scholarly journals Computational Balancing between Wearable Sensor and Smartphone towards Energy-Efficient Remote Healthcare Monitoring

2018 ◽  
Vol 18 (4) ◽  
pp. 3-10 ◽  
Author(s):  
A. SECERBEGOVIC ◽  
A. GOGIC ◽  
N. SULJANOVIC ◽  
M. ZAJC ◽  
A. MUJCIC
Keyword(s):  
Energy Efficient ◽  
Wearable Sensor ◽  
Remote Healthcare ◽  
Healthcare Monitoring

Energy-Efficient Remote Healthcare Monitoring Using IoT

2016 ◽  
Author(s):  
Wan Aida Nadia Wan Abdullah ◽  
Naimah Yaakob ◽  
Mohamed Elshaikh Elobaid ◽  
Mohd Nazri Mohd Warip ◽  
Siti Asilah Yah
Keyword(s):  
Energy Efficient ◽  
Remote Healthcare ◽  
Healthcare Monitoring

Exploring the Applications of Machine Learning in Healthcare

2020 ◽  
Vol 10 (4) ◽  
pp. 458-472
Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Machine Learning ◽  
Disease Risk ◽  
Disease Diagnosis ◽  
Machine Intelligence ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Machine Learning Applications ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

A Microfiber Probe-Based Wearable Sensor for Human Healthcare Monitoring

2021 ◽  
Author(s):  
Shijie Tan ◽  
Yanpeng Li ◽  
Fang Fang ◽  
Liangye Li ◽  
Zhijun Yan ◽  
...  
Keyword(s):  
Wearable Sensor ◽  
Healthcare Monitoring ◽  
Human Healthcare

scholarly journals Remote healthcare monitoring and assessment

2011 ◽  
Vol 19 (4) ◽  
pp. 295-306
Author(s):  
Chris D. Nugent ◽  
Dewar Finlay ◽  
Richard Davies ◽  
Mark Donnelly ◽  
Josef Hallberg ◽  
...  
Keyword(s):  
Remote Healthcare ◽  
Healthcare Monitoring

scholarly journals Wired and Wireless Distributed e-Home Healthcare System

2016 ◽  
pp. 207-250
Author(s):  
Booma Devi Sekar ◽  
JiaLi Ma ◽  
MingChui Dong
Keyword(s):  
Healthcare System ◽  
Point Of Care ◽  
Access Point ◽  
Data Access ◽  
Medical Personnel ◽  
Home Healthcare ◽  
Information Communication ◽  
Healthcare Data ◽  
Remote Healthcare ◽  
Healthcare Monitoring

The proactive development in electronic health (e-health) has introduced seemingly endless number of applications such as telemedicine, electronic records, healthcare score cards, healthcare monitoring etc. Yet, these applications confront the key challenges of network dependence and medical personnel necessity, which hinders the development of universality of e-health services. To mitigate such key challenges, this chapter presents a versatile wired and wireless distributed e-home healthcare system. By exploiting the benefit of body sensor network and information communication technology, the dedicated system model methodically integrates some of the comprehensive functions such as pervasive health monitoring, remote healthcare data access, point-of-care signal interpretation and diagnosis, disease-driven uplink update and synchronization (UUS) scheme and emergency management to design a complete and independent e-home healthcare system.

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