Proposing Real-Time Smart Healthcare Model Using IoT

2020 ◽  
pp. 25-41
Author(s):  
Rashbir Singh ◽  
Prateek Singh ◽  
Latika Kharb
Keyword(s):  
Real Time ◽  
Smart Healthcare ◽  
Healthcare Model

Smart Healthcare

2018 ◽  
pp. 21-33
Author(s):  
R. Rajkumar
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Cost Effective ◽  
Reliable System ◽  
Smart Healthcare ◽  
Cloud Server ◽  
Android App ◽  
The Cost ◽  
Very High ◽  
At Home

Internet of things is a revolutionary domain, when we use it for the wellness of people in a smart way. As of now, the cost to implement IoT-enabled services is very high. So, this chapter introduces a cost effective and a reliable system to monitor patients at home and in hospitals with the help of IoT. The monitored details of a person can be drawn at any time with the help of an android app, which can produce output at real-time. The processed data are stored in the UBIDOTS cloud server, and the patients' needs can be met in time as well lives saved during critical cases with the help of the system proposed in this chapter.

scholarly journals An IoMT-Enabled Smart Healthcare Model to Monitor Elderly People Using Machine Learning Technique

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Muhammad Farrukh Khan ◽  
Taher M. Ghazal ◽  
Raed A. Said ◽  
Areej Fatima ◽  
Sagheer Abbas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Elderly People ◽  
Nearest Neighbor ◽  
Quality Of Healthcare ◽  
Healthcare Services ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Smart Healthcare ◽  
Healthcare Model ◽  
The Cost

The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. The cost of related healthcare will rise as the global population of elderly people grows in parallel with an overall life expectancy that demands affordable healthcare services, solutions, and developments. IoMT may bring revolution in the medical sciences in terms of the quality of healthcare of elderly people while entangled with machine learning (ML) algorithms. The effectiveness of the smart healthcare (SHC) model to monitor elderly people was observed by performing tests on IoMT datasets. For evaluation, the precision, recall, fscore, accuracy, and ROC values are computed. The authors also compare the results of the SHC model with different conventional popular ML techniques, e.g., support vector machine (SVM), K-nearest neighbor (KNN), and decision tree (DT), to analyze the effectiveness of the result.

scholarly journals IoT based on secure personal healthcare using RFID technology and steganography

2021 ◽  
Vol 11 (4) ◽  
pp. 3300
Author(s):  
Haider Ali Khan ◽  
Raed Abdulla ◽  
Sathish Kumar Selvaperumal ◽  
Ammar Bathich
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Medical Model ◽  
Environmental Sensors ◽  
Rfid Technology ◽  
Time Data ◽  
Personal Healthcare ◽  
Smart Healthcare ◽  
Real Time Data ◽  
User Data

Internet of things (IoT) makes it attainable for connecting different various smart objects together with the internet. The evolutionary medical model towards medicine can be boosted by IoT with involving sensors such as environmental sensors inside the internal environment of a small room with a specific purpose of monitoring of person's health with a kind of assistance which can be remotely controlled. RF identification (RFID) technology is smart enough to provide personal healthcare providing part of the IoT physical layer through low-cost sensors. Recently researchers have shown more IoT applications in the health service department using RFID technology which also increases real-time data collection. IoT platform which is used in the following research is Blynk and RFID technology for the user's better health analyses and security purposes by developing a two-level secured platform to store the acquired data in the database using RFID and Steganography. Steganography technique is used to make the user data more secure than ever. There were certain privacy concerns which are resolved using this technique. Smart healthcare medical box is designed using SolidWorks health measuring sensors that have been used in the prototype to analyze real-time data.

scholarly journals SMS: A Secure Healthcare Model for Smart Cities

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1135
Author(s):  
Gautami Tripathi ◽  
Mohd Abdul Ahad ◽  
Sara Paiva
Keyword(s):  
Real Time ◽  
Assisted Living ◽  
Smart City ◽  
Smart Cities ◽  
Ambient Assisted Living ◽  
Critical Time ◽  
Personal Data ◽  
Healthcare Management ◽  
Real Time Analysis ◽  
Healthcare Model

Technological innovations have enabled the realization of a utopian world where all objects of everyday life, as well as humans, are interconnected to form an “Internet of Things (IoT).” These connected technologies and IoT solutions have led to the emergence of smart cities where all components are converted into a connected smart ecosystem. IoT has envisioned several areas of smart cities including the modern healthcare environment like real-time monitoring, patient information management, ambient-assisted living, ambient-intelligence, anomaly detection, and accelerated sensing. IoT has also brought a breakthrough in the medical domain by integrating stake holders, medical components, and hospitals to bring about holistic healthcare management. The healthcare domain is already witnessing promising IoT-based solutions ranging from embedded mobile applications to wearable devices and implantable gadgets. However, with all these exemplary benefits, there is a need to ensure the safety and privacy of the patient’s personal and medical data communicated to and from the connected devices and systems. For a smart city, it is pertinent to have an accessible, effective, and secure healthcare system for its inhabitants. This paper discusses the various elements of technology-enabled healthcare and presents a privacy-preserved and secure “Smart Medical System (SMS)” framework for the smart city ecosystem. For providing real-time analysis and responses, this paper proposes to use the concept of secured Mobile Edge Computing (MEC) for performing critical time-bound computations on the edge itself. In order to protect the medical and personal data of the patients and to make the data tamper-proof, the concept of blockchain has been used. Finally, this paper highlights the ways to capture and store the medical big data generated from IoT devices and sensors.

Smart healthcare model with fog-cloud network architecture

2019 ◽  
Vol 1 (2/3) ◽  
pp. 99
Author(s):  
Mrinal Kanti Naskar ◽  
Amitava Mukherjee ◽  
Rohan Basu Roy ◽  
Arani Roy
Keyword(s):  
Network Architecture ◽  
Smart Healthcare ◽  
Healthcare Model ◽  
Cloud Network

scholarly journals A new smart healthcare framework for real-time heart disease detection based on deep and machine learning

2021 ◽  
Vol 7 ◽  
pp. e646
Author(s):  
Haitham Elwahsh ◽  
Engy El-shafeiy ◽  
Saad Alanazi ◽  
Medhat A. Tawfeek
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Real Time ◽  
Heart Diseases ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Medical Decision ◽  
Rate Condition ◽  
Smart Healthcare ◽  
Learning Techniques

Cardiovascular diseases (CVDs) are the most critical heart diseases. Accurate analytics for real-time heart disease is significant. This paper sought to develop a smart healthcare framework (SHDML) by using deep and machine learning techniques based on optimization stochastic gradient descent (SGD) to predict the presence of heart disease. The SHDML framework consists of two stage, the first stage of SHDML is able to monitor the heart beat rate condition of a patient. The SHDML framework to monitor patients in real-time has been developed using an ATmega32 Microcontroller to determine heartbeat rate per minute pulse rate sensors. The developed SHDML framework is able to broadcast the acquired sensor data to a Firebase Cloud database every 20 seconds. The smart application is infectious in regard to displaying the sensor data. The second stage of SHDML has been used in medical decision support systems to predict and diagnose heart diseases. Deep or machine learning techniques were ported to the smart application to analyze user data and predict CVDs in real-time. Two different methods of deep and machine learning techniques were checked for their performances. The deep and machine learning techniques were trained and tested using widely used open-access dataset. The proposed SHDML framework had very good performance with an accuracy of 0.99, sensitivity of 0.94, specificity of 0.85, and F1-score of 0.87.

scholarly journals SARS-CoV-2 genome datasets analytics for informed infectious disease surveillance

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 919
Author(s):  
Moses Effiong Ekpenyong ◽  
Ifiok James Udo ◽  
Mercy Ernest Edoho ◽  
EnoAbasi Deborah Anwana ◽  
Francis Bukie Osang ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Real Time ◽  
Disease Surveillance ◽  
Emerging Infectious Diseases ◽  
Cost Effective ◽  
Contact Tracing ◽  
Public Healthcare ◽  
Case Definitions ◽  
Smart Healthcare ◽  
Disease Case

Background: The COVID-19 pandemic has ravaged economies, health systems, and lives globally. Concerns surrounding near total economic collapse, loss of livelihood and emotional complications ensuing from lockdowns and commercial inactivity, resulted in governments loosening economic restrictions. These concerns were further exacerbated by the absence of vaccines and drugs to combat the disease, with the fear that the next wave of the pandemic would be more fatal. Consequently, integrating disease surveillance mechanism into public healthcare systems is gaining traction, to reduce the spread of community and cross-border infections and offer informed medical decisions. Methods: Publicly available datasets of coronavirus cases around the globe deposited between December, 2019 and March 15, 2021 were retrieved from GISAID EpiFluTM and processed. Also retrieved from GISAID were data on the different SARS-CoV-2 variant types since inception of the pandemic. Results: Epidemiological analysis offered interesting statistics for understanding the demography of SARS-CoV-2 and helped the elucidation of local and foreign transmission through a history of contact travels. Results of genome pattern visualization and cognitive knowledge mining revealed the emergence of high intra-country viral sub-strains with localized transmission routes traceable to immediate countries, for enhanced contact tracing protocol. Variant surveillance analysis indicates increased need for continuous monitoring of SARS-CoV-2 variants.  A collaborative Internet of Health Things (IoHT) framework was finally proposed to impact the public health system, for robust and intelligent support for modelling, characterizing, diagnosing and real-time contact tracing of infectious diseases. Conclusions: Localizing healthcare disease surveillance is crucial in emerging disease situations and will support real-time/updated disease case definitions for suspected and probable cases. The IoHT framework proposed in this paper will assist early syndromic assessments of emerging infectious diseases and support healthcare/medical countermeasures as well as useful strategies for making informed policy decisions to drive a cost effective, smart healthcare system.

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