Using a Bayesian Network to Predict L5S1 Spinal Compression Force from Posture, Hand Load, Anthropometry, and Disc Injury Status
Author
Source
Applied Bionics and Biomechanics
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-01
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Stochastic biomechanical modeling has become a useful tool most commonly implemented using Monte Carlo simulation, advanced mean value theorem, or Markov chain modeling.
Bayesian networks are a novel method for probabilistic modeling in artificial intelligence, risk modeling, and machine learning.
The purpose of this study was to evaluate the suitability of Bayesian networks for biomechanical modeling using a static biomechanical model of spinal forces during lifting.
A 20-node Bayesian network model was used to implement a well-established static two-dimensional biomechanical model for predicting L5/S1 compression and shear forces.
The model was also implemented as a Monte Carlo simulation in MATLAB.
Mean L5/S1 spinal compression force estimates differed by 0.8%, and shear force estimates were the same.
The model was extended to incorporate evidence about disc injury, which can modify the prior probability estimates to provide posterior probability estimates of spinal compression force.
An example showed that changing disc injury status from false to true increased the estimate of mean L5/S1 compression force by 14.7%.
This work shows that Bayesian networks can be used to implement a whole-body biomechanical model used in occupational biomechanics and incorporate disc injury.
American Psychological Association (APA)
Hughes, Richard E.. 2017. Using a Bayesian Network to Predict L5S1 Spinal Compression Force from Posture, Hand Load, Anthropometry, and Disc Injury Status. Applied Bionics and Biomechanics،Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1120982
Modern Language Association (MLA)
Hughes, Richard E.. Using a Bayesian Network to Predict L5S1 Spinal Compression Force from Posture, Hand Load, Anthropometry, and Disc Injury Status. Applied Bionics and Biomechanics No. 2017 (2017), pp.1-7.
https://search.emarefa.net/detail/BIM-1120982
American Medical Association (AMA)
Hughes, Richard E.. Using a Bayesian Network to Predict L5S1 Spinal Compression Force from Posture, Hand Load, Anthropometry, and Disc Injury Status. Applied Bionics and Biomechanics. 2017. Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1120982
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1120982