scholarly journals Security in IoMT Communications: A Survey

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4828 ◽  
Author(s):  
Dimitris Koutras ◽  
George Stergiopoulos ◽  
Thomas Dasaklis ◽  
Panayiotis Kotzanikolaou ◽  
Dimitris Glynos ◽  
...  
Keyword(s):  
Medical Devices ◽  
Patient Monitoring ◽  
Communication Protocols ◽  
Communication Technologies ◽  
Healthcare Services ◽  
Remote Patient Monitoring ◽  
Application Layer ◽  
Network Layers ◽  
Remote Patient ◽  
Implementation Gaps

The Internet of Medical Things (IoMT) couples IoT technologies with healthcare services in order to support real-time, remote patient monitoring and treatment. However, the interconnectivity of critical medical devices with other systems in various network layers creates new opportunities for remote adversaries. Since most of the communication protocols have not been specifically designed for the needs of connected medical devices, there is a need to classify the available IoT communication technologies in terms of security. In this paper we classify IoT communication protocols, with respect to their application in IoMT. Then we describe the main characteristics of IoT communication protocols used at the perception, network and application layer of medical devices. We examine the inherent security characteristics and limitations of IoMT-specific communication protocols. Based on realistic attacks we identify available mitigation controls that may be applied to secure IoMT communications, as well as existing research and implementation gaps.

scholarly journals Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 776
Author(s):  
Xiaohui Tao ◽  
Thanveer Basha Shaik ◽  
Niall Higgins ◽  
Raj Gururajan ◽  
Xujuan Zhou
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Radio Frequency Identification ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Remote Patient Monitoring ◽  
Rfid Technology ◽  
Rpm System ◽  
Frequency Identification ◽  
Remote Patient

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward.

Leadless and Wireless Cardiac Devices: The Next Frontier in Remote Patient Monitoring

2021 ◽  
Vol 46 (5) ◽  
pp. 100800
Author(s):  
Abdulaziz Joury ◽  
Tamunoinemi Bob-Manuel ◽  
Alexandra Sanchez ◽  
Fnu Srinithya ◽  
Amber Sleem ◽  
...  
Keyword(s):  
Patient Monitoring ◽  
Remote Patient Monitoring ◽  
Cardiac Devices ◽  
Remote Patient

scholarly journals Emerging Role of Remote Patient Monitoring in Pulmonary Care

CHEST Journal ◽  
2021 ◽  
Vol 159 (2) ◽  
pp. 477-478
Author(s):  
Neeraj R. Desai ◽  
Edward J. Diamond
Keyword(s):  
Patient Monitoring ◽  
Remote Patient Monitoring ◽  
Remote Patient

scholarly journals Remote patient monitoring for ED discharges in the COVID-19 pandemic

2021 ◽  
Vol 38 (3) ◽  
pp. 229-231
Author(s):  
Ahmad A Aalam ◽  
Colton Hood ◽  
Crystal Donelan ◽  
Adam Rutenberg ◽  
Erin M Kane ◽  
...  
Keyword(s):  
Patient Monitoring ◽  
Clinical Status ◽  
Remote Patient Monitoring ◽  
Hospital Capacity ◽  
Vulnerable Patients ◽  
Natural Progression ◽  
Extended Access ◽  
Discharged Patients ◽  
The Individual ◽  
Remote Patient

COVID-19 has had a significant effect on healthcare resources worldwide, with our knowledge of the natural progression of the disease evolving for the individual patient. To allow for early detection of worsening clinical status, protect hospital capacity and provide extended access for vulnerable patients, our emergency department developed a remote patient monitoring programme for discharged patients with COVID-19. The programme uses a daily emailed secure link to a survey in which patients submit biometric and symptoms data for monitoring. Patients’ meeting criteria are escalated to a physician for a phone or video visit. Here, we describe the development, implementation and preliminary analysis of utilisation of the programme.

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