Sensor networks for aerospace human-machine systems

Nichakorn Pongsakornsathien, Yixiang Lim, Alessandro Gardi, Samuel Hilton, Lars Planke, Roberto Sabatini, Trevor Kistan, Neta Ezer

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

Intelligent automation and trusted autonomy are being introduced in aerospace cyberphysical systems to support diverse tasks including data processing, decision-making, information sharing and mission execution. Due to the increasing level of integration/collaboration between humans and automation in these tasks, the operational performance of closed-loop human-machine systems can be enhanced when the machine monitors the operator’s cognitive states and adapts to them in order to maximise the effectiveness of the Human-Machine Interfaces and Interactions (HMI2). Technological developments have led to neurophysiological observations becoming a reliable methodology to evaluate the human operator’s states using a variety of wearable and remote sensors. The adoption of sensor networks can be seen as an evolution of this approach, as there are notable advantages if these sensors collect and exchange data in real-time, while their operation is controlled remotely and synchronised. This paper discusses recent advances in sensor networks for aerospace cyber-physical systems, focusing on Cognitive HMI2 (CHMI2) implementations. The key neurophysiological measurements used in this context and their relationship with the operator’s cognitive states are discussed. Suitable data analysis techniques based on machine learning and statistical inference are also presented, as these techniques allow processing both neurophysiological and operational data to obtain accurate cognitive state estimations. Lastly, to support the development of sensor networks for CHMI2 applications, the paper addresses the performance characterisation of various state-of-the-art sensors and the propagation of measurement uncertainties through a machine learning-based inference engine. Results show that a proper sensor selection and integration can support the implementation of effective human-machine systems for various challenging aerospace applications, including Air Traffic Management (ATM), commercial airliner Single-Pilot Operations (SIPO), one-to-many Unmanned Aircraft Systems (UAS), and space operations management.

Original languageBritish English
Article number3465
JournalSensors (Switzerland)
Volume19
Issue number16
DOIs
StatePublished - 2 Aug 2019

Keywords

  • Cognitive cybernetics
  • Cognitive states
  • Human-machine system
  • Mental workload
  • Neurophysiology
  • Physiological response

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