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