This is not a "theoretical" project proposal. The code in this repository powers real hardware and controls real flight simulator environments based on live pilot biotelemetry.

• Captures real-time heart rate data from commercial smartwatches (Pixel Watch 2)
• Calculates Heart Rate Variability (HRV) in real-time using modern algorithms
• Detects pilot physiological states (stress, cognitive overload, complacency)
• Automatically adjusts flight simulator conditions via Microsoft Flight Simulator plugin
• Creates adaptive training environments that respond to pilot state

This project implements a biocybernetic loop for flight training. The system measures a pilot's real-time physiological response via HRV, analyzes their cognitive state, and dynamically adjusts the training environment to optimize learning outcomes.

┌─────────────────────────┐
│  Smartwatch (Worn)      │
│  - PPG Sensor           │ ──▶ Raw heart rate data
│  - CAPHRV App           │ ──▶ HRV calculation
│  - WiFi transmission    │
└─────────────────────────┘
            │
            ▼ (WiFi)
┌─────────────────────────┐
│  Flight Simulator PC    │
│  - MSFS Plugin          │ ──▶ Receives HRV data
│  - Weather Controller   │ ──▶ Modifies conditions
└─────────────────────────┘

Stress Inoculation Mode

• Detects when HRV indicates pilot complacency or under-stimulation
• Automatically introduces challenging conditions (crosswinds, turbulence)
• Maintains optimal training stress by forcing re-engagement

Adaptive Difficulty Mode

• Detects when HRV indicates excessive stress or cognitive overload
• Reduces environmental challenges automatically
• Allows trainee to stabilize and build confidence incrementally

Tested and verified hardware:

• Smartwatch: Pixel Watch 2 or compatible Wear OS device with PPG sensor

  • WiFi connectivity required

• Flight Simulator: PC running Microsoft Flight Simulator

  • MSFS 2020 or later

• Runs directly on Wear OS smartwatches
• Extracts raw photoplethysmogram (PPG) sensor data
• Processes signal peaks and calculates inter-beat intervals (IBI)
• Transmits real-time HRV metrics over Wi-Fi

• Exposes API for external control (receives triggers from CAPHRV application)
• Dynamically modifies weather parameters (wind speed, wind direction, turbulence intensity)

Contact me at andrew.buglione@flwgcap.us for assistance and detailed setup instructions.

This system is ready to deploy for researchers interested in adaptive training technology.

HRV measures the variation in time intervals between heartbeats. It is a well-established physiological indicator of stress levels, cognitive load, and autonomic nervous system state.

This COTS-based system provides objective, real-time physiological measurement to create a data-driven feedback loop for training optimization.

• Consumer-grade electrocardiogram / EKG integration
• Consumer-grade electroencephologram / EEG integration

• Real-world flight training integration
• Operational mission support (crew monitoring)

This work builds on established research in aviation medicine and biocybernetic systems:

• Cao et al. (2019). Heart rate variability and performance of commercial airline pilots during flight simulations. doi:10.3390/ijerph16020237

• Coste et al. (2025). A Comparative Study Between ECG- and PPG-Based Heart Rate Sensors for Heart Rate Variability Measurements. doi:10.3390/s25185745

• Stephens et al. (2018). Biocybernetic Adaptation Strategies: Machine awareness of human engagement for improved operational performance. doi:10.1007/978-3-319-91470-1_9

• Zhang et al. (2019). Motion Artifact Reduction for Wrist-Worn Photoplethysmograph Sensors Based on Different Wavelengths. doi:10.3390/s19030673

• Shaw & Harrell (2023). Integrating physiological monitoring systems in military aviation: a brief narrative review of its importance, opportunities, and risks. doi:10.1080/00140139.2023.2194592

Author: 2d Lt Andrew Buglione Organization: Civil Air Patrol Status: Fully-Working Prototype

Detailed setup instructions available upon request.

1. Install CAPHRV app on compatible Wear OS smartwatch
2. Configure WiFi connection between devices
3. Start the MSFS plugin: uv run caphrv.py

This project is a low-cost "biocybernetic" system that measures a pilot's heartbeats in real-time to optimize training. Using a smartwatch running custom software, the system measures cardiovascular indicators of cognitive focus and feeds the results to its Microsoft Flight Simulator plugin. The simulator then adjusts conditions such as turbulence to keep the pilot in an optimal learning state using biofeedback, avoiding both boredom and overstimulation. I am also exploring other types of heart and brain activity monitors to see how they could be used in real-world flights. These tools could give instructors and crews a clearer picture of how pilots respond under pressure.