Integration of IoT and Data Visualization for Personalized Diabetes Management
A Technological Approach to Chronic Disease Care
DOI:
https://doi.org/10.47756/aihc.y9i1.178Palabras clave:
Data visualization, Diabetes, Chronic diseases, Medical devices, Biomarkers, Patient monitoringResumen
This paper presents a framework for developing diabetes management systems, emphasizing the importance of data collection, storage, analysis, and visualization. It discusses two main methods of data entry: manual and automated, each with its advantages and disadvantages. Data visualization is highlighted as a crucial component, enabling users to interpret their health information clearly and understandably, using graphs and tables that facilitate the identification of trends and patterns. Additionally, the document addresses security and privacy challenges in data storage, both locally and in the cloud. Data analysis allows for generating personalized recommendations that help patients manage their condition more effectively. Overall, the document underscores the need for intuitive and accessible interfaces that enhance user experience and promote proactive diabetes management.
Descargas
Citas
A. R. Kalia, A. Pavshe, D. Shah, and S. Pansambal, “Data visualization and pre-processing techniques-based diabetes prediction system,” in 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), 2021, pp. 1638–1645. DOI: https://doi.org/10.1109/ICESC51422.2021.9532964
International Diabetes Federation, IDF Diabetes Atlas, 10th edn., Brussels, Belgium, 2021.
J. C. Wong, A. B. Neinstein, H. Look, B. Arbiter, N. Chokr, C. Ross, and S. Adi, “Pilot study of a novel application for data visualization in type 1 diabetes,” Journal of Diabetes Science and Technology, vol. 11, no. 4, pp. 800–807, 2017, pMID: 28617628. DOI: https://doi.org/10.1177/1932296817691305
J. Koponen, Data visualization handbook, 1st ed., ser. Aalto University Publication Series. Art, Design and Architecture ; 1. Espoo: Aalto Arts Books, 2019. DOI: https://doi.org/10.21926/rpm.1902001
Nightscout. (2024). Nightscout documentation. https://nightscout.github.io/
R. Arriaga, EAI for Innovation. A, and Association for Computing Machinery, “Proceedings of the 14th eai inter national conference on pervasive computing technologies for healthcare: Pervasivehealth 2020,” 2020, 6-8 October 2020.
Y. Zhang, K. Chanana, and C. Dunne, “Idmvis: Temporal event sequence visualization for type 1 diabetes treatment decision support,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 512–522, 2018. DOI: https://doi.org/10.1109/TVCG.2018.2865076
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
AMexIHC hace todo el esfuerzo para asegurar la precisi´n y rigurosidad de la informaci´ón (el "Contenido") contenida en nuestras publicaciones. Sin embargo, AMexIHC y nuestros representantes no representan o garantizan de ninguna manera la precisi´ón, completitud o pertinencia de el Contenido para ning´ún propósito. Cualquier opinión y punto de vista expresados en esta publicación son las opiniones y puntos de vista de los autores, y no son de ninguna manera los puntos de vista o con anuencia de AMexIHC. La precisi´ón de el Contenido no debería ser confiada en su totalidad y debería ser corroborada con las fuentes primarias de informaci´ón.
