A hybrid neuro-fuzzy approach for spinal force evaluation in manual materials handling tasks

Authors:
Yanfeng Hou;Jacek M. Zurada;Waldemar Karwowski;William S. Marras
Affiliations:
Department of Electrical and Computer Engineering, University of Louisville;Department of Electrical and Computer Engineering, University of Louisville;Department of Industrial Engineering, University of Louisville;Biodynamics Laboratory, Institute for Ergonomics, The Ohio State University
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Year:
2005

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Abstract

Evaluation of the spinal forces from kinematics data is very complicated because it involves the handling of relationship between kinematic variables and electromyography (EMG) responses, as well as the relationship between EMG responses and the forces. A recurrent fuzzy neural network (RFNN) model is proposed to establish the kinematics-EMG-force relationship and model the dynamics of muscular activities. The EMG signals are used as an intermediate output and are fed back to the input layer. Since the EMG signal is a direct reflection of muscular activities, the feedback of this model has a physical meaning. It expresses the dynamics of muscular activities in a straightforward way and takes advantage from the recurrent property. The trained model can then have the forces predicted directly from kinematic variables while bypassing the procedure of measuring EMG signals and avoiding the use of biomechanics model. A learning algorithm is derived for the RFNN.