Development of a neural net paradigm that predicts simulator sickness

Allgood, G O

doi:10.2172/10172277

Title: Development of a neural net paradigm that predicts simulator sickness

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Abstract

A disease exists that affects pilots and aircrew members who use Navy Operational Flight Training Systems. This malady, commonly referred to as simulator sickness and whose symptomatology closely aligns with that of motion sickness, can compromise the use of these systems because of a reduced utilization factor, negative transfer of training, and reduction in combat readiness. A report is submitted that develops an artificial neural network (ANN) and behavioral model that predicts the onset and level of simulator sickness in the pilots and aircrews who sue these systems. It is proposed that the paradigm could be implemented in real time as a biofeedback monitor to reduce the risk to users of these systems. The model captures the neurophysiological impact of use (human-machine interaction) by developing a structure that maps the associative and nonassociative behavioral patterns (learned expectations) and vestibular (otolith and semicircular canals of the inner ear) and tactile interaction, derived from system acceleration profiles, onto an abstract space that predicts simulator sickness for a given training flight.

Authors:: Allgood, G O

Publication Date:: Mon Mar 01 00:00:00 EST 1993

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

OSTI Identifier:: 10172277

Report Number(s):: ORNL/TM-12254
ON: DE93017242; TRN: AHC29305%%27

DOE Contract Number:: AC05-84OR21400

Resource Type:: Technical Report

Resource Relation:: Other Information: PBD: Mar 1993

Country of Publication:: United States

Language:: English

Subject:: 59 BASIC BIOLOGICAL SCIENCES; MILITARY PERSONNEL; BIOLOGICAL STRESS; NERVOUS SYSTEM DISEASES; MATHEMATICAL MODELS; TRAINING; SIMULATORS; MAN-MACHINE SYSTEMS; FORECASTING; NEURAL NETWORKS; REAL TIME SYSTEMS; BEHAVIOR; AIRCRAFT; 550900; PATHOLOGY

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                    Allgood, G O. Development of a neural net paradigm that predicts simulator sickness.  United States: N. p., 1993. 
        Web.  doi:10.2172/10172277.

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                    Allgood, G O. Development of a neural net paradigm that predicts simulator sickness.  United States.  https://doi.org/10.2172/10172277

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                    Allgood, G O. 1993.  
        "Development of a neural net paradigm that predicts simulator sickness".  United States.  https://doi.org/10.2172/10172277.  https://www.osti.gov/servlets/purl/10172277.

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@article{osti_10172277,

  title        = {Development of a neural net paradigm that predicts simulator sickness},

  author       = {Allgood, G O},

  abstractNote = {A disease exists that affects pilots and aircrew members who use Navy Operational Flight Training Systems. This malady, commonly referred to as simulator sickness and whose symptomatology closely aligns with that of motion sickness, can compromise the use of these systems because of a reduced utilization factor, negative transfer of training, and reduction in combat readiness. A report is submitted that develops an artificial neural network (ANN) and behavioral model that predicts the onset and level of simulator sickness in the pilots and aircrews who sue these systems. It is proposed that the paradigm could be implemented in real time as a biofeedback monitor to reduce the risk to users of these systems. The model captures the neurophysiological impact of use (human-machine interaction) by developing a structure that maps the associative and nonassociative behavioral patterns (learned expectations) and vestibular (otolith and semicircular canals of the inner ear) and tactile interaction, derived from system acceleration profiles, onto an abstract space that predicts simulator sickness for a given training flight.},

  doi          = {10.2172/10172277},

  url          = {https://www.osti.gov/biblio/10172277},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Mar 01 00:00:00 EST 1993},

  month        = {Mon Mar 01 00:00:00 EST 1993}

}

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