{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T04:36:07Z","timestamp":1773117367881,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T00:00:00Z","timestamp":1694044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National PhD Program in Artificial Intelligence for Healthcare and Life Sciences (Campus Bio-Medico, University of Rome)","award":["ECS00000036"],"award-info":[{"award-number":["ECS00000036"]}]},{"name":"the MUR\u2014M4C2 1.5 of PNRR","award":["ECS00000036"],"award-info":[{"award-number":["ECS00000036"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gait abnormalities are common in the elderly and individuals diagnosed with Parkinson\u2019s, often leading to reduced mobility and increased fall risk. Monitoring and assessing gait patterns in these populations play a crucial role in understanding disease progression, early detection of motor impairments, and developing personalized rehabilitation strategies. In particular, by identifying gait irregularities at an early stage, healthcare professionals can implement timely interventions and personalized therapeutic approaches, potentially delaying the onset of severe motor symptoms and improving overall patient outcomes. In this paper, we studied older adults affected by chronic diseases and\/or Parkinson\u2019s disease by monitoring their gait due to wearable devices that can accurately detect a person\u2019s movements. In our study, about 50 people were involved in the trial (20 with Parkinson\u2019s disease and 30 people with chronic diseases) who have worn our device for at least 6 months. During the experimentation, each device collected 25 samples from the accelerometer sensor for each second. By analyzing those data, we propose a metric for the \u201cgait quality\u201d based on the measure of entropy obtained by applying the Fourier transform.<\/jats:p>","DOI":"10.3390\/s23187743","type":"journal-article","created":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T08:01:30Z","timestamp":1694160090000},"page":"7743","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Gait Monitoring and Analysis: A Mathematical Approach"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9723-8757","authenticated-orcid":false,"given":"Massimo","family":"Canonico","sequence":"first","affiliation":[{"name":"Department of Sciences and Technological Innovation, University of Piemonte Orientale, 15121 Alessandria, Italy"}]},{"given":"Francesco","family":"Desimoni","sequence":"additional","affiliation":[{"name":"Department of Sciences and Technological Innovation, University of Piemonte Orientale, 15121 Alessandria, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9690-0198","authenticated-orcid":false,"given":"Alberto","family":"Ferrero","sequence":"additional","affiliation":[{"name":"Department of Sciences and Technological Innovation, University of Piemonte Orientale, 15121 Alessandria, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6744-2386","authenticated-orcid":false,"given":"Pietro Antonio","family":"Grassi","sequence":"additional","affiliation":[{"name":"Department of Sciences and Technological Innovation, University of Piemonte Orientale, 15121 Alessandria, Italy"}]},{"given":"Christopher","family":"Irwin","sequence":"additional","affiliation":[{"name":"Department of Sciences and Technological Innovation, University of Piemonte Orientale, 15121 Alessandria, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4621-9150","authenticated-orcid":false,"given":"Daiana","family":"Campani","sequence":"additional","affiliation":[{"name":"Department of Translational Medicine, Universit\u00e0 del Piemonte Orientale, 28100 Novara, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2263-1340","authenticated-orcid":false,"given":"Alberto","family":"Dal Molin","sequence":"additional","affiliation":[{"name":"Department of Translational Medicine, Universit\u00e0 del Piemonte Orientale, 28100 Novara, Italy"}]},{"given":"Massimiliano","family":"Panella","sequence":"additional","affiliation":[{"name":"Department of Translational Medicine, Universit\u00e0 del Piemonte Orientale, 28100 Novara, Italy"}]},{"given":"Luca","family":"Magistrelli","sequence":"additional","affiliation":[{"name":"Department of Translational Medicine, Universit\u00e0 del Piemonte Orientale, 28100 Novara, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1038\/nature06516","article-title":"The coming acceleration of global population ageing","volume":"451","author":"Lutz","year":"2008","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1001\/jama.2010.1923","article-title":"Gait speed and survival in older adults","volume":"305","author":"Studenski","year":"2011","journal-title":"JAMA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1016\/j.gerinurse.2021.06.001","article-title":"Physical exercise and fall prevention: A systematic review and meta-analysis of experimental studies included in Cochrane reviews","volume":"42","author":"Caristia","year":"2021","journal-title":"Geriatr. Nurs."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"101532","DOI":"10.1016\/j.arr.2021.101532","article-title":"The complex associations between late life depression, fear of falling and risk of falls. A systematic review and meta-analysis","volume":"73","author":"Gambaro","year":"2022","journal-title":"Ageing Res. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1111\/phn.12852","article-title":"Home and environmental hazards modification for fall prevention among the elderly","volume":"38","author":"Campani","year":"2021","journal-title":"Public Health Nurs."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1111\/phn.12949","article-title":"Effective, sustainable, and transferable physical exercise interventions for fall prevention among older people","volume":"38","author":"Campani","year":"2021","journal-title":"Public Health Nurs."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1007\/s40520-022-02238-1","article-title":"The prevention of falls in patients with Parkinson\u2019s disease with in-home monitoring using a wearable system: A pilot study protocol","volume":"34","author":"Campani","year":"2022","journal-title":"Aging Clin. Exp. Res."},{"key":"ref_8","unstructured":"Salis, F., Bertuletti, S., Bonci, T., Caruso, M., Scott, K., Alcock, L., Buckley, E., Gazit, E., Hansen, C., and Schwickert, L. (2023, August 28). A Multi-Sensor Wearable System for Gait Assessment in Real-World Conditions: Performance in Individuals with Impaired Mobility. Available online: https:\/\/www.researchsquare.com\/article\/rs-2486943\/v1."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.gaitpost.2013.01.018","article-title":"The evaluation of an inexpensive, 2D, video based gait assessment system for clinical use","volume":"38","author":"Ugbolue","year":"2013","journal-title":"Gait Posture"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1093\/gerona\/glp033","article-title":"Quantitative gait markers and incident fall risk in older adults","volume":"64","author":"Verghese","year":"2009","journal-title":"J. Gerontol. Ser. A"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lee, C.H., Mendoza, T., Huang, C.H., and Sun, T.L. (2023). Comparative analysis of fall risk assessment features in community-elderly and stroke survivors: Insights from sensor-based data. Healthcare, 11.","DOI":"10.3390\/healthcare11131938"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1002\/ana.25548","article-title":"Gait analysis with wearables predicts conversion to parkinson disease","volume":"86","author":"Elshehabi","year":"2019","journal-title":"Ann. Neurol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1016\/j.jstrokecerebrovasdis.2015.02.004","article-title":"Accelerometry-based gait characteristics evaluated using a smartphone and their association with fall risk in people with chronic stroke","volume":"24","author":"Isho","year":"2015","journal-title":"J. Stroke Cerebrovasc. Dis."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1089\/tmj.2011.0132","article-title":"Reliability and validity of gait analysis by android-based smartphone","volume":"18","author":"Nishiguchi","year":"2012","journal-title":"Telemed. e-Health"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Daines, K.J., Baddour, N., Burger, H., Bavec, A., and Lemaire, E.D. (2021). Fall risk classification for people with lower extremity amputations using random forests and smartphone sensor features from a 6-minute walk test. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0247574"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7772","DOI":"10.3390\/s100807772","article-title":"A review of accelerometry-based wearable motion detectors for physical activity monitoring","volume":"10","author":"Yang","year":"2010","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/1743-0003-9-21","article-title":"A review of wearable sensors and systems with application in rehabilitation","volume":"9","author":"Patel","year":"2012","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2048","DOI":"10.1038\/s41591-023-02440-2","article-title":"Wearable movement-tracking data identify Parkinson\u2019s disease years before clinical diagnosis","volume":"29","author":"Schalkamp","year":"2023","journal-title":"Nat. Med."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Williamson, J.R., Telfer, B., Mullany, R., and Friedl, K.E. (2021). Detecting Parkinson\u2019s disease from wrist-worn accelerometry in the UK Biobank. Sensors, 21.","DOI":"10.3390\/s21062047"},{"key":"ref_20","unstructured":"Cola, G., Avvenuti, M., Musso, F., and Vecchio, A. (December, January 28). Gait-based authentication using a wrist-worn device. Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Hiroshima, Japan."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102883","DOI":"10.1109\/ACCESS.2020.2998842","article-title":"Real-world gait bout detection using a wrist sensor: An unsupervised real-life validation","volume":"8","author":"Soltani","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gjoreski, M., Gjoreski, H., Lu\u0161trek, M., and Gams, M. (2016). How accurately can your wrist device recognize daily activities and detect falls?. Sensors, 16.","DOI":"10.3390\/s16060800"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Peraza, L.R., Kinnunen, K.M., McNaney, R., Craddock, I.J., Whone, A.L., Morgan, C., Joules, R., and Wolz, R. (2021). An automatic gait analysis pipeline for wearable sensors: A pilot study in Parkinson\u2019s disease. Sensors, 21.","DOI":"10.3390\/s21248286"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1186\/s12938-015-0103-8","article-title":"Fourier-based integration of quasi-periodic gait accelerations for drift-free displacement estimation using inertial sensors","volume":"14","author":"Sabatini","year":"2015","journal-title":"BioMed. Eng. Online"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Teran-Pineda, D., Thurnhofer-Hemsi, K., and Dominguez, E. (2023). Analysis and recognition of human gait activity based on multimodal sensors. Mathematic, 11.","DOI":"10.1142\/S0129065723500582"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"129","DOI":"10.3991\/ijoe.v13i07.7294","article-title":"A human body gait recognition system based on fourier transform and quartile difference extraction","volume":"13","author":"Wang","year":"2017","journal-title":"Int. J. Online Eng."},{"key":"ref_28","first-page":"249","article-title":"Development of digital gait monitoring software for diagnosis of neuromuscular disorder","volume":"4","author":"Ajani","year":"2018","journal-title":"Int. J. Biosens. Bioelectron."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"719442","DOI":"10.3389\/fneur.2021.719442","article-title":"Quantification of daily-living gait quantity and quality using a wrist-worn accelerometer in Huntington\u2019s disease","volume":"12","author":"Keren","year":"2021","journal-title":"Front. Neurol."},{"key":"ref_30","unstructured":"Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., and Hammock, C. (2020). Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC \u201920) Berkeley, CA, USA, 15\u201317 July 2020, USENIX Association."},{"key":"ref_31","unstructured":"Nielsen, H., Mogul, J., Masinter, L.M., Fielding, R.T., Gettys, J., Leach, P.J., and Berners-Lee, T. (2023, August 28). Hypertext Transfer Protocol\u2014HTTP\/1.1; RFC 2616. Available online: https:\/\/www.rfc-editor.org\/info\/rfc2616."},{"key":"ref_32","unstructured":"Rescorla, E., and Dierks, T. (2023, August 28). The Transport Layer Security (TLS) Protocol Version 1.2; RFC 5246. Available online: https:\/\/www.rfc-editor.org\/info\/rfc5246."},{"key":"ref_33","unstructured":"Hillar, G.C. (2017). MQTT Essentials\u2014A lightweight IoT Protocol, Packt Publishing Ltd."},{"key":"ref_34","unstructured":"Kyle, B., Douglas, G., Peter, B., and Shaun, V. (2016). MongoDB in Action: Covers MongoDB Version 3.0, Simon and Schuster."},{"key":"ref_35","unstructured":"(2023, June 30). Cloud Application Platform. Available online: https:\/\/www.salesforce.com\/ap\/?ir=1."},{"key":"ref_36","unstructured":"Bracewell, R.N., and Bracewell, R.N. (1986). The Fourier Transform and Its Applications, McGraw-Hill."},{"key":"ref_37","unstructured":"Chen, T., and Guestrin, C. (2016). Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201916, San Francisco, CA, USA, 13\u201317 August 2016, ACM."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Walmsley, R., Chan, S., Smith-Byrne, K., Ramakrishnan, R., Woodward, M., Rahimi, K., Dwyer, T., Bennett, D., and Doherty, A. (2020). Reallocating time from device-measured sleep, sedentary behaviour or light physical activity to moderate-to-vigorous physical activity is associated with lower cardiovascular disease risk. medRxiv.","DOI":"10.1101\/2020.11.10.20227769"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7961","DOI":"10.1038\/s41598-018-26174-1","article-title":"Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants","volume":"8","author":"Willetts","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_40","unstructured":"Whitlock, M., and Schluter, D. (2020). The Analysis of Biological Data, Roberts Publishers."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7743\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:47:05Z","timestamp":1760129225000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7743"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,7]]},"references-count":40,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23187743"],"URL":"https:\/\/doi.org\/10.3390\/s23187743","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,7]]}}}