context(),
            'task_context': await self.get_current_task_context(),
            'temporal_patterns': self.analyze_temporal_patterns()
        }

        # AI-based context enrichment
        enriched_context = await self.context_analyzer.enrich(context, event)

        return enriched_context

    async def recognize_intent(self, event: InputEvent, context: Dict) -> Dict:
        """Multi-modal intent recognition met confidence scoring"""
        # Extract features per modaliteit
        gesture_features = self.gesture_processor.extract_features(event)
        gaze_features = self.gaze_tracker.extract_features(event)
        voice_features = self.voice_processor.extract_features(event)

        # Fusion van multi-modal features
        fused_features = self.fuse_multimodal_features(
            gesture_features, gaze_features, voice_features, context
        )

        # Intent classification
        intent_distribution = await self.intent_recognizer.predict(
            fused_features, context
        )

        # Confidence-based filtering
        filtered_intents = self.filter_by_confidence(
            intent_distribution, threshold=0.7
        )

        return {
            'primary_intent': filtered_intents[0] if filtered_intents else None,
            'alternative_intents': filtered_intents[1:3],
            'confidence_scores': intent_distribution,
            'ambiguity_score': self.calculate_ambiguity(intent_distribution)
        }

# Advanced Gesture Recognition System
class GestureRecognitionProcessor:
    def __init__(self):
        # Multi-camera hand tracking
        self.hand_tracker = MediaPipeHandTracker()
        self.pose_estimator = PoseEstimationModel()

        # Temporal gesture recognition
        self.gesture_classifier = TemporalGestureClassifier()
        self.gesture_buffer = TemporalBuffer(size=30)  # 1 second at 30fps

        # Custom gesture learning
        self.custom_gesture_learner = OneShotGestureLearner()

    def process_frame(self, frame: np.ndarray, timestamp: float) -> Dict:
        # Hand landmark detection
        hand_landmarks = self.hand_tracker.process(frame)

        if hand_landmarks:
            # Feature extraction
            features = self.extract_gesture_features(hand_landmarks)

            # Temporal buffering
            self.gesture_buffer.add(features, timestamp)

            # Gesture classification als we genoeg frames hebben
            if len(self.gesture_buffer) >= self.min_sequence_length:
                gesture_prediction = self.gesture_classifier.predict(
                    self.gesture_buffer.get_sequence()
                )

                return {
                    'gesture_class': gesture_prediction['class'],
                    'confidence': gesture_prediction['confidence'],
                    'hand_landmarks': hand_landmarks,
                    'gesture_velocity': self.calculate_gesture_velocity(),
                    'gesture_start_time': gesture_prediction['start_time'],
                    'gesture_duration': gesture_prediction['duration']
                }

        return {'gesture_class': None, 'confidence': 0.0}

    def extract_gesture_features(self, landmarks: np.ndarray) -> np.ndarray:
        """Extract meaningful features voor gesture recognition"""
        # Normalize landmarks relative to hand center
        hand_center = np.mean(landmarks, axis=0)
        normalized_landmarks = landmarks - hand_center

        # Calculate relative distances en angles
        finger_angles = self.calculate_finger_angles(normalized_landmarks)
        palm_orientation = self.calculate_palm_orientation(normalized_landmarks)
        finger_extensions = self.calculate_finger_extensions(normalized_landmarks)

        # Temporal features (velocity, acceleration)
        if len(self.gesture_buffer) > 0:
            velocity = self.calculate_velocity(landmarks)
            acceleration = self.calculate_acceleration(landmarks)
        else:
            velocity = np.zeros_like(landmarks)
            acceleration = np.zeros_like(landmarks)

        # Combine all features
        features = np.concatenate([
            normalized_landmarks.flatten(),
            finger_angles,
            palm_orientation,
            finger_extensions,
            velocity.flatten(),
            acceleration.flatten()
        ])

        return features

# Biometric Authentication & User State Monitoring
class BiometricAuthProcessor:
    def __init__(self):
        # Multi-modal biometrics
        self.face_recognizer = FaceRecognitionModel()
        self.voice_authenticator = VoiceAuthenticationModel()
        self.heart_rate_monitor = HeartRateMonitor()
        self.stress_detector = StressDetectionModel()

        # Continuous authentication
        self.auth_confidence_tracker = ContinuousAuthTracker()
        self.anomaly_detector = UserBehaviorAnomalyDetector()

    async def authenticate_user(self, biometric_data: Dict) -> Dict:
        """Multi-modal biometric authentication"""
        auth_results = {}

        # Face recognition
        if 'face_image' in biometric_data:
            face_result = await self.face_recognizer.authenticate(
                biometric_data['face_image']
            )
            auth_results['face'] = face_result

        # Voice authentication
        if 'voice_sample' in biometric_data:
            voice_result = await self.voice_authenticator.authenticate(
                biometric_data['voice_sample']
            )
            auth_results['voice'] = voice_result

        # Behavioral biometrics
        if 'interaction_pattern' in biometric_data:
            behavior_score = self.analyze_interaction_pattern(
                biometric_data['interaction_pattern']
            )
            auth_results['behavior'] = behavior_score

        # Fusion van authentication scores
        combined_confidence = self.fuse_auth_scores(auth_results)

        # Continuous authentication update
        self.auth_confidence_tracker.update(combined_confidence)

        return {
            'authenticated': combined_confidence > self.auth_threshold,
            'confidence': combined_confidence,
            'auth_methods': auth_results,
            'continuous_confidence': self.auth_confidence_tracker.get_confidence(),
            'anomaly_detected': self.anomaly_detector.check_anomaly(biometric_data)
        }

    def monitor_user_state(self, sensor_data: Dict) -> Dict:
        """Continuous user state monitoring voor adaptive interfaces"""
        user_state = {
            'stress_level': self.stress_detector.predict(sensor_data),
            'attention_level': self.calculate_attention_level(sensor_data),
            'fatigue_level': self.calculate_fatigue_level(sensor_data),
            'cognitive_load': self.estimate_cognitive_load(sensor_data)
        }

        # Adaptive interface recommendations
        adaptations = self.recommend_interface_adaptations(user_state)

        return {
            'user_state': user_state,
            'recommended_adaptations': adaptations,
            'intervention_needed': self.check_intervention_needed(user_state)
        }