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Sensor Networks for Aerospace Human-Machine Systems

Intelligent automation and trusted autonomy are being introduced in aerospace cyber-physical 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...

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Autores principales: Pongsakornsathien, Nichakorn, Lim, Yixiang, Gardi, Alessandro, Hilton, Samuel, Planke, Lars, Sabatini, Roberto, Kistan, Trevor, Ezer, Neta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6720637/
https://www.ncbi.nlm.nih.gov/pubmed/31398917
http://dx.doi.org/10.3390/s19163465
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author Pongsakornsathien, Nichakorn
Lim, Yixiang
Gardi, Alessandro
Hilton, Samuel
Planke, Lars
Sabatini, Roberto
Kistan, Trevor
Ezer, Neta
author_facet Pongsakornsathien, Nichakorn
Lim, Yixiang
Gardi, Alessandro
Hilton, Samuel
Planke, Lars
Sabatini, Roberto
Kistan, Trevor
Ezer, Neta
author_sort Pongsakornsathien, Nichakorn
collection PubMed
description Intelligent automation and trusted autonomy are being introduced in aerospace cyber-physical 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 (HMI(2)). 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 HMI(2) (CHMI(2)) 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 CHMI(2) 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.
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spelling pubmed-67206372019-09-10 Sensor Networks for Aerospace Human-Machine Systems Pongsakornsathien, Nichakorn Lim, Yixiang Gardi, Alessandro Hilton, Samuel Planke, Lars Sabatini, Roberto Kistan, Trevor Ezer, Neta Sensors (Basel) Article Intelligent automation and trusted autonomy are being introduced in aerospace cyber-physical 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 (HMI(2)). 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 HMI(2) (CHMI(2)) 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 CHMI(2) 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. MDPI 2019-08-08 /pmc/articles/PMC6720637/ /pubmed/31398917 http://dx.doi.org/10.3390/s19163465 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pongsakornsathien, Nichakorn
Lim, Yixiang
Gardi, Alessandro
Hilton, Samuel
Planke, Lars
Sabatini, Roberto
Kistan, Trevor
Ezer, Neta
Sensor Networks for Aerospace Human-Machine Systems
title Sensor Networks for Aerospace Human-Machine Systems
title_full Sensor Networks for Aerospace Human-Machine Systems
title_fullStr Sensor Networks for Aerospace Human-Machine Systems
title_full_unstemmed Sensor Networks for Aerospace Human-Machine Systems
title_short Sensor Networks for Aerospace Human-Machine Systems
title_sort sensor networks for aerospace human-machine systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6720637/
https://www.ncbi.nlm.nih.gov/pubmed/31398917
http://dx.doi.org/10.3390/s19163465
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