A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing.
A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing.
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通过利用基于BLE的传感器和实时数据处理，针对糖尿病患者的个性化医疗监测系统。
DOI：
10.3390/s18072183
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发表时间：
2018-07-06
期刊：
影响因子：
--
通讯作者：
Rhee J
中科院分区：
文献类型：
--
作者：
Alfian G;Syafrudin M;Ijaz MF;Syaekhoni MA;Fitriyani NL;Rhee J
当前的技术提供了一种监测个人的个人健康的有效方式。基于蓝牙低功耗（BLE）的传感器可以被认为是监测个人生命体征数据的解决方案。在这项研究中，我们提出了一个个性化的医疗监测系统，利用基于BLE的传感器设备，实时数据处理和基于机器学习的算法，以帮助糖尿病患者更好地自我管理他们的慢性病。BLE用于从传感器节点到智能手机收集用户的生命体征数据，如血压、心率、体重和血糖（BG），而实时数据处理用于管理大量连续生成的传感器数据。提出的实时数据处理利用Apache Kafka作为流媒体平台，MongoDB存储来自患者的传感器数据。结果表明，基于BLE的传感器的商业版本和所提出的实时数据处理足够有效地监测糖尿病患者的生命体征数据。此外，基于机器学习的分类方法在糖尿病数据集上进行了测试，并表明多层感知器可以在用户的传感器数据作为输入的情况下提供糖尿病的早期预测。结果还表明，长短期记忆可以根据当前传感器数据准确预测未来的血糖水平。此外，所提出的糖尿病分类和BG预测可以与个性化饮食和身体活动建议相结合，以提高患者的健康质量并避免将来出现危急状况。
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.
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影响因子：
5
作者：
Arsand, Eirik;Muzny, Miroslav;Hartvigsen, Gunnar
通讯作者：
Hartvigsen, Gunnar
DOI：
10.3390/healthcare3020310
发表时间：
2015-05-21
期刊：
Healthcare (Basel, Switzerland)
影响因子：
--
作者：
Garnweidner-Holme LM;Borgen I;Garitano I;Noll J;Lukasse M
通讯作者：
Lukasse M
DOI：
10.1016/j.future.2013.07.014
发表时间：
2014-07-01
影响因子：
7.5
作者：
Dobre, C.;Xhafa, F.
通讯作者：
Xhafa, F.
影响因子：
6.4
作者：
Hamdi, Takoua;Ben Ali, Jaouher;Ginoux, Jean-Marc
通讯作者：
Ginoux, Jean-Marc
影响因子：
6
作者：
Gentili, M.;Sannino, R.;Petracca, M.
通讯作者：
Petracca, M.