Prediction of dynamic forces on lumbar joint using a recurrent neural network model.
Hou-Y; Zurada-JM; Karwowski-W
Proceedings of the 2004 International Conference on Machine Learning and Applications, December 16-18, 2004, Louisville, Kentucky. Kantardzic-M; Nasraoui-O; Milanova-M, eds., Piscataway, NJ: Institute of Electrical and Electronics Engineers, 2004 Dec; :360-365
We propose a modified recurrent neural network model which establishes the relationship between kinematics and the dynamic forces on lumbar joint. By doing that we can have the forces predicted directly from kinematic variables while bypassing the costly procedure of measuring EMG (electromyography) signals and avoiding the use of biomechanics model. In the proposed model, we introduce the EMG signal as an intermediate output and loop it back to the input layer, instead of looping back the ultimate output, the forces. Since the EMG signal is a direct reflection of muscle activity, the most valuable point of this model is that the back-looping of the intermediate output has physical meaning. It solves the problem that the input and output of the system have no direct and explicit physical connection. At the same time, the advantages of recurrent neural network are utilized.
Models; Computer-models; Mathematical-models; Biomechanical-modeling; Biomechanics; Musculoskeletal-system; Manual-lifting; Materials-handling; Manual-materials-handling
Kantardzic-M; Nasraoui-O; Milanova-M
Proceedings of the 2004 International Conference on Machine Learning and Applications, December 16-18, 2004, Louisville, Kentucky
Ohio State University