{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T02:15:32Z","timestamp":1787883332187,"version":"build-2784847793"},"reference-count":190,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Maryland MPower Seed Grant Award (R.v.C. and M.P.C.), the Rosalyn Newman Foundation (L.M.S.), and the University of Maryland Claude D. Pepper Older Americans Independence Center","award":["P30-AG028747"],"award-info":[{"award-number":["P30-AG028747"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson\u2019s disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.<\/jats:p>","DOI":"10.3390\/s24154983","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T11:34:20Z","timestamp":1722512060000},"page":"4983","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson\u2019s Disease"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0327-013X","authenticated-orcid":false,"given":"Rana M.","family":"Khalil","sequence":"first","affiliation":[{"name":"Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa M.","family":"Shulman","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0190-1973","authenticated-orcid":false,"given":"Ann L.","family":"Gruber-Baldini","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8046-2456","authenticated-orcid":false,"given":"Sunita","family":"Shakya","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rebecca","family":"Fenderson","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maxwell","family":"Van Hoven","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1608-0776","authenticated-orcid":false,"given":"Jeffrey M.","family":"Hausdorff","sequence":"additional","affiliation":[{"name":"Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv 6492416, Israel"},{"name":"Department of Physical Therapy, Faculty of Medicine & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel"},{"name":"Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel"},{"name":"Rush Alzheimer\u2019s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA"},{"name":"Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7651-2780","authenticated-orcid":false,"given":"Rainer","family":"von Coelln","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1467-0334","authenticated-orcid":false,"given":"Michael P.","family":"Cummings","sequence":"additional","affiliation":[{"name":"Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1002\/mds.25945","article-title":"The prevalence of Parkinson\u2019s disease: A systematic review and meta-analysis","volume":"29","author":"Pringsheim","year":"2014","journal-title":"Mov. 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