{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T23:30:34Z","timestamp":1777419034654,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T00:00:00Z","timestamp":1669248000000},"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>Parkinson\u2019s disease is a neurodegenerative disorder impacting patients\u2019 movement, causing a variety of movement abnormalities. It has been the focus of research studies for early detection based on wearable technologies. The benefit of wearable technologies in the domain rises by continuous monitoring of this population\u2019s movement patterns over time. The ubiquity of wrist-worn accelerometry and the fact that the wrist is the most common and acceptable body location to wear the accelerometer for continuous monitoring suggests that wrist-worn accelerometers are the best choice for early detection of the disease and also tracking the severity of it over time. In this study, we use a dataset consisting of one-week wrist-worn accelerometry data collected from individuals with Parkinson\u2019s disease and healthy elderlies for early detection of the disease. Two feature engineering methods, including epoch-based statistical feature engineering and the document-of-words method, were used. Using various machine learning classifiers, the impact of different windowing strategies, using the document-of-words method versus the statistical method, and the amount of data in terms of number of days were investigated. Based on our results, PD was detected with the highest average accuracy value (85% \u00b1 15%) across 100 runs of SVM classifier using a set of features containing features from every and all windowing strategies. We also found that the document-of-words method significantly improves the classification performance compared to the statistical feature engineering model. Although the best performance of the classification task between PD and healthy elderlies was obtained using seven days of data collection, the results indicated that with three days of data collection, we can reach a classification performance that is not significantly different from a model built using seven days of data collection.<\/jats:p>","DOI":"10.3390\/s22239122","type":"journal-article","created":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T04:34:52Z","timestamp":1669264492000},"page":"9122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Detection of Parkinson\u2019s Disease Using Wrist Accelerometer Data and Passive Monitoring"],"prefix":"10.3390","volume":"22","author":[{"given":"Elham","family":"Rastegari","sequence":"first","affiliation":[{"name":"Department of Business Intelligence and Analytics, Business College, Creighton University, Omaha, NE 68178, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hesham","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, College of Information Systems and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vivien","family":"Marmelat","sequence":"additional","affiliation":[{"name":"Department of Biomechanics, College of Education, Health and Human Sciences, University of Nebraska at Omaha, Omaha, NE 68182, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1862","DOI":"10.1109\/JBHI.2015.2464354","article-title":"What engineering technology could do for quality of life in Parkinson\u2019s disease: A review of current needs and opportunities","volume":"19","author":"Stamford","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1038\/nrneurol.2016.152","article-title":"Advances in markers of prodromal Parkinson disease","volume":"12","author":"Postuma","year":"2016","journal-title":"Nat. Rev. Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Habets, J.G., Herff, C., Kubben, P.L., Kuijf, M.L., Temel, Y., Evers, L.J., Bloem, B.R., Starr, P.A., Gilron, R.E., and Little, S. (2021). Rapid dynamic naturalistic monitoring of bradykinesia in Parkinson\u2019s disease using a wrist-worn accelerometer. Sensors, 21.","DOI":"10.1101\/2021.09.03.458142"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1109\/TITB.2009.2033471","article-title":"Monitoring motor fluctuations in patients with Parkinson\u2019s disease using wearable sensors","volume":"13","author":"Patel","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1016\/j.future.2013.01.010","article-title":"Internet of Things (IoT): A vision, architectural elements, and future di-rections","volume":"29","author":"Gubbi","year":"2013","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2688","DOI":"10.1016\/j.comnet.2010.05.003","article-title":"Wireless sensor networks for healthcare: A survey","volume":"54","author":"Alemdar","year":"2010","journal-title":"Comput. Netw."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pmcj.2015.12.007","article-title":"The role of wrist-mounted inertial sensors in detecting gait freeze episodes in Parkinson\u2019s disease","volume":"33","author":"Mazilu","year":"2016","journal-title":"Pervasive Mob. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mazilu, S., Blanke, U., and Tr\u00f6ster, G. (2015, January 23\u201327). Gait, wrist, and sensors: Detecting freezing of gait in Parkinson\u2019s disease from wrist movement. Proceedings of the 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), St. Louis, MI, USA.","DOI":"10.1109\/PERCOMW.2015.7134102"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1002\/mds.25391","article-title":"High-resolution tracking of motor disorders in Parkinson\u2019s disease during unconstrained activity","volume":"28","author":"Roy","year":"2013","journal-title":"Mov. Disord."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1080\/17434440.2016.1198694","article-title":"Wearable inertial sensors for human movement analysis","volume":"13","author":"Iosa","year":"2016","journal-title":"Expert Rev. Med. Devices"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Williamson, J.R., Telfer, B., Mullany, R., and Friedl, K.E. (2021). Detecting Parkinson\u2019s disease from wrist-worn accelerometry in the UK Biobank. Sensors, 21.","DOI":"10.3390\/s21062047"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.parkreldis.2019.08.001","article-title":"Quantifying physical activity in early Parkinson disease using a commercial activity monitor","volume":"66","author":"Pradhan","year":"2019","journal-title":"Park. Relat. Disord."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2144","DOI":"10.1002\/mds.28631","article-title":"Detecting Sensitive Mobility Features for Parkinson\u2019s Disease Stages Via Machine Learning","volume":"36","author":"Mirelman","year":"2021","journal-title":"Mov. Disord."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s40263-012-0012-3","article-title":"Postural instability in patients with Parkinson\u2019s disease","volume":"27","author":"Kim","year":"2012","journal-title":"CNS Drugs"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1136\/jnnp.2007.131045","article-title":"Parkinson\u2019s disease: Clinical features and diagnosis","volume":"79","author":"Jankovic","year":"2008","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1038\/s41746-019-0217-7","article-title":"Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device","volume":"3","author":"Mahadevan","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_17","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."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106494","DOI":"10.1016\/j.asoc.2020.106494","article-title":"Supervised machine learning based gait classification system for early detection and stage classification of Parkinson\u2019s disease","volume":"94","author":"Balaji","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Doherty, A., Jackson, D., Hammerla, N., Pl\u00f6tz, T., Olivier, P., Granat, M.H., White, T., Van Hees, V.T., Trenell, M.I., and Owen, C.G. (2017). Large scale population assessment of physical activity using wrist worn accelerometers: The UK biobank study. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169649"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rastegari, E., and Ali, H. (2017, January 20\u201323). A Correlation Network Model Utilizing Gait Parameters for Evaluating Health Levels. Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Boston, MA, USA. Available online: http:\/\/dl.acm.org\/citation.cfm?id=3107487.","DOI":"10.1145\/3107411.3107487"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Aich, S., Youn, J., Chakraborty, S., Pradhan, P.M., Park, J.H., Park, S., and Park, J. (2020). A supervised machine learning approach to detect the on\/off state in Parkinson\u2019s disease using wearable based gait signals. Diagnostics, 10.","DOI":"10.3390\/diagnostics10060421"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kheirkhahan, M., Mehta, S., Nath, M., Wanigatunga, A.A., Corbett, D.B., Manini, T.M., and Ranka, S. (2017, January 13\u201316). A bag-of-words approach for assessing activities of daily living using wrist accelerometer data. Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA.","DOI":"10.1109\/BIBM.2017.8217735"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100116","DOI":"10.1016\/j.smhl.2020.100116","article-title":"A bag-of-words feature engineering approach for assessing health conditions using accelerometer data","volume":"16","author":"Rastegari","year":"2020","journal-title":"Smart Health"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7181","DOI":"10.3390\/s140407181","article-title":"Smartphone-based solutions for fall detection and prevention: Challenges and open issues","volume":"14","author":"Habib","year":"2014","journal-title":"Sensors"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"eabd7865","DOI":"10.1126\/scitranslmed.abd7865","article-title":"Smartwatch inertial sensors continuously monitor real-world motor fluctuations in Parkinson\u2019s disease","volume":"13","author":"Powers","year":"2021","journal-title":"Sci. Transl. Med."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ossig, C., Gandor, F., Fauser, M., Bosredon, C., Churilov, L., Reichmann, H., Horne, M.K., Ebersbach, G., and Storch, A. (2016). Correlation of quantitative motor state assessment using a kinetograph and patient diaries in advanced PD: Data from an observational study. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0161559"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1002\/mds.27376","article-title":"Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson\u2019s disease clinical trial","volume":"33","author":"Lipsmeier","year":"2018","journal-title":"Mov. Disord."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"431","DOI":"10.3389\/fneur.2017.00431","article-title":"Analysis of correlation between an accelerometer-based algorithm for detecting parkinsonian gait and UPDRS subscales","volume":"8","author":"Alcaine","year":"2017","journal-title":"Front. Neurol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e17986","DOI":"10.2196\/17986","article-title":"The impact of reducing the number of wearable devices on measuring gait in parkinson disease: Noninterventional exploratory study","volume":"7","author":"Czech","year":"2020","journal-title":"JMIR Rehabilitation Assist. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1642","DOI":"10.1007\/s00415-017-8424-0","article-title":"Freezing of gait and fall detection in Parkinson\u2019s disease using wearable sensors: A systematic review","volume":"264","author":"Evers","year":"2017","journal-title":"J. Neurol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1109\/TNSRE.2020.2987020","article-title":"PDMeter: A wrist wearable device for an at-home assessment of the Parkinson\u2019s disease ri-gidity","volume":"28","author":"Raiano","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gokul, H., Suresh, P., Vignesh, B.H., Kumaar, R.P., and Vijayaraghavan, V. (September, January 24). Gait recovery system for parkinson\u2019s disease using machine learning on embedded platforms. Proceedings of the 2020 IEEE International Systems Conference (SysCon), Montreal, QC, Canada.","DOI":"10.1109\/SysCon47679.2020.9275930"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.1021\/acssensors.9b01127","article-title":"Wearable electrochemical microneedle sensor for continuous monitoring of levodopa: Toward Parkinson management","volume":"4","author":"Goud","year":"2019","journal-title":"ACS Sensors"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"N\u00f3brega, L.R., Cabral, A.M., Oliveira, F.H., de Oliveira Andrade, A., Krishnan, S., and Pereira, A.A. (2022). Wrist Movement Variability Assessment in Individuals with Parkinson\u2019s Disease. Healthcare, 10.","DOI":"10.3390\/healthcare10091656"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Juutinen, M., Wang, C., Zhu, J., Haladjian, J., Ruokolainen, J., Puustinen, J., and Vehkaoja, A. (2020). Parkinson\u2019s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0236258"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Rastegari, E., Azizian, S., and Ali, H. (2019, March 17). Machine Learning and Similarity Network Approaches to Support Automatic Classification of Parkinson\u2019s Diseases Using Accelerometer-Based Gait Analysis. Available online: http:\/\/scholarspace.manoa.hawaii.edu\/handle\/10125\/59861.","DOI":"10.24251\/HICSS.2019.511"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kheirkhahan, M., Chakraborty, A., Wanigatunga, A.A., Corbett, D.B., Manini, T.M., and Ranka, S. (2018). Wrist accelerometer shape feature derivation methods for assessing activities of daily living. BMC Med. Inform. Decis. Mak., 18.","DOI":"10.1186\/s12911-018-0671-1"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1002\/mds.26718","article-title":"Free-living monitoring of Parkinson\u2019s disease: Lessons from the field","volume":"31","author":"Godfrey","year":"2016","journal-title":"Mov. Disord."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/S1474-4422(19)30397-7","article-title":"Long-term unsupervised mobility assessment in movement disorders","volume":"19","author":"Warmerdam","year":"2020","journal-title":"Lancet Neurol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1109\/JBHI.2015.2461555","article-title":"An Emerging Era in the Management of Parkinson\u2019s Disease: Wearable Technologies and the Internet of Things","volume":"19","author":"Pasluosta","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"e19068","DOI":"10.2196\/19068","article-title":"Real-life gait performance as a digital bi-omarker for motor fluctuations: The Parkinson@ Home validation study","volume":"22","author":"Evers","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.patrec.2020.09.011","article-title":"A new machine learning based approach to predict Freezing of Gait","volume":"140","author":"Kleanthous","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1186\/s12984-020-00684-4","article-title":"Role of data measurement characteristics in the accurate detection of Parkinson\u2019s disease symptoms using wearable sensors","volume":"17","author":"Shawen","year":"2020","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s12938-021-00872-w","article-title":"Ensemble deep model for continuous estimation of Unified Parkinson\u2019s Disease Rating Scale III","volume":"20","author":"Hssayeni","year":"2021","journal-title":"Biomed. Eng. Online"},{"key":"ref_46","unstructured":"Kuijf, M.L., Kubben, P.L., and Herff, C. (2021). Evaluation of Parkinson\u2019s Disease at Home: Predicting Tremor from Wearable Sensors. Prediction and Real-Life Monitoring of DBS Motor Response in Parkinson\u2019s Disease, Elsevier."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Rastegari, E., Orn, D., and Ali, H. (2020, January 21\u201324). Smart Computational Approaches with Advanced Feature Selection Algorithms for Optimiz-ing the Classification of Mobility Data in Health Informatics. Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, Virtual.","DOI":"10.1145\/3388440.3412426"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.neucom.2016.11.084","article-title":"Bag-of-steps: Predicting lower-limb fracture rehabilitation length by weight loading analysis","volume":"268","author":"Pla","year":"2017","journal-title":"Neurocomputing"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9122\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:25:53Z","timestamp":1760145953000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9122"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,24]]},"references-count":48,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22239122"],"URL":"https:\/\/doi.org\/10.3390\/s22239122","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,24]]}}}