{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T11:36:44Z","timestamp":1773920204956,"version":"3.50.1"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioeng. Biotechnol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Fall risk assessment in Parkinson\u2019s Disease (PD) is critical for preventing patient injuries; however, traditional clinical scales are often constrained by their semi-quantitative nature and time-consuming administration. This study aimed to develop and validate an analytical framework integrating Empirical Mode Decomposition (EMD) and machine learning for the objective classification of fall risk in PD patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data from 32 PD patients under four standing conditions were analyzed, with fall risk labels defined based on a Mini-BESTest score cutoff of \u226421. Center of Pressure (COP) signals were first decomposed into Intrinsic Mode Functions (IMFs) using EMD, from which multi-dimensional features were extracted. Subsequently, the Sequential Forward Floating Selection (SFFS) algorithm was employed to identify a core feature subset, and the performance of five classifiers\u2014Random Forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGB), Logistic Regression (LR), and Support Vector Machine (SVM)\u2014was evaluated using Leave-One-Subject-Out Cross-Validation (LOSO-CV). Finally, a multi-trial probability aggregation strategy was introduced to assess subject-level risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Results indicated that the rigid surface eyes-closed (rs ec) condition yielded the optimal discriminatory capability. The SFFS algorithm identified a parsimonious 3-dimensional feature subset (COPap_IMF1_BinEntropy, COPml_IMF1_STD, COPap_IMF2_TotalPower), which enabled tree-based classifiers to exhibit superior performance. Notably, DT and RF achieved highly balanced sensitivity and specificity, reaching a maximum subject-level AUC of 0.96.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>In conclusion, EMD-derived multi-scale features can precisely capture postural control deficits in PD, offering a promising technical solution for the future development of efficient and objective PD clinical monitoring tools.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fbioe.2026.1790452","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T06:48:28Z","timestamp":1773902908000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Classification of fall risk in Parkinson\u2019s disease using empirical mode decomposition and machine learning"],"prefix":"10.3389","volume":"14","author":[{"given":"Yunfei","family":"Gui","sequence":"first","affiliation":[{"name":"School of Health Science and Engineering, University of Shanghai for Science and Technology","place":["Shanghai, China"]},{"name":"Department of Orthopedics, Jinshan District Central Hospital Affiliated to Shanghai University of Medicine and Health Sciences","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuyi","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Health Science and Engineering, University of Shanghai for Science and Technology","place":["Shanghai, China"]},{"name":"Department of Orthopedics, Jinshan District Central Hospital Affiliated to Shanghai University of Medicine and Health Sciences","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linyan","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Medical Instruments, Shanghai University of Medicine and Health Sciences","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinfeng","family":"Cao","sequence":"additional","affiliation":[{"name":"Periodicals Agency of Shanghai University, Shanghai University","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linlin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Medical Instruments, Shanghai University of Medicine and Health Sciences","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roger","family":"Adams","sequence":"additional","affiliation":[{"name":"Research Institute for Sport and Exercise, University of Canberra","place":["Canberra, ACT, Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Orthopedics, Jinshan District Central Hospital Affiliated to Shanghai University of Medicine and Health Sciences","place":["Shanghai, China"]},{"name":"College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Lyu","sequence":"additional","affiliation":[{"name":"Department of Orthopedics, Jinshan District Central Hospital Affiliated to Shanghai University of Medicine and Health Sciences","place":["Shanghai, China"]},{"name":"College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"906274","DOI":"10.1155\/2013\/906274","article-title":"Recurrent falls in Parkinson\u2019s disease: a systematic review","volume":"2013","author":"Allen","year":"2013","journal-title":"Parkinson\u2019s Disease"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.gaitpost.2015.10.014","article-title":"The use of force-plate posturography in the assessment of postural instability","volume":"44","author":"B\u0142aszczyk","year":"2016","journal-title":"Gait & Posture"},{"key":"B3","doi-asserted-by":"publisher","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. Disorders Official Journal Mov. Disord. Soc."},{"key":"B4","doi-asserted-by":"publisher","first-page":"4071","DOI":"10.3390\/s25134071","article-title":"Determining falls risk in people with parkinson\u2019s disease using wearable sensors: a systematic review","volume":"25","author":"Bradley","year":"2025","journal-title":"Sensors"},{"key":"B5","doi-asserted-by":"publisher","first-page":"2696","DOI":"10.3390\/ijerph18052696","article-title":"Review of the upright balance assessment based on the force plate","volume":"18","author":"Chen","year":"2021","journal-title":"Int. Journal Environmental Research Public Health"},{"key":"B6","doi-asserted-by":"publisher","first-page":"362572","DOI":"10.1155\/2012\/362572","article-title":"Risk of falls in parkinson\u2019s disease: a cross-sectional study of 160 patients","volume":"2012","author":"Contreras","year":"2012","journal-title":"Parkinson\u2019s Dis."},{"key":"B7","doi-asserted-by":"publisher","first-page":"865882","DOI":"10.3389\/fnins.2022.865882","article-title":"A public data set with ground reaction forces of human balance in individuals with parkinson\u2019s disease","volume":"16","author":"De Oliveira","year":"2022","journal-title":"Front. Neuroscience"},{"key":"B8","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1016\/S1474-4422(18)30295-3","article-title":"Global, regional, and national burden of Parkinson\u2019s disease, 1990\u20132016: a systematic analysis for the global burden of disease study 2016","volume":"17","author":"Dorsey","year":"2018","journal-title":"Lancet Neurology"},{"key":"B9","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1590\/S1413-35552010000300003","article-title":"Revision of posturography based on force plate for balance evaluation","volume":"14","author":"Duarte","year":"2010","journal-title":"Braz. J. Physical Therapy"},{"key":"B10","doi-asserted-by":"publisher","first-page":"542","DOI":"10.2522\/ptj.20120302","article-title":"Comparative utility of the BESTest, mini-BESTest, and brief-BESTest for predicting falls in individuals with parkinson disease: a cohort study","volume":"93","author":"Duncan","year":"2013","journal-title":"Phys. Therapy"},{"key":"B11","doi-asserted-by":"crossref","DOI":"10.1201\/9780429246593","volume-title":"An introduction to the bootstrap","author":"Efron","year":"1994"},{"key":"B12","first-page":"2433","article-title":"Early detection of Parkinson\u2019s disease using center of pressure data and machine learning","author":"Fadil","year":"2021"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.1002\/mds.27195","article-title":"Falls in Parkinson\u2019s disease: a complex and evolving picture","volume":"32","author":"Fasano","year":"2017","journal-title":"Mov. Disorders"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1636","DOI":"10.1007\/s10439-015-1479-0","article-title":"Comparing postural stability entropy analyses to differentiate fallers and non-fallers","volume":"44","author":"Fino","year":"2016","journal-title":"Ann. Biomed. Eng."},{"key":"B15","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1002\/mds.22491","article-title":"Rehabilitation treatment of gait in patients with Parkinson\u2019s disease with freezing: a comparison between two physical therapy protocols using visual and auditory cues with or without treadmill training","volume":"24","author":"Frazzitta","year":"2009","journal-title":"Mov. Disord."},{"key":"B16","doi-asserted-by":"publisher","first-page":"7129","DOI":"10.1038\/s41598-018-24783-4","article-title":"Model-based and model-free machine learning techniques for diagnostic prediction and classification of clinical outcomes in Parkinson\u2019s disease","volume":"8","author":"Gao","year":"2018","journal-title":"Sci. Reports"},{"key":"B17","doi-asserted-by":"publisher","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. Disorders Official Journal Mov. Disord. Soc."},{"key":"B18","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1002\/mds.20324","article-title":"Sydney multicenter study of Parkinson\u2019s disease: non-l-dopa\u2013responsive problems dominate at 15 years","volume":"20","author":"Hely","year":"2005","journal-title":"Mov. Disorders Official Journal Mov. Disord. Soc."},{"key":"B19","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1212\/WNL.17.5.427","article-title":"Parkinsonism: onset, progression, and mortality","volume":"17","author":"Hoehn","year":"1967","journal-title":"Neurology"},{"key":"B20","doi-asserted-by":"publisher","first-page":"ii7","DOI":"10.1093\/ageing\/afl077","article-title":"Postural orientation and equilibrium: what do we need to know about neural control of balance to prevent falls?","volume":"35","author":"Horak","year":"2006","journal-title":"Age Ageing"},{"key":"B21","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. Lond. Ser. A Mathematical, Physical Engineering Sciences"},{"key":"B22","doi-asserted-by":"publisher","first-page":"e0123705","DOI":"10.1371\/journal.pone.0123705","article-title":"Wearable sensor use for assessing standing balance and walking stability in people with Parkinson\u2019s disease: a systematic review","volume":"10","author":"Hubble","year":"2015","journal-title":"PloS One"},{"key":"B23","doi-asserted-by":"publisher","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. Neurology, Neurosurgery & Psychiatry"},{"key":"B24","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1002\/mds.25292","article-title":"The current and projected economic burden of Parkinson\u2019s disease in the United States","volume":"28","author":"Kowal","year":"2013","journal-title":"Mov. Disord."},{"key":"B25","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1097\/NPT.0b013e31821a620c","article-title":"Utility of the Mini-BESTest, BESTest, and BESTest sections for balance assessments in individuals with parkinson disease","volume":"35","author":"Leddy","year":"2011","journal-title":"J. Neurologic Physical Therapy"},{"key":"B26","doi-asserted-by":"publisher","first-page":"e70118","DOI":"10.1111\/ene.70118","article-title":"Machine learning predicts risk of falls in parkison\u2019s disease patients in a multicenter observational study","volume":"32","author":"Malaguti","year":"2025","journal-title":"Eur. J. Neurology"},{"key":"B27","first-page":"239","article-title":"The relevance of clinical balance assessment tools to differentiate balance deficits","volume":"46","author":"Mancini","year":"2010","journal-title":"Eur. Journal Physical Rehabilitation Medicine"},{"key":"B28","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1186\/s12984-018-0465-9","article-title":"On the use of approximate entropy and sample entropy with centre of pressure time-series","volume":"15","author":"Montesinos","year":"2018","journal-title":"J. NeuroEngineering Rehabilitation"},{"key":"B29","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1186\/s12984-020-00756-5","article-title":"Classification of Parkinson\u2019s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach","volume":"17","author":"Moon","year":"2020","journal-title":"J. Neuroengineering Rehabilitation"},{"key":"B30","first-page":"1","article-title":"Analysis of center of pressure signals using empirical mode decomposition and fourier-bessel expansion","author":"Pachori","year":"2008"},{"key":"B31","doi-asserted-by":"publisher","first-page":"239","DOI":"10.3390\/brainsci9090239","article-title":"Postural instability in parkinson\u2019s disease: a review","volume":"9","author":"Palakurthi","year":"2019","journal-title":"Brain Sci."},{"key":"B32","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1123\/jsr.11.1.51","article-title":"Center-of-pressure parameters used in the assessment of postural control","volume":"11","author":"Palmieri","year":"2002","journal-title":"J. Sport Rehabilitation"},{"key":"B33","doi-asserted-by":"publisher","first-page":"1892","DOI":"10.1002\/mds.21598","article-title":"A meta-analysis of six prospective studies of falling in Parkinson\u2019s disease","volume":"22","author":"Pickering","year":"2007","journal-title":"Mov. Disord."},{"key":"B34","doi-asserted-by":"publisher","first-page":"2297","DOI":"10.1073\/pnas.88.6.2297","article-title":"Approximate entropy as a measure of system complexity","volume":"88","author":"Pincus","year":"1991","journal-title":"Proc. National Academy Sciences"},{"key":"B35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/nrdp.2017.13","article-title":"Parkinson disease","volume":"3","author":"Poewe","year":"2017","journal-title":"Nat. Reviews Dis. Primers"},{"key":"B36","doi-asserted-by":"publisher","first-page":"2283","DOI":"10.1007\/s00198-013-2300-2","article-title":"Risk of fracture in patients with Parkinson\u2019s disease","volume":"24","author":"Pouwels","year":"2013","journal-title":"Osteoporos. Int."},{"key":"B37","doi-asserted-by":"publisher","first-page":"956","DOI":"10.1109\/10.532130","article-title":"Measures of postural steadiness: differences between healthy young and elderly adults","volume":"43","author":"Prieto","year":"1996","journal-title":"IEEE Trans. Biomedical Engineering"},{"key":"B38","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1016\/0167-8655(94)90127-9","article-title":"Floating search methods in feature selection","volume":"15","author":"Pudil","year":"1994","journal-title":"Pattern Recognition Letters"},{"key":"B39","doi-asserted-by":"publisher","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. Journal Physiology-Heart Circulatory Physiology"},{"key":"B40","article-title":"On empirical mode decomposition and its algorithms","volume-title":"IEEE-EURASIP workshop on nonlinear signal and image processing NSIP-03","author":"Rilling","year":"2003"},{"key":"B41","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1016\/j.jclinepi.2009.11.009","article-title":"Use of brier score to assess binary predictions","volume":"63","author":"Rufibach","year":"2010","journal-title":"J. Clinical Epidemiology"},{"key":"B42","doi-asserted-by":"publisher","first-page":"gix019","DOI":"10.1093\/gigascience\/gix019","article-title":"The need to approximate the use-case in clinical machine learning","volume":"6","author":"Saeb","year":"2017","journal-title":"Gigascience"},{"key":"B43","doi-asserted-by":"publisher","first-page":"283","DOI":"10.3390\/bioengineering9070283","article-title":"EMD-Based method for supervised classification of Parkinson\u2019s disease patients using balance control data","volume":"9","author":"Safi","year":"2022","journal-title":"Bioengineering"},{"key":"B44","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1038\/s41746-024-01311-5","article-title":"Predicting future fallers in Parkinson\u2019s disease using kinematic data over a period of 5 years","volume":"7","author":"Sotirakis","year":"2024","journal-title":"Npj Digit. Med."},{"key":"B45","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1186\/s12916-019-1466-7","article-title":"Calibration: the achilles heel of predictive analytics","volume":"17","author":"Van Calster","year":"2019","journal-title":"BMC Med."},{"key":"B46","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1136\/jnnp.2005.074070","article-title":"Predictors of falls and fractures in bradykinetic rigid syndromes: a retrospective study","volume":"77","author":"Williams","year":"2006","journal-title":"J. Neurology, Neurosurg. & Psychiatry"},{"key":"B47","doi-asserted-by":"publisher","first-page":"1949","DOI":"10.1177\/0269215519877498","article-title":"Measures of balance and falls risk prediction in people with Parkinson\u2019s disease: a systematic review of psychometric properties","volume":"33","author":"Winser","year":"2019","journal-title":"Clin. Rehabilitation"},{"key":"B48","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/0966-6362(96)82849-9","article-title":"Human balance and posture control during standing and walking","volume":"3","author":"Winter","year":"1995","journal-title":"Gait & Posture"},{"key":"B49","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1136\/jnnp.72.6.721","article-title":"Incidence and prediction of falls in Parkinson\u2019s disease: a prospective multidisciplinary study","volume":"72","author":"Wood","year":"2002","journal-title":"J. Neurology, Neurosurg. & Psychiatry"},{"key":"B50","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1093\/ageing\/afi196","article-title":"Development and initial validation of the falls efficacy scale-international (FES-I)","volume":"34","author":"Yardley","year":"2005","journal-title":"Age Ageing"},{"key":"B51","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1%3C32::AID-CNCR2820030106%3E3.0.CO","article-title":"Index for rating diagnostic tests","volume":"3","author":"Youden","year":"1950","journal-title":"Cancer"},{"key":"B52","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.neunet.2018.12.012","article-title":"Classification of gait patterns between patients with Parkinson\u2019s disease and healthy controls using phase space reconstruction (PSR), empirical mode decomposition (EMD) and neural networks","volume":"111","author":"Zeng","year":"2019","journal-title":"Neural Netw."}],"container-title":["Frontiers in Bioengineering and Biotechnology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbioe.2026.1790452\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T06:48:29Z","timestamp":1773902909000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbioe.2026.1790452\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,19]]},"references-count":52,"alternative-id":["10.3389\/fbioe.2026.1790452"],"URL":"https:\/\/doi.org\/10.3389\/fbioe.2026.1790452","relation":{},"ISSN":["2296-4185"],"issn-type":[{"value":"2296-4185","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,19]]},"article-number":"1790452"}}