A method to determine the interdependent relationships between biomechanical variables in artificial neural network models: the case of lower extremity muscle activity and body sway
This study examined the possibility of using artificial neural network weights to quantify inputoutput relationship. We derived a first-order coefficient to calculate the relative contribution from the ith input to the kth output, called Qki. Theoretical analysis and numerical simulation suggested that Qki depended on the magnitude, frequency and phase shift of the input/output function, making it possible to identify those inputs that have the greatest contribution to the output. This approach was applied to investigate the relationship between muscle activities and body sway during human quiet stance. The results demonstrated its potential for studying human postural control issues.
artificial neural network weights inputoutput relationship postural control