{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T06:29:20Z","timestamp":1777444160501,"version":"3.51.4"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,7,5]],"date-time":"2019-07-05T00:00:00Z","timestamp":1562284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002784","name":"Canada Excellence Research Chairs, Government of Canada","doi-asserted-by":"publisher","award":["950-231214"],"award-info":[{"award-number":["950-231214"]}],"id":[{"id":"10.13039\/501100002784","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002790","name":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2015-03853"],"award-info":[{"award-number":["RGPIN-2015-03853"]}],"id":[{"id":"10.13039\/501100002790","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>The analysis of knee kinematic data, which come in the form of a small sample of discrete curves that describe repeated measurements of the temporal variation of each of the knee three fundamental angles of rotation during a subject walking cycle, can inform knee pathology classification because, in general, different pathologies have different kinematic data patterns. However, high data dimensionality and the scarcity of reference data, which characterize this type of application, challenge classification and make it prone to error, a problem Duda and Hart refer to as the curse of dimensionality. The purpose of this study is to investigate a sample-based classifier which evaluates data proximity by the two-sample Hotelling     T 2     statistic. This classifier uses the whole sample of an individual\u2019s measurements for a better support to classification, and the Hotelling     T 2     hypothesis testing made applicable by dimensionality reduction. This method was able to discriminate between femero-rotulian (FR) and femero-tibial (FT) knee osteoarthritis kinematic data with an accuracy of     88.1 %    , outperforming significantly current state-of-the-art methods which addressed similar problems. Extended to the much harder three-class problem involving pathology categories FR and FT, as well as category FR-FT which represents the incidence of both diseases FR and FT in a same individual, the scheme was able to reach a performance that justifies its further use and investigation in this and other similar applications.<\/jats:p>","DOI":"10.3390\/make1030045","type":"journal-article","created":{"date-parts":[[2019,7,5]],"date-time":"2019-07-05T11:44:16Z","timestamp":1562327056000},"page":"768-784","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Pattern Classification by the Hotelling Statistic and Application to Knee Osteoarthritis Kinematic Signals"],"prefix":"10.3390","volume":"1","author":[{"given":"Badreddine","family":"Ben Nouma","sequence":"first","affiliation":[{"name":"INRS-\u00c9nergie mat\u00e9riaux et t\u00e9l\u00e9communications, Montreal, QC H5A 1K6, Canada"}]},{"given":"Amar","family":"Mitiche","sequence":"additional","affiliation":[{"name":"INRS-\u00c9nergie mat\u00e9riaux et t\u00e9l\u00e9communications, Montreal, QC H5A 1K6, Canada"}]},{"given":"Youssef","family":"Ouakrim","sequence":"additional","affiliation":[{"name":"Centre de recherche LICEF, TELUQ university, Montreal, QC H2S 3L5, Canada"},{"name":"Laboratoire de recherche en imagerie et orthop\u00e9die, Centre de recherche du CHUM, Montreal, QC H2X 0A9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5935-4570","authenticated-orcid":false,"given":"Neila","family":"Mezghani","sequence":"additional","affiliation":[{"name":"Centre de recherche LICEF, TELUQ university, Montreal, QC H2S 3L5, Canada"},{"name":"Laboratoire de recherche en imagerie et orthop\u00e9die, Centre de recherche du CHUM, Montreal, QC H2X 0A9, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,5]]},"reference":[{"key":"ref_1","unstructured":"Duda, R., Hart, P., and Stork, D. (2012). Pattern Classification, JohnWiley & Sons, INC."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.eswa.2016.12.005","article-title":"Spatial features selection for unsupervised speaker segmentation and clustering","volume":"73","author":"Pardo","year":"2017","journal-title":"J. Experts Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3079","DOI":"10.1016\/S0167-8655(03)00167-3","article-title":"Regularized discriminant analysis for the small sample size problem in face recognition","volume":"24","author":"Lu","year":"2003","journal-title":"Pattern Recognit. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1109\/TNN.2006.885038","article-title":"Weighted piecewise LDA for solving the small sample size problem in face verification","volume":"18","author":"Kyperountas","year":"2007","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1528","DOI":"10.1016\/j.patcog.2007.10.024","article-title":"Kernel quadratic discriminant analysis for small sample size problem","volume":"41","author":"Wang","year":"2008","journal-title":"Pattern Recognit."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yu, Y., McKelvey, T., and Kung, S. (2013, January 26\u201331). A classification scheme for \u2018high-dimensional-small-sample-size\u2019 data using soda and ridge-SVM with microwave measurement applications. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638317"},{"key":"ref_7","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"Volume 1","author":"Krizhevsky","year":"2012","journal-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS\u201912)"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., and Greenspan, H. (2018, January 4\u20137). Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363576"},{"key":"ref_9","first-page":"10","article-title":"Automated Classification of Gait Abnormalities in Children with Autism Spectrum Disorders Based on Kinematic Data","volume":"2","author":"Hasan","year":"2017","journal-title":"Int. J. Psychiatry Psychother."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0202348","article-title":"An Analysis of 3D Knee Kinematic Data Complexity in Knee Osteoarthritis and Asymptomatic Controls","volume":"13","author":"Mezghani","year":"2018","journal-title":"PLoS ONE"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1177\/1759720X09342132","article-title":"Focusing osteoarthritis management on modifiable risk factors and future therapeutic prospects","volume":"1","author":"Hunter","year":"2009","journal-title":"Ther. Adv. Musculoskelet. Dis."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.clinbiomech.2010.10.004","article-title":"Effects of physiotherapy treatment on knee osteoarthritis gait data using principal component analysis","volume":"26","author":"Gaudreault","year":"2010","journal-title":"Clin. Biomech."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ben Nouma, B., Mezghani, N., Mitiche, A., and Ouakrim, Y. (2018, January 27\u201328). A variational method to determine the most representative shape of a set of curves and its application to knee kinematic data for pathology classification. Proceedings of the 2nd Mediterranean Conference on Pattern Recognition and Artificial Intelligence (MedPRAI \u201918), Rabat, Morocco.","DOI":"10.1145\/3177148.3180095"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"32","DOI":"10.5430\/jbei.v4n1p32","article-title":"Knee kinematic curve representation and application to knee pathology classification","volume":"4","author":"Mitiche","year":"2018","journal-title":"J. Biomed. Eng. Inform."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mechmeche, I., Mitiche, A., Ouakrim, Y., De Guise, J., and Mezghani, N. (2016, January 16\u201320). Data correction to determine a representative pattern of a set of 3D knee kinematic measurements. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590842"},{"key":"ref_16","first-page":"1","article-title":"Classification of asymptomatic and osteoarthritic knee gait patterns using gait analysis via deterministic learning","volume":"2","author":"Zeng","year":"2018","journal-title":"Artif. Intell. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1016\/j.patrec.2010.01.003","article-title":"A multi-classifier for grading knee osteoarthritis using gait analysis","volume":"31","author":"Koktas","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jbiomech.2016.12.022","article-title":"Mechanical biomarkers of medial compartment knee osteoarthritis diagnosis and severity grading: Discovery phase","volume":"52","author":"Mezghani","year":"2017","journal-title":"J. Biomech."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kotz, S., and Johnson, N.L. (1992). The Generalization of Student\u2019s Ratio. Breakthroughs in Statistics, Springer.","DOI":"10.1007\/978-1-4612-4380-9"},{"key":"ref_20","unstructured":"Johnson, R., and Wichern, D. (2007). Applied Multivariate Statistical Analysis, Prentice-Hall, Inc.. [6 ed.]."},{"key":"ref_21","first-page":"1","article-title":"Similarity-based methods: A general framework for classification, approximation and association","volume":"29","author":"Duch","year":"2000","journal-title":"Control. Cybern."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ben Nouma, B., Mitiche, A., and Mezghani, N. (2019). A Sample-Encoding Generalization of the Kohonen Associative Memory and Application to Knee Kinematic Data Representation and Pathology Classification. Appl. Sci., 9.","DOI":"10.3390\/app9091741"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1007\/BF02834632","article-title":"Mahalanobis distance","volume":"4","author":"McLachlan","year":"1999","journal-title":"Resonance"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Daubechies, I. (1992). Ten Lectures on Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics.","DOI":"10.1137\/1.9781611970104"},{"key":"ref_25","first-page":"65","article-title":"Dimensionality Reduction for Motor Imagery Signal Classification using Wavelet Analysis","volume":"10","author":"Akanksha","year":"2017","journal-title":"Int. J. Control. Theory Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Thepade, S., and Erandole, S. (2013, January 11\u201312). Extended performance comparison of tiling based image compression using wavelet transforms & hybrid wavelet transforms. Proceedings of the 2013 IEEE Conference on Information & Communication Technologies, Tamil Nadu, India.","DOI":"10.1109\/CICT.2013.6558273"},{"key":"ref_27","first-page":"380","article-title":"Wavelet Coefficients Reduction Method Based On Standard Deviation Concept For High Quality Compressed Image","volume":"79","author":"Taujuddin","year":"2015","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1016\/j.mcm.2010.02.019","article-title":"Wavelet Transform application to the compression of images","volume":"52","author":"Boix","year":"2010","journal-title":"Math. Comput. Model."},{"key":"ref_29","first-page":"218","article-title":"RELIEF: Feature Selection Approach","volume":"4","author":"Rosario","year":"2015","journal-title":"Int. J. Innov. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1007\/s00167-011-1867-4","article-title":"The Knee KG system: a review of the literature","volume":"20","author":"Lustig","year":"2012","journal-title":"Knee Surg. Sports Traumatol. Arthrosc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1926","DOI":"10.1016\/j.jbiomech.2005.05.013","article-title":"A reproducible method for studying three-dimensional knee kinematics","volume":"38","author":"Hagemeister","year":"2005","journal-title":"J. Biomech."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.gaitpost.2007.11.002","article-title":"Reliability of a method for analyzing three-dimensional knee kinematics during gait","volume":"28","author":"Labbe","year":"2007","journal-title":"Gait Posture"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6190","DOI":"10.1016\/j.eswa.2010.11.050","article-title":"Wavelet basis functions in biomedical signal processing","volume":"38","author":"Rafiee","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1109\/LGRS.2008.2001282","article-title":"Limitations of Principal Components Analysis for Hyperspectral Target Recognition","volume":"5","author":"Prasad","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","unstructured":"(2018, October 10). Hotelling T-Squared Testing Procedures for Multivariate Samples. Available online: https:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/2844-hotellingt2."},{"key":"ref_36","first-page":"89","article-title":"Damage detection using large-scale covariance matrix","volume":"Volume 5","author":"Balsamo","year":"2014","journal-title":"Proceedings of the Conference and Exposition on Structural Dynamics"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1162\/neco.1996.8.3.629","article-title":"A Theoretical and Experimental Account of n-Tuple Classifier Performance","volume":"8","author":"Rohwer","year":"1996","journal-title":"Neural Comput."},{"key":"ref_38","unstructured":"Bishop, C.M. (2012). Pattern Recognition and Machine Learning, Elsevier."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7472039","DOI":"10.1155\/2019\/7472039","article-title":"Knee Joint Biomechanical Gait Data Classification for Knee Pathology Assessment: A Literature Review","volume":"2019","author":"Abid","year":"2019","journal-title":"Appl. Bionics Biomech."},{"key":"ref_40","unstructured":"Kira, K., and Rendell, L. (1992, January 1\u20133). A Practical Approach to Feature Selection. Proceedings of the Ninth International Workshop on Machine Learning (ML \u201992), San Francisco, CA, USA."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/1\/3\/45\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:03:01Z","timestamp":1760187781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/1\/3\/45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,5]]},"references-count":40,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["make1030045"],"URL":"https:\/\/doi.org\/10.3390\/make1030045","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,5]]}}}