TY - GEN
T1 - Eye-tracking sensors for adaptive aerospace human-machine interfaces and interactions
AU - Lim, Yixiang
AU - Gardi, Alessandro
AU - Ezer, Neta
AU - Kistan, Trevor
AU - Sabatini, Roberto
N1 - Funding Information:
ACKNOWLEDGMENT The authors wish to thank and acknowledge THALES ATM Australia and Northrop Grumman Systems Corporation for separately supporting different aspects of this work under the collaborative research projects 0200315666, 0200316323 and 0200317164 respectively.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/31
Y1 - 2018/8/31
N2 - Whereas current aerospace Human-Machine Interfaces and Interactions (HMI2) are mostly static in their behaviour/appearance and require direct input from human operators, innovative HMI2 concepts are being proposed which allow for multimodal interactions or sense the functional state of human operators and dynamically adapt the level of automation. In particular, to facilitate trust between the human and machine, such systems need sensors that can reliably detect changes in the operator state. However, a number of environmental factors can affect the sensor's accuracy and precision. When used together with other novel sensors, eye tracking hasa significant potential to enhance the adaptiveness of aerospace HMI2. This paper presents the activities carried out to quantify the uncertainty associated with eye tracking equipment available in the Avionics and Air Traffic Management (ATM) systems laboratory of RMIT University, whichis being used to support the development of Cognitive HMI2. The presented methodology is used to characterise its measurement uncertainty based on a number of considerations, including the calibration error as well as gaze angle in static and dynamic conditions. The uncertainty associated with the eye tracker is used for error budgeting of a Cognitive HMI2 system which employs fuzzy logics to infer operator cognitive states based on eye tracking inputs.
AB - Whereas current aerospace Human-Machine Interfaces and Interactions (HMI2) are mostly static in their behaviour/appearance and require direct input from human operators, innovative HMI2 concepts are being proposed which allow for multimodal interactions or sense the functional state of human operators and dynamically adapt the level of automation. In particular, to facilitate trust between the human and machine, such systems need sensors that can reliably detect changes in the operator state. However, a number of environmental factors can affect the sensor's accuracy and precision. When used together with other novel sensors, eye tracking hasa significant potential to enhance the adaptiveness of aerospace HMI2. This paper presents the activities carried out to quantify the uncertainty associated with eye tracking equipment available in the Avionics and Air Traffic Management (ATM) systems laboratory of RMIT University, whichis being used to support the development of Cognitive HMI2. The presented methodology is used to characterise its measurement uncertainty based on a number of considerations, including the calibration error as well as gaze angle in static and dynamic conditions. The uncertainty associated with the eye tracker is used for error budgeting of a Cognitive HMI2 system which employs fuzzy logics to infer operator cognitive states based on eye tracking inputs.
KW - Eye tracking
KW - Human Factors Engineering
KW - Human-Machine Interfaces and Interactions (HMI2)
KW - Uncertainty analysis
UR - http://www.scopus.com/inward/record.url?scp=85053900993&partnerID=8YFLogxK
U2 - 10.1109/MetroAeroSpace.2018.8453509
DO - 10.1109/MetroAeroSpace.2018.8453509
M3 - Conference contribution
AN - SCOPUS:85053900993
SN - 9781538624746
T3 - 5th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2018 - Proceedings
SP - 311
EP - 316
BT - 5th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2018
Y2 - 20 June 2018 through 22 June 2018
ER -