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Prediction of EMG signals of trunk muscles in manual lifting using a neural network model.
Hou-Y; Zurada-JM; Karwowski-W
Proceedings of International Joint Conference on Neural Networks, July 25-29, 2004, Budapest, Hungary. Piscataway, NJ: Institute of Electrical and Electronics Engineers, 2004 Jul; 3:1935-1940
An EMG (electromyography) signal prediction model is built using artificial neural network. Kinematics variables and subject variables are selected as inputs of this model. A novel structure of feedforward neural network is proposed in This work to obtain better accuracy of prediction. By adding regional connections between the input and the output, the new architecture of the neural network can have both global features and regional features extracted from the input. The global connections put more emphasis on the whole picture and determine the global trend of the predicted curve, while the regional connections concentrate on each point and modify the prediction locally. Back-propagation algorithm is used in the modeling. A basic structure of neural network designed for this problem is discussed. Then to overcome its drawbacks, we propose a new structure.
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 25-29, 2004, Budapest, Hungary
Ohio State University
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