A Hybrid Neuro-fuzzy Approach for Spinal Force Evaluation in Manual Materials Handling Tasks

  • Yanfeng Hou
  • Jacek M. Zurada
  • Waldemar Karwowski
  • William S. Marras
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3612)


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.


Membership Function Fuzzy System Fuzzy Rule Kinematic Variable Trunk Muscle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yanfeng Hou
    • 1
  • Jacek M. Zurada
    • 1
  • Waldemar Karwowski
    • 2
  • William S. Marras
    • 3
  1. 1.Department of Electrical and Computer EngineeringUniversity of Louisville 
  2. 2.Department of Industrial EngineeringUniversity of Louisville 
  3. 3.Biodynamics Laboratory, Institute for ErgonomicsThe Ohio State University 

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