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.
Kaczmarczyk
Katarzyna
K
Jozef Pilsudski University of Physical Education, Marymoncka 34, Warsaw, Poland. katarzyna.kaczmarczyk@gmail.com
Wit
Andrzej
A
Krawczyk
Maciej
M
Zaborski
Jacek
J
D000328:Adult; D000368:Aged; D053587:Arthrometry, Articular; D001696:Biomechanical Phenomena; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted; D016002:Discriminant Analysis; D005260:Female; D020233:Gait Disorders, Neurologic / Q000145:classification* / Q000209:etiology / Q000503:physiopathology*; D006429:Hemiplegia / Q000209:etiology / Q000503:physiopathology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D020521:Stroke / Q000150:complications*