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Identification of low back injury from EMG signals using a neural network model.

Authors
Hou-Y; Zurada-JM; Karwowski-W; Marras-WS
Source
Proceedings of International Joint Conference on Neural Networks, July 16-21, 2006, Vancouver, BC, Canada. Piscataway, NJ: Institute of Electrical and Electronics Engineers, 2006 Jul; :5309-5315
NIOSHTIC No.
20041200
Abstract
We propose a novel neural network model for the identification of low back injury using electromyography (EMG) data. By connecting task condition variables to the second hidden-layer of the neural network, the importance of those variables can be improved. A 4-muscle method and a 10-muscle method are discussed. A higher classification accuracy was achieved by the 10-muscle method since it takes the correlation between muscle activities into account. We also found that identification accuracy decreases when the object weight or the lifting height increases. The obtained results improve our understanding of low back disorders and provide important guidance for future experimental studies.
Keywords
Models; Computer-models; Mathematical-models; Biomechanical-modeling; Biomechanics; Musculoskeletal-system; Manual-lifting; Materials-handling; Manual-materials-handling
Publication Date
20060716
Document Type
Conference/Symposia Proceedings
Funding Type
Grant
Fiscal Year
2006
NTIS Accession No.
NTIS Price
ISBN No.
9780780394902
Identifying No.
Grant-Number-R01-OH-007787
Source Name
Proceedings of International Joint Conference on Neural Networks, July 16-21, 2006, Vancouver, BC, Canada
State
OH; KY
Performing Organization
Ohio State University
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