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An analysis of 3D knee kinematic data complexity in knee osteoarthritis and asymptomatic controls [r-libre/1537]

Mezghani, Neila; Mechmeche, Imene; Mitiche, Amar; Ouakrim, Youssef et De Guise, Jacques A. (2018). An analysis of 3D knee kinematic data complexity in knee osteoarthritis and asymptomatic controls. PLoS ONE, 13 (10), e0202348. https://doi.org/10.1371/journal.pone.0202348

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[img]  PDF - 2018-PlosOne(Imen).pdf
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Catégorie de document : Articles de revues
Évaluation par un comité de lecture : Oui
Étape de publication : Publié
Résumé : Three-dimensional (3D) knee kinematic data, measuring flexion/extension, abduction/ adduction, and internal/external rotation angle variations during locomotion, provide essential information to diagnose, classify, and treat musculoskeletal knee pathologies. However, and so across genders, the curse of dimensionality, intra-class high variability, and interclass proximity make this data usually difficult to interpret, particularly in tasks such as knee pathology classification. The purpose of this study is to use data complexity analysis to get some insight into this difficulty. Using 3D knee kinematic measurements recorded from osteoarthritis and asymptomatic subjects, we evaluated both single feature complexity, where each feature is taken individually, and global feature complexity, where features are considered simultaneously. These evaluations afford a characterization of data complexity independent of the used classifier and, therefore, provide information as to the level of classification performance one can expect. Comparative results, using reference databases, reveal that knee kinematic data are highly complex, and thus foretell the difficulty of knee pathology classification.
Adresse de la version officielle : https://journals.plos.org/plosone/article?id=10.13...
Déposant: Mezghani, Neila
Responsable : Neila Mezghani
Dépôt : 23 oct. 2018 13:42
Dernière modification : 23 oct. 2018 13:42

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