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A hybrid neuro-fuzzy approach for spinal force evaluation in manual materials handling tasks.

Authors
Hou-Y; Zurada-JM; Karwowski-W; Marras-WS
Source
Advances in Natural Computation: Proceedings of the First International Conference, ICNC 2005, August 27-29, 2005, Changsha, China. Wang L, Chen K, Ong YS, eds., New York: Springer, 2005 Jan; 3612(Part III):1216-1225
NIOSHTIC No.
20041201
Abstract
Evaluation of the spinal forces from kinematics data is very complicated because it involves the handling of relationship between kinematic variables and electromyography (EMG) responses, as well as the relationship between EMG responses 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 the EMG signal 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 procedure of measuring EMG signals and avoiding the use of biomechanics model. A learning algorithm is derived for the RFNN.
Keywords
Models; Computer-models; Mathematical-models; Biomechanical-modeling; Biomechanics; Musculoskeletal-system; Manual-lifting; Materials-handling; Manual-materials-handling
Publication Date
20050101
Document Type
Conference/Symposia Proceedings
Editors
Wang-L; Chen-K; Ong-YS
Funding Type
Grant
Fiscal Year
2005
NTIS Accession No.
NTIS Price
ISBN No.
9783540283201
Identifying No.
Grant-Number-R01-OH-007787
ISSN
0302-9743
Source Name
Advances in Natural Computation: Proceedings of the First International Conference, ICNC 2005, August 27-29, 2005, Changsha, China
State
OH; KY
Performing Organization
Ohio State University
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