Estimation of the dynamic spinal forces using a recurrent fuzzy neural network.
Estimation of the dynamic spinal forces from kinematics data is very complicated because it involves the handling of the relationship between kinematic variables and electromyography (EMG) signals, as well as the relationship between EMG signals 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 EMG 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 costly procedure of measuring EMG signals and avoiding the use of a biomechanics model. A learning algorithm is derived for the RFNN model.
Hou
Yanfeng
Y
Department of Electrical and Computer Engineering, University of Louisville, KY 40292, USA. y0hou002@louisville.edu
Zurada
Jacek M
JM
Karwowski
Waldemar
W
Marras
William S
WS
Davis
Kermit
K
D000465:Algorithms; D001185:Artificial Intelligence; D001696:Biomechanical Phenomena / Q000379:methods; D032701:Biomimetics / Q000379:methods*; D003198:Computer Simulation; D017143:Fuzzy Logic; D006801:Humans; D009068:Movement / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D016571:Neural Networks, Computer; D012984:Software; D013131:Spine / Q000502:physiology*; D013314:Stress, Mechanical; D013598:Systems Theory