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Sensor Networks for Aerospace Human-Machine Systems.

Nichakorn Pongsakornsathien1, Yixiang Lim1, Alessandro Gardi1

  • 1RMIT University-School of Engineering, Bundoora, VIC 3083, Australia.

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|August 11, 2019
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Summary
This summary is machine-generated.

Intelligent automation in aerospace can adapt to pilot cognitive states using sensor networks. This enhances human-machine interactions for safer, more effective aerospace operations.

Keywords:
cognitive cyberneticscognitive stateshuman-machine systemmental workloadneurophysiologyphysiological response

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Area of Science:

  • Aerospace Engineering
  • Human-Computer Interaction
  • Neuroscience

Background:

  • Intelligent automation and autonomy are increasingly integrated into aerospace cyber-physical systems.
  • Human-machine collaboration requires systems to monitor and adapt to operator cognitive states for optimal performance.
  • Neurophysiological monitoring via sensors offers a reliable method for assessing operator cognitive states.

Purpose of the Study:

  • To review advances in sensor networks for Cognitive Human-Machine Interfaces and Interactions (CHMI²) in aerospace.
  • To discuss neurophysiological measurements and their link to cognitive states.
  • To present data analysis techniques for accurate cognitive state estimation.

Main Methods:

  • Utilizing wearable and remote sensors for real-time neurophysiological data collection.
  • Employing machine learning and statistical inference for data analysis.
  • Characterizing sensor performance and analyzing uncertainty propagation in machine learning models.

Main Results:

  • Sensor networks offer advantages for real-time, synchronized data collection in aerospace systems.
  • Machine learning effectively processes neurophysiological and operational data for cognitive state estimation.
  • Proper sensor selection and integration are crucial for effective human-machine systems.

Conclusions:

  • Sensor networks and Cognitive HMI² are vital for advancing aerospace operations.
  • Accurate cognitive state monitoring enhances human-machine system effectiveness.
  • This approach supports applications like Air Traffic Management and Unmanned Aircraft Systems operations.