{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:51:41Z","timestamp":1760151101522,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T00:00:00Z","timestamp":1645401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Operational Programme &quot;Competitiveness, Entrepreneurship and Innovation&quot; (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).","award":["\u201cMEGATRON\u201d (MIS 5047227) which is implemented under the Action \u201cReinforcement of the Research and Innovation Infrastructure\u201d"],"award-info":[{"award-number":["\u201cMEGATRON\u201d (MIS 5047227) which is implemented under the Action \u201cReinforcement of the Research and Innovation Infrastructure\u201d"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Hemiplegia affects a significant portion of the human population. It is a condition that causes motor impairment and severely reduces the patient\u2019s quality of life. This paper presents an automatic system for identifying the hemiplegia type (right or left part of the body is affected). The proposed system utilizes the data taken from patients and healthy subjects using the accelerometer sensor from the RehaGait mobile gait analysis system. The collected data undergo a pre-processing procedure followed by a feature extraction stage. The extracted features are then sent to a neural network trained by the Levenberg-Marquardt backpropagation (LM-BP) algorithm. The experimental part of this research involved creating a custom-created dataset containing entries taken from ten healthy and twenty non-healthy subjects. The data were taken from seven different sensors placed in specific areas of the subjects\u2019 bodies. These sensors can capture a three-dimensional (3D) signal using the accelerometer, magnetometer, and gyroscope device types. The proposed system used the signals taken from the accelerometers, which were split into 2-sec windows. The proposed system achieved a classification accuracy of 95.12% and was compared with fourteen commonly used machine learning approaches.<\/jats:p>","DOI":"10.3390\/info13020101","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T20:47:45Z","timestamp":1645476465000},"page":"101","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Automatic Hemiplegia Type Detection (Right or Left) Using the Levenberg-Marquardt Backpropagation Method"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3231-8852","authenticated-orcid":false,"given":"Vasileios","family":"Christou","sequence":"first","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, GR47100 Arta, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandros","family":"Arjmand","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, GR47100 Arta, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6920-1500","authenticated-orcid":false,"given":"Dimitrios","family":"Dimopoulos","sequence":"additional","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, University of Ioannina, S. Niarchos Ave, GR45110 Ioannina, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3639-9274","authenticated-orcid":false,"given":"Dimitrios","family":"Varvarousis","sequence":"additional","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, University of Ioannina, S. Niarchos Ave, GR45110 Ioannina, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioannis","family":"Tsoulos","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, GR47100 Arta, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9043-1290","authenticated-orcid":false,"given":"Alexandros T.","family":"Tzallas","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, GR47100 Arta, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1113-8462","authenticated-orcid":false,"given":"Christos","family":"Gogos","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, GR47100 Arta, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6757-1698","authenticated-orcid":false,"given":"Markos G.","family":"Tsipouras","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Western Macedonia, GR50100 Kozani, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evripidis","family":"Glavas","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, GR47100 Arta, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6828-3196","authenticated-orcid":false,"given":"Avraam","family":"Ploumis","sequence":"additional","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, University of Ioannina, S. Niarchos Ave, GR45110 Ioannina, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0615-783X","authenticated-orcid":false,"given":"Nikolaos","family":"Giannakeas","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, GR47100 Arta, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,21]]},"reference":[{"key":"ref_1","unstructured":"Davies, P.M. (2000). Steps to Follow: The Comprehensive Treatment of Patients with Hemiplegia, Springer Science & Business Media."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1161\/01.STR.14.3.438","article-title":"Understanding stroke and its rehabilitation","volume":"14","author":"Ruskin","year":"1983","journal-title":"Stroke"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.gaitpost.2016.07.269","article-title":"Mobile inertial sensor based gait analysis: Validity and reliability of spatiotemporal gait characteristics in healthy seniors","volume":"49","author":"Donath","year":"2016","journal-title":"Gait Posture"},{"key":"ref_4","unstructured":"HASOMED (2021, September 03). RehaGait\u2014Mobile Gait Analysis. Available online: https:\/\/hasomed.de\/en\/products\/rehagait\/."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1177\/0269215512452880","article-title":"Can falls be predicted with gait analytical and posturographic measurement systems? A prospective follow-up study in a nursing home population","volume":"27","author":"Schwesig","year":"2013","journal-title":"Clin. Rehabilit."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lee, J., Park, S., and Shin, H. (2018). Detection of Hemiplegic Walking Using a Wearable Inertia Sensing Device. Sensors, 18.","DOI":"10.3390\/s18061736"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ji, N., Zhou, H., Guo, K., Samuel, O.W., Huang, Z., Xu, L., and Li, G. (2019). Appropriate mother wavelets for continuous gait event detection based on time-frequency analysis for hemiplegic and healthy individuals. Sensors, 19.","DOI":"10.3390\/s19163462"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.bbe.2016.03.002","article-title":"Gait patterns classification based on cluster and bicluster analysis","volume":"36","author":"Pauk","year":"2016","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_9","first-page":"39","article-title":"Early Detection of Hemiplegia by Analyzing the Gait Characteristics and Walking Patterns Using","volume":"Volume 1118","author":"Patil","year":"2019","journal-title":"Proceedings of the Soft Computing and Signal Processing, Proceedings of the 2nd ICSCSP 2019, Hyderabad, India, 21\u201322 June 2019"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Padilla, U. Fuzzy Classification of Hemiplegic Gait Using Kinematic Indicators in Knee. Proceedings of the VI Latin American Congress on Biomedical Engineering CLAIB 2014 Paran\u00e1, Argentina, 29\u201331 October 2014.","DOI":"10.1007\/978-3-319-13117-7_152"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2014\/939316","article-title":"Gait Patterns in Hemiplegic Patients with Equinus Foot Deformity","volume":"2014","author":"Manca","year":"2014","journal-title":"BioMed Res. Int."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1007\/s12541-015-0051-z","article-title":"Gait patterns of chronic ambulatory hemiplegic elderly compared with normal Age-Matched elderly","volume":"16","author":"Kim","year":"2015","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"LeMoyne, R., Kerr, W., Mastroianni, T., and Hessel, A. (2014, January 3\u20136). Implementation of machine learning for classifying hemiplegic gait disparity through use of a force plate. Proceedings of the 2014 13th International Conference on Machine Learning and Applications, Detroit, MI, USA.","DOI":"10.1109\/ICMLA.2014.67"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jung, S., Bong, J., Kim, S.-J., and Park, S. (2021). DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients. Appl. Sci., 11.","DOI":"10.3390\/app11073163"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yardimci, A. (2007). Fuzzy Logic Based Gait Classification for Hemiplegic Patients. International Symposium on Intelligent Data Analysis, Springer.","DOI":"10.1007\/978-3-540-74825-0_31"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Luo, H., and Luo, J. (2018, January 25\u201327). Evaluating the Intra-limb Coordination during Gait in Hemiplegia. Proceedings of the 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS), Shenzhen, China.","DOI":"10.1109\/CBS.2018.8612239"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1016\/j.apmr.2003.11.039","article-title":"Foot contact pattern analysis in hemiplegic stroke patients: An implication for neurologic status determination","volume":"85","author":"Wong","year":"2004","journal-title":"Arch. Phys. Med. Rehabilit."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"LeMoyne, R., and Mastroianni, T. (2018, January 17\u201320). Implementation of a smartphone as a wearable and wireless gyroscope platform for machine learning classification of hemiplegic gait through a multi-layer perceptron neural network. Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00153"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1007\/s11634-020-00427-2","article-title":"Automatic gait classification patterns in spastic hemiplegia","volume":"14","author":"Aguilera","year":"2020","journal-title":"Adv. Data Anal. Classif."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1109\/TNSRE.2021.3076366","article-title":"Machine-learning-based prediction of gait events from EMG in cerebral palsy children","volume":"29","author":"Morbidoni","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabilit. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Agostini, V., Knaflitz, M., Nascimberi, A., and Gaffuri, A. (2014, January 11\u201312). Gait measurements in hemiplegic children: An automatic analysis of foot-floor contact sequences and electromyographic patterns. Proceedings of the 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Lisboa, Portugal.","DOI":"10.1109\/MeMeA.2014.6860061"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Di Nardo, F. (2019). EMG-based characterization of walking asymmetry in children with mild hemiplegic cerebral palsy. Biosensors, 9.","DOI":"10.3390\/bios9030082"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1177\/0309364613506911","article-title":"Validation of the activPAL activity monitor in children with hemiplegic gait patterns resultant from cerebral palsy","volume":"38","author":"McAloon","year":"2014","journal-title":"Prosthet. Orthot. Int."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.gaitpost.2012.07.030","article-title":"Effect of fine wire electrode insertion on gait patterns in children with hemiplegic cerebral palsy","volume":"37","author":"Krzak","year":"2013","journal-title":"Gait Posture"},{"key":"ref_25","first-page":"1578","article-title":"Gait analysis of children with spastic hemiplegic cerebral palsy","volume":"7","author":"Wang","year":"2012","journal-title":"Neural Regen. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Aguilera, A., Subero, A., and Mata-Toledo, R. (2013). Application of Data Mining Techniques on EMG Registers of Hemiplegic Patients. Industrial Conference on Data Mining, Springer.","DOI":"10.1007\/978-3-642-39736-3_20"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Abaid, N., Cappa, P., Palermo, E., Petrarca, M., and Porfiri, M. (2013). Gait detection in children with and without hemiplegia using single-axis wearable gyroscopes. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0073152"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Watanabe, T., and Miyazawa, T.A. (2015). Validation Test of a Simple Method of Stride Length Measurement Only with Inertial Sensors and a Preliminary Test in FES-assisted Hemiplegic Gait. World Congress on Medical Physics and Biomedical Engineering Toronto, Ontario, Canada, Springer.","DOI":"10.1007\/978-3-319-19387-8_270"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/1350-4533(95)97321-F","article-title":"A body-worn gait analysis system for evaluating hemiplegic gait","volume":"17","author":"Granat","year":"1995","journal-title":"Med. Eng. Phys."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1002\/eej.23152","article-title":"Evaluation of hemiplegia caused by stroke by using joint detection of depth sensors-case of SIAS","volume":"206","author":"Ohnishi","year":"2019","journal-title":"Electr. Eng. Jpn."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kumari, P., Cooney, N.J., Kim, T.-S., and Minhas, A.S. (2018, January 10\u201312). Gait analysis in Spastic Hemiplegia and Diplegia cerebral palsy using a wearable activity tracking device-a data quality analysis for deep convolutional neural networks. Proceedings of the 2018 5th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), Nadi, Fiji.","DOI":"10.1109\/APWConCSE46201.2018.8950057"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/s00371-016-1330-0","article-title":"Classification of gait anomalies from kinect","volume":"34","author":"Li","year":"2018","journal-title":"Vis. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pandit, T., Nahane, H., Lade, D., and Rao, V. (2019, January 16\u201319). Abnormal gait detection by classifying inertial sensor data using transfer learning. Proceedings of the 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA.","DOI":"10.1109\/ICMLA.2019.00236"},{"key":"ref_34","unstructured":"Azlan, W.N.W., Zakaria, W.N.W., Othman, N., Mohd, M.N.H., and Ghani, M.N.A. Evaluation of Leap Motion Controller Usability in Development of Hand Gesture Recognition for Hemiplegia Patients. Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"80300","DOI":"10.1109\/ACCESS.2019.2923077","article-title":"Automatic detection of compensatory movement patterns by a pressure distribution mattress using machine learning methods: A pilot study","volume":"7","author":"Cai","year":"2019","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Christou, V. (2021, January 24\u201326). Neural network-based approach for hemiplegia detection via accelerometer signals. Proceedings of the 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference, Preveza, Greece.","DOI":"10.1109\/SEEDA-CECNSM53056.2021.9566216"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Priya, S.J., Rani, A.J., Subathra, M., Mohammed, M.A., Dama\u0161evi\u010dius, R., and Ubendran, N. (2021). Local pattern transformation based feature extraction for recognition of Parkinson\u2019s disease based on gait signals. Diagnostics, 11.","DOI":"10.3390\/diagnostics11081395"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/S0893-6080(05)80056-5","article-title":"A scaled conjugate gradient algorithm for fast supervised learning","volume":"6","year":"1993","journal-title":"Neural Netw."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1090\/qam\/10666","article-title":"A method for the solution of certain non-linear problems in least squares","volume":"2","author":"Levenberg","year":"1944","journal-title":"Q. Appl. Math."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1137\/0111030","article-title":"An Algorithm for Least-Squares Estimation of Nonlinear Parameters","volume":"11","author":"Marquardt","year":"1963","journal-title":"J. Soc. Ind. Appl. Math."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Scales, L. (1985). Introduction to Non-Linear Optimization, Macmillan International Higher Education.","DOI":"10.1007\/978-1-349-17741-7"},{"key":"ref_42","first-page":"1591","article-title":"BFGS method: A new search direction","volume":"43","author":"Hery","year":"2014","journal-title":"Sains Malays."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1162\/neco.1992.4.2.141","article-title":"First-and second-order methods for learning: Between steepest descent and Newton\u2019s method","volume":"4","author":"Battiti","year":"1992","journal-title":"Neural Comput."},{"key":"ref_44","first-page":"10","article-title":"Cauchy and the gradient method","volume":"251","year":"2012","journal-title":"Doc. Math. Extra."},{"key":"ref_45","unstructured":"Riedmiller, M., and Braun, H. (April, January 28). A direct adaptive method for faster backpropagation learning: The RPROP algorithm. Proceedings of the IEEE International Conference on Neural Networks, San Francisco, CA, USA."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1162\/neco.1992.4.3.415","article-title":"Bayesian interpolation","volume":"4","author":"MacKay","year":"1992","journal-title":"Neural Comput."},{"key":"ref_47","unstructured":"Foresee, F.D., and Hagan, M.T. (1997, January 12). Gauss-Newton approximation to Bayesian learning. Proceedings of the International Conference on Neural Networks (ICNN\u201997), Houston, TX, USA."},{"key":"ref_48","unstructured":"Tieleman, T., and Hinton, G. (2012). Coursera: Neural Networks for Machine Learning-Lecture 6.5: RMSprop, University of Toronto."},{"key":"ref_49","unstructured":"Kingma, D.P., and Adam, J.B. (2015, January 7\u20139). Adam: A method for stochastic optimizatio. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_50","first-page":"7","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"Duchi","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1109\/TNN.2009.2036259","article-title":"OP-ELM: Optimally pruned extreme learning machine","volume":"21","author":"Miche","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"555","DOI":"10.5194\/isprsarchives-XL-1-W5-555-2015","article-title":"Snow depth estimation using time series passive microwave imagery via genetically support vector regression (case study urmia lake basin)","volume":"40","author":"Zahir","year":"2015","journal-title":"ISPRS\u2014Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1016\/B978-0-444-63456-6.50160-5","article-title":"Product Quality Monitoring Using Extreme Learning Machines and Bat Algorithms: A Case Study in Second-Generation Ethanol Production","volume":"Volume 33","author":"Farias","year":"2014","journal-title":"Computer Aided Chemical Engineering"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/2\/101\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:23:48Z","timestamp":1760135028000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/2\/101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,21]]},"references-count":54,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["info13020101"],"URL":"https:\/\/doi.org\/10.3390\/info13020101","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2022,2,21]]}}}