Potential of the genetic algorithm neural network in the assessment of gait patterns in ankle arthrodesis

The aim of this study was to develop an empirical model of parameter-based gait data, based on an artificial neural network and a genetic algorithm, f or the assessment of patients after ankle arthrodesis. Ground reaction forc e vectors were measured by force platforms during level walking. Nine force parameters expressed in percentage of body weight and their chronologic in cidence of occurrence expressed in percentage of stance phase period were u sed in modeling. Ten healthy persons and ten patients who had solid arthrod esis of the ankle were recruited in this study fur developing the model. By applying the genetic algorithm neural network, the percentage of correct c lassification was 98.8% and the subset of discriminant parameters was be re duced to 9 out of 18. These key parameters were mainly related to the loadi ng response and propulsive phase. This indicates that there was a reduction in the abilities in cushion impact and push off in the patients after ankl e arthrodesis. Finally, the relative distance (D-r) was defined in this stu dy and used in two new patients' examinations to demonstrate its clinical u tility. (C) 2001 Biomedical Engineering Society.