Motion Sickness: Quantitative, algorithmic Malaise Indication in Real Time.
Abstract
Physiological data were collected on human volunteers to study the effects of motion sickness. Data were analyzed and correlated using least squares curve fitting and other statistical methods that are described. An equation is developed that relates five separate physiological signals to subjective motion sickness. A computer program is presented and described which takes the physiological signals in real time and computes the motion sickness. A pattern recognition type of approach which uses a neural net is presented and discussed as an alternative to the equation model. The two models are compared. This disorder is characterized by a variety of symptoms; the most prevalent are nausea, pallor, sweating, and vomiting. Other possible symptoms include salivation, feeling of warmth, light-headedness, depression or apathy, yawning and drowsiness, belching or flatulence, headache, and occasionally hyperventilation. They are brought on by unusual or provocative motion stimulus, either real or perceived. The leading theory about the mechanism of motion sickness is the sensory conflict theory. It says that when there is a conflict between different parts of the balance system, motion sickness can result. Drug treatment is the easiest method to apply for alleviating motion sickness; however, drugs have undesirable side effects. Biofeedback is a promising treatment method for long-term protection. A problem with biofeedback, however, is identifying physiologic parameters that are good indicators of motion sickness and can also be brought under voluntary control.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 1987
- Accession Number
- ADA189674
Entities
People
- Edward L. Fix
Organizations
- Air Force Institute of Technology