عنوان انگلیسی مقاله:
Classification of foot drop gait characteristic due to lumbar radiculopathy using machine learning algorithms
ترجمه فارسی عنوان مقاله:
طبقه بندی ویژگی راه رفتن قطره پا به دلیل رادیکولوپاتی کمری با استفاده از الگوریتم های یادگیری ماشین
Sciencedirect - Elsevier - Gait & Posture, 71 (2019) 234-240: doi:10:1016/j:gaitpost:2019:05:010
Shiva Sharif Bidabadia,⁎, Iain Murrayb, Gabriel Yin Foo Leec,d, Susan Morrise, Tele Tana
Background: Recently, the study of walking gait has received significant attention due to the importance of
identifying disorders relating to gait patterns. Characterisation and classification of different common gait
disorders such as foot drop in an effective and accurate manner can lead to improved diagnosis, prognosis
assessment, and treatment. However, currently visual inspection is the main clinical method to evaluate gait
disorders, which is reliant on the subjectivity of the observer, leading to inaccuracies.
Research question: This study examines if it is feasible to use commercial off-the-shelf Inertial measurement
unit sensors and supervised learning methods to distinguish foot drop gait disorder from the normal walking gait
Method: The gait data collected from 56 adults diagnosed with foot drop due to L5 lumbar radiculopathy
(with MRI verified compressive pathology), and 30 adults with normal gait during multiple walking trials on a
flat surface. Machine learning algorithms were applied to the inertial sensor data to investigate the feasibility of
classifying foot drop disorder.
Results: The best three performing results were 88.45%, 86.87% and 86.08% accuracy derived from the
Random Forest, SVM, and Naive Bayes classifiers respectively. After applying the wrapper feature selection
technique, the top performance was from the Random Forest classifier with an overall accuracy of 93.18%.
Significance: It is demonstrated that the combination of inertial sensors and machine learning algorithms,
provides a promising and feasible solution to differentiating L5 radiculopathy related foot drop from normal
walking gait patterns. The implication of this finding is to provide an objective method to help clinical decision
Keywords: Foot drop | Inertial measurement unit | Machine learning | Gait classification