Artificial neural network-based prediction of human posture

Authors:
Mohammad Bataineh;Timothy Marler;Karim Abdel-Malek
Affiliations:
Virtual Soldier Research Program---Center for Computer-Aided Design, The University of Iowa, Iowa City, IA;Virtual Soldier Research Program---Center for Computer-Aided Design, The University of Iowa, Iowa City, IA;Virtual Soldier Research Program---Center for Computer-Aided Design, The University of Iowa, Iowa City, IA
Venue:
DHM'13 Proceedings of the 4th international conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management: human body modeling and ergonomics - Volume Part II
Year:
2013

Citing 5
Cited 0

Advanced Methods in Neural Computing

Advanced Methods in Neural Computing
Radial Basis Functions

Radial Basis Functions
A unified architecture for natural language processing: deep neural networks with multitask learning

Proceedings of the 25th international conference on Machine learning
Development of the virtual-human Santos®

ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
A general regression neural network

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

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.