Gait classification in post-stroke patients using artificial neural networks.

The aim of this study was to test three methods for classifying the gait patterns of post-stroke patients into homogenous groups. First, qualitative test results were found to correctly classify patients' gait patterns with an average success rate of 85%. Seeking further improvement, two quantitative methods were then tested. Analysis of min/max angle values in three lower limb joints, however, was less successful, showing a correct classification rate of below 50%. The best classification results were seen using an artificial neural network (ANN) to analyze the full progression of lower limb joint angle changes as a function of the gait cycle (with success rates from 100% for the knee joint to 86% for the frontal motion of the hip joint). These findings may help clinicians improve targeted therapy.