Statistical Impact Acceleration Injury Prediction Models Based on -Gx Accelerator Data and Initial Head Conditions

Abstract

Statistical impact acceleration injury prediction models are developed using data from 23 high-level -G sub X acceleration runs. These runs involve Rhesus monkeys with securely restrained torsos and unrestrained heads. The models are based on peak sled acceleration and initial head conditions. The model predictions are compared with those given in an earlier report based on different data and an estimate of Fisher's information matrix is used to evaluate the relative worth of the two data bases.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1983
Accession Number
ADA129353

Entities

People

  • Dennis E. Smith
  • Kevin C. Burns

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Angular Acceleration
  • Data Sets
  • Databases
  • Dynamic Response
  • Fatalities
  • Impact Acceleration
  • Information Science
  • Military Research
  • New York
  • Observation
  • Operations Research
  • Predictive Modeling
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Rhesus Monkeys
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Explosive Engineering.
  • Regression Analysis.