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A variational method to determine the most representative shape of a set of curves and its application to knee kinematic data for pathology classification

Authors:

Badreddine Ben Nouma,

Neila Mezghani,

Amar Mitiche,

Youssef OuakrimAuthors Info & Claims

MedPRAI '18: Proceedings of the 2nd Mediterranean Conference on Pattern Recognition and Artificial Intelligence

Pages 22 - 26

https://doi.org/10.1145/3177148.3180095

Published: 27 March 2018 Publication History

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Abstract

The purpose of this study is to investigate a variational method to determine the most representative shape of a family of curves and its application to three-dimensional knee kinematic data for knee pathology classification. High variability and the presence of outliers are characteristic of the data in this application. This method determines the most representative shape by averaging the family curves corrected to account for outliers occurrence and family variability. To this effect, the correction is performed by simultaneous minimization of a set of objective functions, one for each family curve consisting of two terms: a data term of conformity of the corrected curve to the given family curve, and a regularization term of proximity of the corrected curve to the mean of the corrected curves to inhibit the influence of outliers in the family. Minimization is carried out efficiently by particle swarm optimization, a method which, in contrast to gradient descent, is robust to the presence of outliers. Experimental results using real-world data in knee osteoarthritis pattern classification demonstrate the validity and efficiency of the method. Comparisons to conventional methods used to determine the most representative shape are given.

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Cited By

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Abid MOuakrim YMitiche AVendittoli PHagemeister NMezghani N(2020)A Comparative Study of End-To-End Discriminative Deep Learning Models for Knee Joint Kinematic Time Series ClassificationBiomedical Signal Processing10.1007/978-3-030-67494-6_2(33-61)Online publication date: 19-Dec-2020
https://doi.org/10.1007/978-3-030-67494-6_2
Abid MOuakrim YVendittoli PHagemeister NMezghani N(2020)Representative Knee Kinematic Patterns Identification Using Within-Subject Variability AnalysisComputer Methods, Imaging and Visualization in Biomechanics and Biomedical Engineering10.1007/978-3-030-43195-2_39(483-494)Online publication date: 1-Apr-2020
https://doi.org/10.1007/978-3-030-43195-2_39
Ben Nouma BMitiche AOuakrim YMezghani N(2019)Pattern Classification by the Hotelling Statistic and Application to Knee Osteoarthritis Kinematic SignalsMachine Learning and Knowledge Extraction10.3390/make10300451:3(768-784)Online publication date: 5-Jul-2019
https://doi.org/10.3390/make1030045
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MedPRAI '18: Proceedings of the 2nd Mediterranean Conference on Pattern Recognition and Artificial Intelligence

March 2018

135 pages

ISBN:9781450352901

DOI:10.1145/3177148

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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IAPR: International Association for Pattern Recognition

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Published: 27 March 2018

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MedPRAI '18: Mediterranean Conference on Pattern Recognition and Artificial Intelligence

March 27 - 28, 2018

Rabat, Morocco

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Cited By

View all

Abid MOuakrim YMitiche AVendittoli PHagemeister NMezghani N(2020)A Comparative Study of End-To-End Discriminative Deep Learning Models for Knee Joint Kinematic Time Series ClassificationBiomedical Signal Processing10.1007/978-3-030-67494-6_2(33-61)Online publication date: 19-Dec-2020
https://doi.org/10.1007/978-3-030-67494-6_2
Abid MOuakrim YVendittoli PHagemeister NMezghani N(2020)Representative Knee Kinematic Patterns Identification Using Within-Subject Variability AnalysisComputer Methods, Imaging and Visualization in Biomechanics and Biomedical Engineering10.1007/978-3-030-43195-2_39(483-494)Online publication date: 1-Apr-2020
https://doi.org/10.1007/978-3-030-43195-2_39
Ben Nouma BMitiche AOuakrim YMezghani N(2019)Pattern Classification by the Hotelling Statistic and Application to Knee Osteoarthritis Kinematic SignalsMachine Learning and Knowledge Extraction10.3390/make10300451:3(768-784)Online publication date: 5-Jul-2019
https://doi.org/10.3390/make1030045
Ben Nouma BMitiche AMezghani N(2019)A Sample-Encoding Generalization of the Kohonen Associative Memory and Application to Knee Kinematic Data Representation and Pathology ClassificationApplied Sciences10.3390/app90917419:9(1741)Online publication date: 26-Apr-2019
https://doi.org/10.3390/app9091741

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