This repository contains a comprehensive SysML (Systems Modeling Language) model for an Adaptive Cognitive Cockpit Sensing System. The system is designed to continuously monitor pilot cognitive state, assess workload and attention, and dynamically adapt cockpit interfaces to optimize human-machine interaction, enhance safety, and reduce cognitive overload.

The Adaptive Cognitive Cockpit Sensing System represents a next-generation human-centered design approach for aviation cockpits. It leverages multimodal sensing, artificial intelligence, and adaptive interfaces to create a responsive environment that adjusts to the pilot's cognitive state in real-time.

• Multimodal Sensing: Integrates visual (eye tracking), biometric (heart rate, GSR), environmental (light, noise), and audio (voice analysis) sensors
• Cognitive Load Assessment: Real-time evaluation of pilot cognitive workload with 85%+ accuracy
• Adaptive Interfaces: Dynamic adjustment of display complexity, layout, and information presentation
• Intelligent Alert Management: Context-aware alert timing, modality selection, and prioritization
• Machine Learning: Continuous learning of individual pilot patterns and preferences
• Safety-First Design: Fail-safe operation with pilot override capabilities

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├── README.md                           # This file
├── sysml/
│   ├── diagrams/                       # PlantUML SysML diagrams
│   │   ├── 01-block-definition-diagram.puml
│   │   ├── 02-internal-block-diagram.puml
│   │   ├── 03-requirements-diagram.puml
│   │   ├── 04-activity-diagram.puml
│   │   ├── 05-state-machine-diagram.puml
│   │   └── 06-use-case-diagram.puml
│   └── models/                         # Model specifications (future)
└── docs/                               # Additional documentation (future)

File: sysml/diagrams/01-block-definition-diagram.puml

Defines the system architecture and component hierarchy:

• Sensing Subsystem: Visual, biometric, environmental, and audio sensors
• Cognitive Processing Subsystem: AI/ML engine, data fusion, cognitive load analyzer
• Adaptation Subsystem: Decision engine, interface adapter, alert manager
• Output Subsystem: Visual displays, audio output, haptic feedback
• Data Storage Subsystem: Operational data storage and retrieval

The BDD shows the structural relationships and composition hierarchy of all system components.

File: sysml/diagrams/02-internal-block-diagram.puml

Illustrates internal component connections and data flows:

• Sensor data flows from sensing to cognitive processing
• Processed data flows to adaptation subsystem
• Adaptation commands control output modalities
• Feedback loops enable continuous learning
• Historical data supports predictive modeling

Key interfaces defined:

• SensorDataInterface: Raw sensor readings
• ProcessedDataInterface: Fused and analyzed data
• AdaptationCommandInterface: Interface modification commands
• OutputControlInterface: Display/audio/haptic parameters
• FeedbackInterface: Performance and effectiveness metrics

File: sysml/diagrams/03-requirements-diagram.puml

Comprehensive requirements hierarchy covering:

Functional Requirements (FR):

• REQ-FR-001: Continuous pilot state monitoring
• REQ-FR-002: Multimodal sensing
• REQ-FR-003: Real-time processing (< 100ms latency)
• REQ-FR-004: Cognitive load assessment (85% accuracy)
• REQ-FR-005: Dynamic interface adaptation
• REQ-FR-006: Intelligent alert management
• REQ-FR-007: Learning capability
• REQ-FR-008: Multimodal data fusion

Performance Requirements (PR):

• REQ-PR-001: Adaptation response time < 200ms
• REQ-PR-002: Sensor sampling rates (60-100 Hz)
• REQ-PR-003: System availability (99.9% uptime)
• REQ-PR-004: Prediction accuracy (80% at 5 seconds ahead)
• REQ-PR-005: Concurrent multimodal processing

Safety Requirements (SAF):

• REQ-SAF-001: Fail-safe operation (critical)
• REQ-SAF-002: Non-intrusive monitoring (critical)
• REQ-SAF-003: Alert fatigue prevention
• REQ-SAF-004: Privacy protection (critical)
• REQ-SAF-005: Pilot override capability (critical)

Interface Requirements (IR):

• REQ-IR-001: Visual display adaptation
• REQ-IR-002: Multimodal output
• REQ-IR-003: Contextual alert presentation
• REQ-IR-004: Information filtering

Technical Requirements (TR):

• REQ-TR-001: Standard sensor integration
• REQ-TR-002: Data storage (30 days minimum)
• REQ-TR-003: Model update support
• REQ-TR-004: Per-pilot calibration
• REQ-TR-005: Maintainability and diagnostics

File: sysml/diagrams/04-activity-diagram.puml

Describes the dynamic behavior and process flows:

Sensing Phase:

1. Parallel capture from all sensor modalities
2. Data synchronization and validation
3. Quality checks and error handling

Cognitive Processing Phase:

1. Multimodal data fusion
2. Parallel analysis of gaze, physiology, and task demands
3. Cognitive load calculation
4. ML-based state prediction

Adaptation Phase:

1. Context analysis and option evaluation
2. Strategy selection with conflict resolution
3. Parallel execution across visual, audio, and haptic channels

Feedback & Learning:

1. Performance monitoring
2. Effectiveness calculation
3. Model retraining and optimization

The activity diagram shows the continuous closed-loop process running at 50-100 Hz.

File: sysml/diagrams/05-state-machine-diagram.puml

Defines system states and transitions:

Main States:

• Power Off: System inactive
• Initialization: Sensor calibration, model loading, profile loading
• Operational: Primary operating state with substates:
  • Monitoring: Normal cognitive load, standard interface
  • Adaptive Mode: Active adaptation with substates:
    • Evaluating Adaptation
    • Executing Adaptation
    • High Cognitive Load (> 80%)
    • Critical Alert Active
  • Learning Mode: Model training and optimization (off-duty)
• Standby: Sensors paused, state preserved
• Error Handling: Diagnostics, safe mode, recovery
• Maintenance: Configuration and calibration updates

Key Transitions:

• Automatic transition to safe mode on failures
• Cognitive load thresholds trigger adaptations
• Learning scheduled during off-duty periods
• Pilot override returns to monitoring state

File: sysml/diagrams/06-use-case-diagram.puml

Shows interactions between actors and system functions:

Actors:

• Pilot: Primary user, receives adaptations, can override
• Co-Pilot: Secondary user, monitors system
• System Administrator: Configuration and management
• Maintenance Technician: Calibration and diagnostics
• Safety Officer: Audits and reviews
• External Systems: Flight Management System, Aircraft Sensors

Primary Use Cases:

• Monitor Pilot State
• Assess Cognitive Load
• Adapt Interface
• Manage Alerts
• Provide Multimodal Feedback
• Learn Pilot Patterns
• Handle Emergency Situations

Supporting Use Cases:

• Configuration & maintenance functions
• Safety & override functions
• Data management & privacy enforcement

Adaptive Cognitive Cockpit Sensing System
├── Sensing Subsystem
│   ├── Visual Sensors (eye tracking, gaze detection)
│   ├── Biometric Sensors (heart rate, GSR, temperature)
│   ├── Environmental Sensors (light, temperature, noise)
│   └── Audio Sensors (voice commands, vocal stress)
├── Cognitive Processing Subsystem
│   ├── Data Fusion Module (multimodal integration)
│   ├── AI/ML Engine (prediction, learning)
│   └── Cognitive Load Analyzer (workload assessment)
├── Adaptation Subsystem
│   ├── Decision Engine (strategy selection)
│   ├── Interface Adapter (display modification)
│   └── Alert Manager (modality and timing)
├── Output Subsystem
│   ├── Visual Display (HUD, panels)
│   ├── Audio Output (alerts, feedback)
│   └── Haptic Feedback (tactile notifications)
└── Data Storage Subsystem
    └── Operational data, historical patterns, pilot profiles

1. Sensing → Processing: Raw sensor data flows continuously at 50-100 Hz
2. Processing → Adaptation: Cognitive assessment triggers adaptation decisions
3. Adaptation → Output: Commands modify interface parameters
4. Output → Feedback: User interactions provide performance metrics
5. Feedback → Learning: Effectiveness data updates ML models

• Eye Tracking: High-frequency gaze tracking and attention mapping
• Biometric Monitoring: Non-intrusive physiological measurement
• Voice Analysis: Vocal stress and fatigue detection
• Environmental Sensing: Cockpit condition monitoring

• Sensor Fusion: Kalman filtering and multimodal integration
• Cognitive Models: Workload estimation and attention prediction
• Adaptive Learning: Online learning and personalization
• Decision Systems: Rule-based and learned adaptation strategies

• Visual Adaptation: Dynamic layout, complexity reduction, color schemes
• Alert Modality: Context-appropriate channel selection (visual/audio/haptic)
• Information Filtering: Priority-based content management
• Timing Optimization: Load-aware notification scheduling

• System defaults to standard interface on any failure
• No adaptation occurs during critical flight phases without pilot confirmation
• All safety-critical alerts bypass adaptive filtering
• Redundant sensor paths for critical measurements

• Pilot can override any adaptation at any time
• Manual mode available for complete system bypass
• Transparent adaptation reasoning available on request
• Training mode for familiarization without operational impact

• Biometric data encrypted at rest and in transit
• Access control for sensitive pilot performance data
• Anonymized data for system improvement research
• Compliance with aviation privacy regulations

During approach in poor weather:

1. System detects elevated heart rate and reduced blink rate
2. Cognitive load assessed at 85% (high)
3. Interface simplified: non-critical displays hidden
4. Non-urgent alerts deferred until after landing
5. Critical alerts presented via haptic + visual (redundant)
6. Post-landing: interface restored, deferred alerts presented

During long-haul cruise:

1. System detects prolonged fixations and reduced saccades
2. Physiological markers indicate fatigue
3. Gentle audio alert suggests rest break
4. Co-pilot notified discreetly
5. Brightness increased, contrast enhanced for alertness
6. System monitors recovery and adjusts accordingly

Engine failure scenario:

1. Critical alert detected from aircraft systems
2. System immediately presents multimodal alert
3. Interface switches to emergency mode
4. Relevant checklists and systems highlighted
5. Non-essential information hidden
6. Pilot focus tracked to ensure procedure following
7. Adaptation suspended until situation resolved

Regular pilot on familiar route:

1. System loads pilot profile with learned preferences
2. Adapts baseline interface to pilot's preferred layout
3. Adjusts alert thresholds based on historical responses
4. Recognizes pilot's attention patterns during cruise
5. Proactively adjusts before cognitive load peaks
6. Learns from pilot overrides to refine future adaptations

The diagrams are created using PlantUML syntax. To view them:

1. Visit PlantUML Online Server
2. Copy the contents of any .puml file
3. Paste and render

1. Install PlantUML: brew install plantuml (macOS) or download from plantuml.com
2. Generate diagrams:

   plantuml sysml/diagrams/*.puml

3. Open generated PNG/SVG files

1. Install "PlantUML" extension in VS Code
2. Open any .puml file
3. Press Alt+D to preview

docker run -v $(pwd):/data plantuml/plantuml sysml/diagrams/*.puml

• Contextual AI: Enhanced context awareness using flight phase and weather data
• Collaborative Adaptation: Coordinated adaptation for pilot and co-pilot
• Predictive Alerting: Anticipatory warnings based on trajectory analysis
• AR Integration: Augmented reality overlay for enhanced situation awareness
• Neurophysiological Sensing: EEG-based direct cognitive load measurement

• Multi-Crew Coordination: Adaptation strategies for multi-pilot operations
• Cross-Platform Learning: Transfer learning across aircraft types
• Explainable AI: Transparent reasoning for adaptation decisions
• Certification Framework: Regulatory pathways for adaptive systems
• Human Factors Validation: Longitudinal studies of effectiveness

This model is designed with consideration for:

• DO-178C: Software considerations in airborne systems
• DO-254: Hardware design assurance
• RTCA/EUROCAE: Aviation standards
• ISO 9241: Ergonomics of human-system interaction
• SAE ARP4754A: Development of civil aircraft systems
• MIL-STD-1472: Human engineering design criteria

• Wickens, C. D. (2008). Multiple resources and mental workload. Human Factors
• Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse
• Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems
• Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX

• Multimodal sensor fusion algorithms
• Real-time cognitive load assessment
• Adaptive user interface design
• Human-centered automation
• Machine learning for personalization

This is a model specification repository. Contributions welcome for:

• Additional diagrams (sequence, parametric, etc.)
• Refined requirements
• Implementation specifications
• Validation scenarios
• Documentation improvements

This SysML model specification is provided for research and development purposes.

For questions about this model or collaboration opportunities, please open an issue in this repository.

Document Version: 1.0 Last Updated: 2025-12-17 Model Maturity: Conceptual Design Phase