Smart Wearable Devices for Remote Patient Monitoring in Healthcare 4.0

2021 ◽  
pp. 117-135
Author(s):  
U. Hariharan ◽  
K. Rajkumar ◽  
T. Akilan ◽  
J. Jeyavel
Keyword(s):  
Patient Monitoring ◽  
Wearable Devices ◽  
Remote Patient Monitoring ◽  
Remote Patient ◽  
Smart Wearable

An Intelligent Multi-Objective Framework of Pervasive Information Computing

2020 ◽  
pp. 456-469
Author(s):  
Basant Tiwari ◽  
Vivek Tiwari
Keyword(s):  
Real Time ◽  
Patient Monitoring ◽  
Healthcare Quality ◽  
Wearable Devices ◽  
Data Driven ◽  
Remote Patient Monitoring ◽  
Network Efficiency ◽  
Monitoring Technology ◽  
Elderly Individuals ◽  
Remote Patient

This article describes how electronic healthcare has been the key application of pervasive computing innovations to enhance healthcare quality and protect human lives. Specific patients of constant sicknesses and elderly individuals, by and large, may oblige continuous observing of their wellbeing status wherever they are. In this regard, remote patient monitoring technology plays the various important role through wearable devices to monitor patient's physiological figures. But, this must ensure some broad issues like, wearability, adaptability, interoperability, integration, security, and network efficiency. This article proposes a data-driven multi-layer architecture for pervasively remote patient monitoring that incorporates aforesaid issues. It enables the patient's care at the real time and supports anywhere and anytime requirement with using network infrastructure efficiently.

An Intelligent Multi-Objective Framework of Pervasive Information Computing

2018 ◽  
Vol 13 (4) ◽  
pp. 15-27
Author(s):  
Basant Tiwari ◽  
Vivek Tiwari
Keyword(s):  
Real Time ◽  
Pervasive Computing ◽  
Patient Monitoring ◽  
Healthcare Quality ◽  
Wearable Devices ◽  
Data Driven ◽  
Remote Patient Monitoring ◽  
Network Efficiency ◽  
Elderly Individuals ◽  
Remote Patient

This article describes how electronic healthcare has been the key application of pervasive computing innovations to enhance healthcare quality and protect human lives. Specific patients of constant sicknesses and elderly individuals, by and large, may oblige continuous observing of their wellbeing status wherever they are. In this regard, remote patient monitoring technology plays the various important role through wearable devices to monitor patient's physiological figures. But, this must ensure some broad issues like, wearability, adaptability, interoperability, integration, security, and network efficiency. This article proposes a data-driven multi-layer architecture for pervasively remote patient monitoring that incorporates aforesaid issues. It enables the patient's care at the real time and supports anywhere and anytime requirement with using network infrastructure efficiently.

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
Sign in / Sign up

Export Citation Format

Share Document