{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T05:39:44Z","timestamp":1778823584721,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T00:00:00Z","timestamp":1548979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The success of medication adjustment in Parkinson\u2019s disease (PD) patients with motor fluctuation relies on the knowledge about their fluctuation severity. However, because of the temporal and spatial variability in motor fluctuations, a single clinical examination often fails to capture the spectrum of motor impairment experienced in routine daily life. In this study, we developed an algorithm to estimate the degree of motor fluctuation severity from two wearable sensors\u2019 data during subjects\u2019 free body movements. Specifically, we developed a new hybrid feature extraction method to represent the longitudinal changes of motor function from the sensor data. Next, we developed a classification model based on random forest to learn the changes in the patterns of the sensor data as the severity of the motor function changes. We evaluated our algorithm using data from 24 subjects with idiopathic PD as they performed a variety of daily routine activities. A leave-one-subject-out assessment of the algorithm resulted in 83.33% accuracy, indicating that our approach holds a great promise to passively detect degree of motor fluctuation severity from continuous monitoring of an individual\u2019s free body movements. Such a sensor-based assessment system and algorithm combination could provide the objective and comprehensive information about the fluctuation severity that can be used by the treating physician to effectively adjust therapy for PD patients with troublesome motor fluctuation.<\/jats:p>","DOI":"10.3390\/e21020137","type":"journal-article","created":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T11:19:58Z","timestamp":1549019998000},"page":"137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Hybrid Feature Extraction for Detection of Degree of Motor Fluctuation Severity in Parkinson\u2019s Disease Patients"],"prefix":"10.3390","volume":"21","author":[{"given":"Murtadha D.","family":"Hssayeni","sequence":"first","affiliation":[{"name":"Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA"}]},{"given":"Joohi","family":"Jimenez-Shahed","sequence":"additional","affiliation":[{"name":"Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0075-7663","authenticated-orcid":false,"given":"Behnaz","family":"Ghoraani","sequence":"additional","affiliation":[{"name":"Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,1]]},"reference":[{"key":"ref_1","unstructured":"Lescher, P.J. (2011). Parkinson\u2019s Disease: Challenges, Progress, and Promise, F.A. Davis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1093\/ajhp\/57.10.953","article-title":"Impact of Parkinson\u2019s disease and its pharmacologic treatment on quality of life and economic outcomes","volume":"57","author":"Scheife","year":"2000","journal-title":"Am. J. Health-Syst. Pharm."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"179","DOI":"10.4155\/tde.15.96","article-title":"A review of current and novel levodopa formulations for the treatment of Parkinson\u2019s disease","volume":"7","year":"2016","journal-title":"Ther. Deliv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1001\/archneurol.2009.295","article-title":"The clinically important difference on the unified Parkinson\u2019s disease rating scale","volume":"67","author":"Shulman","year":"2010","journal-title":"Arch. Neurol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1002\/mds.10473","article-title":"The unified Parkinson\u2019s disease rating scale (UPDRS): Status and recommendations","volume":"18","author":"Goetz","year":"2003","journal-title":"Mov. Disord."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"202","DOI":"10.3389\/fnins.2018.00202","article-title":"Quantifying motor impairment in movement disorders","volume":"12","author":"FitzGerald","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1002\/mds.20640","article-title":"Unified Parkinson\u2019s disease rating scale motor examination: are ratings of nurses, residents in neurology, and movement disorders specialists interchangeable?","volume":"20","author":"Post","year":"2005","journal-title":"Mov. Disord."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"de Lima, A.L.S., Hahn, T., Evers, L.J., de Vries, N.M., Cohen, E., Afek, M., Bataille, L., Daeschler, M., Claes, K., and Boroojerdi, B. (2017). Feasibility of large-scale deployment of multiple wearable sensors in Parkinson\u2019s disease. PloS ONE, 12.","DOI":"10.1371\/journal.pone.0189161"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1109\/TBME.2015.2480242","article-title":"A method for automatic and objective scoring of bradykinesia using orientation sensors and classification algorithms","volume":"63","author":"Roosma","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2778","DOI":"10.1109\/TBME.2010.2049573","article-title":"Ambulatory monitoring of activities and motor symptoms in Parkinson\u2019s disease","volume":"57","author":"Zwartjes","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_11","unstructured":"Sharma, V., Fowler, A., Lyons, K., and Pahwa, R. (2017). Personal KinetiGraph (PKG) for Parkinson\u2019s disease: experience at a tertiary care center. Movement Disorders, Wiley."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"pe37","DOI":"10.1016\/j.parkreldis.2015.10.051","article-title":"Usefulness of Parkinson\u2019s KinetiGraph in a Parkinson\u2019s disease clinic\u2014Survey of 82 patients","volume":"22","author":"Klingelhoefer","year":"2016","journal-title":"Parkinsonism Relat. Disord."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1109\/TBME.2006.886670","article-title":"Quantification of tremor and bradykinesia in Parkinson\u2019s disease using a novel ambulatory monitoring system","volume":"54","author":"Salarian","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1147\/JRD.2017.2768739","article-title":"Decomposition of complex movements into primitives for Parkinson\u2019s disease assessment","volume":"62","author":"Pissadaki","year":"2018","journal-title":"IBM J. Res. Dev."},{"key":"ref_15","first-page":"1341","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 Trans. Biomed. Eng."},{"key":"ref_16","first-page":"803","article-title":"Automatic UPDRS evaluation in the sit-to-stand task of Parkinsonians: Kinematic analysis and comparative outlook on the leg agility task","volume":"19","author":"Giuberti","year":"2018","journal-title":"J. Biomed. Health Inform."},{"key":"ref_17","unstructured":"Adranly, A., Lu, S., and Zoumot, Y. (2018). Unsupervised Parkinson\u2019s Disease Assessment. [Ph.D. Thesis, Santa Clara University]."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1109\/JBHI.2015.2472640","article-title":"Body-sensor-network-based kinematic characterization and comparative outlook of UPDRS scoring in leg agility, sit-to-stand, and Gait tasks in Parkinson\u2019s disease","volume":"19","author":"Parisi","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Vasquez-Correa, J.C., Arias-Vergara, T., Orozco-Arroyave, J.R., Eskofier, B.M., Klucken, J., and Noth, E. (2018). Multimodal assessment of Parkinson\u2019s disease: A deep learning approach. J. Biomed. Health Inform.","DOI":"10.1109\/JBHI.2018.2866873"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"160011","DOI":"10.1038\/sdata.2016.11","article-title":"The mPower study, Parkinson disease mobile data collected using ResearchKit","volume":"3","author":"Bot","year":"2016","journal-title":"Sci. Data"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1788","DOI":"10.1016\/j.procs.2018.05.154","article-title":"Predicting Severity of Parkinson\u2019s Disease Using Deep Learning","volume":"132","author":"Grover","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"36825","DOI":"10.1109\/ACCESS.2018.2851382","article-title":"Deep multi-layer perceptron classifier for behavior analysis to estimate parkinson\u2019s disease severity using smartphones","volume":"6","author":"Wan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_23","first-page":"1","article-title":"A hybrid spatio-temporal model for detection and severity rating of Parkinson\u2019s Disease from gait data","volume":"135","author":"Zhao","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_24","first-page":"11","article-title":"Parkinsonism: Onset, progression and mortality","volume":"57","author":"Hoehn","year":"2001","journal-title":"Neurology"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"399","DOI":"10.3233\/JPD-120166","article-title":"Objective motion sensor assessment highly correlated with scores of global levodopa-induced dyskinesia in Parkinson\u2019s disease","volume":"3","author":"Mera","year":"2013","journal-title":"Parkinson\u2019s Dis."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/TBME.2017.2697764","article-title":"Continuous Assessment of Levodopa Response in Parkinson\u2019s Disease Using Wearable Motion Sensors","volume":"65","author":"Pulliam","year":"2018","journal-title":"IEEE Trans. Biom. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hssayeni, M.D., Burack, M.A., and Ghoraani, B. (2016, January 16\u201320). Automatic assessment of medication states of patients with Parkinson\u2019s disease using wearable sensors. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7592116"},{"key":"ref_28","unstructured":"Hssayeni, M.D., Burack, M.A., Jimenez-Shahed, J., and Ghoraani, B. (2019, January 30). Wearable-based Mediation State Detection in Individuals with Parkinson\u2019s Disease. Available online: https:\/\/arxiv.org\/abs\/1809.06973."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kyan, M., Muneesawang, P., Jarrah, K., and Guan, L. (2014). Unsupervised Learning: A Dynamic Approach, John Wiley & Sons.","DOI":"10.1002\/9781118875568"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3178582","article-title":"A survey of random forest based methods for intrusion detection systems","volume":"51","author":"Resende","year":"2018","journal-title":"ACM Comput. Surv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jneumeth.2017.12.010","article-title":"Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer\u2019s disease patients: From the alzheimer\u2019s disease neuroimaging initiative (ADNI) database","volume":"302","author":"Dimitriadis","year":"2018","journal-title":"J. Neurosci. Methods"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mehrang, S., Pietil\u00e4, J., and Korhonen, I. (2018). An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial Accelerometer Wrist-Band. Sensors, 18.","DOI":"10.3390\/s18020613"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Berhane, T.M., Lane, C.R., Wu, Q., Autrey, B.C., Anenkhonov, O.A., Chepinoga, V.V., and Liu, H. (2018). Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory. Remote Sens., 10.","DOI":"10.3390\/rs10040580"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1001\/jamaneurol.2018.0809","article-title":"Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score","volume":"75","author":"Zhan","year":"2018","journal-title":"JAMA Neurol."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/2\/137\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:30:18Z","timestamp":1760185818000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/2\/137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,1]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["e21020137"],"URL":"https:\/\/doi.org\/10.3390\/e21020137","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,1]]}}}