{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T11:20:39Z","timestamp":1767612039855,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:00:00Z","timestamp":1659484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The application of machine learning (ML) has made an unprecedented change in the field of medicine, showing a significant potential to automate tasks and to achieve objectives that are closer to human cognitive capabilities. Human gait, in particular, is a series of continuous metabolic interactions specific for humans. The need for an intelligent recognition of dynamic changes of gait enables physicians in clinical practice to early identify impaired gait and to reach proper decision making. Because of the underlying complexity of the biological system, it can be difficult to create an accurate detection and analysis of imbalanced gait. This paper proposes a novel Criticality Analysis (CA) methodology as a feasible method to extract the dynamic interactions involved in human gait. This allows a useful scale-free representation of multivariate dynamic data in a nonlinear representation space. To quantify the effectiveness of the CA methodology, a Support Vector Machine (SVM) algorithm is implemented in order to identify the nonlinear relationships and high-order interactions between multiple gait data variables. The gait features extracted from the CA method were used for training and testing the SVM algorithm. The simulation results of this paper show that the implemented SVM model with the support of the CA method increases the accuracy and enhances the efficiency of gait analysis to extremely high levels. Therefore, it can perform as a robust classification tool for detection of dynamic disturbances of biological data patterns and creates a tremendous opportunity for clinical diagnosis and rehabilitation.<\/jats:p>","DOI":"10.3390\/computers11080120","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T20:52:01Z","timestamp":1659559921000},"page":"120","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Novel Criticality Analysis Technique for Detecting Dynamic Disturbances in Human Gait"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8987-6591","authenticated-orcid":false,"given":"Shadi","family":"Eltanani","sequence":"first","affiliation":[{"name":"School of Engineering, Computing and Mathematics, Faculty of Technology, Design and Environment, Oxford Brookes University, Wheatley Campus, Wheatley, Oxford OX33 1HX, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1441-1444","authenticated-orcid":false,"given":"Tjeerd V.","family":"olde Scheper","sequence":"additional","affiliation":[{"name":"School of Engineering, Computing and Mathematics, Faculty of Technology, Design and Environment, Oxford Brookes University, Wheatley Campus, Wheatley, Oxford OX33 1HX, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2933-5213","authenticated-orcid":false,"given":"Helen","family":"Dawes","sequence":"additional","affiliation":[{"name":"College of Medicine and Health, University of Exeter, St. Luke\u2019s Campus, Exeter EX1 2LU, UK"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Eltanani, S., Scheper, T.O., and Dawes, H.K. (2021, January 5\u20138). Nearest Neighbor Algorithm: Proposed Solution for Human Gait Data Classification. Proceedings of the IEEE Symposium on Computers and Communications (ISCC), Athens, Greece.","DOI":"10.1109\/ISCC53001.2021.9631454"},{"key":"ref_2","first-page":"103122","article-title":"Biologically Inspired Rate Control of Chaos. Chaos: An Interdiscip","volume":"27","year":"2017","journal-title":"J. Nonlinear Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1016\/S0960-0779(03)00070-5","article-title":"Chaos in a Bienzymatic Cyclic Model with Two Autocatalytic Loops","volume":"18","author":"Berry","year":"2003","journal-title":"Chaos Solitons Fractals"},{"key":"ref_4","unstructured":"olde Scheper, T.V. (2021). Self-Organised Criticality Equation Files [Data set]. Zenodo."},{"key":"ref_5","unstructured":"olde Scheper, T.V. (2017, January 26\u201328). Criticality in Biocomputation. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"043126","DOI":"10.1063\/1.3270048","article-title":"Control of Spatiotemporal Patterns in the Gray\u2013Scott Model","volume":"19","author":"Kyrychko","year":"2009","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_7","unstructured":"Vapnik, V. (1998). Statistical Learning Theory, Wiley."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cristianini, N., and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press.","DOI":"10.1017\/CBO9780511801389"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Boyd, S., and Vandenberghe, L. (2004). Convex Optimization, Cambridge University Press.","DOI":"10.1017\/CBO9780511804441"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Shalev-Shwartz, S., and Ben-David, S. (2014). Understanding Machine Learning\u2014From Theory to Algorithms, Cambridge University Press.","DOI":"10.1017\/CBO9781107298019"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TPAMI.2019.2906594","article-title":"Back to the Future: Radial Basis Function Network Revisited","volume":"Volume 42","author":"Que","year":"2020","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref_13","unstructured":"Panchapakesan, C., Ralph, D., and Palaniswami, M. (1998, January 4\u20139). Effects of Moving the Centers in an RBF Network. Proceedings of the IEEE International Joint Conference on Neural Networks Proceedings, IEEE World Congress on Computational Intelligence (Cat. No.98CH36227), Anchorage, AK, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1016\/j.jbiomech.2009.03.049","article-title":"IMU: Inertial Sensing of Vertical CoM Movement","volume":"42","author":"Esser","year":"2009","journal-title":"J. Biomech."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1016\/j.gaitpost.2013.02.016","article-title":"Insights into Gait Disorders: Walking Variability Using Phase Plot Analysis, Parkinson\u2019s Disease","volume":"38","author":"Esser","year":"2013","journal-title":"Gait Posture"},{"key":"ref_16","unstructured":"(2022, April 01). AX3 GUI \u00b7 digitalinteraction\/openmovement Wiki. Available online: https:\/\/github.com\/digitalinteraction\/openmovement\/wiki\/AX3-GUI."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"olde Scheper, T.V. (2022). Controlled Bio-Inspired Self-Organised Criticality. Plos ONE, 17.","DOI":"10.1371\/journal.pone.0260016"},{"key":"ref_18","unstructured":"(2022, May 15). CVX: Matlab Software for Disciplined Convex Programming|CVX Research, Inc. Available online: http:\/\/cvxr.com\/cvx."},{"key":"ref_19","first-page":"1692","article-title":"Multi-Class Associative Classification Based on Intersection Method and Extended Chi-Square Testing","volume":"28","author":"Sun","year":"2008","journal-title":"J. Comput. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Varrecchia, T., Castiglia, S.F., Ranavolo, A., Conte, C., Tatarelli, A., Coppola, G., Di Lorenzo, C., Draicchio, F., Pierelli, F., and Serrao, M. (2021). An Artificial Neural Network Approach to Detect Presence and Severity of Parkinson\u2019s Disease via Gait Parameters. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0244396"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, F.-C., Chen, S.-F., Lin, C.-H., Shih, C.-J., Lin, A.-C., Yuan, W., Li, Y.-C., and Kuo, T.-Y. (2021). Detection and Classification of Stroke Gaits by Deep Neural Networks Employing Inertial Measurement Units. Sensors, 21.","DOI":"10.3390\/s21051864"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.clinbiomech.2019.12.031","article-title":"Automatic Classification of Gait Patterns in Children with Cerebral Palsy Using Fuzzy Clustering Method","volume":"73","author":"Darbandi","year":"2020","journal-title":"Clin. Biomech."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Saleem, F., Khan, M.A., Alhaisoni, M., Tariq, U., Armghan, A., Alenezi, F., Choi, J.-I., and Kadry, S. (2021). Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion. Sensors, 21.","DOI":"10.3390\/s21227584"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Trabassi, D., Serrao, M., Varrecchia, T., Ranavolo, A., Coppola, G., De Icco, R., Tassorelli, C., and Castiglia, S.F. (2022). Machine Learning Approach to Support the Detection of Parkinson\u2019s Disease in IMU-Based Gait Analysis. 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