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A neural network model for predicting postures during non-repetitive manual materials handling tasks.

Authors
Perez-MA; Nussbaum-MA
Source
Ergonomics 2008 Oct; 51(10):1549-1564
NIOSHTIC No.
20043309
Abstract
Posture prediction can be useful in facilitating the design and evaluation processes for manual materials handling tasks. This study evaluates the ability of artificial neural network models to predict initial and final lifting postures in 2-D and 3-D scenarios. Descriptors for the participant and condition of interest were input to the models; outputs consisted of posture-defining joint angles. Models were trained with subsets of an existing posture database before predictions were generated. Trained models predictions were then evaluated using the remaining data, which included conditions not presented during training. Prediction errors were consistent across these data subsets, suggesting the models generalised well to novel conditions. The models generally predicted whole-body postures with per-joint errors in the 5 degrees -20 degrees range, though some errors were larger, particularly for 3-D conditions. These models provided reasonably accurate predictions, even outperforming some computational approaches previously proposed for similar purposes. Suggestions for future refinement of such models are presented. The models in this investigation provide a means to predict initial and final postures in commonly occurring manual materials handling tasks. In addition, the model structures provide information about potential lifting strategies that may be used by individuals with particular anthropometry or strength characteristics.
Keywords
Posture; Manual-materials-handling; Models; Training; Humans; Men; Women; Age-groups; Biomechanics; Physiological-function; Physiology; Physical-reactions; Physical-capacity; Author Keywords: posture prediction; artificial neural networks; manual materials handling; simulation
Contact
Miguel A. Perez, Center for Automotive Safety Research, Virginia Tech Transportation Institute, 3500 Transportation Research Plaza, Blacksburg, VA 24061
CODEN
ERGOAX
Publication Date
20081001
Document Type
Journal Article
Email Address
mperez@vt.edu
Funding Type
Cooperative Agreement
Fiscal Year
2009
NTIS Accession No.
NTIS Price
Identifying No.
Cooperative-Agreement-Number-U19-OH-008308
Issue of Publication
10
ISSN
0014-0139
Source Name
Ergonomics
State
VA
Performing Organization
Virginia Polytechnic Institute and State University
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