U-SCM Research Center, Nano Information Technology Academy, Dongguk University, Seoul 100-715, Korea.
Department of Industrial and Systems Engineering, Dongguk University, Seoul 100-715, Korea.
Sensors (Basel). 2018 Jul 6;18(7):2183. doi: 10.3390/s18072183.
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.
当前的技术为个人健康监测提供了高效的手段。基于蓝牙低能耗（BLE）的传感器可以被视为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的个性化医疗监测系统，以帮助糖尿病患者更好地管理他们的慢性疾病。BLE 被用于从传感器节点收集用户的生命体征数据，如血压、心率、体重和血糖（BG）到智能手机，而实时数据处理则用于管理大量连续生成的传感器数据。所提出的实时数据处理利用 Apache Kafka 作为流平台和 MongoDB 来存储来自患者的传感器数据。结果表明，商用版的 BLE 传感器和所提出的实时数据处理足以有效地监测糖尿病患者的生命体征数据。此外，基于糖尿病数据集的机器学习分类方法测试表明，多层感知机可以根据用户的传感器数据作为输入，对糖尿病进行早期预测。结果还表明，长短期记忆可以根据当前的传感器数据准确预测未来的 BG 水平。此外，所提出的糖尿病分类和 BG 预测可以与个性化的饮食和体育活动建议相结合，以提高患者的健康质量，并避免未来的危急情况。
