A Telemedicine Service System Exploiting BT/BLE Wireless Sensors for Remote Management of Chronic Patients

Donati, Massimiliano; Celli, Alessio; Ruiu, Alessio; Saponara, Sergio; Fanucci, Luca

doi:10.3390/technologies7010013

Cited by 21 publications

(10 citation statements)

References 25 publications

(29 reference statements)

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“…Many research works have focused on demonstrating the use of Bluetooth Low Energy (BLE) for developing wearable IoT sensor networks for health applications [44][45][46][47][48]. These BLE health sensor networks implement different types of monitoring including temperature, humidity, and carbon dioxide for environmental observations or body temperature and heart rate for physiological conditions [44,46].…”

Section: Healthmentioning

confidence: 99%

“…Remote management of patients for chronic diseases in general has also been the focus of research lately. The remote monitoring kits include wireless sensors with Bluetooth Low Energy connectivity to a local gateway (tablet/smartphone with specialized software) and a network connection to a centralized cloud-oriented application for data processing and storage [48].…”

Section: Healthmentioning

confidence: 99%

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25 Years of Bluetooth Technology

Zeadally¹,

Siddiqui²,

Baig³

2019

Future Internet

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Bluetooth technology started off as a wireless, short-range cable replacement technology but it has undergone significant developments over the last two decades. Bluetooth radios are currently embedded in almost all computing devices including personal computers, smart phones, smart watches, and even micro-controllers. For many of us, Bluetooth is an essential technology that we use every day. We provide an insight into the history of Bluetooth and its significant design developments over the last 25 years. We also discuss related issues (including security) and Bluetooth as a driving technology for the Internet of Things (IoT). Finally, we also present recent research results obtained with Bluetooth technology in various application areas.

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Section: Healthmentioning

confidence: 99%

Section: Healthmentioning

confidence: 99%

25 Years of Bluetooth Technology

Zeadally¹,

Siddiqui²,

Baig³

2019

Future Internet

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“…The telemedicine environment comprises three connected tiers [4]. These tiers include (1) sensor-based (Tier 1) used for data collection purposes [5], (2) gateway-based (Tier 2) used to represent the patients' side that is mainly responsible for signal transferring [6] and (3) server-based (Tier 3) used to represent the remote server that mainly serve the remote monitoring processes for patients [7]. In telemedicine, triage is identified as the patients sorting concept for treating them based on their necessity within a large-scale emergency [4].…”

Section: Introductionmentioning

confidence: 99%

A Uniform Intelligent Prioritisation for Solving Diverse and Big Data Generated From Multiple Chronic Diseases Patients Based on Hybrid Decision-Making and Voting Method

Mohammed¹,

Othman

Zaidan³

et al. 2020

IEEE Access

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Telemedicine is increasingly used in the modern health care system because it provides health care services to patients amidst distant locations. However, the prioritisation process for patients with multiple chronic diseases (MCDs) over telemedicine is becoming increasingly complex due to diverse and big data generated from multiple disease conditions. To solve such a problem, massive datasets must be collected, and high velocity must be acquired, specifically in real-time processing. This process requires decision-making (DM) regarding the emergency degree of each chronic disease for every patient. Multi-criteria decisionmaking (MCDM) approaches (i.e. direct aggregation, distance measurement and compromise ranking) are the main solutions for dealing with this complex situation. However, each MCDM approach provides a unique rank from those of other methods. By far, the preferred DM approach that can provide an ideal rank better than other approaches has not been established. This study proposes an extension of the technique for reorganising opinion order to interval levels (TROOIL). Such an extension is called Hybrid DM and Voting Method (HDMVM) which is based on different DM approaches (i.e. direct aggregation, distance measurement and compromise ranking). HDMVM is used to prioritise big data of patients with MCDs in real-time through the remote health-monitoring procedure. In this paper, we propose a methodology that is based on three sequential stages. The first stage illustrates how the big data of patients with MCDs can be recognised in the telemedicine environment and identifies the target telemedicine tier in this study. The second stage describes the steps of the proposed HDMVM sequentially. The third stage applies the proposed method by prioritising the case study of big data of patients with MCDs based on the above DM approaches. Moreover, dataset of remote patients with MCDs (n = 500) is adopted, which contains three diseases, namely, chronic heart diseases and high and low blood pressures. The prioritisation results vary among direct aggregation, distance measurement and compromise approaches. The proposed HDMVM effectively provides a uniform and final ranking result for big data of patients with MCDs. A statistical method (i.e. mean) is performed to objectively validate the ranking results. Significant differences between the scores of the groups are identified in the objective validation, signifying identical ranking results. The evaluation of the proposed work with the benchmark study indicates that this study has tackled issues relevant to big data and diversity of MCDM approaches in the prioritisation of patients with MCDs.

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“…In [4], Majumder et al provide a survey on wearable sensors used for health monitoring, which includes electrocardiograms (EKGs), electrodermal activity (EDA) sensors, blood oxygen saturation (SpO 2 ) sensors, and activity monitoring devices. In [5][6][7], further remote health monitoring systems are presented. In their review, Marques et al [8] detail internet of things (IoT)-related technologies used in health care, which are essential for connecting sensors to health monitoring systems.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Promoting Autonomy in Care: Combining Sensor Technology and Social Robotics for Health Monitoring

Neef¹,

Richert²

2020

7th International Electronic Conference on Sensors and Applications

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As the world’s population grows significantly older, there are not enough caregivers in many countries for all the elderly people in need of care. To promote their autonomy while also supporting their caregivers, we propose a health monitoring system comprised of a social robot, and various wearable and non-wearable sensors. Through the use of patient-reported outcome measures (PROMs), captured in conversation with the social robot, the subjective health status of the user is determined. This is supplemented by objective information gathered from wearable and non-wearable sensors used to measure numerous biosignals. By combining the subjective data obtained from interaction with the user and the objective data from the sensor network, a health report for both users and caregivers is generated. The data are visualized for the user and caregiver in a customizable and easily accessible health monitoring dashboard, which also warns the user and their caregivers when the data deviate from the expected values or ranges. The goal is to use this information to improve the quality of care, as changes in the user’s health status can be determined more quickly by themselves and their caregivers. The proposed system establishes a good base for further testing and optimization together with the user, to ensure a useful and appropriate combination of sensors and technological devices that the user is comfortable with.

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A Telemedicine Service System Exploiting BT/BLE Wireless Sensors for Remote Management of Chronic Patients

Cited by 21 publications

References 25 publications

25 Years of Bluetooth Technology

25 Years of Bluetooth Technology

A Uniform Intelligent Prioritisation for Solving Diverse and Big Data Generated From Multiple Chronic Diseases Patients Based on Hybrid Decision-Making and Voting Method

Promoting Autonomy in Care: Combining Sensor Technology and Social Robotics for Health Monitoring

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