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Estimation of the dynamic spinal forces using a recurrent fuzzy neural network.

Authors
Hou-Y; Zurada-JM; Karwowski-W; Marras-WS; Davis-K
Source
IEEE Trans Syst Man Cybern, Part B, Cybern 2007 Feb; 37(1):100-109
NIOSHTIC No.
20041191
Abstract
Estimation of the dynamic spinal forces from kinematics data is very complicated because it involves the handling of the relationship between kinematic variables and electromyography (EMG) signals, as well as the relationship between EMG signals and the forces. A recurrent fuzzy neural network (RFNN) model is proposed to establish the kinematics-EMG-force relationship and model the dynamics of muscular activities. The EMG signals are used as an intermediate output and are fed back to the input layer. Since EMG is a direct reflection of muscular activities, the feedback of this model has a physical meaning. It expresses the dynamics of muscular activities in a straightforward way and takes advantage from the recurrent property. The trained model can then have the forces predicted directly from kinematic variables while bypassing the costly procedure of measuring EMG signals and avoiding the use of a biomechanics model. A learning algorithm is derived for the RFNN model.
Keywords
Models; Computer-models; Mathematical-models; Biomechanical-modeling; Biomechanics; Musculoskeletal-system; Manual-lifting; Materials-handling; Manual-materials-handling; Author Keywords: Fuzzy neural networks; spinal force
CODEN
ITSCFI
Publication Date
20070201
Document Type
Journal Article
Funding Type
Grant
Fiscal Year
2007
NTIS Accession No.
NTIS Price
Identifying No.
Grant-Number-R01-OH-007787
Issue of Publication
1
ISSN
1083-4419
Source Name
IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics
State
OH; KY
Performing Organization
Ohio State University
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