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Open AccessArticle

Adaptive Neuro-Fuzzy Fusion of Multi-Sensor Data for Monitoring a Pilot’s Workload Condition

1
College of Civil Aviation, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, China
2
Shanghai Aircraft Design & Research Institute, Commercial Aircraft Corporation of China, Ltd., Shanghai 201210, China
3
School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(16), 3629; https://doi.org/10.3390/s19163629
Received: 19 July 2019 / Revised: 13 August 2019 / Accepted: 16 August 2019 / Published: 20 August 2019
(This article belongs to the Collection Multi-Sensor Information Fusion)
To realize an early warning of unbalanced workload in the aircraft cockpit, it is required to monitor the pilot’s real-time workload condition. For the purpose of building the mapping relationship from physiological and flight data to workload, a multi-source data fusion model is proposed based on a fuzzy neural network, mainly structured using a principal components extraction layer, fuzzification layer, fuzzy rules matching layer, and normalization layer. Aiming at the high coupling characteristic variables contributing to workload, principal component analysis reconstructs the feature data by reducing its dimension. Considering the uncertainty for a single variable to reflect overall workload, a fuzzy membership function and fuzzy control rules are defined to abstract the inference process. An error feedforward algorithm based on gradient descent is utilized for parameter learning. Convergence speed and accuracy can be adjusted by controlling the gradient descent rate and error tolerance threshold. Combined with takeoff and initial climbing tasks of a Boeing 737–800 aircraft, crucial performance indicators—including pitch angle, heading, and airspeed—as well as physiological indicators—including electrocardiogram (ECG), respiration, and eye movements—were featured. The mapping relationship between multi-source data and the comprehensive workload level synthesized using the NASA task load index was established. Experimental results revealed that the predicted workload corresponding to different flight phases and difficulty levels showed clear distinctions, thereby proving the validity of data fusion. View Full-Text
Keywords: aircraft pilot; workload; multi-source data fusion; fuzzy neural network; principal component analysis; parameter learning aircraft pilot; workload; multi-source data fusion; fuzzy neural network; principal component analysis; parameter learning
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Zhang, X.; Sun, Y.; Qiu, Z.; Bao, J.; Zhang, Y. Adaptive Neuro-Fuzzy Fusion of Multi-Sensor Data for Monitoring a Pilot’s Workload Condition. Sensors 2019, 19, 3629.

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