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2021 | OriginalPaper | Chapter

Approaches to Classify Knee Osteoarthritis Using Biomechanical Data

Authors : Tiago Franco, P. R. Henriques, P. Alves, M. J. Varanda Pereira

Published in: Optimization, Learning Algorithms and Applications

Publisher: Springer International Publishing

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Abstract

Knee osteoarthritis (KOA) is a degenerative disease that mainly affects the elderly. The development of this disease is associated with a complex set of factors that cause abnormalities in motor functions. The purpose of this review is to understand the composition of works that combine biomechanical data and machine learning techniques to classify KOA progress. This study was based on research articles found in the search engines Scopus and PubMed between January 2010 and April 2021. The results were divided into data acquisition, feature engineering, and algorithms to synthesize the discovered content. Several approaches have been found for KOA classification with significant accuracy, with an average of 86% overall and three papers reaching 100%; that is, they did not fail once in their tests. The acquisition of data proved to be the divergent task between the works, the most considerable correlation in this stage was the use of the ground reaction force (GRF) sensor. Although three studies reached 100% in the classification, two did not use a gradual evaluation scale, classifying between KOA or healthy individuals. Thus, we can get out of this work that machine learning techniques are promising for identifying KOA using biomechanical data. However, the classification of pathological stages is a complex problem to discuss, mainly due to the difficult access and lack of standardization in data acquisition.

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Title: Approaches to Classify Knee Osteoarthritis Using Biomechanical Data
Authors: Tiago Franco
P. R. Henriques
P. Alves
M. J. Varanda Pereira
Publisher: Springer International Publishing
Book: Optimization, Learning Algorithms and Applications
Print ISBN: 978-3-030-91884-2

Electronic ISBN: 978-3-030-91885-9

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-91885-9_31

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