期刊
GAIT & POSTURE
卷 30, 期 2, 页码 207-210出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.gaitpost.2009.04.010
关键词
Stroke; Hemiplegia; ANN analysis; Gait patterns
资金
- Ministry of Science and Higher Education [404 3302 33]
The aim of this Study was to test three methods for classifying the gait patterns of post-stroke patients' gait into homogenous groups. First, qualitative test results were found to correctly classify patients 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. (C) 2009 Elsevier B.V. All rights reserved.
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