{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T06:28:26Z","timestamp":1776407306661,"version":"3.51.2"},"reference-count":54,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T00:00:00Z","timestamp":1618531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH \u2013CREATE \u2013INNOVATE","award":["\u03a41\u0395\u0394\u039a-01888"],"award-info":[{"award-number":["\u03a41\u0395\u0394\u039a-01888"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gait analysis is crucial for the detection and management of various neurological and musculoskeletal disorders. The identification of gait events is valuable for enhancing gait analysis, developing accurate monitoring systems, and evaluating treatments for pathological gait. The aim of this work is to introduce the Smart-Insole Dataset to be used for the development and evaluation of computational methods focusing on gait analysis. Towards this objective, temporal and spatial characteristics of gait have been estimated as the first insight of pathology. The Smart-Insole dataset includes data derived from pressure sensor insoles, while 29 participants (healthy adults, elderly, Parkinson\u2019s disease patients) performed two different sets of tests: The Walk Straight and Turn test, and a modified version of the Timed Up and Go test. A neurologist specialized in movement disorders evaluated the performance of the participants by rating four items of the MDS-Unified Parkinson\u2019s Disease Rating Scale. The annotation of the dataset was performed by a team of experienced computer scientists, manually and using a gait event detection algorithm. The results evidence the discrimination between the different groups, and the verification of established assumptions regarding gait characteristics of the elderly and patients suffering from Parkinson\u2019s disease.<\/jats:p>","DOI":"10.3390\/s21082821","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T06:35:53Z","timestamp":1618814153000},"page":"2821","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["The Smart-Insole Dataset: Gait Analysis Using Wearable Sensors with a Focus on Elderly and Parkinson\u2019s Patients"],"prefix":"10.3390","volume":"21","author":[{"given":"Chariklia","family":"Chatzaki","sequence":"first","affiliation":[{"name":"Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71004 Heraklion, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3279-8016","authenticated-orcid":false,"given":"Vasileios","family":"Skaramagkas","sequence":"additional","affiliation":[{"name":"Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology\u2014Hellas, Vassilika Vouton, 71110 Heraklion, Greece"}]},{"given":"Nikolaos","family":"Tachos","sequence":"additional","affiliation":[{"name":"Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece"},{"name":"Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology\u2014Hellas, 45110 Ioannina, Greece"}]},{"given":"Georgios","family":"Christodoulakis","sequence":"additional","affiliation":[{"name":"Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology\u2014Hellas, Vassilika Vouton, 71110 Heraklion, Greece"}]},{"given":"Evangelia","family":"Maniadi","sequence":"additional","affiliation":[{"name":"Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71004 Heraklion, Greece"}]},{"given":"Zinovia","family":"Kefalopoulou","sequence":"additional","affiliation":[{"name":"Neurology Department, Patras University Hospital, 26404 Patra, Greece"}]},{"given":"Dimitrios I.","family":"Fotiadis","sequence":"additional","affiliation":[{"name":"Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece"},{"name":"Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology\u2014Hellas, 45110 Ioannina, Greece"}]},{"given":"Manolis","family":"Tsiknakis","sequence":"additional","affiliation":[{"name":"Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71004 Heraklion, Greece"},{"name":"Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology\u2014Hellas, Vassilika Vouton, 71110 Heraklion, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.gaitpost.2017.09.003","article-title":"One-year persistence of individual gait patterns identified in a follow-up study\u2014A call for individualised diagnose and therapy","volume":"58","author":"Horst","year":"2017","journal-title":"Gait Posture"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"13","DOI":"10.4236\/jcc.2015.33003","article-title":"El Gait-Ground Reaction Force Sensors Selection Based on ROC Curve Evaluation","volume":"3","author":"Alkhatib","year":"2015","journal-title":"J. Comput. Commun."},{"key":"ref_3","unstructured":"Whittle, M.W. (2007). Gait Analysis\u2014An Intoduction, Butterworth-Heinemann Ltd.. [4th ed.]."},{"key":"ref_4","unstructured":"M\u00fcller, B., and Wolf, S.I. (2018). Handbook of Human Motion, Springer International Publishing."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0268-0033(87)90002-7","article-title":"Two steps equals one stride equals what? The applicability of normal gait nomenclature to abnormal walking patterns","volume":"2","author":"Wall","year":"1987","journal-title":"Clin. Biomech."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Taborri, J., Palermo, E., Rossi, S., and Cappa, P. (2016). Gait partitioning methods: A systematic review. Sensors, 16.","DOI":"10.3390\/s16010066"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1016\/j.gaitpost.2013.10.009","article-title":"Gait phase varies over velocities","volume":"39","author":"Liu","year":"2014","journal-title":"Gait Posture"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1109\/JBHI.2016.2608720","article-title":"Toward pervasive gait analysis with wearable sensors: A systematic review","volume":"20","author":"Chen","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"D\u00edaz, S., Stephenson, J.B., and Labrador, M.A. (2020). Use of wearable sensor technology in gait, balance, and range of motion analysis. Appl. Sci., 10.","DOI":"10.3390\/app10010234"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"167830","DOI":"10.1109\/ACCESS.2020.3022818","article-title":"Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review","volume":"8","author":"Saboor","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Vu, H.T.T., Gomez, F., Cherelle, P., Lefeber, D., Now\u00e9, A., and Vanderborght, B. (2018). ED-FNN: A new deep learning algorithm to detect percentage of the gait cycle for powered prostheses. Sensors, 18.","DOI":"10.3390\/s18072389"},{"key":"ref_12","unstructured":"Neumann, D. (2009). Kinesiology of the Musculoskeletal System, Mosby. [2nd ed.]."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Webster, J.B., and Darter, B.J. (2019). 4-Principles of Normal and Pathologic Gait. Atlas of Orthoses and Assistive Devices, Elsevier Inc.. [5th ed.].","DOI":"10.1016\/B978-0-323-48323-0.00004-4"},{"key":"ref_14","unstructured":"(2020, December 14). Moticon-SCIENCE. Available online: https:\/\/www.moticon.de\/."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/s13047-015-0111-8","article-title":"Validation and reliability testing of a new, fully integrated gait analysis insole","volume":"8","author":"Braun","year":"2015","journal-title":"J. Foot Ankle Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1080\/02640414.2016.1161205","article-title":"Validation of Moticon\u2019s OpenGo sensor insoles during gait, jumps, balance and cross-country skiing specific imitation movements","volume":"35","author":"Martiner","year":"2017","journal-title":"J. Sports Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kakarla, T.P., Varma, K.A., Preejith, S.P., Joseph, J., and Sivaprakasam, M. (2019). Accuracy Enhancement of Total Force by Capacitive Insoles. Medical Measurements and Applications, MeMeA 2019\u2014Symposium Proceedings, Institute of Electrical and Electronics Engineers Inc.","DOI":"10.1109\/MeMeA.2019.8802146"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1111\/j.1532-5415.1991.tb01616.x","article-title":"The timed \u201cUp & Go\u201d: A test of basic functional mobility for frail elderly persons","volume":"39","author":"Podsiadlo","year":"1991","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1159\/000314963","article-title":"Properties of the \u201cTimed Up and Go\u201d test: More than meets the eye","volume":"57","author":"Herman","year":"2011","journal-title":"Gerontology"},{"key":"ref_20","unstructured":"McGrath, D., Greene, B.R., Doheny, E.P., McKeown, D.J., De Vito, G., and Caulfield, B. (September, January 30). Reliability of quantitative TUG measures of mobility for use in falls risk assessment. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Boston, MA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1109\/TBME.2012.2227317","article-title":"On-shoe wearable sensors for gait and turning assessment of patients with parkinson\u2019s disease","volume":"60","author":"Mariani","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1080\/003655099444533","article-title":"Plantar force distribution in Parkinsonian gait: A comparison between patients and age-matched control subjects","volume":"31","author":"Nieuwboer","year":"1999","journal-title":"Scand. J. Rehabil. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1002\/mds.22894","article-title":"Obstacle avoidance to elicit freezing of gait during treadmill walking","volume":"25","author":"Snijders","year":"2010","journal-title":"Mov. Disord."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"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_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/964790","article-title":"Stride-Time Variability and Fall Risk in Persons with Multiple Sclerosis","volume":"2015","author":"Moon","year":"2015","journal-title":"Mult. Scler. Int."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.gaitpost.2018.03.021","article-title":"Detecting gait abnormalities after concussion or mild traumatic brain injury: A systematic review of single-task, dual-task, and complex gait","volume":"62","author":"Fino","year":"2018","journal-title":"Gait Posture"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1136\/jnnp.2005.068742","article-title":"Multiple balance tests improve the assessment of postural stability in subjects with Parkinson\u2019s disease","volume":"77","author":"Jacobs","year":"2006","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Brauer, S.G., Woollacott, M.H., Lamont, R., Clewett, S., O\u2019Sullivan, J., Silburn, P., Mellick, G.D., and Morris, M.E. (2011). Single and dual task gait training in people with Parkinson\u2019s Disease: A protocol for a randomised controlled trial. BMC Neurol., 11.","DOI":"10.1186\/1471-2377-11-90"},{"key":"ref_29","doi-asserted-by":"crossref","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. Disord."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kluge, F., Ga\u00dfner, H., Hannink, J., Pasluosta, C., Klucken, J., and Eskofier, B.M. (2017). Towards mobile gait analysis: Concurrent validity and test-retest reliability of an inertial measurement system for the assessment of spatio-temporal gait parameters. Sensors, 17.","DOI":"10.3390\/s17071522"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1016\/j.gaitpost.2013.10.019","article-title":"Short-distance walking speed tests in people with Parkinson disease: Reliability, responsiveness, and validity","volume":"39","author":"Combs","year":"2014","journal-title":"Gait Posture"},{"key":"ref_32","first-page":"1","article-title":"The MDS-sponsored Revision of the Unified Parkinson\u2019s Disease Rating Scale","volume":"1","author":"Goetz","year":"2008","journal-title":"J. Mov. Disord."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Dorsey, E.R., Darwin, K.C., Mohammed, S., Donohue, S., Tethal, A., Achey, M.A., Ward, S., Caughey, E., Conley, E.D., and Eriksson, N. (2015). Virtual research visits and direct-to-consumer genetic testing in Parkinson\u2019s disease. Digit. Health, 1.","DOI":"10.1177\/2055207615592998"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"505","DOI":"10.3233\/JPD-150549","article-title":"Feasibility of virtual research visits in fox trial finder","volume":"5","author":"Dorsey","year":"2015","journal-title":"J. Parkinson\u2019s Dis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1002\/acn3.51236","article-title":"Design of a virtual longitudinal observational study in Parkinson\u2019s disease (AT-HOME PD)","volume":"8","author":"Schneider","year":"2020","journal-title":"Ann. Clin. Transl. Neurol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4802570","DOI":"10.1155\/2016\/4802570","article-title":"Remotely assessing symptoms of Parkinson\u2019s disease using videoconferencing: A feasibility study","volume":"2016","author":"Stillerova","year":"2016","journal-title":"Neurol. Res. Int."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tarolli, C., Andrzejewski, K., Bull, M., Goldenthal, S., O\u2019Brien, M., Simuni, T., Zimmerman, G., Biglan, K., and Dorsey, E.R. (2017). Virtual research visits in individuals with Parkinson disease enrolled in a clinical trial: REACT-PD Study Interim Analysis (P4.005). Neurology, 88, Available online: http:\/\/n.neurology.org\/content\/88\/16_Supplement\/P4.005.abstract.","DOI":"10.1212\/WNL.88.16_supplement.P4.005"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"R\u00f6cker, C., O\u2019Donoghue, J., Ziefle, M., Helfert, M., and Molloy, W. (2017). Human Daily Activity and Fall Recognition Using a Smartphone\u2019s Acceleration Sensor. Information and Communication Technologies for Ageing Well and e-Health, Springer International Publishing.","DOI":"10.1007\/978-3-319-62704-5"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/7333.928571","article-title":"A reliable gait phase detection system","volume":"9","author":"Pappas","year":"2001","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_40","unstructured":"(2004). Tekscan Research Software User Manual, Tekscan Inc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.gaitpost.2008.01.019","article-title":"Detection of gait events using an F-Scan in-shoe pressure measurement system","volume":"28","author":"Catalfamo","year":"2008","journal-title":"Gait Posture"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1109\/ICSENS.2002.1037264","article-title":"A reliable, gyroscope based gait phase detection sensor embedded in a shoe insole","volume":"2","author":"Pappas","year":"2002","journal-title":"Proc. IEEE Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.gaitpost.2006.10.011","article-title":"Classification of idiopathic toe walking based on gait analysis: Development and application of the ITW severity classification","volume":"26","author":"Alvarez","year":"2007","journal-title":"Gait Posture"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2914","DOI":"10.1152\/jn.1999.81.6.2914","article-title":"Turning strategies during human walking","volume":"81","author":"Hase","year":"1999","journal-title":"J. Neurophysiol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-54271-2","article-title":"Mechanics of very slow human walking","volume":"9","author":"Wu","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1589\/jpts.29.722","article-title":"Estimated lower speed boundary at which the walk ratio constancy is broken in healthy adults","volume":"29","author":"Murakami","year":"2017","journal-title":"J. Phys. Ther. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_48","unstructured":"(2021, February 08). Shimmer. Available online: http:\/\/www.shimmersensing.com\/."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/978-3-319-73013-4_12","article-title":"HuGaDB: Human gait database for activity recognition from wearable inertial sensor networks","volume":"Volume 10716","author":"Chereshnev","year":"2017","journal-title":"Analysis of Images, Social Networks and Texts"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3603","DOI":"10.1016\/j.patcog.2012.03.008","article-title":"A cascade fusion scheme for gait and cumulative foot pressure image recognition","volume":"45","author":"Zheng","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_51","unstructured":"Kobayashi, M.M.Y., Hida, N., Nakajima, K., and Fujimoto, M. (2020, December 05). 2019: AIST Gait Database 2019. Available online: https:\/\/unit.aist.go.jp\/harc\/ExPART\/GDB2019.html."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1519\/JPT.0b013e3181ff262c","article-title":"Spatial and temporal parameters of self-selected and fast walking speeds in healthy community-living adults aged 72\u201398 years","volume":"33","author":"Chui","year":"2010","journal-title":"J. Geriatr. Phys. Ther."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Jerome, G.J., Ko, S., Kauffman, D., Studenski, S.A., Ferrucci, L., and Simonsick, E.M. (2015). Gait Characteristics Associated with Walking Speed Decline in Older Adults: Results from the Baltimore Longitudinal Study of Aging, Elsevier Ireland Limited.","DOI":"10.1016\/j.archger.2015.01.007"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1109\/JBHI.2015.2450232","article-title":"Classification of Parkinson\u2019s disease gait using spatial-temporal gait features","volume":"19","author":"Wahid","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/8\/2821\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:49:03Z","timestamp":1760161743000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/8\/2821"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,16]]},"references-count":54,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21082821"],"URL":"https:\/\/doi.org\/10.3390\/s21082821","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,16]]}}}