{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T03:01:42Z","timestamp":1790996502751,"version":"4.1.0"},"reference-count":101,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,10]],"date-time":"2019-01-10T00:00:00Z","timestamp":1547078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This article presents a machine learning methodology for diagnosing Parkinson\u2019s disease (PD) based on the use of vertical Ground Reaction Forces (vGRFs) data collected from the gait cycle. A classification engine assigns subjects to healthy or Parkinsonian classes. The diagnosis process involves four steps: data pre-processing, feature extraction and selection, data classification and performance evaluation. The selected features are used as inputs of each classifier. Feature selection is achieved through a wrapper approach established using the random forest algorithm. The proposed methodology uses both supervised classification methods including K-nearest neighbour (K-NN), decision tree (DT), random forest (RF), Na\u00efve Bayes (NB), support vector machine (SVM) and unsupervised classification methods such as K-means and the Gaussian mixture model (GMM). To evaluate the effectiveness of the proposed methodology, an online dataset collected within three different studies is used. This data set includes vGRF measurements collected from eight force sensors placed under each foot of the subjects. Ninety-three patients suffering from Parkinson\u2019s disease and 72 healthy subjects participated in the experiments. The obtained performances are compared with respect to various metrics including accuracy, precision, recall and F-measure. The classification performance evaluation is performed using the leave-one-out cross validation. The results demonstrate the ability of the proposed methodology to accurately differentiate between PD subjects and healthy subjects. For the purpose of validation, the proposed methodology is also evaluated with an additional dataset including subjects with neurodegenerative diseases (Amyotrophic Lateral Sclerosis (ALS) and Huntington\u2019s disease (HD)). The obtained results show the effectiveness of the proposed methodology to discriminate PD subjects from subjects with other neurodegenerative diseases with a relatively high accuracy.<\/jats:p>","DOI":"10.3390\/s19020242","type":"journal-article","created":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T04:10:16Z","timestamp":1547179816000},"page":"242","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":105,"title":["Data-Driven Based Approach to Aid Parkinson\u2019s Disease Diagnosis"],"prefix":"10.3390","volume":"19","author":[{"given":"Nicolas","family":"Khoury","sequence":"first","affiliation":[{"name":"Laboratory of Images, Signals and Intelligent Systems (LISSI), University of Paris-Est Cr\u00e9teil (UPEC), 122 rue Paul Armangot, 94400 Vitry-Sur-Seine, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1668-7620","authenticated-orcid":false,"given":"Ferhat","family":"Attal","sequence":"additional","affiliation":[{"name":"Laboratory of Images, Signals and Intelligent Systems (LISSI), University of Paris-Est Cr\u00e9teil (UPEC), 122 rue Paul Armangot, 94400 Vitry-Sur-Seine, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yacine","family":"Amirat","sequence":"additional","affiliation":[{"name":"Laboratory of Images, Signals and Intelligent Systems (LISSI), University of Paris-Est Cr\u00e9teil (UPEC), 122 rue Paul Armangot, 94400 Vitry-Sur-Seine, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Latifa","family":"Oukhellou","sequence":"additional","affiliation":[{"name":"French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR), University of Paris-Est, COSYS, GRETTIA, F-77447 Marne la Vall\u00e9e, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samer","family":"Mohammed","sequence":"additional","affiliation":[{"name":"Laboratory of Images, Signals and Intelligent Systems (LISSI), University of Paris-Est Cr\u00e9teil (UPEC), 122 rue Paul Armangot, 94400 Vitry-Sur-Seine, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1016\/j.eswa.2009.06.040","article-title":"Comparison of multiple classification methods for diagnosis of Parkinson disease","volume":"37","author":"Das","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1016\/j.cmpb.2014.01.004","article-title":"A new hybrid intelligent system for accurate detection of Parkinson\u2019s disease","volume":"113","author":"Hariharan","year":"2014","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"10764","DOI":"10.1016\/j.eswa.2012.02.189","article-title":"Parkinson\u2019s Disease tremor classification\u2013A comparison between Support Vector Machines and neural networks","volume":"39","author":"Pan","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1212\/01.wnl.0000247740.47667.03","article-title":"Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030","volume":"68","author":"Dorsey","year":"2007","journal-title":"Neurology"},{"key":"ref_5","unstructured":"(2015, October 15). Circulaire SG\/DGOS\/R4\/DGS\/MC3\/DGCS\/3A\/CNSA no 2015-281 du 7 Septembre 2015 Relative \u00e0 la Mise en Oeuvre du Plan Maladies Neuro-D\u00e9G\u00e9N\u00e9Ratives 2014\u20132019. Available online: https:\/\/solidarites-sante.gouv.fr\/fichiers\/bo\/2015\/15-09\/ste_20150009_0000_0056.pdf."},{"key":"ref_6","unstructured":"(2019, January 10). FRANCE PARKINSON et CGE DISTRIBUTION: Un Accord de M\u00e9C\u00e9Nat Pour Faire Connaitre la Maladie dans le Milieu de L\u2019entreprise. Available online: https:\/\/www.franceparkinson.fr\/wp-content\/uploads\/2016\/10\/CP-FRANCE-PARKINSON-et-CGE-DISTRIBUTION.pdf."},{"key":"ref_7","unstructured":"(2013, April 25). Parkinson et Souffrances de vie. Available online: https:\/\/neurologies.fr\/parkinson-et-souffrances-de-vie\/."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/S0140-6736(14)61682-2","article-title":"Global, regional, and national age\u2013sex specific all-cause and cause-specific mortality for 240 causes of death, 1990\u20132013","volume":"385","author":"Beyene","year":"2015","journal-title":"Lancet"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/S0140-6736(15)60692-4","article-title":"Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990\u20132013: A systematic analysis for the Global Burden of Disease Study 2013","volume":"386","author":"Vos","year":"2015","journal-title":"Lancet"},{"key":"ref_10","unstructured":"Mosley, A.D. (2010). The Encyclopedia of Parkinson\u2019s Disease, Facts on File. [2nd ed.]."},{"key":"ref_11","first-page":"153","article-title":"Members of the UPDRS Development Committee","volume":"Volume 2","author":"Fahn","year":"1987","journal-title":"Recent Development in Parkinson\u2019s Disease"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1212\/WNL.17.5.427","article-title":"Parkinsonism onset, progression, and mortality","volume":"17","author":"Hoehn","year":"1967","journal-title":"Neurology"},{"key":"ref_13","unstructured":"Gillingham, F.J., and Donaldson, I.M.L. (1969). Projection technique for evaluating surgery in Parkinson\u2019s disease. Third Symposium on Parkinson\u2019s Disease, Scientific Research Publishing."},{"key":"ref_14","first-page":"61","article-title":"The Barthel Index","volume":"14","author":"Mahoney","year":"1965","journal-title":"Md State Med. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/0022-3956(75)90026-6","article-title":"Mini-mental state. A practical method for grading the cognitive state of patients for the clinician","volume":"12","author":"Folstein","year":"1975","journal-title":"J. Psychiatr. Res."},{"key":"ref_16","unstructured":"Schmand, B., Lindeboom, J., and Van Harskamp, F. (1992). Dutch Adult Reading Test, Swets & Zeitlinger."},{"key":"ref_17","unstructured":"Lezak, M.D. (2004). Neuropsychological Assessment, Oxford University Press."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1260\/2040-2295.6.3.281","article-title":"Computer-aided diagnosis of Parkinson\u2019s disease using complex-valued neural networks and mRMR feature selection algorithm","volume":"6","author":"Peker","year":"2015","journal-title":"J. Healthc. Eng."},{"key":"ref_19","unstructured":"Kenneth, R., Gorunescu, F., and Salem, A.M. (2009, January 12\u201314). Feature selection in Parkinson\u2019s disease: A rough sets approach. Proceedings of the International Multiconference on Computer Science and Information Technology (IMCSIT\u201909), Mr\u0105gowo, Poland."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yadav, G., Kumar, Y., and Sahoo, G. (2012, January 21\u201322). Predication of Parkinson\u2019s disease using data mining methods: A comparative analysis of tree, statistical and support vector machine classifiers. Proceedings of the National Conference on Computing and Communication Systems (NCCCS), Durgapur, India.","DOI":"10.1109\/NCCCS.2012.6413034"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Safi, K., Diab, A., Albertsen, I.M., Hutin, E., Mohammed, S., Khalil, M., Amirat, Y., and Gracies, J.M. (2015, January 16\u201318). Non-linear analysis of human stability during static posture. Proceedings of the International Conference on Advances in Biomedical Engineering (ICABME), Beirut, Lebanon.","DOI":"10.1109\/ICABME.2015.7323308"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/86.242405","article-title":"Characterization and modeling of postural steadiness in the elderly: A review","volume":"1","author":"Prieto","year":"1993","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"ref_23","unstructured":"(2018, October 22). Diagnosing Parkinson\u2019s Disease. Available online: https:\/\/www.medicalnewstoday.com\/articles\/323412.php."},{"key":"ref_24","first-page":"210","article-title":"Basic gait parameters: Reference data for normal subjects, 10\u201379 years of age","volume":"30","author":"Oberg","year":"1993","journal-title":"J. Rehabil. Res. Dev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s10916-014-0147-5","article-title":"HMM for classification of Parkinson\u2019s disease based on the raw gait data","volume":"38","author":"Khorasani","year":"2014","journal-title":"J. Med. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1007\/s004150070070","article-title":"Assessment of gait in subcortical vascular encephalopathy by computerized analysis: A cross-sectional and longitudinal study","volume":"247","author":"Oster","year":"2000","journal-title":"J. Neurol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.humov.2007.05.003","article-title":"Gait dynamics, fractals and falls: Finding meaning in the stride-to-stride fluctuations of human walking","volume":"26","author":"Hausdorff","year":"2007","journal-title":"Hum. Mov. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1111\/j.1460-9568.2005.04298.x","article-title":"Dual tasking, gait rhythmicity, and Parkinson\u2019s disease: Which aspects of gait are attention demanding?","volume":"22","author":"Yogev","year":"2005","journal-title":"Eur. J. Neurosci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1037\/0033-2909.109.2.163","article-title":"Half a century of research on the Stroop effect: An integrative review","volume":"109","author":"MacLeod","year":"1991","journal-title":"Psychol. Bull."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.neuroimage.2003.08.027","article-title":"fMRI of healthy older adults during Stroop interference","volume":"21","author":"Langenecker","year":"2004","journal-title":"Neuroimage"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dwolatzky, T., Whitehead, V., Doniger, G.M., Simon, E.S., Schweiger, A., Jaffe, D., and Chertkow, H. (2003). Validity of a novel computerized cognitive battery for mild cognitive impairment. BMC Geriatr., 3.","DOI":"10.1186\/1471-2318-3-4"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s00221-006-0676-3","article-title":"Gait asymmetry in patients with Parkinson\u2019s disease and elderly fallers: When does the bilateral coordination of gait require attention?","volume":"177","author":"Yogev","year":"2007","journal-title":"Exp. Brain Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1111\/j.1460-9568.2007.05810.x","article-title":"Rhythmic auditory stimulation modulates gait variability in Parkinson\u2019s disease","volume":"26","author":"Hausdorff","year":"2007","journal-title":"Eur. J. Neurosci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1002\/mds.870130310","article-title":"Gait variability and basal ganglia disorders: Stride-to-stride variations of gait cycle timing in parkinson\u2019s disease and Huntington\u2019s disease","volume":"13","author":"Hausdorff","year":"1998","journal-title":"Mov. Disord."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/1743-0003-2-23","article-title":"Effect of gait speed on gait rhythmicity in Parkinson\u2019s disease: Variability of stride time and swing time respond differently","volume":"2","author":"Giladi","year":"2005","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_36","unstructured":"Jeon, H.S., Han, J., Yi, W.J., Jeon, B., and Park, K.S. (2008, January 20\u201324). Classification of Parkinson gait and normal gait using spatial-temporal image of plantar pressure. Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1136\/jnnp.36.3.329","article-title":"Comparison of stretch reflexes and shortening reactions in activated normal subjects with those in Parkinson\u2019s disease","volume":"36","author":"Andrews","year":"1973","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ashhar, K., Soh, C.B., and Kong, K.H. (2017, January 11\u201315). A wearable ultrasonic sensor network for analysis of bilateral gait symmetry. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, Korea.","DOI":"10.1109\/EMBC.2017.8037845"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1093\/brain\/awh189","article-title":"Electromyographic profiles of gait prior to onset of freezing episodes in patients with Parkinsons disease","volume":"127","author":"Nieuwboer","year":"2004","journal-title":"Brain"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/S0966-6362(98)00020-4","article-title":"The initiation of gait in young, elderly, and Parkinson\u2019s disease subjects","volume":"8","author":"Halliday","year":"1998","journal-title":"Gait Posture"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1177\/1545968308320639","article-title":"A kinematic and electromyographic analysis of turning in people with Parkinson disease","volume":"23","author":"Hong","year":"2009","journal-title":"Neurorehabil. Neural Repair"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"685","DOI":"10.2169\/internalmedicine.43.685","article-title":"Lifecorder: A new device for the long-term monitoring of motor activities for Parkinsons disease","volume":"43","author":"Saito","year":"2004","journal-title":"Intern. Med."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1002\/1531-8257(200101)16:1<58::AID-MDS1018>3.0.CO;2-9","article-title":"Accelerometric assessment of levodopa-induced dyskinesias in Parkinson\u2019s disease","volume":"16","author":"Hoff","year":"2001","journal-title":"Mov. Disord. Off. J. Mov. Disord. Soc."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1109\/TBME.2004.827933","article-title":"Gait assessment in Parkinson\u2019s disease: Toward an ambulatory system for long-term monitoring","volume":"51","author":"Salarian","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1109\/TNSRE.2010.2047606","article-title":"iTUG, a sensitive and reliable measure of mobility","volume":"18","author":"Salarian","year":"2010","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1109\/TBME.2012.2227317","article-title":"On-shoe wearable sensors for gait and turning assessment of patients with Parkinson\u2019s disease","volume":"6","author":"Mariani","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1136\/jnnp.58.3.326","article-title":"Anticipatory postural adjustments during self inflicted and predictable perturbations in Parkinson\u2019s disease","volume":"58","author":"Latash","year":"1995","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"7033","DOI":"10.1016\/j.eswa.2008.08.076","article-title":"A vision-based analysis system for gait recognition in patients with Parkinson\u2019s disease","volume":"36","author":"Cho","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1016\/j.gaitpost.2014.01.008","article-title":"Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson\u2019s disease","volume":"39","author":"Galna","year":"2014","journal-title":"Gait Posture"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Pachoulakis, I., and Kourmoulis, K. (2014, January 28\u201330). Building a gait analysis framework for Parkinson\u2019s disease patients: Motion capture and skeleton 3D representation. Proceedings of the International Conference on IEEE Telecommunications and Multimedia (TEMU), Heraklion, Greece.","DOI":"10.1109\/TEMU.2014.6917764"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Dror, B., Yanai, E., Frid, A., Peleg, N., Goldenthal, N., Schlesinger, I., Hel-Or, H., and Raz, S. (2014, January 3\u20135). Automatic assessment of Parkinson\u2019s Disease from natural hands movements using 3D depth sensor. Proceedings of the 2014 IEEE 28th Convention of Electrical & Electronics Engineers in Israel (IEEEI), Eilat, Israel.","DOI":"10.1109\/EEEI.2014.7005763"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Dyshel, M., Arkadir, D., Bergman, H., and Weinshall, D. (2015, January 7\u201313). Quantifying Levodopa-Induced Dyskinesia Using Depth Camera. Proceedings of the IEEE International Conference on Computer Vision Workshops, Santiago, Chile.","DOI":"10.1109\/ICCVW.2015.73"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1016\/j.parkreldis.2015.05.006","article-title":"Abnormal thermography in Parkinson\u2019s disease","volume":"21","year":"2015","journal-title":"Parkinsonism Relat. Disord."},{"key":"ref_54","first-page":"386962","article-title":"Altered dynamic postural control during step turning in persons with early-stage Parkinsons disease","volume":"2012","author":"Song","year":"2012","journal-title":"Parkinson\u2019s Dis."},{"key":"ref_55","first-page":"692150","article-title":"Improved dynamic postural task performance without improvements in postural responses: The blessing and the curse of dopamine replacement","volume":"2012","author":"Foreman","year":"2012","journal-title":"Parkinson\u2019s Dis."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1016\/j.jbiomech.2009.10.018","article-title":"Comparison among probabilistic neural network, support vector machine and logistic regression for evaluating the effect of subthalamic stimulation in Parkinson disease on ground reaction force during gait","volume":"43","author":"Muniz","year":"2010","journal-title":"J. Biomech."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/S0966-6362(03)00034-1","article-title":"Coordination of axial rotation and step execution: Deficits in Parkinson\u2019s disease","volume":"18","author":"Vaugoyeau","year":"2003","journal-title":"Gait Posture"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.3390\/s120202255","article-title":"Gait analysis using wearable sensors","volume":"12","author":"Tao","year":"2012","journal-title":"Sensors"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.bspc.2014.11.008","article-title":"Characterizing gait asymmetry via frequency sub-band components of the ground reaction force","volume":"18","author":"Su","year":"2015","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.neulet.2016.09.043","article-title":"Parkinson\u2019s disease classification using gait analysis via deterministic learning","volume":"633","author":"Zeng","year":"2016","journal-title":"Neurosci. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1016\/j.measurement.2012.04.013","article-title":"Automatic diagnosis of neuro-degenerative diseases using gait dynamics","volume":"45","author":"Daliri","year":"2012","journal-title":"Measurement"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"31314","DOI":"10.3390\/s151229858","article-title":"Physical human activity recognition using wearable sensors","volume":"15","author":"Attal","year":"2015","journal-title":"Sensors"},{"key":"ref_63","unstructured":"Russell, S., and Norvig, P. (1995). Artificial Intelligence: A Modern Approach, Prentice-Hall."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"7338","DOI":"10.1016\/j.eswa.2012.01.084","article-title":"Parkinson\u2019s disease classification using gait characteristics and wavelet-based feature extraction","volume":"39","author":"Lee","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.bspc.2012.04.007","article-title":"Chi-square distance kernel of the gaits for the diagnosis of Parkinson\u2019s disease","volume":"8","author":"Daliri","year":"2013","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1109\/TNSRE.2009.2033062","article-title":"Statistical analysis of gait rhythm in patients with Parkinson\u2019s disease","volume":"18","author":"Wu","year":"2010","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.neulet.2011.10.002","article-title":"Modeling the gait of normal and Parkinsonian persons for improving the diagnosis","volume":"509","author":"Sarbaz","year":"2012","journal-title":"Neurosci. Lett."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.gaitpost.2017.02.012","article-title":"Metric learning for Parkinsonian identification from IMU gait measurements","volume":"54","author":"Cuzzolin","year":"2017","journal-title":"Gait Posture"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.jbi.2016.01.014","article-title":"A Q-backpropagated time delay neural network for diagnosing severity of gait disturbances in Parkinson\u2019s disease","volume":"60","author":"Jane","year":"2016","journal-title":"J. Biomed. Inform."},{"key":"ref_70","unstructured":"(2019, January 10). Gait in Parkinson\u2019s Disease. Available online: https:\/\/physionet.org\/pn3\/gaitpdb\/."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.eswa.2016.03.018","article-title":"Detection of Parkinson\u2019s disease by shifted one dimensional local binary patterns from gait","volume":"56","author":"Kaya","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"A\u00e7ici, K., Erda\u015f, \u00c7.B., A\u015furo\u011flu, T., Toprak, M.K., Erdem, H., and O\u011ful, H. (2017, January 25\u201327). A Random Forest Method to Detect Parkinson\u2019s Disease via Gait Analysis. Proceedings of the International Conference on Engineering Applications of Neural Networks, Athens, Greece.","DOI":"10.1007\/978-3-319-65172-9_51"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.cmpb.2017.04.007","article-title":"An automatic non-invasive method for Parkinson\u2019s disease classification","volume":"145","author":"Joshi","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.bspc.2016.08.022","article-title":"Measuring signal fluctuations in gait rhythm time series of patients with Parkinson\u2019s disease using entropy parameters","volume":"31","author":"Wu","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Alam, M.N., Garg, A., Munia, T.T.K., Fazel-Rezai, R., and Tavakolian, K. (2017). Vertical ground reaction force marker for Parkinson\u2019s disease. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0175951"},{"key":"ref_76","unstructured":"Khoury, N., Attal, F., Amirat, Y., Chibani, A., and Mohammed, S. (2018, January 25\u201327). CDTW-based classification for Parkinson\u2019s Disease diagnosis. Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Bruges, Belgium."},{"key":"ref_77","first-page":"19","article-title":"Classification and Clustering of Parkinson\u2019s and Healthy Control Gait Dynamics Using LDA and K-means","volume":"21","author":"Bhoi","year":"2017","journal-title":"Int. J. Bioautom."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"011012","DOI":"10.1115\/1.4038810","article-title":"A Practical Measurement of Parkinson\u2019s Patients Gait Using Simple Walker-Based Motion Sensing and Data Analysis","volume":"12","author":"Aharonson","year":"2018","journal-title":"J. Med. Devices"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Haji Ghassemi, N., Hannink, J., Martindale, C.F., Gabner, H., Muller, M., Klucken, J., and Eskofier, B.M. (2018). Segmentation of Gait Sequences in Sensor-Based Movement Analysis: A Comparison of Methods in Parkinson\u2019s Disease. Sensors, 18.","DOI":"10.3390\/s18010145"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1002\/mds.20507","article-title":"Treadmill walking as an external pacemaker to improve gait rhythm and stability in Parkinson\u2019s disease","volume":"20","author":"Giladi","year":"2005","journal-title":"Mov. Disord."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1053\/apmr.2001.24893","article-title":"Gait variability and fall risk in community-living older adults: A 1-year prospective study","volume":"82","author":"Hausdorff","year":"2001","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2045","DOI":"10.1152\/jappl.2000.88.6.2045","article-title":"Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis","volume":"88","author":"Hausdorff","year":"2000","journal-title":"J. Appl. Physiol."},{"key":"ref_83","unstructured":"Liu, H., Motoda, H., Setiono, R., and Zhao, Z. (2010, January 21). Feature selection: An ever evolving frontier in data mining?. Proceedings of the Fourth Workshop on Feature Selection in Data Mining, Hyderabad, India."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TKDE.2005.66","article-title":"Toward integrating feature selection algorithms for classification and clustering","volume":"17","author":"Liu","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","article-title":"Wrappers for feature subset selection","volume":"97","author":"Kohavi","year":"1997","journal-title":"Artif. Intel."},{"key":"ref_86","unstructured":"Das, S. (July, January 28). Filters, wrappers and a boosting-based hybrid for feature selection. Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williamstown, MA, USA."},{"key":"ref_87","unstructured":"Attal, F. (2015). Classification de Situations de Conduite et D\u00e9tection des \u00c9v\u00e9nements Critiques d\u2019un Deux Roues Motoris\u00e9. [Ph.D. Thesis, Universit\u00e9 Paris-Est]."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbors pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_89","unstructured":"Vapnik, V.N. (2013). The Nature of Statistical Learning Theory, Springer Science & Business Media."},{"key":"ref_90","unstructured":"Breiman, L., Friedman, J., Stone, C.J., and Olshen, R.A. (1984). Classification and Regression Trees, CRC Press."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_92","unstructured":"Quinlan, J.R. (2014). C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_94","unstructured":"Neapolitan, R.E. (2004). Learning Bayesian Networks, Pearson Prentice Hall."},{"key":"ref_95","unstructured":"Nielsen, T.D., and Jensen, F.V. (2009). Bayesian Networks and Decision Graphs, Springer."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_97","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","volume":"Volume 1","author":"MacQueen","year":"1967","journal-title":"Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Rayhan, F., Ahmed, S., Mahbub, A., Jani, M., Shatabda, S., and Farid, D.M. (arXiv, 2017). CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification, arXiv.","DOI":"10.1109\/CSITSS.2017.8447534"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Yang, M., Zheng, H., Wang, H., and McClean, S. (2009, January 1\u20133). Feature selection and construction for the discrimination of neurodegenerative diseases based on gait analysis. Proceedings of the 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare, London, UK.","DOI":"10.4108\/ICST.PERVASIVEHEALTH2009.6053"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Zheng, H., Yang, M., Wang, H., and McClean, S. (2009). Machine learning and statistical approaches to support the discrimination of neuro-degenerative diseases based on gait analysis. Intelligent Patient Management, Springer.","DOI":"10.1007\/978-3-642-00179-6_4"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/2\/242\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:24:54Z","timestamp":1760185494000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/2\/242"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,10]]},"references-count":101,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["s19020242"],"URL":"https:\/\/doi.org\/10.3390\/s19020242","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,10]]}}}