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.
This work was supported by FCT - Fundação para a Ciência
e a Tecnologia under Projects UIDB/05757/2020, UIDB/00319/2020 and individual
research grant 2020.05704.BD, funded by Ministério da Ciência, Tecnologia e Ensino
Superior (MCTES) and Fundo Social Europeu (FSE) through The Programa Operacional
Regional Norte.
