دانلود مقاله انگلیسی رایگان:طبقه بندی ویژگی راه رفتن قطره پا به دلیل رادیکولوپاتی کمری با استفاده از الگوریتم های یادگیری ماشین - 2019
دانلود بهترین مقالات isi همراه با ترجمه فارسی
دانلود مقاله انگلیسی یادگیری ماشین رایگان
  • Classification of foot drop gait characteristic due to lumbar radiculopathy using machine learning algorithms Classification of foot drop gait characteristic due to lumbar radiculopathy using machine learning algorithms
    Classification of foot drop gait characteristic due to lumbar radiculopathy using machine learning algorithms

    سال انتشار:

    2019


    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


    منبع:

    Sciencedirect - Elsevier - Gait & Posture, 71 (2019) 234-240: doi:10:1016/j:gaitpost:2019:05:010


    نویسنده:

    Shiva Sharif Bidabadia,⁎, Iain Murrayb, Gabriel Yin Foo Leec,d, Susan Morrise, Tele Tana


    چکیده انگلیسی:

    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 pattern. 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 making.
    Keywords: Foot drop | Inertial measurement unit | Machine learning | Gait classification


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 7
    حجم فایل: 2142 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




اگر این مقاله را پسندیدید آن را در شبکه های اجتماعی به اشتراک بگذارید (برای به اشتراک گذاری بر روی ایکن های زیر کلیک کنید)

تعداد نظرات : 0

الزامی
الزامی
الزامی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی