Authors:
Susana M. Vieira
1
;
Alexandra Moutinho
1
;
Margarida Solas
1
;
José F. Loureiro
1
;
Maria B. Silva
1
;
Sara Zorro
2
;
Luís Patrão
3
;
Joaquim Gabriel
4
and
Jorge Silva
2
Affiliations:
1
Universidade de Lisboa, Portugal
;
2
Universidade de Lisboa and Universidade da Beira Interior, Portugal
;
3
Universidade da Beira Interior, Portugal
;
4
Universidade do Porto, Portugal
Keyword(s):
Light-sport Aviation, Classification, Prediction, Neural Networks Model, Decision Support System.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Business Analytics
;
Cardiovascular Technologies
;
Computational Intelligence
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Engineering Applications
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge-Based Systems
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
Symbolic Systems
Abstract:
Several applications require humans to be in high-altitude environments, whether for recreational purposes,
like mountaineering or light sport aviation, or for labour, as miners. Although in these conditions the monitoring
of physiological variables is, per se, of interest, the direct correlation of these variables with altitude itself
is not usually explored towards the development of decision-support systems and/or critical event alarms. This
paper proposes two neural networks approaches to assess and explore this correlation. One, based on dynamic
SISO models, estimates physiological variables using the aircraft pressure altitude as input. A second approach
uses static MISO networks to classify the flight stage (and therefore the altitude variation) from physiological
variables. Both models were developed and validated using real data acquired in hypobaric chamber tests
simulating a real flight. The good results obtained indicate the viability of the approach.