Prediction of helicopter simulator sickness
- Tennessee Univ., Knoxville, TN (USA). Dept. of Electrical and Computer Engineering
- Oak Ridge National Lab., TN (USA)
Machine learning methods from artificial intelligence are used to identify information in sampled accelerometer signals and associative behavioral patterns which correlates pilot simulator sickness with helicopter simulator dynamics. These simulators are used to train pilots in fundamental procedures, tactics, and response to emergency conditions. Simulator sickness induced by these systems represents a risk factor to both the pilot and manufacturer. Simulator sickness symptoms are closely aligned with those of motion sickness. Previous studies have been performed by behavioral psychologists using information gathered with surveys and motor skills performance measures; however, the results are constrained by the limited information which is accessible in this manner. In this work, accelerometers were installed in the simulator cab, enabling a complete record of flight dynamics and the pilot's control response as a function of time. Given the results of performance measures administered to detect simulator sickness symptoms, the problem was then to find functions of the recorded data which could be used to help predict the simulator sickness level and susceptibility. Methods based upon inductive inference were used, which yield decision trees whose leaves indicate the degree of simulator-induced sickness. The long-term goal is to develop a gauge'' which can provide an on-line prediction of simulator sickness level, given a pilot's associative behavioral patterns (learned expectations). This will allow informed decisions to be made on when to terminate a hop and provide an effective basis for determining training and flight restrictions placed upon the pilot after simulator use. 6 refs., 6 figs.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- DOE/ER; National Science Foundation (NSF)
- DOE Contract Number:
- AC05-84OR21400
- OSTI ID:
- 6759880
- Report Number(s):
- CONF-901209-2; ON: DE90016530
- Resource Relation:
- Conference: 29. IEEE conference on decision and control, Honolulu, HI (USA), 5-7 Dec 1990
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
42 ENGINEERING
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
HELICOPTERS
NAUSEA
FORECASTING
SIMULATORS
OPERATION
ACCELEROMETERS
ARTIFICIAL INTELLIGENCE
BEHAVIOR
COMPUTER ARCHITECTURE
DATA ACQUISITION SYSTEMS
ENGINEERED SAFETY SYSTEMS
FLOWSHEETS
HUMAN FACTORS ENGINEERING
LEARNING
MEASURING METHODS
PERSONNEL
RANDOMNESS
REAL TIME SYSTEMS
SAMPLING
SENSITIVITY
VULNERABILITY
AIRCRAFT
ANALOG SYSTEMS
DIAGRAMS
ENGINEERING
FUNCTIONAL MODELS
MEASURING INSTRUMENTS
SYMPTOMS
550100* - Behavioral Biology
420200 - Engineering- Facilities
Equipment
& Techniques
990200 - Mathematics & Computers