NIOSHTIC-2 Publications Search

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

Hou-Y; Zurada-JM; Karwowski-W; Marras-WS

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

http://dx.doi.org/10.1109/IJCNN.2006.247287

20041200

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.

Models; Computer-models; Mathematical-models; Biomechanical-modeling; Biomechanics; Musculoskeletal-system; Manual-lifting; Materials-handling; Manual-materials-handling

20060716

Conference/Symposia Proceedings

Grant

2006

9780780394902

Grant-Number-R01-OH-007787

Proceedings of International Joint Conference on Neural Networks, July 16-21, 2006, Vancouver, BC, Canada

OH; KY

Ohio State University