Abstract
Diabetes is a disease that requires monitoring of healthy habits for its treatment. A recent study suggests that sensor-based activity recognition approaches are a suitable tool for monitoring such habits that are established between clinical personnel and the patient through a therapeutic contract. To date, there is no fully described system architecture for implementing a sensor-based activity recognition approach to monitor healthy habits. In this paper, we present the advanced architecture of a system for monitoring healthy habits of multiple diabetes patients in their homes. The presented system, called AI2EPD, features a complex architecture that encompasses from the sensor devices deployed in each patient’s home to persistence in the central server and visualisation in the technical and clinical interface. The system has been deployed in the municipality of Cabra in Córdoba (Spain) in collaboration with the Cabra Health Centre. The approach for sensor installation in the patient’s home, as well as the issues and challenges encountered during system deployment, are presented in this section.
This result has been partially supported through the Spanish Government by the project PID2021-127275OB-I00, FEDER “Una manera de hacer Europa”, the European Union’s Horizon 2020 research and innovation programme under grant agreement No 8571 and the Consejería de Salud y Familias de la Junta de Andalucía for the project with code AP-0233-2019.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdallah, Z.S., Gaber, M.M., Srinivasan, B., Krishnaswamy, S.: Activity recognition with evolving data streams: a review. ACM Comput. Surv. (CSUR) 51(4), 1–36 (2018)
Albin-Rodriguez, A.P., De-La-Fuente-Robles, Y.M., Lopez-Ruiz, J.L., Verdejo-Espinosa, A., Espinilla Estévez, M.: UJAmI location: a fuzzy indoor location system for the elderly. Int. J. Environ. Res. Public Health 18(16), 8326 (2021)
Assistant, H.: Home assistant (2023). https://www.home-assistant.io/
Atlas, I.D.: Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res. Clin. Pract. 103(2), 137–49 (2014)
Baena-Díez, J.M., et al.: Risk of cause-specific death in individuals with diabetes: a competing risks analysis. Diabetes Care 39(11), 1987–1995 (2016)
Beprevent: Objetos inteligentes, discretos observadores. https://beprevent.es/
Dambha-Miller, H., Feldman, A.L., Kinmonth, A.L., Griffin, S.J.: Association between primary care practitioner empathy and risk of cardiovascular events and all-cause mortality among patients with type 2 diabetes: a population-based prospective cohort study. Ann. Family Med. 17(4), 311–318 (2019)
Hassan, M.M., Uddin, M.Z., Mohamed, A., Almogren, A.: A robust human activity recognition system using smartphone sensors and deep learning. Futur. Gener. Comput. Syst. 81, 307–313 (2018)
Herath, S., Harandi, M., Porikli, F.: Going deeper into action recognition: a survey. Image Vis. Comput. 60, 4–21 (2017)
Hills, A.P., Misra, A., Gill, J.M., Byrne, N.M., Soares, M.J., Ramachandran, A., Palaniappan, L., Street, S.J., Jayawardena, R., Khunti, K., et al.: Public health and health systems: implications for the prevention and management of type 2 diabetes in South Asia. Lancet Diabetes Endocrinol. 6(12), 992–1002 (2018)
Jiang, W., Yin, Z.: Human activity recognition using wearable sensors by deep convolutional neural networks. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 1307–1310 (2015)
Lazarou, C., Panagiotakos, D., Matalas, A.L.: The role of diet in prevention and management of type 2 diabetes: implications for public health. Crit. Rev. Food Sci. Nutr. 52(5), 382–389 (2012)
Lopez-Medina, M., Espinilla, M., Cleland, I., Nugent, C., Medina, J.: Fuzzy cloud-fog computing approach application for human activity recognition in smart homes. J. Intell. Fuzzy Syst. 38(1), 709–721 (2020)
Montagut-Martínez, P., et al.: Feasibility of an activity control system in patients with diabetes: a study protocol of a randomised controlled trial. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, pp. 2683–2691 (2022)
Zhang, M., Sawchuk, A.A.: USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors. In: Proceedings of the 2012 ACM conference on Ubiquitous Computing, pp. 1036–1043 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Díaz Jiménez, D., López Ruiz, J.L., Montoro Lendínez, A., González Lama, J., Espinilla Estévez, M. (2023). Advanced Home-Based Diabetes Monitoring System: Initial Real-World Experiences. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14134. Springer, Cham. https://doi.org/10.1007/978-3-031-43085-5_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-43085-5_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43084-8
Online ISBN: 978-3-031-43085-5
eBook Packages: Computer ScienceComputer Science (R0)