{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T04:49:23Z","timestamp":1793335763992,"version":"4.5.0"},"reference-count":42,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2025,8,9]],"date-time":"2025-08-09T00:00:00Z","timestamp":1754697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Military University of Technology","award":["531-000023-W500-22"],"award-info":[{"award-number":["531-000023-W500-22"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study explores the use of machine learning models to assess the severity of Parkinson\u2019s disease symptoms based on data from wearable and smartphone sensors. It presents models to predict the severities of individual symptoms\u2014tremor, bradykinesia, stiffness, and dyskinesia\u2014as well as the overall state of patients, using both clinician and patient self-assessments as labels. The dataset, although limited and imbalanced, enabled the identification of key trends. The best performance was achieved when combining data from both the MYO armband and smartphone, and when using patient self-assessments as targets. Tremor was the most predictable symptom, while others proved more challenging\u2014especially at higher severity levels, which were poorly represented in the dataset. These results highlight the value of multimodal data and the importance of patient input in symptom monitoring. However, they also point to the need for more balanced and extensive datasets to improve prediction accuracy across all severity levels and symptoms.<\/jats:p>","DOI":"10.3390\/s25164924","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T09:59:13Z","timestamp":1754906353000},"page":"4924","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Machine Learning-Based Assessment of Parkinson\u2019s Disease Symptoms Using Wearable and Smartphone Sensors"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3980-4343","authenticated-orcid":false,"given":"Tomasz","family":"Gutowski","sequence":"first","affiliation":[{"name":"Faculty of Cybernetics, Military University of Technology, gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8791-7192","authenticated-orcid":false,"given":"Olga","family":"Stodulska","sequence":"additional","affiliation":[{"name":"Department of Neurology, Faculty of Health Sciences, Medical University of Warsaw, \u017bwirki i Wigury 61, 02-091 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aleksandra","family":"\u0106wikli\u0144ska","sequence":"additional","affiliation":[{"name":"Department of Neurology, Faculty of Health Sciences, Medical University of Warsaw, \u017bwirki i Wigury 61, 02-091 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katarzyna","family":"Gutowska","sequence":"additional","affiliation":[{"name":"Department of Neurology, Faculty of Health Sciences, Medical University of Warsaw, \u017bwirki i Wigury 61, 02-091 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3898-5775","authenticated-orcid":false,"given":"Kamila","family":"Kope\u0107","sequence":"additional","affiliation":[{"name":"Department of Neurology, Faculty of Health Sciences, Medical University of Warsaw, \u017bwirki i Wigury 61, 02-091 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta","family":"Betka","sequence":"additional","affiliation":[{"name":"Department of Neurology, Faculty of Health Sciences, Medical University of Warsaw, \u017bwirki i Wigury 61, 02-091 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6749-6563","authenticated-orcid":false,"given":"Ryszard","family":"Antkiewicz","sequence":"additional","affiliation":[{"name":"Faculty of Cybernetics, Military University of Technology, gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dariusz","family":"Koziorowski","sequence":"additional","affiliation":[{"name":"Department of Neurology, Faculty of Health Sciences, Medical University of Warsaw, \u017bwirki i Wigury 61, 02-091 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4188-5901","authenticated-orcid":false,"given":"Stanis\u0142aw","family":"Szlufik","sequence":"additional","affiliation":[{"name":"Department of Neurology, Faculty of Health Sciences, Medical University of Warsaw, \u017bwirki i Wigury 61, 02-091 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"950","DOI":"10.14336\/AD.2021.0530","article-title":"Neurological and Neurodegenerative Disorders: Novel Concepts and Treatment","volume":"12","author":"Kirmani","year":"2021","journal-title":"Aging Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1111\/jnc.13691","article-title":"The Clinical Symptoms of Parkinson\u2019s Disease","volume":"139","author":"Sveinbjornsdottir","year":"2016","journal-title":"J. Neurochem."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1016\/S0140-6736(21)00218-X","article-title":"Parkinson\u2019s Disease","volume":"397","author":"Bloem","year":"2021","journal-title":"Lancet"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/S1474-4422(06)70471-9","article-title":"Epidemiology of Parkinson\u2019s Disease","volume":"5","author":"Breteler","year":"2006","journal-title":"Lancet Neurol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1670","DOI":"10.1001\/jama.2014.3654","article-title":"Pharmacological Treatment of Parkinson Disease: A Review","volume":"311","author":"Connolly","year":"2014","journal-title":"JAMA"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1097\/WCO.0000000000000118","article-title":"Treatment of Advanced Parkinson\u2019s Disease","volume":"27","author":"Giugni","year":"2014","journal-title":"Curr. Opin. Neurol."},{"key":"ref_7","first-page":"222","article-title":"A Review on Parkinson\u2019s Disease Treatment","volume":"8","author":"Lee","year":"2021","journal-title":"Neurosciences"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"112821","DOI":"10.1016\/j.eswa.2019.112821","article-title":"A Systematic Survey of Computer-Aided Diagnosis in Medicine: Past and Present Developments","volume":"138","author":"Yanase","year":"2019","journal-title":"Expert. Syst. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shehab, M., Abualigah, L., Shambour, Q., Abu-Hashem, M.A., Shambour, M.K.Y., Alsalibi, A.I., and Gandomi, A.H. (2022). Machine Learning in Medical Applications: A Review of State-of-the-Art Methods. Comput. Biol. Med., 145.","DOI":"10.1016\/j.compbiomed.2022.105458"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Oung, Q.W., Hariharan, M., Lee, H.L., Basah, S.N., Sarillee, M., and Lee, C.H. (2015, January 27\u201329). Wearable Multimodal Sensors for Evaluation of Patients with Parkinson Disease. Proceedings of the 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015, Penang, Malaysia.","DOI":"10.1109\/ICCSCE.2015.7482196"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1109\/TITB.2009.2033471","article-title":"Monitoring Motor Fluctuations in Patients with Parkinsons Disease Using Wearable Sensors","volume":"13","author":"Patel","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1038\/s41746-021-00414-7","article-title":"Crowdsourcing Digital Health Measures to Predict Parkinson\u2019s Disease Severity: The Parkinson\u2019s Disease Digital Biomarker DREAM Challenge","volume":"4","author":"Sieberts","year":"2021","journal-title":"npj Digit. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/JBHI.2017.2777926","article-title":"A Treatment-Response Index from Wearable Sensors for Quantifying Parkinson\u2019s Disease Motor States","volume":"22","author":"Thomas","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"47","DOI":"10.3233\/JPD-2012-11071","article-title":"Automated Assessment of Bradykinesia and Dyskinesia in Parkinson\u2019s Disease","volume":"2","author":"Griffiths","year":"2012","journal-title":"J. Parkinsons Dis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1109\/THMS.2025.3570704","article-title":"Fine-Grained Assessment of Upper-Limb Bradykinesia Through Multimodal Feature Enhancement and Deep Learning","volume":"55","author":"Lin","year":"2025","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"ref_16","first-page":"5787563","article-title":"Continuous and Unconstrained Tremor Monitoring in Parkinson\u2019s Disease Using Supervised Machine Learning and Wearable Sensors","volume":"2024","author":"Rodriguez","year":"2024","journal-title":"Parkinsons Dis."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gutowski, T. (2022, January 5\u20137). Deep Learning for Parkinson\u2019s Disease Symptom Detection and Severity Evaluation Using Accelerometer Signal. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2022), Bruges, Belgium, Online Event.","DOI":"10.14428\/esann\/2022.ES2022-107"},{"key":"ref_18","first-page":"1","article-title":"Technical Features and Functionalities of Myo Armband: An Overview on Related Literature and Advanced Applications of Myoelectric Armbands Mainly Focused on Arm Prostheses","volume":"11","author":"Visconti","year":"2018","journal-title":"Int. J. Smart Sens. Intell. Syst."},{"key":"ref_19","unstructured":"Gutowski, T. (2024). Optimization of Medicine Dosing in Parkinson\u2019s Disease, Based on Signals from Sensor Measurements, Military University of Technology."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2934","DOI":"10.1016\/j.jbiomech.2007.02.019","article-title":"Adaptive Usage of the Butterworth Digital Filter","volume":"40","author":"Erer","year":"2007","journal-title":"J. Biomech."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bazgir, O., Frounchi, J., Habibi, S.A.H., Palma, L., and Pierleoni, P. (2015, January 25\u201327). A Neural Network System for Diagnosis and Assessment of Tremor in Parkinson Disease Patients. Proceedings of the 2015 22nd Iranian Conference on Biomedical Engineering, ICBME 2015, Tehran, Iran.","DOI":"10.1109\/ICBME.2015.7404105"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/TNSRE.2013.2265887","article-title":"A Comprehensive Assessment of Gait Accelerometry Signals in Time, Frequency and Time-Frequency Domains","volume":"22","author":"Sejdic","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.artmed.2012.03.003","article-title":"An Automated Methodology for Levodopa-Induced Dyskinesia: Assessment Based on Gyroscope and Accelerometer Signals","volume":"55","author":"Tsipouras","year":"2012","journal-title":"Artif. Intell. Med."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Alam, M.N., Johnson, B., Gendreau, J., Tavakolian, K., Combs, C., and Fazel-Rezai, R. (2016, January 19\u201321). Tremor Quantification of Parkinson\u2019s Disease \u2013A Pilot Study. Proceedings of the IEEE International Conference on Electro Information Technology, IEEE Computer Society, Grand Forks, ND, USA.","DOI":"10.1109\/EIT.2016.7535334"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Eskofier, B.M., Lee, S.I., Daneault, J.F., Golabchi, F.N., Ferreira-Carvalho, G., Vergara-Diaz, G., Sapienza, S., Costante, G., Klucken, J., and Kautz, T. (2016, January 16\u201320). Recent Machine Learning Advancements in Sensor-Based Mobility Analysis: Deep Learning for Parkinson\u2019s Disease Assessment. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2016, Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590787"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"San-Segundo, R., Zhang, A., Cebulla, A., Panev, S., Tabor, G., Stebbins, K., Massa, R.E., Whitford, A., de la Torre, F., and Hodgins, J. (2020). Parkinson\u2019s Disease Tremor Detection in the Wild Using Wearable Accelerometers. Sensors, 20.","DOI":"10.3390\/s20205817"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0165-1684(90)90158-U","article-title":"Fast Fourier Transforms: A Tutorial Review and a State of the Art","volume":"19","author":"Duhamel","year":"1990","journal-title":"Signal Process"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Delgado-Bonal, A., and Marshak, A. (2019). Approximate Entropy and Sample Entropy: A Comprehensive Tutorial. Entropy, 21.","DOI":"10.3390\/e21060541"},{"key":"ref_29","first-page":"1157","article-title":"An Introduction to Variable and Feature Selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"110558","DOI":"10.1016\/j.asoc.2023.110558","article-title":"Multi-Objective Binary Grey Wolf Optimization for Feature Selection Based on Guided Mutation Strategy","volume":"145","author":"Li","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","article-title":"A Comparative Analysis of Gradient Boosting Algorithms","volume":"54","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107681","DOI":"10.1016\/j.engappai.2023.107681","article-title":"Permutation Importance Based Modified Guided Regularized Random Forest in Human Activity Recognition with Smartphone","volume":"129","author":"Thakur","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_33","first-page":"4","article-title":"Dimensionality Reduction and Classification through PCA and LDA","volume":"122","author":"Archana","year":"2015","journal-title":"Int. J. Comput. Appl."},{"key":"ref_34","unstructured":"(2024, June 14). 1.13. Feature Selection\u2014Scikit-Learn 1.5.0 Documentation. Available online: https:\/\/scikit-learn.org\/stable\/modules\/feature_selection.html#selectfrommodel."},{"key":"ref_35","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics), Springer New York, Inc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s44150-021-00015-8","article-title":"Error Metrics and Performance Fitness Indicators for Artificial Intelligence and Machine Learning in Engineering and Sciences","volume":"3","author":"Naser","year":"2021","journal-title":"Archit. Struct. Constr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1016\/j.patcog.2015.03.009","article-title":"Performance Evaluation of Classification Algorithms by K-Fold and Leave-One-out Cross Validation","volume":"48","author":"Wong","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2129","DOI":"10.1002\/mds.22340","article-title":"Movement Disorder Society-Sponsored Revision of the Unified Parkinson\u2019s Disease Rating Scale (MDS-UPDRS): Scale Presentation and Clinimetric Testing Results","volume":"23","author":"Goetz","year":"2008","journal-title":"Mov. Disord."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1097\/WNF.0b013e31820b570a","article-title":"A Pharmacokinetic-Pharmacodynamic Model for Duodenal Levodopa Infusion","volume":"34","author":"Westin","year":"2011","journal-title":"Clin. Neuropharmacol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1002\/mds.20383","article-title":"Identification of Motor and Nonmotor Wearing-off in Parkinson\u2019s Disease: Comparison of a Patient Questionnaire versus a Clinician Assessment","volume":"20","author":"Stacy","year":"2005","journal-title":"Mov. Disord."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1002\/mds.29962","article-title":"Distinct Clinical Implications of Patient- Versus Clinician-Rated Motor Symptoms in Parkinson\u2019s Disease","volume":"39","author":"Kikuya","year":"2024","journal-title":"Mov. Disord."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1080\/01621459.1959.10501526","article-title":"Remarks on Zeros and Ties in the Wilcoxon Signed Rank Procedures","volume":"54","author":"Pratt","year":"1959","journal-title":"J. Am. Stat. Assoc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/16\/4924\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:27:11Z","timestamp":1760034431000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/16\/4924"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,9]]},"references-count":42,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["s25164924"],"URL":"https:\/\/doi.org\/10.3390\/s25164924","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,9]]}}}