Intelligent Health Monitoring Based on Pervasive Technologies and Cloud Computing

2014 ◽  
Vol 23 (03) ◽  
pp. 1460001 ◽  
Author(s):  
Ilias Maglogiannis ◽  
Charalampos Doukas
Keyword(s):  
Cloud Computing ◽  
Health Monitoring ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Healthcare Applications ◽  
Pervasive Healthcare ◽  
Health Monitoring System ◽  
User Acceptability ◽  
Acquisition Management ◽  
Initial Results

The proper management of patient data and their accessibility are still remaining issues that prevent the full deployment and usage of pervasive healthcare applications. This paper presents an integrated health monitoring system based on mobile pervasive technologies. The system utilizes Cloud Computing for providing robust and scalable resources for sensor data acquisition, management and communication with external applications like health information systems. A prototype has been developed using both mobile and wearable sensors for demonstrating the usability of the proposed platform. Initial results regarding the performance of the system, the efficiency in data management and user acceptability have been quite promising.

IOT based Remote Health Monitoring System

2021 ◽  
Vol 9 (VI) ◽  
pp. 80-85
Author(s):  
Manish Chaudhary
Keyword(s):  
Monitoring System ◽  
Health Monitoring ◽  
Human Life ◽  
New Technology ◽  
Healthcare Sector ◽  
Remote Health Monitoring ◽  
Healthcare Applications ◽  
Health Monitoring System ◽  
Remote Health Monitoring System ◽  
Remote Health

As the technology changing every year so, there has been an attempt to apply the new technology in numerous areas to increase the quality of human life. One of the main fields of research that has seen an implementation of the technology is the healthcare sector. Consequently, our paper is an effort to solve a healthcare problem currently people are facing. Main objective of our paper is to enterprise a remote healthcare system. It covers of three key parts. The first part is, detection of patient’s condition with the proposed system, second is to storing data on cloud storage and the last part is to provide the data for isolated viewing. Remote observing of the data empowers a doctor or custodian to television a patient’s health advancement from anywhere. In this project, we have obtainable an IoT architecture personalized for healthcare applications. The main motive of this scheme is to come up with a Remote Health Monitoring System that will completed with locally available sensors with a view to manufacture it reasonable for everybody

scholarly journals Stochastic Recognition of Physical Activity and Healthcare Using Tri-Axial Inertial Wearable Sensors

2020 ◽  
Vol 10 (20) ◽  
pp. 7122
Author(s):  
Ahmad Jalal ◽  
Mouazma Batool ◽  
Kibum Kim
Keyword(s):  
Physical Activity ◽  
Human Health ◽  
Activity Recognition ◽  
Human Activity ◽  
Health Monitoring ◽  
Wearable Sensors ◽  
Gaussian Mixture ◽  
Sensor Data ◽  
Tree Classifier ◽  
Combined Features

The classification of human activity is becoming one of the most important areas of human health monitoring and physical fitness. With the use of physical activity recognition applications, people suffering from various diseases can be efficiently monitored and medical treatment can be administered in a timely fashion. These applications could improve remote services for health care monitoring and delivery. However, the fixed health monitoring devices provided in hospitals limits the subjects’ movement. In particular, our work reports on wearable sensors that provide remote monitoring that periodically checks human health through different postures and activities to give people timely and effective treatment. In this paper, we propose a novel human activity recognition (HAR) system with multiple combined features to monitor human physical movements from continuous sequences via tri-axial inertial sensors. The proposed HAR system filters 1D signals using a notch filter that examines the lower/upper cutoff frequencies to calculate the optimal wearable sensor data. Then, it calculates multiple combined features, i.e., statistical features, Mel Frequency Cepstral Coefficients, and Gaussian Mixture Model features. For the classification and recognition engine, a Decision Tree classifier optimized by the Binary Grey Wolf Optimization algorithm is proposed. The proposed system is applied and tested on three challenging benchmark datasets to assess the feasibility of the model. The experimental results show that our proposed system attained an exceptional level of performance compared to conventional solutions. We achieved accuracy rates of 88.25%, 93.95%, and 96.83% over MOTIONSENSE, MHEALTH, and the proposed self-annotated IM-AccGyro human-machine dataset, respectively.

Cloud Based Wireless Infrastructure for Health Monitoring

2020 ◽  
pp. 34-55 ◽  
Author(s):  
Ajay Chaudhary ◽  
Sateesh Kumar Peddoju ◽  
Suresh Kumar Peddoju
Keyword(s):  
Cloud Computing ◽  
Health Monitoring ◽  
Healthcare Services ◽  
Power Source ◽  
Vital Role ◽  
Healthcare Applications ◽  
Related Data ◽  
Health Related ◽  
Healthcare Monitoring ◽  
Wireless Infrastructure

The wireless infrastructure based devices can collect data for long period of time even with a tiny power source as they perform specific function of collection of health related data and sending to gateways. The sensing data of healthcare monitoring consumes low power but they had limited computation power to process this data, where the cloud computing plays a vital role and compliment the loophole of wireless infrastructure based systems. In cloud computing with its immense computation power for easily deployment of healthcare monitoring algorithms and helps to process sensed data. As these two technologies did great jobs in their respective fields a conflate framework of these two technologies may lead to a great architecture for healthcare applications. This chapter reviews complete state-of-the-art and several use cases related to healthcare monitoring using different wireless infrastructure and adapting cloud based technologies in providing the healthcare services.

The design of an m-Health monitoring system based on a cloud computing platform

2015 ◽  
Vol 11 (1) ◽  
pp. 17-36 ◽  
Author(s):  
Boyi Xu ◽  
Lida Xu ◽  
Hongming Cai ◽  
Lihong Jiang ◽  
Yang Luo ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Monitoring System ◽  
Health Monitoring ◽  
Health Monitoring System ◽  
Computing Platform ◽  
Cloud Computing Platform

Ontology-based sharing of structural health monitoring data

2019 ◽  
Author(s):  
Seppo Törmä ◽  
Markku Kiviniemi ◽  
Mikko Lampi ◽  
Pekka Toivola ◽  
Päivi Puntila ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Service Providers ◽  
Facility Management ◽  
Monitoring Data ◽  
Sensor Data ◽  
Health Monitoring System ◽  
Structural Health ◽  
Monitoring Model ◽  
Monitoring Service

<p>A structural health monitoring system installed in a bridge produces a vast amount of sensor data that is analyzed and periodically reported to a bridge owner at an aggregate level. The data itself typically remains in the monitoring service of a service provider; it may be accessible to clients and third parties through a dedicated user interface and API. This paper presents an ontology to defining the monitoring model based on the Semantic Sensor Network Ontology by W3C. The goal is to enable an asset owner to utilize preferred tools to view and access monitoring data from different service providers, and in longer term, increase the utilization of monitoring data in facility management. The ultimate aim is to use BrIM as a digital twin of a bridge and to link external datasets to improve information management and maintenance over its lifecycle.</p>

A WBAN-Based Framework for Health Condition Monitoring and Faulty Sensor Node Detection Applying ANN

2021 ◽  
Vol 10 (2) ◽  
pp. 44-65
Author(s):  
Koushik Karmakar ◽  
Sohail Saif ◽  
Suparna Biswas ◽  
Sarmistha Neogy
Keyword(s):  
Condition Monitoring ◽  
Health Monitoring ◽  
Wearable Sensors ◽  
Wireless Body Area Network ◽  
Health Condition ◽  
Sensor Data ◽  
Area Network ◽  
Cardiac Condition ◽  
Artificial Neural Network Ann ◽  
Sensor Detection

Remote health monitoring framework using wireless body area network with ubiquitous support is gaining popularity. However, faulty sensor data may prove to be critical. Hence, faulty sensor detection is necessary in sensor-based health monitoring. In this paper, an artificial neural network (ANN)-based framework for learning about health condition of patients as well as fault detection in the sensors is proposed. This experiment is done based on human cardiac condition monitoring setup. Related physiological parameters have been collected using wearable sensors from different people. These data are then analyzed using ANN for health condition identification and faulty node detection. Libelium MySignals HW (eHealth Medical Development Shield for Arduino) v2 sensors such as ECG sensor, pulse oximeter sensor, and body temperature sensor have been used for data collection and ARDINO UNO R3 as microcontroller device. ANN method detects faulty sensor data with classification accuracy of 98%. Experimental results and analyses are given to prove the claim.

scholarly journals Internet of things (IoT) based health monitoring system and challenges

2018 ◽  
Vol 7 (1.7) ◽  
pp. 175 ◽  
Author(s):  
M Sathya ◽  
S Madhan ◽  
K Jayanthi
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
Health Monitoring ◽  
Healthcare Services ◽  
The Body ◽  
Healthcare Applications ◽  
Health Monitoring System ◽  
The People ◽  
Improved Outcomes ◽  
The World

Among the applications that Internet of Things (IoT) facilitated to the world, Healthcare applications are most important. In general, IoT has been widely used to interconnect the advanced medical resources and to offer smart and effective healthcare services to the people.  The advanced sensors can be either worn or be embedded into the body of the patients, so as to continously monitor their health. The information collected in such manner, can be analzed, aggregated and mined to do the early prediction of diseases.  The processing algorithms assist the physicians for the personalization of treatment and it helps to make the health care economical, at the same time, with improved outcomes. Also, in this paper, we highlight the challenges in the implementation of IoT health monitoring system in real world.

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