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Classification of asymptomatic and osteoarthritic knee gait patterns using gait analysis via deterministic learning

Classification of asymptomatic and osteoarthritic knee gait patterns using gait analysis via... Gait measures have received increasing attention in the evaluation of patients with knee osteoarthritis (OA). Comprehending gait parameters is an essential requirement for studying the causes of knee disorders. The aim of this work is to develop a new method to distinguish between asymptomatic (AS) and osteoarthritic knee gait patterns using gait analysis via deterministic learning. Spatiotemporal parameters and three-dimensional knee joint rotations and translations are measured and compared in 19 patients with knee OA and 28 AS control subjects during level walking. The classification approach consists of two stages: a training stage and a classification stage. In the training stage, gait features representing gait dynamics, including knee rotations and translations, are derived from the kinematic data of the knees in six-degree-of-freedom. Gait dynamics underlying gait patterns of AS control subjects and patients with knee OA are locally accurately approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. Gait patterns of AS control subjects and patients with knee OA constitute a training set. In the classification stage, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of gait dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test knee OA gait pattern to be classified, a set of classification errors are generated. The average $$L_1$$ L 1 norms of the errors are taken as the classification measure between the dynamics of the training gait patterns and the dynamics of the test knee OA gait pattern according to the smallest error principle. Finally, experiments are carried out to demonstrate that the proposed method can effectively separate the gait patterns between the groups of AS control subjects and patients with knee OA. By using the two-fold cross-validation and leave-one-out cross-validation styles, the correct classification rates for knee OA gait patterns are reported to be 95.7 and 97.9%, respectively. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

Classification of asymptomatic and osteoarthritic knee gait patterns using gait analysis via deterministic learning

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References (37)

Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Nature B.V.
Subject
Computer Science; Artificial Intelligence; Computer Science, general
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/s10462-018-9645-z
Publisher site
See Article on Publisher Site

Abstract

Gait measures have received increasing attention in the evaluation of patients with knee osteoarthritis (OA). Comprehending gait parameters is an essential requirement for studying the causes of knee disorders. The aim of this work is to develop a new method to distinguish between asymptomatic (AS) and osteoarthritic knee gait patterns using gait analysis via deterministic learning. Spatiotemporal parameters and three-dimensional knee joint rotations and translations are measured and compared in 19 patients with knee OA and 28 AS control subjects during level walking. The classification approach consists of two stages: a training stage and a classification stage. In the training stage, gait features representing gait dynamics, including knee rotations and translations, are derived from the kinematic data of the knees in six-degree-of-freedom. Gait dynamics underlying gait patterns of AS control subjects and patients with knee OA are locally accurately approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. Gait patterns of AS control subjects and patients with knee OA constitute a training set. In the classification stage, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of gait dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test knee OA gait pattern to be classified, a set of classification errors are generated. The average $$L_1$$ L 1 norms of the errors are taken as the classification measure between the dynamics of the training gait patterns and the dynamics of the test knee OA gait pattern according to the smallest error principle. Finally, experiments are carried out to demonstrate that the proposed method can effectively separate the gait patterns between the groups of AS control subjects and patients with knee OA. By using the two-fold cross-validation and leave-one-out cross-validation styles, the correct classification rates for knee OA gait patterns are reported to be 95.7 and 97.9%, respectively.

Journal

Artificial Intelligence Review – Springer Journals

Published: Jul 10, 2018

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