{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T18:19:06Z","timestamp":1787249946788,"version":"build-2736575974"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,3,22]],"date-time":"2021-03-22T00:00:00Z","timestamp":1616371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81430022"],"award-info":[{"award-number":["81430022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91332107"],"award-info":[{"award-number":["91332107"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81371407"],"award-info":[{"award-number":["81371407"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Hum. Neurosci."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>The purpose of this study was to introduce an orthogonal experimental design (OED) to improve the efficiency of building and optimizing models for freezing of gait (FOG) prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A random forest (RF) model was developed to predict FOG by using acceleration signals and angular velocity signals to recognize possible precursor signs of FOG (preFOG). An OED was introduced to optimize the feature extraction parameters.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The main effects and interaction among the feature extraction hyperparameters were analyzed. The false-positive rate, hit rate, and mean prediction time (MPT) were 27%, 68%, and 2.99 s, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The OED was an effective method for analyzing the main effects and interactions among the feature extraction parameters. It was also beneficial for optimizing the feature extraction parameters of the FOG prediction model.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnhum.2021.636414","type":"journal-article","created":{"date-parts":[[2021,3,22]],"date-time":"2021-03-22T04:22:55Z","timestamp":1616386975000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Prediction of Freezing of Gait in Parkinson\u2019s Disease Using a Random Forest Model Based on an Orthogonal Experimental Design: A Pilot Study"],"prefix":"10.3389","volume":"15","author":[{"given":"Zhonelue","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyan","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Ling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoliu","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengdi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,3,22]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.parkreldis.2008.03.010","article-title":"Impaired attention predicts falling in Parkinson\u2019s disease.","volume":"15","author":"Allcock","year":"2009","journal-title":"Parkinsonism Relat. Disord."},{"key":"B2","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.neubiorev.2016.03.016","article-title":"Animal behavioral assessments in current research of Parkinson\u2019s disease.","volume":"65","author":"Asakawa","year":"","journal-title":"Neurosci. Biobehav. Rev."},{"key":"B3","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1016\/j.neubiorev.2016.06.036","article-title":"Human behavioral assessments in current research of Parkinson\u2019s disease.","volume":"68","author":"Asakawa","year":"","journal-title":"Neurosci. Biobehav. Rev."},{"key":"B4","doi-asserted-by":"publisher","first-page":"69","DOI":"10.2176\/nmc.ra.2018-0045","article-title":"Can the latest computerized technologies revolutionize conventional assessment tools and therapies for a neurological disease? the example of Parkinson\u2019s disease.","volume":"59","author":"Asakawa","year":"2019","journal-title":"Neurol. Med. Chir. (Tokyo)"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.32457\/ejhr.v7i1.1395","article-title":"Online detection of freezing of gait in parkinson\u2019s disease patients: a performance characterization","author":"B\u00e4chlin","year":"2009","journal-title":"Proceedings of the 4th International Conference on Body Area Networks"},{"key":"B6","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1109\/titb.2009.2036165","article-title":"Wearable assistant for Parkinson\u2019s disease patients with the freezing of gait symptom.","volume":"14","author":"Bachlin","year":"2009","journal-title":"IEEE Trans. Inform. Technol. Biomed."},{"key":"B7","author":"Cavazzuti","year":"2012","journal-title":"Optimization Methods: From Theory to Design Scientific and Technological Aspects in Mechanics."},{"key":"B8","article-title":"Risk of falls in Parkinson\u2019s disease: a cross-sectional study of 160 patients.","volume":"2012","author":"Contreras","year":"2012","journal-title":"Parkinsons Dis."},{"key":"B9","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1109\/tnsre.2013.2287241","article-title":"Automatic identification and classification of freezing of gait episodes in Parkinson\u2019s disease patients.","volume":"22","author":"Djuri\u0107-Jovi\u0107i\u0107","year":"2013","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"B10","first-page":"159","article-title":"Gait parameters change prior to freezing in Parkinson\u2019s disease: a data-driven study with wearable inertial units","author":"Ferster","year":"2015","journal-title":"Proceedings of the 10th EAI International Conference on Body Area Networks"},{"key":"B11","doi-asserted-by":"crossref","first-page":"1863","DOI":"10.1109\/IEMBS.2003.1279781","article-title":"Gait analysis for freezing detection in patients with movement disorder using three dimensional acceleration system","author":"Han","year":"2003","journal-title":"Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No. 03CH37439)"},{"key":"B12","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1109\/EMBC.2012.6345873","article-title":"The detection of Freezing of Gait in Parkinson\u2019s disease patients using EEG signals based on Wavelet decomposition","author":"Handojoseno","year":"2012","journal-title":"Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society"},{"key":"B13","doi-asserted-by":"crossref","first-page":"5151","DOI":"10.1109\/IEMBS.2009.5334257","article-title":"deFOG\u2014A real time system for detection and unfreezing of gait of Parkinson\u2019s patients","author":"Jovanov","year":"2009","journal-title":"Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.1002\/mds.22561","article-title":"Clinical and physiological assessments for elucidating falls risk in Parkinson\u2019s disease.","volume":"24","author":"Latt","year":"2009","journal-title":"Mov. Disord."},{"key":"B15","doi-asserted-by":"crossref","DOI":"10.1186\/s40035-019-0162-z","article-title":"Parkinson\u2019s disease in China: a forty-year growing track of bedside work.","volume":"8","author":"Li","year":"2019","journal-title":"Transl. Neurodegener."},{"key":"B16","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1191\/0269215505cr906oa","article-title":"Effects of external rhythmical cueing on gait in patients with Parkinson\u2019s disease: a systematic review.","volume":"19","author":"Lim","year":"2005","journal-title":"Clin. Rehabil."},{"key":"B17","first-page":"579","article-title":"Gait, wrist, and sensors: detecting freezing of gait in Parkinson\u2019s disease from wrist movement","author":"Mazilu","year":"","journal-title":"Proceedings of the 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1843","DOI":"10.1109\/jbhi.2015.2465134","article-title":"Prediction of freezing of gait in Parkinson\u2019s from physiological wearables: an exploratory study.","volume":"19","author":"Mazilu","year":"","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"B19","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/978-3-642-39712-7_11","article-title":"Feature learning for detection and prediction of freezing of gait in Parkinson\u2019s disease","author":"Mazilu","year":"2013","journal-title":"Proceedings of the International Workshop on Machine Learning and Data Mining in Pattern Recognition"},{"key":"B20","first-page":"123","article-title":"Online detection of freezing of gait with smartphones and machine learning techniques","author":"Mazilu","year":"2012","journal-title":"Proceedings of the 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops"},{"key":"B21","first-page":"1","article-title":"Optimal window lengths, features and subsets thereof for freezing of gait classification","author":"Mikos","year":"2017","journal-title":"Proceedings of the 2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)"},{"key":"B22","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.jneumeth.2007.08.023","article-title":"Ambulatory monitoring of freezing of gait in Parkinson\u2019s disease.","volume":"167","author":"Moore","year":"2008","journal-title":"J. Neurosci. Methods"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1186\/1743-0003-10-19","article-title":"Autonomous identification of freezing of gait in Parkinson\u2019s disease from lower-body segmental accelerometry.","volume":"10","author":"Moore","year":"2013","journal-title":"J. Neuroeng. Rehabil."},{"key":"B24","doi-asserted-by":"crossref","first-page":"VII27","DOI":"10.1007\/s00415-006-7007-2","article-title":"Freezing of gait in Parkinson\u2019s disease.","volume":"253","author":"Okuma","year":"2006","journal-title":"J. Neurol."},{"key":"B25","first-page":"1","article-title":"Predicting freezing of gait in Parkinsons disease patients using machine learning","author":"Orphanidou","year":"2018","journal-title":"Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC)"},{"key":"B26","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/ICCE.2015.7066386","article-title":"Smartphone based freezing of gait detection for parkinsonian patients","author":"Pepa","year":"2015","journal-title":"Proceedings of the 2015 IEEE International Conference on Consumer Electronics (ICCE)"},{"key":"B27","doi-asserted-by":"publisher","first-page":"2719","DOI":"10.1109\/tbme.2017.2665438","article-title":"Freezing of gait detection in parkinson\u2019s disease: a subject-independent detector using anomaly scores.","volume":"64","author":"Pham","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"B28","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1002\/mds.26424","article-title":"MDS clinical diagnostic criteria for Parkinson\u2019s disease.","volume":"30","author":"Postuma","year":"2015","journal-title":"Mov. Disord."},{"key":"B29","doi-asserted-by":"crossref","DOI":"10.1186\/s40035-015-0047-8","article-title":"Pilot study of atomoxetine in patients with Parkinson\u2019s disease and dopa-unresponsive Freezing of Gait.","volume":"4","author":"Revuelta","year":"2015","journal-title":"Transl. Neurodegener."},{"key":"B30","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. Neurol. Neurosurg. Psychiatry"},{"key":"B31","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.bspc.2018.07.015","article-title":"Evaluation of deep convolutional neural networks for detection of freezing of gait in Parkinson\u2019s disease patients.","volume":"46","author":"Xia","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"B32","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1016\/j.parkreldis.2015.09.051","article-title":"Identifying freezing of gait in Parkinson\u2019s disease during freezing provoking tasks using waist-mounted accelerometry.","volume":"21","author":"Zach","year":"2015","journal-title":"Parkinsonism Relat. Disord."},{"key":"B33","first-page":"92","article-title":"A feature selection-based framework for human activity recognition using wearable multimodal sensors","author":"Zhang","year":"2011","journal-title":"Proceedings of the 6th International Conference on Body Area Networks: BodyNets"},{"key":"B34","article-title":"Can a smartphone diagnose parkinson disease? a deep neural network method and telediagnosis system implementation.","volume":"2017","author":"Zhang","year":"2017","journal-title":"Parkinsons Dis."}],"container-title":["Frontiers in Human Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnhum.2021.636414\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,22]],"date-time":"2021-03-22T04:23:06Z","timestamp":1616386986000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnhum.2021.636414\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,22]]},"references-count":34,"alternative-id":["10.3389\/fnhum.2021.636414"],"URL":"https:\/\/doi.org\/10.3389\/fnhum.2021.636414","relation":{},"ISSN":["1662-5161"],"issn-type":[{"value":"1662-5161","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,22]]},"article-number":"636414"}}