ArticlePloS one2018
An analysis of 3D knee kinematic data complexity in knee osteoarthritis and asymptomatic controls.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Altered Spatiotemporal and Kinematic Gait in Patients with Knee Osteoarthritis.Journal of functional morphology and kinesiology · 2026Article
- Defining and measuring varus thrust in knee osteoarthritis: A scoping review of current evidence and challenges.Osteoarthritis and cartilage open · 2025Review
- Impact of combined balance and strength exercise program on lower limb energy flow in individuals with knee osteoarthritis.Frontiers in sports and active living · 2025Article
- The knee kinematic patterns and associated factors in healthy Thai adults.BMC musculoskeletal disorders · 2023Article
- A patient-specific 3D model of the knee to compare the femoral rollback before and after total knee arthroplasty (TKA).Journal of experimental orthopaedics · 2021Article
- A multivariate relationship between the kinematic and clinical parameters of knee osteoarthritis population.Biomedical engineering online · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
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 inter-class 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.
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Registered trials
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