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Continuous glucose monitoring (CGM) and biomedical sensors enable ongoing glucose data acquisition alongside other signals, while machine learning can model glycemic patterns for forecasting. Constrained IoT devices limit computation, so the approach leverages edge computing, lightweight ML, and preprocessing/feature extraction to enable local prediction. Results test constrained devices to locally predict glycemia up to 45 minutes ahead using random forest with acceptable accuracy.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/constrained-iot-based-machine-learning-for-accurate-glycemia-forecasting-in-type-1-diabetes-patients/290565/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/constrained-iot-based-machine-learning-for-accurate-glycemia-forecasting-in-type-1-diabetes-patients/290565.png","ImageObject",300,407,{"name":92,"@type":93},"Liam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is accurate glycemia forecasting important for type 1 diabetes patients?","Question",{"text":112,"@type":113},"DM1 patients rely on frequent glucose monitoring and related factors to decide insulin amounts, aiming to keep blood glucose within a normal range and avoid both hyper- and hypoglycemia.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does continuous glucose monitoring (CGM) support machine learning-based forecasting?",{"text":117,"@type":113},"CGM provides frequent glucose measurements and can also collect other biomedical variables, creating data that helps machine learning model glycemic patterns for future prediction.",{"name":119,"@type":110,"acceptedAnswer":120},"What makes glycemia forecasting difficult on constrained IoT devices, and how is it addressed?",{"text":121,"@type":113},"Constrained devices have limited computational power for complex ML models; the document explains using edge computing, lightweight machine learning, and preprocessing/feature extraction to run forecasting locally. It reports local prediction on a smartphone up to 45 minutes ahead using random forest.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290565,1789642090,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143},8796095461564,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","sensors   \nArticle  \nConstrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients  \nIgnacio Rodr½guez-Rodr½guez 1, *, Mar½a Campo-Valera 2, Jos²-V½ctor Rodr½guez 2 and Alberto Frisa-Rubio 3  \nCitation: Rodríguez-Rodríguez, I.; Campo-Valera, M.; Rodríguez, J.-V.; Frisa-Rubio, A. Constrained  \nIoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients. Sensors 2023, 23, 3665. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/s23073665](10.3390/s23073665)  \nAcademic Editor: Laura Cercenelli  \nReceived: 7 March 2023  \nRevised: 21 March 2023  \nAccepted: 28 March 2023  \nPublished: 31 March 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Departamento de Ingenier½a de las Comunicaciones, Universidad de M¡laga, 29010 M¡laga, Spain  \n2 Departamento de Tecnolog½as de la Informaciân y las Comunicaciones, Universidad Polit²cnica de Cartagena, 30202 Cartagena, Spain  \n3 CIRCE—Centro Tecnolâgico (Research Centre for Energy Resources and Consumption), Av. Ranillas, Edf.  \nDinamiza 3D, 50018 Zaragoza, Spain  \n* Correspondence: [ignacio.rodriguez@ic.uma.es](ignacio.rodriguez@ic.uma.es)  \nAbstract: 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.  \nKeywords: constrained devices; diabetes; IoT; monitoring; machine learning  \n1. Introduction: The Forecasting Problem in Type 1 Diabetes Mellitus  \nDue to insulin deficiency, diabetes causes elevated blood sugar. Glycemic homeostasis is a closed-loop process that regulates blood sugar in healthy people. Thus, the pancreas produces insulin from 􀀌 cells that are sensitive to high glucose levels and enables glucose to enter cells, reducing hyperglycemia.  \nPeople with type 1 diabetes mellitus (DM1) cannot naturally regulate their insulin. DM1 patients who do not make insulin must inject or hold an insulin pump to lower their glycemic levels. Diabetics must also monitor their glucose levels multiple times a day and use these data and other variables, such as meals, exercise, and others, to anticipate their glycemia. Then, they must determine how much insulin they need to maintain normal blood glucose levels (avoiding both hyper-and hypoglycemia) . Thus, precise blood glucose prediction is crucial for insulin dosing [1] .  \nThankfully, modern technology opens up new diabetes control alternatives. Continuous glucose monitoring is now essential to di","cbCaimxTS8VCl5Wx","https://ap.wps.com/l/cbCaimxTS8VCl5Wx","pdf",2422325,"English","# Introduction: The Forecasting Problem in Type 1 Diabetes Mellitus\n## Glucose regulation and insulin dosing in DM1\n## Continuous glucose monitoring and sensor-based variables\n## Wireless body area networks and edge vs cloud processing","[{\"question\":\"Why is accurate glycemia forecasting important for type 1 diabetes patients?\",\"answer\":\"DM1 patients rely on frequent glucose monitoring and related factors to decide insulin amounts, aiming to keep blood glucose within a normal range and avoid both hyper- and hypoglycemia.\"},{\"question\":\"How does continuous glucose monitoring (CGM) support machine learning-based forecasting?\",\"answer\":\"CGM provides frequent glucose measurements and can also collect other biomedical variables, creating data that helps machine learning model glycemic patterns for future prediction.\"},{\"question\":\"What makes glycemia forecasting difficult on constrained IoT devices, and how is it addressed?\",\"answer\":\"Constrained devices have limited computational power for complex ML models; the document explains using edge computing, lightweight machine learning, and preprocessing/feature extraction to run forecasting locally. It reports local prediction on a smartphone up to 45 minutes ahead using random forest.\"}]","Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients | PDF",48]