{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T20:39:46Z","timestamp":1792442386258,"version":"4.5.0"},"reference-count":139,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,24]],"date-time":"2019-11-24T00:00:00Z","timestamp":1574553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["."],"award-info":[{"award-number":["."]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004490","name":"University of Waterloo","doi-asserted-by":"publisher","award":["."],"award-info":[{"award-number":["."]}],"id":[{"id":"10.13039\/501100004490","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 serious gait disturbance, common in mid- and late-stage Parkinson\u2019s disease, that affects mobility and increases fall risk. Wearable sensors have been used to detect and predict FOG with the ultimate aim of preventing freezes or reducing their effect using gait monitoring and assistive devices. This review presents and assesses the state of the art of FOG detection and prediction using wearable sensors, with the intention of providing guidance on current knowledge, and identifying knowledge gaps that need to be filled and challenges to be considered in future studies. This review searched the Scopus, PubMed, and Web of Science databases to identify studies that used wearable sensors to detect or predict FOG episodes in Parkinson\u2019s disease. Following screening, 74 publications were included, comprising 68 publications detecting FOG, seven predicting FOG, and one in both categories. Details were extracted regarding participants, walking task, sensor type and body location, detection or prediction approach, feature extraction and selection, classification method, and detection and prediction performance. The results showed that increasingly complex machine-learning algorithms combined with diverse feature sets improved FOG detection. The lack of large FOG datasets and highly person-specific FOG manifestation were common challenges. Transfer learning and semi-supervised learning were promising for FOG detection and prediction since they provided person-specific tuning while preserving model generalization.<\/jats:p>","DOI":"10.3390\/s19235141","type":"journal-article","created":{"date-parts":[[2019,11,25]],"date-time":"2019-11-25T03:10:00Z","timestamp":1574651400000},"page":"5141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":192,"title":["Wearable-Sensor-Based Detection and Prediction of Freezing of Gait in Parkinson\u2019s Disease: A Review"],"prefix":"10.3390","volume":"19","author":[{"given":"Scott","family":"Pardoel","sequence":"first","affiliation":[{"name":"Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Kofman","sequence":"additional","affiliation":[{"name":"Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julie","family":"Nantel","sequence":"additional","affiliation":[{"name":"School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1N 6N5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4693-2623","authenticated-orcid":false,"given":"Edward D.","family":"Lemaire","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, ON K1H 8M2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,24]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1002\/mds.21458","article-title":"Predictors of freezing in Parkinson\u2019s disease: A survey of 6620 patients","volume":"22","author":"Macht","year":"2007","journal-title":"Mov. Disord."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1002\/mds.870130310","article-title":"Gait variability and basal ganglia disorders: Stride-to-stride variations of gait cycle timing in Parkinson\u2019s disease and Huntington\u2019s disease","volume":"13","author":"Hausdorff","year":"1998","journal-title":"Mov. Disord."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1111\/j.1460-9568.2006.05033.x","article-title":"Marked alterations in the gait timing and rhythmicity of patients with de novo Parkinson\u2019s disease","volume":"24","author":"Baltadjieva","year":"2006","journal-title":"Eur. J. Neurosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"026113:1","DOI":"10.1063\/1.3147408","article-title":"Gait dynamics in Parkinson\u2019s disease: Common and distinct behavior among stride length, gait variability, and fractal-like scaling","volume":"19","author":"Hausdorff","year":"2009","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1016\/j.apmr.2004.08.012","article-title":"Quantitative gait analysis in Parkinson\u2019s disease: Comparison with a healthy control group","volume":"86","author":"Sofuwa","year":"2005","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1002\/mds.22144","article-title":"Clinimetrics of freezing of gait","volume":"23","author":"Snijders","year":"2008","journal-title":"Mov. Disord."},{"key":"ref_9","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_10","first-page":"255","article-title":"Freezing of gait and falls in Parkinson\u2019s disease","volume":"4","author":"Okuma","year":"2014","journal-title":"J. Park. Dis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.parkreldis.2017.10.013","article-title":"A prospective study of falls in relation to freezing of gait and response fluctuations in Parkinson\u2019s disease","volume":"46","author":"Okuma","year":"2018","journal-title":"Park. Relat. Disord."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.parkreldis.2014.01.017","article-title":"The effect of medication and the role of postural instability in different components of freezing of gait (FOG)","volume":"20","author":"Nantel","year":"2014","journal-title":"Park. Relat. Disord."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1093\/ageing\/30.suppl_4.3","article-title":"Epidemiology of falls","volume":"30","author":"Masud","year":"2001","journal-title":"Age Ageing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.gaitpost.2014.09.006","article-title":"How fear of falling can increase fall-risk in older adults: Applying psychological theory to practical observations","volume":"41","author":"Young","year":"2015","journal-title":"Gait Posture"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1353\/cja.2005.0028","article-title":"Falls and fear of falling among community-dwelling seniors: The dynamic tension between exercising precaution and striving for independence","volume":"23","author":"Hobson","year":"2004","journal-title":"Can. J. Aging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1002\/mds.10396","article-title":"Fear of falling and postural control in Parkinson\u2019s disease","volume":"18","author":"Adkin","year":"2003","journal-title":"Mov. Disord."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1007\/s004150170047","article-title":"Prospective assessment of falls in Parkinson\u2019s disease","volume":"248","author":"Bloem","year":"2001","journal-title":"J. Neurol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1089\/tmj.2017.0215","article-title":"Validation of freezing-of-gait monitoring using smartphone","volume":"24","author":"Kim","year":"2018","journal-title":"Telemed. e-Health"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pham, T.T., Nguyen, D.N., Dutkiewicz, E., McEwan, A.L., and Leong, P.H.W. (2017, January 11\u201313). An anomaly detection technique in wearable wireless monitoring systems for studies of gait freezing in Parkinson\u2019s disease. Proceedings of the 2017 International Conference on Information Networking (ICOIN), Da Nang, Vietnam.","DOI":"10.1109\/ICOIN.2017.7899471"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.rehab.2017.08.002","article-title":"Cueing for people with Parkinson\u2019s disease with freezing of gait: A narrative review of the state-of-the-art and novel perspectives","volume":"61","author":"Ginis","year":"2018","journal-title":"Ann. Phys. Rehabil. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1002\/mds.21978","article-title":"Cueing for freezing of gait in patients with Parkinson\u2019s disease: A rehabilitation perspective","volume":"23","author":"Nieuwboer","year":"2008","journal-title":"Mov. Disord."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jovanov, E., Wang, E., Verhagen, L., Fredrickson, M., and Fratangelo, R. (2009, January 3\u20136). deFOG\u2014A real time system for detection and unfreezing of gait of Parkinson\u2019s patients. Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, Minneapolis, MN, USA.","DOI":"10.1109\/IEMBS.2009.5334257"},{"key":"ref_24","doi-asserted-by":"crossref","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":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mazilu, S., Blanke, U., Hardegger, M., Troster, G., Gazit, E., Dorfman, M., and Hausdorff, J.M. (2014, January 24\u201328). GaitAssist: A wearable assistant for gait training and rehabilitation in Parkinson\u2019s disease. Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS), Budapest, Hungary.","DOI":"10.1109\/PerComW.2014.6815179"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1093\/brain\/awh189","article-title":"Electromyographic profiles of gait prior to onset of freezing episodes in patients with Parkinson\u2019s disease","volume":"127","author":"Nieuwboer","year":"2004","journal-title":"Brain"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ferster, M.L., Mazilu, S., and Tr\u00f6ster, G. (2015, January 28\u201330). Gait parameters change prior to freezing in Parkinson\u2019s disease: A data-driven study with wearable inertial units. Proceedings of the 10th EAI International Conference on Body Area Networks, Sydney, Australia.","DOI":"10.4108\/eai.28-9-2015.2261411"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Vander SLoten, J., Verdonck, P., Nyssen, M., and Haueisen, J. (2008, January 23\u201327). Power spectral distribution analysis for detection of freezing of gait in patients with Parkinson\u2019s disease. Proceedings of the 4th European Conference of the International Federation for Medical and Biological Engineering, IFMBE, Antwerp, Belgium.","DOI":"10.1007\/978-3-540-89208-3"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bachlin, M., Roggen, D., Troster, G., Plotnik, M., Inbar, N., Meidan, I., Herman, T., Brozgol, M., Shaviv, E., and Giladi, N. (2009, January 4\u20137). Potentials of Enhanced Context Awareness in Wearable Assistants for Parkinson\u2019s Disease Patients with the Freezing of Gait Syndrome. Proceedings of the 2009 International Symposium on Wearable Computers, Linz, Austria.","DOI":"10.1109\/ISWC.2009.14"},{"key":"ref_32","unstructured":"Troester, G., Connelly, K., and Arnrich, B. (2009, January 1\u20133). Parkinson\u2019s disease patients\u2019 perspective on context aware wearable technology for auditive assistance. Proceedings of the 3rd International Conference on Pervasive Computing Technologies for Healthcare\u2014Pervasive Health 2009, London, UK."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"88","DOI":"10.3414\/ME09-02-0003","article-title":"A wearable system to assist walking of parkinso\u0144s disease patients benefits and challenges of context-triggered acoustic cueing","volume":"49","author":"Plotnik","year":"2010","journal-title":"Methods Inf. Med."},{"key":"ref_34","unstructured":"Kaiser, W., and Lu, C. (2009, January 1\u20133). Online detection of freezing of gait in Parkinson\u2019s disease patients: A performance characterization. Proceedings of the BODYNETS 2009\u20144th International ICST Conference on Body Area Networks, Los Angeles, CA, USA."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Djuric-Jovicic, M., Jovicic, N.S., Milovanovic, I., Radovanovic, S., Kresojevic, N., and Popovic, M.B. (2010, January 23\u201325). Classification of walking patterns in Parkinson\u2019s disease patients based on inertial sensor data. Proceedings of the 10th Symposium on Neural Network Applications in Electrical Engineering, Belgrade, Serbia.","DOI":"10.1109\/NEUREL.2010.5644040"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1590\/S0100-879X2010007500077","article-title":"A simple method to assess freezing of gait in Parkinson\u2019s disease patients","volume":"43","author":"Popovic","year":"2010","journal-title":"Braz. J. Med. Res."},{"key":"ref_38","unstructured":"Cole, B.T., Roy, S.H., and Nawab, S.H. (September, January 30). Detecting freezing-of-gait during unscripted and unconstrained activity. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMB, Boston, MA, USA."},{"key":"ref_39","unstructured":"Lin, J., and Nikita, K.S. (2010, January 18\u201320). On assessing motor disorders in Parkinson\u2019s disease. Proceedings of the Second International ICST Conference, MobiHealth 2010, Ayia Napa, Cyprus."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Niazmand, K., Tonn, K., Zhao, Y., Fietzek, U.M., Schroeteler, F., Ziegler, K., Ceballos-Baumann, A.O., and Lueth, T.C. (2011, January 10\u201312). Freezing of Gait detection in Parkinson\u2019s disease using accelerometer based smart clothes. Proceedings of the 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS), San Diego, CA, USA.","DOI":"10.1109\/BioCAS.2011.6107762"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Tonn, K., Niazmand, K., Fietzek, U.M., D\u2019Angelo, L.T., Ceballos-Baumann, A., and Lueth, T.C. (2012, January 2\u20137). Online FOG identification in Parkinson\u2019s disease with a time-frequency combined algorithm. Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics, Hong Kong and Shenzhen, China.","DOI":"10.1109\/BHI.2012.6211542"},{"key":"ref_42","unstructured":"Arriaga, R., and Matic, A. (2012, January 21\u201324). Online detection of freezing of gait with smartphones and machine learning techniques. Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare, San Diego, CA, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.cmpb.2012.10.016","article-title":"Automatic detection of freezing of gait events in patients with Parkinson\u2019s disease","volume":"110","author":"Tripoliti","year":"2013","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"19:1","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_45","doi-asserted-by":"crossref","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","volume":"Volume 7988","author":"Perner","year":"2013","journal-title":"Machine Learning and Data Mining in Pattern Recognition"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"6819","DOI":"10.3390\/s140406819","article-title":"Detection of freezing of gait in Parkinson disease: Preliminary results","volume":"14","author":"Coste","year":"2014","journal-title":"Sensors"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sijobert, B., Denys, J., Coste, C.A., and Geny, C. (2014, January 17\u201319). IMU based detection of freezing of gait and festination in Parkinson\u2019s disease. Proceedings of the 2014 IEEE 19th International Functional Electrical Stimulation Society Annual Conference, IFESS, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IFESS.2014.7036751"},{"key":"ref_48","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_49","doi-asserted-by":"crossref","unstructured":"Pepa, L., Ciabattoni, L., Verdini, F., Capecci, M., and Ceravolo, M.G.G. (2014, January 10\u201312). Smartphone based fuzzy logic freezing of gait detection in Parkinson\u2019s Disease. Proceedings of the 10th IEEE\/ASME International Conference on Mechatronic and Embedded Systems and Applications, Senigallia, Italy.","DOI":"10.1109\/MESA.2014.6935630"},{"key":"ref_50","doi-asserted-by":"crossref","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":"Jovicic","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_51","unstructured":"Kanza, Y., Gudes, O., and Goldberg, D.W. (2014, January 4). Prediction of freezing of gait from parkinson\u2019s disease movement time series using conditional random fields. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on the Use of GIS in Public Health, HealthGIS 2014\u2014In Conjuncture with the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM GIS 2014, Dallas, TX, USA."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Mazilu, S., Blanke, U., and Troster, G. (2015, January 23\u201327). Gait, wrist, and sensors: Detecting freezing of gait in Parkinson\u2019s disease from wrist movement. Proceedings of the IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), St. Louis, MI, USA.","DOI":"10.1109\/PERCOMW.2015.7134102"},{"key":"ref_53","doi-asserted-by":"crossref","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":"Park. Relat. Disord."},{"key":"ref_54","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 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Handojoseno, A.M.A., Gilat, M., Quynh Tran Ly, Q.T., Chamtie, H., Shine, J.M., Nguyen, T.N., Tran, Y., Lewis, S.J.G., and Nguyen, H.T. (2015, January 25\u201329). An EEG study of turning freeze in Parkinson\u2019s disease patients: The alteration of brain dynamic on the motor and visual cortex. Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7319910"},{"key":"ref_56","first-page":"13679","article-title":"Prediction of freezing of gait (FOG) episodes in Parkinson\u2019s disease patients by gait analysis","volume":"5","author":"Venu","year":"2016","journal-title":"Int. J. Innov. Res. Sci. Eng. Technol."},{"key":"ref_57","unstructured":"Nebot, \u00c0., Binefa, X., and de M\u00e1ntaras, R.L. (2016, January 19\u201321). Comparison of features, window sizes and classifiers in detecting freezing of gait in patients with Parkinson\u2019s disease through a waist-worn accelerometer. Proceedings of the 19th International Conference of the Catalan Association for Artificial Intelligence, Barcelona, Spain."},{"key":"ref_58","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_59","first-page":"8220","article-title":"Mobile devices for the real-time detection of specific human motion disorders","volume":"16","author":"Lorenzi","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.proeng.2015.08.626","article-title":"Smart sensing systems for the detection of human motion disorders","volume":"120","author":"Lorenzi","year":"2015","journal-title":"Procedia Eng."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Lorenzi, P., Rao, R., Romano, G., Kita, A., Serpa, M., Filesi, F., Irrera, F., Bologna, M., Suppa, A., and Berardelli, A. (2015, January 18\u201319). Smart sensors for the recognition of specific human motion disorders in Parkinson\u2019s disease. Proceedings of the 2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI), Gallipoli, Italy.","DOI":"10.1109\/IWASI.2015.7184973"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Kita, A., Lorenzi, P., Romano, G., Rao, R., Parisi, R., Suppa, A., Bologna, M., Berardelli, A., and Irrera, F. (2016, January 21\u201323). Smart Sensing System for the Detection of Specific Human Motion Symptoms of the Parkinson\u2019s Disease. Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIODEVICES, Rome, Italy.","DOI":"10.5220\/0005666801520159"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Rezvanian, S., and Lockhart, T.E. (2016). Towards real-time detection of freezing of gait using wavelet transform on wireless accelerometer data. Sensors, 16.","DOI":"10.3390\/s16040475"},{"key":"ref_64","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_65","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.gaitpost.2016.08.018","article-title":"A smartphone-based architecture to detect and quantify freezing of gait in Parkinson\u2019s disease","volume":"50","author":"Capecci","year":"2016","journal-title":"Gait Posture"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Ly, Q.T., Handojoseno, A.M.A., Gilat, M., Nguyen, N., Chai, R., Tran, Y., Lewis, S.J.G., and Nguyen, H.T. (2016, January 16\u201320). Identifying montages that best detect the electroencephalogram power spectrum alteration during freezing of gait in Parkinson\u2019s disease patients. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7592119"},{"key":"ref_67","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_68","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.parkreldis.2011.08.002","article-title":"Assessing the utility of Freezing of gait questionnaires in Parkinson\u2019s disease","volume":"18","author":"Shine","year":"2012","journal-title":"Park. Relat. Disord."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Pham, T.T., Nguyen, D.N., Dutkiewicz, E., McEwan, A.L., and Leong, P.H.W. (2017, January 21\u201325). Wearable healthcare systems: A single channel accelerometer based anomaly detector for studies of gait freezing in Parkinson\u2019s disease. Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France.","DOI":"10.1109\/ICC.2017.7997415"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2394","DOI":"10.1109\/TBME.2017.2655344","article-title":"Smart gait-aid glasses for Parkinson\u2019s disease patients","volume":"64","author":"Ahn","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_71","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_72","doi-asserted-by":"crossref","first-page":"406:1","DOI":"10.3389\/fneur.2017.00406","article-title":"L-DOPA and freezing of gait in Parkinson\u2019s disease: Objective assessment through a wearable wireless system","volume":"8","author":"Suppa","year":"2017","journal-title":"Front. Neurol."},{"key":"ref_73","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_74","first-page":"e0171764:1","article-title":"Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer","volume":"12","author":"Cabestany","year":"2017","journal-title":"PLoS ONE"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Mart\u00edn, D., P\u00e9rez-L\u00f3pez, C., Sam\u00e0, A., Catal\u00e0, A., Arostegui, J.M.M., Cabestany, J., Mestre, B., Alcaine, S., Prats, A., and De La Cruz Crespo, M. (2017). A waist-worn inertial measurement unit for long-term monitoring of Parkinson\u2019s disease patients. Sensors, 17.","DOI":"10.3390\/s17040827"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Ly, Q.T., Handojoseno, A.M.A., Gilat, M., Chai, R., Martens, K.A.E., Georgiades, M., Naik, G.R., Tran, Y., Lewis, S.J.G., and Nguyen, H.T. (2017, January 11\u201315). Detection of turning freeze in Parkinson\u2019s disease based on S-transform decomposition of EEG signals. Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, Korea.","DOI":"10.1109\/EMBC.2017.8037499"},{"key":"ref_77","unstructured":"Rodrigues, J.J.P.C., Ni\u017eeti\u0107, S., Patrono, L., Milanovi\u0107, Z., \u0160oli\u0107, P., Vukojevi\u0107, K., and Perkovi\u0107, T. (2017, January 12\u201314). An unobtrusive expert system to detect freezing of gait during daily living in people with Parkinson\u2019s disease. Proceedings of the 2017 2nd International Multidisciplinary Conference on Computer and Energy Science, Split, Croatia."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Wang, K., Li, B., Gu, D., Dai, K., and Zhou, L. (2017, January 6\u20138). A smartphone based system for freezing of gait monitoring for Parkinson\u2019s disease patients. Proceedings of the 9th IEEE International Conference on Communication Software and Networks, ICCSN 2017, Guangzhou, China.","DOI":"10.1109\/ICCSN.2017.8230364"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Punin, C., Barzallo, B., Huerta, M., Bermeo, A., Bravo, M., and Llumiguano, C. (2017, January 16\u201320). Wireless devices to restart walking during an episode of FOG on patients with Parkinson\u2019s disease. Proceedings of the 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), Salinas, Ecuador.","DOI":"10.1109\/ETCM.2017.8247520"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1007\/s13042-015-0480-0","article-title":"Detection of freezing of gait for Parkinson\u2019s disease patients with multi-sensor device and Gaussian neural networks","volume":"8","author":"Saad","year":"2017","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_81","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_82","doi-asserted-by":"crossref","first-page":"2152","DOI":"10.1109\/TBME.2017.2785625","article-title":"Modeling, detecting, and tracking freezing of gait in Parkinson disease using inertial sensors","volume":"65","author":"Prateek","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_83","unstructured":"Yassine, I., Basha, T., and Eldeib, .A. (2018, January 20\u201322). Patient-dependent freezing of gait detection using signals from multi-accelerometer sensors in Parkinson\u2019s disease. Proceedings of the 9th Cairo International Biomedical Engineering Conference, CIBEC 2018, Cairo, Egypt."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Roffo, G., Melzi, S., and Cristani, M. (2015, January 7\u201313). Infinite Feature Selection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.478"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.knosys.2017.10.017","article-title":"Deep learning for freezing of gait detection in Parkinson\u2019s disease patients in their homes using a waist-worn inertial measurement unit","volume":"139","author":"Camps","year":"2018","journal-title":"Knowl.-Based Syst."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Oung, Q.W., Basah, S.N., Muthusamy, H., Vijean, V., Lee, H., Khairunizam, W., Bakar, S.A., Razlan, Z.M., and Ibrahim, Z. (2018, January 15\u201317). Objective evaluation of freezing of gait in patients with Parkinson\u2019s disease through machine learning approaches. Proceedings of the 2018 International Conference on Computational Approach in Smart Systems Design and Applications, ICASSDA, Kuching, Malaysia.","DOI":"10.1109\/ICASSDA.2018.8477606"},{"key":"ref_87","unstructured":"Xu, B. (2018, January 25\u201327). Automatic detection system for freezing of gait in Parkinson\u2019s disease based on the clustering algorithm. Proceedings of the 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC, Xi\u2019an, China."},{"key":"ref_88","first-page":"871","article-title":"Real-time patient adaptivity for freezing of gait classification through semi-supervised neural networks","volume":"Volume 1","author":"Chen","year":"2018","journal-title":"Proceedings of the 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Mikos, V., Heng, C.-H., Tay, A., Yen, S.-C., Chia, N.S.Y., Koh, K.M.L., Tan, D.M.L., and Au, W.L. (2018, January 5\u20137). A neural network accelerator with integrated feature extraction processor for a freezing of gait detection system. Proceedings of the IEEE Asian Solid-State Circuits Conference, Tainan, Taiwan.","DOI":"10.1109\/ASSCC.2018.8579283"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Mohammadian Rad, N., van Laarhoven, T., Furlanello, C., and Marchiori, E. (2018). Novelty detection using deep normative modeling for imu-based abnormal movement monitoring in parkinson\u2019s disease and autism spectrum disorders. Sensors, 18.","DOI":"10.3390\/s18103533"},{"key":"ref_91","first-page":"117","article-title":"Hybrid DWT-FFT features for detecting freezing of gait in Parkinson\u2019s disease","volume":"314","author":"Ashour","year":"2019","journal-title":"Front. Artif. Intell. Appl."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Punin, C., Barzallo, B., Clotet, R., Bermeo, A., Bravo, M., Bermeo, J.P., and Llumiguano, C. (2019). A non-invasive medical device for parkinson\u2019s patients with episodes of freezing of gait. Sensors, 19.","DOI":"10.3390\/s19030737"},{"key":"ref_93","first-page":"865","article-title":"Wireless assistance system during episodes of freezing of gait by means superficial electrical stimulation","volume":"Volume 68","author":"Lhotska","year":"2019","journal-title":"Proceedings of the World Congress on Medical Physics and Biomedical Engineering 2018, IFMBE"},{"key":"ref_94","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_95","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. Informatics"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1109\/TNSRE.2014.2381254","article-title":"Analysis and prediction of the freezing of gait using EEG brain dynamics","volume":"23","author":"Shine","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Zia, J., Tadayon, A., McDaniel, T., and Panchanathan, S. (2016, January 23\u201326). Utilizing neural networks to predict freezing of gait in Parkinson\u2019s patients. Proceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS 2016, Reno, NV, USA.","DOI":"10.1145\/2982142.2982194"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"394:1","DOI":"10.3389\/fneur.2017.00394","article-title":"Identification of characteristic motor patterns preceding freezing of gait in Parkinson\u2019s disease using wearable sensors","volume":"8","author":"Palmerini","year":"2017","journal-title":"Front. Neurol."},{"key":"ref_99","first-page":"124","article-title":"Prediction of freezing of gait in patients with Parkinson\u2019s disease using EEG signals","volume":"Volume 246","author":"Maeder","year":"2018","journal-title":"Telehealth for our Ageing Society"},{"key":"ref_100","unstructured":"Wani, M.A., Sayed-Mouchaweh, M., Kantardzic, M., Gama, J., and Lughofer, E. (2018, January 17\u201318). Deep domain adaptation to predict freezing of gait in patients with Parkinson\u2019s disease. Proceedings of the 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, Orlando, FL, USA."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Mazilu, S., Blanke, U., Roggen, D., Tr\u00f6ster, G., Gazit, E., and Hausdorff, J.M. (2013, January 7\u20138). Engineers meet clinicians. Proceedings of the 4th Augmented Human International Conference, Stuttgart, Germany.","DOI":"10.1145\/2459236.2459257"},{"key":"ref_102","unstructured":"(2019, October 02). REMPARK\u2014Personal Health Device for the Remote and Autonomous Management of Parkinson\u2019s Disease. Available online: https:\/\/rempark.cetpd.upc.edu\/project."},{"key":"ref_103","unstructured":"Verleysen, M., P\u00e9rez L\u00f3pez, C., Rodr\u00edguez Mart\u00edn, D.M., Cabestany Moncus\u00ed, J., Moreno Ar\u00f3stegui, J.M., and Rodr\u00edguez Molinero, A. (2013, January 24\u201326). A heterogeneous database for movement knowledge extraction in Parkinson\u2019s Disease. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN 2013, Bruges, Belgium."},{"key":"ref_104","unstructured":"(2019, May 10). MASPARK. Available online: https:\/\/www.aicos.fraunhofer.pt\/en\/our_work\/projects\/maspark.html."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Niazmand, K., Tonn, K., Kalaras, A., Fietzek, U.M., Mehrkens, J.H., and Lueth, T.C. (2011, January 27\u201330). Quantitative evaluation of Parkinson\u2019s disease using sensor based smart glove. Proceedings of the 24th International Symposium on Computer-Based Medical Systems (CBMS), Bristol, UK.","DOI":"10.1109\/CBMS.2011.5999113"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Nilsson, J.-O., Gupta, A.K., and Handel, P. (2014, January 27\u201330). Foot-mounted inertial navigation made easy. Proceedings of the 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Busan, Korea.","DOI":"10.1109\/IPIN.2014.7275464"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0966-6362(02)00190-X","article-title":"Assessment of spatio-temporal gait parameters from trunk accelerations during human walking","volume":"18","author":"Zijlstra","year":"2003","journal-title":"Gait Posture"},{"key":"ref_108","unstructured":"Laudanski, A., Yang, S., and Li, Q. (September, January 30). A concurrent comparison of inertia sensor-based walking speed estimation methods. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA."},{"key":"ref_109","unstructured":"Madgwick, S.O.H., Harrison, A.J.L., and Vaidyanathan, R. (July, January 29). Estimation of IMU and MARG orientation using a gradient descent algorithm. Proceedings of the IEEE International Conference on Rehabilitation Robotics, Zurich, Switzerland."},{"key":"ref_110","unstructured":"Cole, B.T., Roy, S.H., De Luca, C.J., and Nawab, S.H. (September, January 31). Dynamic neural network detection of tremor and dyskinesia from wearable sensor data. Proceedings of the Proceeding of the Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.gaitpost.2006.09.011","article-title":"Long-term monitoring of gait in Parkinson\u2019s disease","volume":"26","author":"Moore","year":"2007","journal-title":"Gait Posture"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Khan, A.M., Lee, Y.K., and Kim, T.-S. (2008, January 20\u201325). Accelerometer signal-based human activity recognition using augmented autoregressive model coefficients and artificial neural nets. Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada.","DOI":"10.1109\/IEMBS.2008.4650379"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/BF00198091","article-title":"A new method of the description of the information flow sources","volume":"65","author":"Kaminiski","year":"1991","journal-title":"Biol. Cybern."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1073\/pnas.17.5.315","article-title":"Hamiltonian Systems and Transformation in Hilbert Space","volume":"17","author":"Koopman","year":"1931","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.physa.2015.12.059","article-title":"Using dynamic mode decomposition to extract cyclic behavior in the stock market","volume":"448","author":"Hua","year":"2016","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_116","first-page":"71","article-title":"A Novel Feature Extraction for Robust EMG Pattern Recognition","volume":"1","author":"Phinyomark","year":"2009","journal-title":"J. Comput."},{"key":"ref_117","first-page":"27","article-title":"Feature extraction and reduction of wavelet transform coefficients for EMG pattern classification","volume":"122","author":"Phinyomark","year":"2012","journal-title":"Electron. Electr. Eng."},{"key":"ref_118","first-page":"225","article-title":"Biomedical signal processing (in four parts) Part 3 The power spectrum and coherence function","volume":"29","author":"Challis","year":"1991","journal-title":"Med. Biol. Eng."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"21329","DOI":"10.3390\/s141121329","article-title":"PERFORM: A system for monitoring, assessment and management of patients with Parkinson\u2019s disease","volume":"14","author":"Tzallas","year":"2014","journal-title":"Sensors"},{"key":"ref_120","unstructured":"Shiavi, R. (2007). Introduction to Applied Statistical Signal Analysis: Guide to Biomedical and Electrical Engineering Applications, Academic Press. [3rd ed.]."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1016\/j.neuroimage.2011.01.055","article-title":"An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias","volume":"55","author":"Vinck","year":"2011","journal-title":"Neuroimage"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1002\/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C","article-title":"Measuring phase synchrony in brain signals","volume":"8","author":"Lachaux","year":"1999","journal-title":"Hum. Brain Mapp."},{"key":"ref_123","unstructured":"Tay, A., Yen, S.C., Lee, P.Y., Wang, C.Y., Neo, A., Phan, S.W., Yogaprakash, K., Liew, S., and Au, W.L. (June, January 31). Freezing of gait (FoG) detection for Parkinson disease. Proceedings of the 10th Asian Control Conference (ASCC), Kota Kinabalu, Malaysia."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","article-title":"Top 10 algorithms in data mining","volume":"14","author":"Wu","year":"2008","journal-title":"Knowl. Inf. Syst."},{"key":"ref_125","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer-Verlag. [1st ed.]."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","article-title":"Additive logistic regression: A statistical view of boosting","volume":"28","author":"Friedman","year":"2000","journal-title":"Ann. Stat."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Seiffert, C., Khoshgoftaar, T.M., Van Hulse, J., and Napolitano, A. (2008, January 8\u201311). RUSBoost: Improving classification performance when training data is skewed. Proceedings of the 19th International Conference on Pattern Recognition, Tampa Bay, FL, USA.","DOI":"10.1109\/ICPR.2008.4761297"},{"key":"ref_128","unstructured":"Freund, Y. (2009). A More Robust Boosting Algorithm. arXiv."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"2279","DOI":"10.1016\/S0031-3203(01)00178-9","article-title":"Image processing with neural networks\u2014A review","volume":"35","author":"Handels","year":"2002","journal-title":"Patt. Recognit."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional neural networks: An overview and application in radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Imaging"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-018-1088-1","article-title":"Medical Image Analysis using Convolutional Neural Networks: A Review","volume":"42","author":"Anwar","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Sak, H., Senior, A., and Beaufays, F. (2014). Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition. arXiv.","DOI":"10.21437\/Interspeech.2014-80"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"350:1","DOI":"10.1007\/s11910-013-0350-7","article-title":"Freezing of gait in Parkinson\u2019s disease: Where are we now?","volume":"13","author":"Heremans","year":"2013","journal-title":"Curr. Neurol. Neurosci. Rep."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"1812","DOI":"10.1109\/TNSRE.2017.2687100","article-title":"Prospective fall-risk prediction models for older adults based on wearable sensors","volume":"25","author":"Howcroft","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"e0153240:1","DOI":"10.1371\/journal.pone.0153240","article-title":"Wearable-sensor-based classification models of faller status in older adults","volume":"11","author":"Howcroft","year":"2016","journal-title":"PLoS ONE"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1475-925X-10-1","article-title":"Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry","volume":"10","author":"Caby","year":"2011","journal-title":"Biomed. Eng. Online"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1109\/JSEN.2011.2146246","article-title":"Sensors-based wearable systems for monitoring of human movement and falls","volume":"12","author":"Shany","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A review of feature selection techniques in bioinformatics","volume":"23","author":"Saeys","year":"2007","journal-title":"Bioinformatics"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5141\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:37:04Z","timestamp":1760189824000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,24]]},"references-count":139,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19235141"],"URL":"https:\/\/doi.org\/10.3390\/s19235141","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,24]]}}}