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Open AccessArticle

A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing

1
U-SCM Research Center, Nano Information Technology Academy, Dongguk University, Seoul 100-715, Korea
2
Department of Industrial and Systems Engineering, Dongguk University, Seoul 100-715, Korea
*
Authors to whom correspondence should be addressed.
Sensors 2018, 18(7), 2183; https://doi.org/10.3390/s18072183
Received: 4 June 2018 / Revised: 25 June 2018 / Accepted: 5 July 2018 / Published: 6 July 2018
(This article belongs to the Special Issue Wireless Body Area Networks and Connected Health)
Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users’ vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data. The proposed real-time data processing utilized Apache Kafka as a streaming platform and MongoDB to store the sensor data from the patient. The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients. Furthermore, machine learning–based classification methods were tested on a diabetes dataset and showed that a Multilayer Perceptron can provide early prediction of diabetes given the user’s sensor data as input. The results also reveal that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data. In addition, the proposed diabetes classification and BG prediction could be combined with personalized diet and physical activity suggestions in order to improve the health quality of patients and to avoid critical conditions in the future. View Full-Text
Keywords: diabetes; BLE; real-time data processing; classification; forecasting diabetes; BLE; real-time data processing; classification; forecasting
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MDPI and ACS Style

Alfian, G.; Syafrudin, M.; Ijaz, M.F.; Syaekhoni, M.A.; Fitriyani, N.L.; Rhee, J. A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing. Sensors 2018, 18, 2183. https://doi.org/10.3390/s18072183

AMA Style

Alfian G, Syafrudin M, Ijaz MF, Syaekhoni MA, Fitriyani NL, Rhee J. A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing. Sensors. 2018; 18(7):2183. https://doi.org/10.3390/s18072183

Chicago/Turabian Style

Alfian, Ganjar; Syafrudin, Muhammad; Ijaz, Muhammad F.; Syaekhoni, M. A.; Fitriyani, Norma L.; Rhee, Jongtae. 2018. "A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing" Sensors 18, no. 7: 2183. https://doi.org/10.3390/s18072183

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