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

Alfian, Ganjar; Syafrudin, Muhammad; Ijaz, Muhammad Fazal; Syaekhoni, M. Alex; Fitriyani, Norma Latif; Rhee, Jongtae

doi:10.3390/s18072183

Cited by 193 publications

(114 citation statements)

References 69 publications

(120 reference statements)

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“…Optical images before and after assembling with the CP electrode are shown in Figure 11F,G. Other diseases such as diabetes are also targeted in eHealth diagnosis because of their relevance worldwide [103][104][105][106]. In this paper we present many approaches based on microfluidics, and we summarize their most relevant features in terms of analytes, detection method, and limits of detection in Table 1.…”

Section: A B Cmentioning

confidence: 99%

Microfluidic Point-of-Care Devices: New Trends and Future Prospects for eHealth Diagnostics

Mejía‐Salazar

Cruz

Vásques

et al. 2020

Sensors

145

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Point-of-care (PoC) diagnostics is promising for early detection of a number of diseases, including cancer, diabetes, and cardiovascular diseases, in addition to serving for monitoring health conditions. To be efficient and cost-effective, portable PoC devices are made with microfluidic technologies, with which laboratory analysis can be made with small-volume samples. Recent years have witnessed considerable progress in this area with “epidermal electronics”, including miniaturized wearable diagnosis devices. These wearable devices allow for continuous real-time transmission of biological data to the Internet for further processing and transformation into clinical knowledge. Other approaches include bluetooth and WiFi technology for data transmission from portable (non-wearable) diagnosis devices to cellphones or computers, and then to the Internet for communication with centralized healthcare structures. There are, however, considerable challenges to be faced before PoC devices become routine in the clinical practice. For instance, the implementation of this technology requires integration of detection components with other fluid regulatory elements at the microscale, where fluid-flow properties become increasingly controlled by viscous forces rather than inertial forces. Another challenge is to develop new materials for environmentally friendly, cheap, and portable microfluidic devices. In this review paper, we first revisit the progress made in the last few years and discuss trends and strategies for the fabrication of microfluidic devices. Then, we discuss the challenges in lab-on-a-chip biosensing devices, including colorimetric sensors coupled to smartphones, plasmonic sensors, and electronic tongues. The latter ones use statistical and big data analysis for proper classification. The increasing use of big data and artificial intelligence methods is then commented upon in the context of wearable and handled biosensing platforms for the Internet of things and futuristic healthcare systems.

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Section: A B Cmentioning

confidence: 99%

Microfluidic Point-of-Care Devices: New Trends and Future Prospects for eHealth Diagnostics

Mejía‐Salazar

Cruz

Vásques

et al. 2020

Sensors

145

111

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“…They have shown good results as compared with the finger-pricking readings. The impact of a real-time prognosis algorithm has also been analyzed in recent proposals [24]. In the case of wireless technologies, limitations persist for both personal use and short-range technologies, especially when they are handled by older persons.…”

Section: Related Workmentioning

confidence: 99%

An IoT-Based Glucose Monitoring Algorithm to Prevent Diabetes Complications

et al. 2020

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Diabetes mellitus (DM) is a metabolic disorder characterized by blood glucose levels above normal limits. The impact of this disease on the population has increased in recent years. It is already a public health problem worldwide and one of the leading causes of death. Recently, several proposals have been developed for better and regular monitoring of glucose. However, theses proposals do not discard erroneous readings and they are not able to anticipate a critical condition. In this work, we propose an algorithm based on the double moving average supported by an IoT architecture to prevent possible complications in elderly patients. The algorithm uses historical readings to construct a series. Given a number of periods, it is possible to calculate averages of different subsets and trends for the next periods and, in this way, the prognosis is obtained. With the prognosis, it is possible to notify the doctor and relatives in advance about a possible critical condition in the patient. The aim of our work is to validate the architecture and prognosis algorithm used for elderly persons. Tests of the algorithm and the architecture were performed with different readings and it was shown that the system generated corresponding notifications before the glucose values were higher than those defined by the WHO (World Health Organization), thus avoiding unnecessary alarms.

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“…Figure 8a shows prototype real-time monitoring system architecture to filter false positives, ensuring only products actually moved through the gate are sent to the representational state transfer application programming interface (REST API), for presentation in web dashboard(s) and/or database storage. We used the MongoDB V3.4.9 database since it can efficiently store continuously-generated sensor/RFID data from manufacturing [56][57][58], healthcare [59], and supply chain [60]. The real-time monitoring system used java programming language V1.8.0 to receive tag information from the readers, filter false positives using trained RF, and send products moved through the gate information to the server via REST API.…”

Section: Management Implicationsmentioning

confidence: 99%

False Positive RFID Detection Using Classification Models

et al. 2019

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Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners.

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

Cited by 193 publications

References 69 publications

Microfluidic Point-of-Care Devices: New Trends and Future Prospects for eHealth Diagnostics

Microfluidic Point-of-Care Devices: New Trends and Future Prospects for eHealth Diagnostics

An IoT-Based Glucose Monitoring Algorithm to Prevent Diabetes Complications

False Positive RFID Detection Using Classification Models

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