{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T07:19:56Z","timestamp":1787901596014,"version":"build-2784847793"},"reference-count":61,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T00:00:00Z","timestamp":1680220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Individuals with diabetes mellitus type 1 (DM1) tend to check their blood sugar levels multiple times daily and utilize this information to predict their future glycemic levels. Based on these predictions, patients decide on the best approach to regulate their glucose levels with considerations such as insulin dosage and other related factors. Nevertheless, modern developments in Internet of Things (IoT) technology and innovative biomedical sensors have enabled the constant gathering of glucose level data using continuous glucose monitoring (CGM) in addition to other biomedical signals. With the use of machine learning (ML) algorithms, glycemic level patterns can be modeled, enabling accurate forecasting of this variable. Constrained devices have limited computational power, making it challenging to run complex machine learning algorithms directly on these devices. However, by leveraging edge computing, using lightweight machine learning algorithms, and performing preprocessing and feature extraction, it is possible to run machine learning algorithms on constrained devices despite these limitations. In this paper we test the burdens of some constrained IoT devices, probing that it is feasible to locally predict glycemia using a smartphone, up to 45 min in advance and with acceptable accuracy using random forest.<\/jats:p>","DOI":"10.3390\/s23073665","type":"journal-article","created":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T02:32:27Z","timestamp":1680489147000},"page":"3665","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0118-3406","authenticated-orcid":false,"given":"Ignacio","family":"Rodr\u00edguez-Rodr\u00edguez","sequence":"first","affiliation":[{"name":"Departamento de Ingenier\u00eda de las Comunicaciones, Universidad de M\u00e1laga, 29010 M\u00e1laga, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0084-3844","authenticated-orcid":false,"given":"Mar\u00eda","family":"Campo-Valera","sequence":"additional","affiliation":[{"name":"Departamento de Tecnolog\u00edas de la Informaci\u00f3n y las Comunicaciones, Universidad Polit\u00e9cnica de Cartagena, 30202 Cartagena, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3298-6439","authenticated-orcid":false,"given":"Jos\u00e9-V\u00edctor","family":"Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Departamento de Tecnolog\u00edas de la Informaci\u00f3n y las Comunicaciones, Universidad Polit\u00e9cnica de Cartagena, 30202 Cartagena, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9059-2569","authenticated-orcid":false,"given":"Alberto","family":"Frisa-Rubio","sequence":"additional","affiliation":[{"name":"CIRCE\u2014Centro Tecnol\u00f3gico (Research Centre for Energy Resources and Consumption), Av. 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