{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T02:50:16Z","timestamp":1774493416199,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T00:00:00Z","timestamp":1597017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000864","name":"Michael J. Fox Foundation for Parkinson's Research","doi-asserted-by":"publisher","award":["11774"],"award-info":[{"award-number":["11774"]}],"id":[{"id":"10.13039\/100000864","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Freezing of gait (FOG) is a debilitating motor phenomenon that is common among individuals with advanced Parkinson\u2019s disease. Objective and sensitive measures are needed to better quantify FOG. The present work addresses this need by leveraging wearable devices and machine-learning methods to develop and evaluate automated detection of FOG and quantification of its severity. Seventy-one subjects with FOG completed a FOG-provoking test while wearing three wearable sensors (lower back and each ankle). Subjects were videotaped before (OFF state) and after (ON state) they took their antiparkinsonian medications. Annotations of the videos provided the \u201cground-truth\u201d for FOG detection. A leave-one-patient-out validation process with a training set of 57 subjects resulted in 84.1% sensitivity, 83.4% specificity, and 85.0% accuracy for FOG detection. Similar results were seen in an independent test set (data from 14 other subjects). Two derived outcomes, percent time frozen and number of FOG episodes, were associated with self-report of FOG. Both derived-metrics were higher in the OFF state than in the ON state and in the most challenging level of the FOG-provoking test, compared to the least challenging level. These results suggest that this automated machine-learning approach can objectively assess FOG and that its outcomes are responsive to therapeutic interventions.<\/jats:p>","DOI":"10.3390\/s20164474","type":"journal-article","created":{"date-parts":[[2020,8,11]],"date-time":"2020-08-11T09:28:57Z","timestamp":1597138137000},"page":"4474","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Using Wearable Sensors and Machine Learning to Automatically Detect Freezing of Gait during a FOG-Provoking Test"],"prefix":"10.3390","volume":"20","author":[{"given":"Tal","family":"Reches","sequence":"first","affiliation":[{"name":"Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo 6492416, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moria","family":"Dagan","sequence":"additional","affiliation":[{"name":"Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo 6492416, Israel"},{"name":"Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Talia","family":"Herman","sequence":"additional","affiliation":[{"name":"Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo 6492416, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eran","family":"Gazit","sequence":"additional","affiliation":[{"name":"Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo 6492416, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natalia","family":"Gouskova","sequence":"additional","affiliation":[{"name":"Harvard Medical School, Boston, MA 02115, USA"},{"name":"Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA 02131, USA"},{"name":"Division of Gerontology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nir","family":"Giladi","sequence":"additional","affiliation":[{"name":"Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo 6492416, Israel"},{"name":"Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel"},{"name":"Department of Neurology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brad","family":"Manor","sequence":"additional","affiliation":[{"name":"Harvard Medical School, Boston, MA 02115, USA"},{"name":"Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA 02131, USA"},{"name":"Division of Gerontology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1608-0776","authenticated-orcid":false,"given":"Jeffrey","family":"Hausdorff","sequence":"additional","affiliation":[{"name":"Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo 6492416, Israel"},{"name":"Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel"},{"name":"Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel"},{"name":"Rush Alzheimer\u2019s Disease Center and Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2192","DOI":"10.1002\/mds.21659","article-title":"Freezing of gait affects quality of life of peoples with Parkinson\u2019s disease beyond its relationships with mobility and gait","volume":"22","author":"Moore","year":"2007","journal-title":"Mov. Disord."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1016\/S1474-4422(11)70143-0","article-title":"Freezing of gait: Moving forward on a mysterious clinical phenomenon","volume":"10","author":"Nutt","year":"2011","journal-title":"Lancet Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","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. Disord."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.parkreldis.2015.03.028","article-title":"Prevalence and associated features of self-reported freezing of gait in Parkinson disease: The DEEP FOG study","volume":"21","author":"Amboni","year":"2015","journal-title":"Parkinsonism. Relat. Disord."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1046\/j.1468-1331.2003.00611.x","article-title":"Characterization of freezing of gait subtypes and the response of each to levodopa in Parkinson\u2019s disease","volume":"10","author":"Schaafsma","year":"2003","journal-title":"Eur. J. Neurol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1002\/mds.27709","article-title":"Clinical and methodological challenges for assessing freezing of gait: Future perspectives","volume":"34","author":"Mancini","year":"2019","journal-title":"Mov. Disord."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"667","DOI":"10.3233\/JPD-160927","article-title":"The Practicalities of Assessing Freezing of Gait","volume":"6","author":"Barthel","year":"2016","journal-title":"J. Parkinsons. Dis."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.neucli.2015.09.009","article-title":"Characterization and quantification of freezing of gait in Parkinson\u2019s disease: Can detection algorithms replace clinical expert opinion?","volume":"45","author":"Delval","year":"2015","journal-title":"Neurophysiol. Clin."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1002\/mdc3.12893","article-title":"The New Freezing of Gait Questionnaire: Unsuitable as an Outcome in Clinical Trials?","volume":"7","author":"Hulzinga","year":"2020","journal-title":"Mov. Disord. Clin. Pract."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1002\/mds.22993","article-title":"A new rating instrument to assess festination and freezing gait in Parkinsonian patients","volume":"25","author":"Ziegler","year":"2010","journal-title":"Mov. Disord."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Herman, T., Dagan, M., Shema-Shiratzky, S., Reches, T., Brozgol, M., Giladi, N., Manor, B., and Hausdorff, J.M. (2020). Advantages of timing the duration of a freezing of gait-provoking test in individuals with Parkinson\u2019s disease. J. Neurol.","DOI":"10.1007\/s00415-020-09856-7"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.parkreldis.2012.03.001","article-title":"A comparison of clinical and objective measures of freezing of gait in Parkinson\u2019s disease","volume":"18","author":"Morris","year":"2012","journal-title":"Parkinsonism Relat. Disord."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pardoel, S., Kofman, J., Nantel, J., and Lemaire, E.D. (2019). Wearable-sensor-based detection and prediction of freezing of gait in parkinson\u2019s disease: A review. Sensors, 19.","DOI":"10.3390\/s19235141"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"19","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":"ref_15","doi-asserted-by":"crossref","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":"ref_16","doi-asserted-by":"crossref","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":"ref_17","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.neuroscience.2016.11.045","article-title":"The clinical significance of freezing while turning in Parkinson\u2019s disease","volume":"343","author":"Mancini","year":"2017","journal-title":"Neuroscience"},{"key":"ref_18","first-page":"1709","article-title":"A practical method for the detection of freezing of gait in patients with Parkinson\u2019s disease","volume":"9","author":"Kwon","year":"2014","journal-title":"Clin. Interv. Aging"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1899","DOI":"10.1109\/JSEN.2017.2659780","article-title":"Reliable and Robust Detection of Freezing of Gait Episodes With Wearable Electronic Devices","volume":"17","author":"Kita","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.1002\/mds.23159","article-title":"Objective detection of subtle freezing of gait episodes in Parkinson\u2019s disease","volume":"25","author":"Delval","year":"2010","journal-title":"Mov. Disord."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Mart\u00edn, D., Sam\u00e0, A., P\u00e9rez-L\u00f3pez, C., Catal\u00e0, A., Arostegui, J.M.M., Cabestany, J., Bay\u00e9s, \u00c0., Alcaine, S., Mestre, B., and Prats, A. (2017). Home detection of freezing of gait using Support Vector Machines through a single waist-worn triaxial accelerometer. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0171764"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s11517-015-1395-3","article-title":"Detecting freezing of gait with a tri-axial accelerometer in Parkinson\u2019s disease patients","volume":"54","author":"Ahlrichs","year":"2016","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.patrec.2017.05.009","article-title":"Determining the optimal features in freezing of gait detection through a single waist accelerometer in home environments","volume":"105","author":"Alcaine","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tahafchi, P., Molina, R., Roper, J.A., Sowalsky, K., Hass, C.J., Gunduz, A., Okun, M.S., and Judy, J.W. (2017, January 11\u201315). Freezing-of-Gait Detection Using Temporal, Spatial, and Physiological Features with a Support-Vector-Machine Classifier. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, Korea.","DOI":"10.1109\/EMBC.2017.8037455"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Aich, S., Pradhan, P.M., Park, J., Sethi, N., Vathsa, V.S.S., and Kim, H.C. (2018). A validation study of freezing of gait (FoG) detection and machine-learning-based FoG prediction using estimated gait characteristics with a wearable accelerometer. Sensors, 18.","DOI":"10.3390\/s18103287"},{"key":"ref_26","unstructured":"Kim, H., Lee, H.J., Lee, W., Kwon, S., Kim, S.K., Jeon, H.S., Park, H., Shin, C.W., Yi, W.J., and Jeon, B.S. (2015, January 25\u201329). Unconstrained Detection of Freezing of Gait in Parkinson\u2019s Disease Patients Using Smartphone. Proceedings of the Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, Milan, Italy."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mazilu, S., Hardegger, M., Zhu, Z., Roggen, D., Troester, G., Plotnik, M., and Hausdorff, J. (2012, January 21\u201324). Online Detection of Freezing of Gait with Smartphones and Machine Learning Techniques. Proceedings of the 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, San Diego, CA, USA.","DOI":"10.4108\/icst.pervasivehealth.2012.248680"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1088\/0967-3334\/32\/12\/009","article-title":"An instrumented timed up and go: The added value of an accelerometer for identifying fall risk in idiopathic fallers","volume":"32","author":"Weiss","year":"2011","journal-title":"Physiol. Meas"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Vervoort, D., Vuillerme, N., Kosse, N., Hortob\u00e1gyi, T., and Lamoth, C.J.C. (2016). Multivariate analyses and classification of inertial sensor data to identify aging effects on the timed-Up-and-Go test. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0155984"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bernard, B.A., and Goldman, J.G. (2010). MMSE\u2014Mini-Mental State Examination. Encyclopedia of Movement Disorders, Elsevier Inc.","DOI":"10.1016\/B978-0-12-374105-9.00186-6"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.gaitpost.2009.07.108","article-title":"Reliability of the new freezing of gait questionnaire: Agreement between patients with Parkinson\u2019s disease and their carers","volume":"30","author":"Nieuwboer","year":"2009","journal-title":"Gait Posture"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.2522\/ptj.20150580","article-title":"Balance and Gait Represent Independent Domains of Mobility in Parkinson Disease","volume":"96","author":"Horak","year":"2016","journal-title":"Phys. Ther."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Orphanidou, N.K., Hussain, A., Keight, R., Lishoa, P., Hind, J., and Al-Askar, H. (2018, January 8\u201313). Predicting Freezing of Gait in Parkinsons Disease Patients Using Machine Learning. Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil.","DOI":"10.1109\/CEC.2018.8477909"},{"key":"ref_34","unstructured":"Ding, C., and Peng, H. (2003, January 11). Minimum redundancy feature selection from microarray gene expression data. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB, Stanford, CA, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/S0378-4371(02)01744-2","article-title":"Time series analysis of leg movements during freezing of gait in Parkinson\u2019s disease: Akinesia, rhyme or reason?","volume":"321","author":"Hausdorff","year":"2003","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_36","unstructured":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, Lawrence Erlbaum Associates. [2nd ed.]."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sigcha, L., Costa, N., Pav, I., Costa, S., Arezes, P., Manuel, J., and Arcas, G. (2020). De Deep Learning Approaches for Detecting Freezing of Gait in Parkinson\u2019s Disease Patients through On-Body Acceleration Sensors. Sensors, 20.","DOI":"10.3390\/s20071895"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mazzetta, I., Zampogna, A., Suppa, A., Gumiero, A., Pessione, M., and Irrera, F. (2019). Wearable sensors system for an improved analysis of freezing of gait in Parkinson\u2019s disease using electromyography and inertial signals. Sensors, 19.","DOI":"10.3390\/s19040948"},{"key":"ref_39","doi-asserted-by":"crossref","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":"2015","journal-title":"IEEE J. Biomed. Heal. Inform."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"631","DOI":"10.3233\/JPD-191813","article-title":"Freezing of Gait in People with Parkinson\u2019s Disease: Nature, Occurrence, and Risk Factors","volume":"10","author":"Lord","year":"2020","journal-title":"J. Parkinsons. Dis."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4474\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:58:53Z","timestamp":1760176733000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4474"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,10]]},"references-count":40,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["s20164474"],"URL":"https:\/\/doi.org\/10.3390\/s20164474","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,10]]}}}