{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T06:45:42Z","timestamp":1782801942135,"version":"3.54.5"},"reference-count":66,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Regional Development Fund of the European Union and Greek national funds","award":["T1EDK-01888"],"award-info":[{"award-number":["T1EDK-01888"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Parkinson\u2019s disease (PD) is one of the most prevalent neurological diseases, described by complex clinical phenotypes. The manifestations of PD include both motor and non-motor symptoms. We constituted an experimental protocol for the assessment of PD motor signs of lower extremities. Using a pair of sensor insoles, data were recorded from PD patients, Elderly and Adult groups. Assessment of PD patients has been performed by neurologists specialized in movement disorders using the Movement Disorder Society\u2014Unified Parkinson\u2019s Disease Rating Scale (MDS-UPDRS)-Part III: Motor Examination, on both ON and OFF medication states. Using as a reference point the quantified metrics of MDS-UPDRS-Part III, severity levels were explored by classifying normal, mild, moderate, and severe levels of PD. Elaborating the recorded gait data, 18 temporal and spatial characteristics have been extracted. Subsequently, feature selection techniques were applied to reveal the dominant features to be used for four classification tasks. Specifically, for identifying relations between the spatial and temporal gait features on: PD and non-PD groups; PD, Elderly and Adults groups; PD and ON\/OFF medication states; MDS-UPDRS: Part III and PD severity levels. AdaBoost, Extra Trees, and Random Forest classifiers, were trained and tested. Results showed a recognition accuracy of 88%, 73% and 81% for, the PD and non-PD groups, PD-related medication states, and PD severity levels relevant to MDS-UPDRS: Part III ratings, respectively.<\/jats:p>","DOI":"10.3390\/s22249937","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T09:31:01Z","timestamp":1671442261000},"page":"9937","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Can Gait Features Help in Differentiating Parkinson\u2019s Disease Medication States and Severity Levels? A Machine Learning Approach"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1312-8401","authenticated-orcid":false,"given":"Chariklia","family":"Chatzaki","sequence":"first","affiliation":[{"name":"Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Crete, Greece"},{"name":"Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology\u2014Hellas, Vassilika Vouton, 71110 Heraklion, Crete, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3279-8016","authenticated-orcid":false,"given":"Vasileios","family":"Skaramagkas","sequence":"additional","affiliation":[{"name":"Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Crete, Greece"},{"name":"Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology\u2014Hellas, Vassilika Vouton, 71110 Heraklion, Crete, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zinovia","family":"Kefalopoulou","sequence":"additional","affiliation":[{"name":"Department of Neurology, Patras University Hospital, 26404 Patra, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikolaos","family":"Tachos","sequence":"additional","affiliation":[{"name":"Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece"},{"name":"Biomedical Research Institute, Foundation for Research and Technology\u2014Hellas, 45110 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2444-1962","authenticated-orcid":false,"given":"Nicholas","family":"Kostikis","sequence":"additional","affiliation":[{"name":"PD Neurotechnology Ltd., 45500 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Foivos","family":"Kanellos","sequence":"additional","affiliation":[{"name":"PD Neurotechnology Ltd., 45500 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eleftherios","family":"Triantafyllou","sequence":"additional","affiliation":[{"name":"Department of Neurology, Patras University Hospital, 26404 Patra, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisabeth","family":"Chroni","sequence":"additional","affiliation":[{"name":"Department of Neurology, Patras University Hospital, 26404 Patra, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7362-5082","authenticated-orcid":false,"given":"Dimitrios I.","family":"Fotiadis","sequence":"additional","affiliation":[{"name":"Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece"},{"name":"Biomedical Research Institute, Foundation for Research and Technology\u2014Hellas, 45110 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8454-1450","authenticated-orcid":false,"given":"Manolis","family":"Tsiknakis","sequence":"additional","affiliation":[{"name":"Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Crete, Greece"},{"name":"Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology\u2014Hellas, Vassilika Vouton, 71110 Heraklion, Crete, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"ref_1","first-page":"S3","article-title":"The emerging evidence of the parkinson pandemic","volume":"8","author":"Dorsey","year":"2018","journal-title":"J. Park. Dis."},{"key":"ref_2","unstructured":"Stoker, T.B., and Greenland, J.C. (2018). Parkinson\u2019s Disease: Pathogenesis and Clinical Aspects, Codon Publications."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"666737","DOI":"10.3389\/fneur.2021.666737","article-title":"The pathogenesis of parkinson\u2019s disease: A complex interplay between astrocytes, microglia, and T lymphocytes?","volume":"12","author":"Copas","year":"2021","journal-title":"Front. Neurol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"612","DOI":"10.3389\/fnins.2018.00612","article-title":"Parkinson\u2019s disease: Biomarkers, treatment, and risk factors","volume":"12","author":"Emamzadeh","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S1474-4422(21)00030-2","article-title":"Challenges in the diagnosis of Parkinson\u2019s disease","volume":"20","author":"Tolosa","year":"2021","journal-title":"Lancet Neurol."},{"key":"ref_6","first-page":"1","article-title":"Parkinson disease-associated cognitive impairment","volume":"7","author":"Aarsland","year":"2021","journal-title":"Nat. Rev. Dis. Prim."},{"key":"ref_7","first-page":"9","article-title":"Parkinson\u2019s disease-associated dysarthria: Prevalence, impact and management strategies","volume":"9","author":"Levy","year":"2019","journal-title":"Res. Rev. Park."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"S85","DOI":"10.1016\/S1353-8020(11)70027-X","article-title":"Parkinson\u2019s disease tremor: Pathophysiology","volume":"18","author":"Hallett","year":"2012","journal-title":"Park. Relat. Disord."},{"key":"ref_9","unstructured":"Gandhi, K.R., and Saadabadi, A. (2022, July 05). Levodopa (L-Dopa), Available online: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK482140\/."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1007\/s13311-020-00939-x","article-title":"Surgical treatment of parkinson\u2019s disease: Devices and lesion approaches","volume":"17","author":"Sharma","year":"2020","journal-title":"Neurotherapeutics"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"156","DOI":"10.3389\/fneur.2018.00156","article-title":"Diagnostic criteria for Parkinson\u2019s disease: From James Parkinson to the concept of prodromal disease","volume":"9","author":"Marsili","year":"2018","journal-title":"Front. Neurol."},{"key":"ref_12","first-page":"1","article-title":"The MDS-sponsored Revision of the Unified Parkinson\u2019s Disease Rating Scale","volume":"1","author":"Goetz","year":"2008","journal-title":"J. Mov. Disord."},{"key":"ref_13","first-page":"S11","article-title":"Parkinsonism: Onset, progression, and mortality 1967","volume":"57","author":"Hoehn","year":"2001","journal-title":"Neurology"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Schlachetzki, J.C.M., Barth, J., Marxreiter, F., Gossler, J., Kohl, Z., Reinfelder, S., Gassner, H., Aminian, K., Eskofier, B.M., and Winkler, J. (2017). Wearable sensors objectively measure gait parameters in Parkinson\u2019s disease. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0183989"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Subramaniam, S., Majumder, S., Faisal, A.I., and Deen, M.J. (2022). Insole-based systems for health monitoring: Current solutions. Sensors, 22.","DOI":"10.3390\/s22020438"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chatzaki, C., Skaramagkas, V., Tachos, N., Christodoulakis, G., Maniadi, E., Kefalopoulou, Z., Fotiadis, D., and Tsiknakis, M. (2021). The smart-insole dataset: Gait analysis using wearable sensors with a focus on elderly and Parkinson\u2019s patients. Sensors, 21.","DOI":"10.3390\/s21082821"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"102179","DOI":"10.1016\/j.media.2021.102179","article-title":"Quantifying Parkinson\u2019s disease motor severity under uncertainty using MDS-UPDRS videos","volume":"73","author":"Lu","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1080\/00207721.2012.724114","article-title":"New machine-learning algorithms for prediction of Parkinson\u2019s disease","volume":"45","author":"Mandal","year":"2014","journal-title":"Int. J. Syst. Sci."},{"key":"ref_19","first-page":"1","article-title":"Parkinson\u2019s disease motor symptoms in machine learning: A review","volume":"2","author":"Ahlrichs","year":"2013","journal-title":"Health Inform. Int. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"201","DOI":"10.3390\/signals2020016","article-title":"A study on the essential and parkinson\u2019s arm tremor classification","volume":"2","author":"Skaramagkas","year":"2021","journal-title":"Signals"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Skaramagkas, V., Andrikopoulos, G., Kefalopoulou, Z., and Polychronopoulos, P. (2020, January 15\u201318). Towards differential diagnosis of essential and parkinson\u2019s tremor via machine learning. Proceedings of the 2020 28th Mediterranean Conference on Control and Automation (MED), Saint-Rapha\u00ebl, Saint-Rapha\u00ebl, France.","DOI":"10.1109\/MED48518.2020.9182922"},{"key":"ref_22","unstructured":"Papadopoulos, A., Kyritsis, K., Klingelhoefer, L., Bostanjopoulou, S., Chaudhuri, K.R., and Delopoulos, A. (2022, February 03). Detecting parkinsonian tremor from IMU data collected in-the-wild using deep multiple-instance learning. Available online: https:\/\/zenodo.org\/record\/3519213."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1007\/978-3-030-46133-1_24","article-title":"Wearable-based parkinson\u2019s disease severity monitoring using deep learning","volume":"11908","author":"Goschenhofer","year":"2019","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_24","first-page":"5996","article-title":"The design of a parkinson\u2019s tremor predictor and estimator using a hybrid convolutional-multilayer perceptron neural network","volume":"Volume 2020","author":"Ibrahim","year":"2020","journal-title":"Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1111\/ane.12248","article-title":"Technical and clinical view on ambulatory assessment in Parkinson\u2019s disease","volume":"130","author":"Hobert","year":"2014","journal-title":"Acta Neurol. Scand."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.jns.2014.07.026","article-title":"Automated gait and balance parameters diagnose and correlate with severity in Parkinson disease","volume":"345","author":"Dewey","year":"2014","journal-title":"J. Neurol. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kyrarini, M., Wang, X., and Graser, A. (2015, January 7\u20139). Comparison of vision-based and sensor-based systems for joint angle gait analysis. Proceedings of the 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings, Turin, Italy.","DOI":"10.1109\/MeMeA.2015.7145231"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Moro, M., Marchesi, G., Hesse, F., Odone, F., and Casadio, M. (2022). Markerless vs. marker-based gait analysis: A proof of concept study. Sensors, 22.","DOI":"10.3390\/s22052011"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1109\/TITB.2009.2036165","article-title":"Wearable assistant for Parkinsons disease patients with the freezing of gait symptom","volume":"14","author":"Bachlin","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1038\/s41598-020-80768-2","article-title":"Gait parameters of Parkinson\u2019s disease compared with healthy controls: A systematic review and meta-analysis","volume":"11","author":"Zanardi","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/s13047-015-0111-8","article-title":"Validation and reliability testing of a new, fully integrated gait analysis insole","volume":"8","author":"Braun","year":"2015","journal-title":"J. Foot Ankle Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1080\/02640414.2016.1161205","article-title":"Validation of Moticon\u2019s OpenGo sensor insoles during gait, jumps, balance and cross-country skiing specific imitation movements","volume":"35","author":"Martiner","year":"2017","journal-title":"J. Sports Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kakarla, T.P., Varma, K.A., Preejith, S.P., Joseph, J., and Sivaprakasam, M. (2019, January 26\u201328). Accuracy Enhancement of Total Force by Capacitive Insoles. Proceedings of the 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Istanbul, Turkey.","DOI":"10.1109\/MeMeA.2019.8802146"},{"key":"ref_35","unstructured":"(2022, September 12). Moticon-SCIENCE. Available online: https:\/\/www.moticon.de\/."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1002\/mds.20115","article-title":"Falls and freezing of Gait in Parkinson\u2019s disease: A review of two interconnected, episodic phenomena","volume":"19","author":"Bloem","year":"2004","journal-title":"Mov. Disord."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Brognara, L., Palumbo, P., Grimm, B., and Palmerini, L. (2019). Assessing gait in Parkinson\u2019s disease using wearable motion sensors: A systematic review. Diseases, 7.","DOI":"10.3390\/diseases7010018"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1111\/j.1532-5415.1991.tb01616.x","article-title":"The timed \u2018Up & Go\u2019: A test of basic functional mobility for frail elderly persons","volume":"39","author":"Podsiadlo","year":"1991","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1159\/000314963","article-title":"Properties of the \u2018Timed Up and Go\u2019 test: More than meets the eye","volume":"57","author":"Herman","year":"2011","journal-title":"Gerontology"},{"key":"ref_40","first-page":"466","article-title":"Reliability of quantitative TUG measures of mobility for use in falls risk assessment","volume":"Volume 2011","author":"McGrath","year":"2011","journal-title":"Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society"},{"key":"ref_41","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":"60","author":"Mariani","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1002\/mds.22894","article-title":"Obstacle avoidance to elicit freezing of gait during treadmill walking","volume":"25","author":"Snijders","year":"2010","journal-title":"Mov. Disord."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1136\/jnnp.2005.068742","article-title":"Multiple balance tests improve the assessment of postural stability in subjects with Parkinson\u2019s disease","volume":"77","author":"Jacobs","year":"2006","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1186\/1471-2377-11-90","article-title":"Single and dual task gait training in people with Parkinson\u2019s Disease: A protocol for a randomised controlled trial","volume":"11","author":"Brauer","year":"2011","journal-title":"BMC Neurol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1002\/mds.22993","article-title":"A new rating instrument to assess festination and freezing gait in Parkinsonian patients","volume":"25","author":"Ziegler","year":"2010","journal-title":"Mov. Disord."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kluge, F., Ga\u00dfner, H., Hannink, J., Pasluosta, C., Klucken, J., and Eskofier, B.M. (2017). Towards mobile gait analysis: Concurrent validity and test-retest reliability of an inertial measurement system for the assessment of spatio-temporal gait parameters. Sensors, 17.","DOI":"10.3390\/s17071522"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1016\/j.gaitpost.2013.10.019","article-title":"Short-distance walking speed tests in people with Parkinson disease: Reliability, responsiveness, and validity","volume":"39","author":"Combs","year":"2014","journal-title":"Gait Posture"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.parkreldis.2014.10.026","article-title":"Parkinson\u2019s disease severity levels and MDS-Unified Parkinson\u2019s Disease Rating Scale","volume":"21","author":"Alvarez","year":"2015","journal-title":"Park. Relat. Disord."},{"key":"ref_49","unstructured":"Kefalopoulou, Z., Chatzaki, V., Skaramagkas, C., Chroni, E., Tachos, N., Fotiadis, D.I., and Tsiknakis, M. (2022, October 20). Pressure Sensor Insole Gait Assessment for Parkinson\u2019s Disease Patients: A Pilot Study [Abstract]. Movement Disorder 2022 International Congress, Available online: https:\/\/www.mdsabstracts.org\/abstract\/pressure-sensor-insole-gait-assessment-for-parkinsons-disease-patients-a-pilot-study\/."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"225","DOI":"10.2147\/CIA.S290071","article-title":"Normative data for gait speed and height norm speed in \u2265 60-year-old men and women","volume":"16","year":"2021","journal-title":"Clin. Interv. Aging"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1093\/gerona\/gls174","article-title":"Gait speed as a measure in geriatric assessment in clinical settings: A systematic review","volume":"68","author":"Peel","year":"2013","journal-title":"J. Gerontol. Ser. A Biol. Sci. Med. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3675","DOI":"10.1589\/jpts.27.3675","article-title":"Gait speed and related factors in parkinson\u2019s disease","volume":"27","author":"Paker","year":"2015","journal-title":"J. Phys. Ther. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1097\/MRR.0b013e328347be02","article-title":"Walk ratio (step length\/cadence) as a summary index of neuromotor control of gait: Application to multiple sclerosis","volume":"34","author":"Rota","year":"2011","journal-title":"Int. J. Rehabil. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"18079","DOI":"10.1038\/s41598-019-54271-2","article-title":"Mechanics of very slow human walking","volume":"9","author":"Wu","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1589\/jpts.29.722","article-title":"Estimated lower speed boundary at which the walk ratio constancy is broken in healthy adults","volume":"29","author":"Murakami","year":"2017","journal-title":"J. Phys. Ther. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Vila, M.H., P\u00e9rez, R., Mollinedo, I., and Cancela, J.M. (2021). Analysis of gait for disease stage in patients with parkinson\u2019s disease. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18020720"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.gaitpost.2017.06.266","article-title":"Recuperation of slow walking in de novo Parkinson\u2019s disease is more closely associated with increased cadence, rather than with expanded stride length","volume":"58","author":"Kwon","year":"2017","journal-title":"Gait Posture"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"26766","DOI":"10.1109\/ACCESS.2021.3056407","article-title":"Metaheuristic algorithms on feature selection: A survey of one decade of research (2009\u20132019)","volume":"9","author":"Agrawal","year":"2021","journal-title":"IEEE Access"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"R\u00e1cz, A., Bajusz, D., and H\u00e9berger, K. (2019). Multi-level comparison of machine learning classifiers and their performance metrics. Molecules, 24.","DOI":"10.3390\/molecules24152811"},{"key":"ref_60","unstructured":"Gholamy, A., Kreinovich, V., and Kosheleva, O. (2022, November 30). Why 70\/30 or 80\/20 Relation between Training and Testing Sets: A Pedagogical Explanation. Departmental Technical Reports (CS). Feburary. Available online: https:\/\/scholarworks.utep.edu\/cs_techrep\/1209\/."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Over-fitting and model tuning. Applied Predictive Modeling, Springer Nature.","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1002\/mds.26269","article-title":"Levodopa is a double-edged sword for balance and gait in people with parkinson\u2019s disease","volume":"30","author":"Curtze","year":"2015","journal-title":"Mov. Disord."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1640","DOI":"10.1111\/ejn.14328","article-title":"Impairments in gait kinematics and postural control may not correlate with dopamine transporter depletion in individuals with mild to moderate Parkinson\u2019s disease","volume":"49","author":"Cabeleira","year":"2019","journal-title":"Eur. J. Neurosci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"8020","DOI":"10.1111\/ejn.15522","article-title":"The effects of levodopa in the spatiotemporal gait parameters are mediated by self-selected gait speed in Parkinson\u2019s disease","volume":"54","author":"Oliveira","year":"2021","journal-title":"Eur. J. Neurosci."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Loh, H.W., Hong, W., Ooi, C.P., Chakraborty, S., Barua, P.D., Deo, R.C., Soar, J., Palmer, E.E., and Acharya, U.R. (2021). Application of Deep Learning Models for Automated Identification of Parkinson\u2019s Disease: A Review (2011\u20132021). Sensors, 21.","DOI":"10.3390\/s21217034"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1618","DOI":"10.1109\/JBHI.2018.2866873","article-title":"Multimodal Assessment of Parkinson\u2019s Disease: A Deep Learning Approach","volume":"23","author":"Eskofier","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9937\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:42:57Z","timestamp":1760146977000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9937"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,16]]},"references-count":66,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22249937"],"URL":"https:\/\/doi.org\/10.3390\/s22249937","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,16]]}}}