NDLI: Prediction of dynamic forces on lumbar joint using a recurrent neural network model

Content Provider	IEEE Xplore Digital Library
Author	Yanfeng Jou Zurada, J.M. Karwowski, W.
Copyright Year	2004
Abstract	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.
Starting Page	360
Ending Page	365
File Size	258822
Page Count	6
File Format	PDF
ISBN	0780388232
DOI	10.1109/ICMLA.2004.1383536
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biomechanics Recurrent neural networks Force measurement Neural networks Kinematics Predictive models Muscles Electromyography Electronic mail Neurofeedback
Content Type	Text
Resource Type	Article

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