AGATA
AGATA (Automated Glucose dATa Analysis) is an open-source toolbox to analyse glucose data. 
Posts by Collection
portfolio
bmthw
bmthw: Material for the laboratory of the course of Biomedical Wearable Technology for Healthcare and Wellbeing, Master’s degree in Bioengineering, Department of Information Engineering (DEI), University of Padova.
dexcom_flutter
dexcom_flutter: A Flutter package to make your life easier when dealing with Dexcom Web APIs. 
Fitbitter
Fitbitter: A Flutter package to make your life easier when dealing with Fitbit APIs.
ReplayBG
ReplayBG is a novel open-source MATLAB® toolbox to assess new strategies for type 1 diabetes management on retrospective already collected patient data. 
publications
Heterogeneity and Nearest-Neighbour Coupling can Explain Small-Worldness and Wave Properties in Pancreatic Islets
Published in Chaos, 2016
Recommended citation: Cappon G, Pedersen MG. Heterogeneity and Nearest-Neighbour Coupling can Explain Small-Worldness and Wave Properties in Pancreatic Islets. Chaos. 2016 May;26(5):053103. doi: 10.1063/1.4949020. PMID: 27249943. http://gcappon.github.io/files/cappon_chaos_2016.pdf
A Neural-Network-Based Approach to Personalize Insulin Bolus Calculation Using Continuous Glucose Monitoring
Published in Journal of Diabetes Science and Technology, 2018
Recommended citation: Cappon G, Vettoretti M, Marturano F, Facchinetti A, Sparacino G. A Neural-Network-Based Approach to Personalize Insulin Bolus Calculation Using Continuous Glucose Monitoring. J Diabetes Sci Technol. 2018 Mar;12(2):265-272. doi: 10.1177/1932296818759558. PMID: 29493356; PMCID: PMC5851237. http://gcappon.github.io/files/cappon_jdst_2018.pdf
In Silico Assessment of Literature Insulin Bolus Calculation Methods Accounting for Glucose Rate of Change
Published in Journal of Diabetes Science and Technology, 2018
Recommended citation: Cappon G, Marturano F, Vettoretti M, Facchinetti A, Sparacino G. In Silico Assessment of Literature Insulin Bolus Calculation Methods Accounting for Glucose Rate of Change. J Diabetes Sci Technol. 2019 Jan;13(1):103-110. doi: 10.1177/1932296818777524. Epub 2018 May 31. PMID: 29848104; PMCID: PMC6313276. http://gcappon.github.io/files/cappon_jdst_2018_2.pdf
Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications
Published in Diabetes & Metabolism Journal, 2019
Recommended citation: Cappon G, Vettoretti M, Sparacino G, Facchinetti A. Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications. Diabetes Metab J. 2019 Aug;43(4):383-397. doi: 10.4093/dmj.2019.0121. PMID: 31441246; PMCID: PMC6712232. http://gcappon.github.io/files/cappon_dmj_2019.pdf
Classification of Postprandial Glycemic Status with Application to Insulin Dosing in Type 1 Diabetes-An In Silico Proof-of-Concept
Published in Sensors (Basel), 2019
Recommended citation: Cappon G, Facchinetti A, Sparacino G, Georgiou P, Herrero P. Classification of Postprandial Glycemic Status with Application to Insulin Dosing in Type 1 Diabetes-An In Silico Proof-of-Concept. Sensors (Basel). 2019 Jul 18;19(14):3168. doi: 10.3390/s19143168. PMID: 31323886; PMCID: PMC6679291. http://gcappon.github.io/files/cappon_sensors_2019.pdf
Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers
Published in IEEE Journal of Biomedical Informatics, 2019
Recommended citation: Guemes A, Cappon G, Hernandez B, Reddy M, Oliver N, Georgiou P, Herrero P. Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers. IEEE J Biomed Health Inform. 2020 May;24(5):1439-1446. doi: 10.1109/JBHI.2019.2938305. Epub 2019 Sep 13. PMID: 31536025. http://gcappon.github.io/files/guemes_jbhi_2019.pdf
Machine-Learning Based Model to Improve Insulin Bolus Calculation in Type 1 Diabetes Therapy
Published in IEEE Transactions on Biomedical Engineering, 2021
Recommended citation: Noaro G, Cappon G, Vettoretti M, Sparacino G, Favero SD, Facchinetti A. Machine-Learning Based Model to Improve Insulin Bolus Calculation in Type 1 Diabetes Therapy. IEEE Trans Biomed Eng. 2021 Jan;68(1):247-255. doi: 10.1109/TBME.2020.3004031. Epub 2020 Dec 21. PMID: 32746033. http://gcappon.github.io/files/noaro_tbme_2021.pdf
An Integrated Mobile Platform for Automated Data Collection and Real-Time Patient Monitoring in Diabetes Clinical Trials
Published in Journal of Diabetes Science and Technology, 2021
Recommended citation: Cappon G, Cossu L, Boscari F, Bruttomesso D, Sparacino G, Facchinetti A. An Integrated Mobile Platform for Automated Data Collection and Real-Time Patient Monitoring in Diabetes Clinical Trials. J Diabetes Sci Technol. 2021 Jul 3:19322968211024620. doi: 10.1177/19322968211024620. Epub ahead of print. PMID: 34218721. http://gcappon.github.io/files/cappon_jdst_2021.pdf
Methods for Insulin Bolus Adjustment Based on the Continuous Glucose Monitoring Trend Arrows in Type 1 Diabetes: Performance and Safety Assessment in an In Silico Clinical Trial
Published in Journal of Diabetes Science and Technology, 2021
Recommended citation: Noaro G, Cappon G, Sparacino G, Boscari F, Bruttomesso D, Facchinetti A. Methods for Insulin Bolus Adjustment Based on the Continuous Glucose Monitoring Trend Arrows in Type 1 Diabetes: Performance and Safety Assessment in an In Silico Clinical Trial. J Diabetes Sci Technol. 2021 Sep 6:19322968211043162. doi: 10.1177/19322968211043162. Epub ahead of print. PMID: 34486426. http://gcappon.github.io/files/noaro_jdst_2021.pdf
AGATA: A Toolbox for Automated Glucose Data Analysis
Published in Journal of Diabetes Science and Technology, 2023
Recommended citation: Cappon G, Sparacino G, Facchinetti A. AGATA: A Toolbox for Automated Glucose Data Analysis. J Diabetes Sci Technol. 2023 Jan 5:19322968221147570. doi: 10.1177/19322968221147570. Epub ahead of print. PMID: 36602030. http://gcappon.github.io/files/cappon_jdst_2023.pdf
Individualized Models for Glucose Prediction in Type 1 Diabetes: Comparing Black-box Approaches To a Physiological White-box One
Published in IEEE Transactions on Biomedical Engineering, 2023
Recommended citation: Cappon G, Prendin F, Sparacino G, Facchinetti A, Del Favero S. Individualized Models for Glucose Prediction in Type 1 Diabetes: Comparing Black-box Approaches To a Physiological White-box One. IEEE Trans Biomed Eng. 2023 May 17. doi: 10.1109/TBME.2023.3276193. Epub ahead of print. PMID: 37195837. http://gcappon.github.io/files/cappon_prendin_tbme_2023.pdf
ReplayBG: A Digital Twin-Based Methodology to Identify a Personalized Model from Type 1 Diabetes Data and Simulate Glucose Concentrations to Assess Alternative Therapies
Published in IEEE Transactions on Biomedical Engineering, 2023
Recommended citation: Cappon G, Vettoretti M, Sparacino G, Del Favero S, Facchinetti A. ReplayBG: A Digital Twin-Based Methodology to Identify a Personalized Model from Type 1 Diabetes Data and Simulate Glucose Concentrations to Assess Alternative Therapies. IEEE Trans Biomed Eng. 2023 Jun 27. doi: 10.1109/TBME.2023.3286856. Epub ahead of print. PMID: 37368794. http://gcappon.github.io/files/cappon_tbme_2023.pdf
talks
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teaching
Medical Informatics, 2020/21
Masters course, University of Padova, Department of Information Engineering, 2020
Contract professor for the course unit Medical Informatics of the Bioengineering course (Master’s degree), School of Engineering, University of Padova (3 credits/CFU, SSD ING/INF-06) (~120 students).
Biomedical Wearable Technologies for Healthcare and Wellbeing, 2021/22
Masters course, University of Padova, Department of Information Engineering, 2022
Contract professor for the course unit Biomedical Wearable Technologies for Healthcare and Wellbeing of the Bioengineering course (Master’s degree), School of Engineering, University of Padova (3 credits/CFU, SSD ING/INF-06) (~80 students).
Biomedical Wearable Technologies for Healthcare and Wellbeing, 2022/23
Masters course, University of Padova, Department of Information Engineering, 2023
Contract professor for the course unit Biomedical Wearable Technologies for Healthcare and Wellbeing of the Bioengineering course (Master’s degree), School of Engineering, University of Padova (3 credits/CFU, SSD ING/INF-06) (~90 students).
Biomedical Wearable Technologies for Healthcare and Wellbeing, 2023/24
Masters course, University of Padova, Department of Information Engineering, 2024
Professor for the course unit Biomedical Wearable Technologies for Healthcare and Wellbeing of the Bioengineering course (Master’s degree), School of Engineering, University of Padova (6 credits/CFU, SSD ING/INF-06) (~120 students).