Artificial Neural Network-Based Prediction of Human Posture

Mohammad Bataineh; Timothy Marler; Karim Abdel-Malek

doi:10.1007/978-3-642-39182-8_36

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Artificial Neural Network-Based Prediction of Human Posture

Book chapter

Open access

Peer reviewed

Artificial Neural Network-Based Prediction of Human Posture

Mohammad Bataineh, Timothy Marler and Karim Abdel-Malek

Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management. Human Body Modeling and Ergonomics, pp.305-313

Lecture Notes in Computer Science, Springer Berlin Heidelberg

2013

DOI: 10.1007/978-3-642-39182-8_36

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Abstract

The use of an artificial neural network (ANN) in many practical complicated problems encourages its implementation in the digital human modeling (DHM) world. DHM problems are complicated and need powerful tools like ANN to provide acceptable solutions. Human posture prediction is a DHM field that has been studied thoroughly in recent years. This work focuses on using a general regression neural network (GRNN) for human posture prediction. This type of ANN has advantages over others when incorporated in DHM problems like posture prediction. A new heuristic approach is also presented in this study to determine the GRNN parameters that lead to the best performance and prediction capability. The results are promising: a high success rate is obtained for predicting 41 outputs, which represent the upper-body degrees of freedom of a human model. This work initiates future focus on embedding GRNN to generalize human posture prediction in a task-based manner.

artificial neural network

Digital human modeling and simulation

posture prediction

Details

Title: Subtitle: Artificial Neural Network-Based Prediction of Human Posture
Creators: Mohammad Bataineh - University of Iowa
Timothy Marler - University of Iowa
Karim Abdel-Malek
Resource Type: Book chapter
Publication Details: Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management. Human Body Modeling and Ergonomics, pp.305-313
Publisher: Springer Berlin Heidelberg; Berlin, Heidelberg
Series: Lecture Notes in Computer Science
DOI: 10.1007/978-3-642-39182-8_36
eISSN: 1611-3349
ISSN: 0302-9743
Language: English
Date published: 2013
Academic Unit: Mechanical Engineering; Roy J. Carver Department of Biomedical Engineering
Record Identifier: 9984196556302771

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