Improving health care with IoT - Research into a weight monitoring bed - ECIS 2017 paper.
Resulting from smart furniture applications research project in Germany, Oliver Neuland and partners from AUT developed a smart bed concept which utilizes weight monitoring for AAL and elderly care. Initially strategies were applied to find meaningful use cases, later a prototype was developed. Here a paper presented during ECIS in Portugal which describes the architecture of the prototype.
Intelligent Healthcare Monitoring in IoTIJAEMSJORNAL
The developing of IoT-based health care systems must ensure and increase the safety of the patients, their quality of life and other health care activities. We may not be aware of the health condition of the patient during the sleeping hours. To overcome this problem. This paper proposes an intelligent healthcare monitoring system which monitors and maintains the patient health condition at regular intervals. The heart rate sensor and temperature sensor would help us analyze the patients’ current health condition. In case of major fluctuations in consecutive intervals a buzzer is run in order to notify the hospital staff and doctors. The monitored details are stored in the cloud "ThingSpeak". The doctor can view the patient health condition using Virtuino simulator. This system would help in reducing the random risks of tracing a patient medical highly. Arduino UNO is used to implement this intelligent healthcare monitoring system.
Real-time Heart Pulse Monitoring Technique Using Wireless Sensor Network and ...IJECEIAES
Wireless Sensor Networks (WSNs) for healthcare have emerged in the recent years. Wireless technology has been developed and used widely for different medical fields. This technology provides healthcare services for patients, especially who suffer from chronic diseases. Services such as catering continuous medical monitoring and get rid of disturbance caused by the sensor of instruments. Sensors are connected to a patient by wires and become bed-bound that less from the mobility of the patient. In this paper, proposed a real-time heart pulse monitoring system via conducted an electronic circuit architecture to measure Heart Pulse (HP) for patients and display heart pulse measuring via smartphone and computer over the network in real-time settings. In HP measuring application standpoint, using sensor technology to observe heart pulse by bringing the fingerprint to the sensor via used Arduino microcontroller with Ethernet shield to connect heart pulse circuit to the internet and send results to the web server and receive it anywhere. The proposed system provided the usability by the user (userfriendly) not only by the specialist. Also, it offered speed andresults accuracy, the highest availability with the user on an ongoing basis, and few cost.
There are a number of scopes for IoT in order to make a difference in lives of patients. The devices can capture as well as monitor related data regarding patient and allows the providers to obtain the insights without bringing the patients visiting. The procedure can assist the patient results as well as preventing the possible communications for the process that involves risk. However, lack of electronic health record (EHR) system integration is one of the major issues faced while using IoT in healthcare. Some of the EHR systems allow the patients importing data into the record. However, it remains limited to a few dominant where the EHR players as well as leaves providers unspecific of the processing data that can be helpful for the organization to use the process. The challenges for interoperability in order to keep data in distinctive medical devices depend on the purpose and ordering physician. by Vishal Dineshkumar Soni 2018. An IoT Based Patient Health Monitoring System. International Journal on Integrated Education. 1, 1 (Dec. 2018), 43-48. DOI:https://doi.org/10.31149/ijie.v1i1.481. https://journals.researchparks.org/index.php/IJIE/article/view/481/458 https://journals.researchparks.org/index.php/IJIE/article/view/481
Survey of IOT based Patient Health Monitoring Systemdbpublications
The Internet of things has provided a promising opportunity and applications for medical services is one of the most important way or solution for taking care of population which is in rapid growth. Internet of things consists of communication and sensors; wireless body area network is highly suitable tool for the medical IOT device. In this survey we discuss mainly on practical issues for implementation of WBAN to health care service tool for the medical devices. The IoT applications are key enabling technologies in industries. A main aim of this survey paper is that it summarizes the present state-of-the-art IOT in industries and also in workflow hospitals systematically. In recent years wide range of opportunity and powerful of IOT applications are developed in industry. The health monitoring system is a big challenge for several researchers. In this paper introduced on the survey of different IOT applications are used for the health monitoring system. The IoT applications are used to decrease the problems which are related to health care system.
Intelligent Healthcare Monitoring in IoTIJAEMSJORNAL
The developing of IoT-based health care systems must ensure and increase the safety of the patients, their quality of life and other health care activities. We may not be aware of the health condition of the patient during the sleeping hours. To overcome this problem. This paper proposes an intelligent healthcare monitoring system which monitors and maintains the patient health condition at regular intervals. The heart rate sensor and temperature sensor would help us analyze the patients’ current health condition. In case of major fluctuations in consecutive intervals a buzzer is run in order to notify the hospital staff and doctors. The monitored details are stored in the cloud "ThingSpeak". The doctor can view the patient health condition using Virtuino simulator. This system would help in reducing the random risks of tracing a patient medical highly. Arduino UNO is used to implement this intelligent healthcare monitoring system.
Real-time Heart Pulse Monitoring Technique Using Wireless Sensor Network and ...IJECEIAES
Wireless Sensor Networks (WSNs) for healthcare have emerged in the recent years. Wireless technology has been developed and used widely for different medical fields. This technology provides healthcare services for patients, especially who suffer from chronic diseases. Services such as catering continuous medical monitoring and get rid of disturbance caused by the sensor of instruments. Sensors are connected to a patient by wires and become bed-bound that less from the mobility of the patient. In this paper, proposed a real-time heart pulse monitoring system via conducted an electronic circuit architecture to measure Heart Pulse (HP) for patients and display heart pulse measuring via smartphone and computer over the network in real-time settings. In HP measuring application standpoint, using sensor technology to observe heart pulse by bringing the fingerprint to the sensor via used Arduino microcontroller with Ethernet shield to connect heart pulse circuit to the internet and send results to the web server and receive it anywhere. The proposed system provided the usability by the user (userfriendly) not only by the specialist. Also, it offered speed andresults accuracy, the highest availability with the user on an ongoing basis, and few cost.
There are a number of scopes for IoT in order to make a difference in lives of patients. The devices can capture as well as monitor related data regarding patient and allows the providers to obtain the insights without bringing the patients visiting. The procedure can assist the patient results as well as preventing the possible communications for the process that involves risk. However, lack of electronic health record (EHR) system integration is one of the major issues faced while using IoT in healthcare. Some of the EHR systems allow the patients importing data into the record. However, it remains limited to a few dominant where the EHR players as well as leaves providers unspecific of the processing data that can be helpful for the organization to use the process. The challenges for interoperability in order to keep data in distinctive medical devices depend on the purpose and ordering physician. by Vishal Dineshkumar Soni 2018. An IoT Based Patient Health Monitoring System. International Journal on Integrated Education. 1, 1 (Dec. 2018), 43-48. DOI:https://doi.org/10.31149/ijie.v1i1.481. https://journals.researchparks.org/index.php/IJIE/article/view/481/458 https://journals.researchparks.org/index.php/IJIE/article/view/481
Survey of IOT based Patient Health Monitoring Systemdbpublications
The Internet of things has provided a promising opportunity and applications for medical services is one of the most important way or solution for taking care of population which is in rapid growth. Internet of things consists of communication and sensors; wireless body area network is highly suitable tool for the medical IOT device. In this survey we discuss mainly on practical issues for implementation of WBAN to health care service tool for the medical devices. The IoT applications are key enabling technologies in industries. A main aim of this survey paper is that it summarizes the present state-of-the-art IOT in industries and also in workflow hospitals systematically. In recent years wide range of opportunity and powerful of IOT applications are developed in industry. The health monitoring system is a big challenge for several researchers. In this paper introduced on the survey of different IOT applications are used for the health monitoring system. The IoT applications are used to decrease the problems which are related to health care system.
Activity and health monitoring systems
This paper presents an Open Platform Activity and health monitoring systems which are also called e-Health systems. These systems measure and store parameters that reflect changes in the human body. Due to continuous monitoring (e.g. in rest state and in physical effort state), a specialist can learn about the individual's physiological parameters. Because the human body is a complex system, the examiner can notice some changes within the body by looking at the physiological parameters. Six different sensors ensure us that the patient's individual parameters are monitored. The main components of the device are: A Raspberry Pi 3 small single-board computer, an e-Health Sensor Platform by Cooking-Hacks, a Raspberry Pi to Arduino Shields Connection Bridge and a 7-inch Raspberry Pi 3 touch screen. The processing unit is the Raspberry Pi 3 board. The Raspbian operating system runs on the Raspberry Pi 3, which provides a solid base for the software. Every examination can be controlled by the touch screen. The measurements can be started with the graphical interface by pressing a button and every measured result can be represented on the GUI’s label or on the graph. The results of every examination can be stored in a database. From that database the specialist can retrieve every personalized data
An Intelligent Healthcare Serviceto Monitor Vital Signs in Daily Life – A Cas...IJERA Editor
Vital signs monitoring for elderly in daily life environment is a promising concept that efficiently can provide medical services to people at home. However, make the system self-served and functioning as personalized provision makes the challenge even larger. This paper presents a case study on a Health-IoT system where an intelligent healthcare service is developed to monitor vital signs in daily life. Here, a generic Health-IoT framework with a Clinical Decision Support System (CDSS) is presented. The generic framework is mainly focused on the supporting sensors, communication media, secure and safe data communication, cloud-based storage, and remote accesses of the data. The CDSS is used to provide a personalized report on persons‟ health condition based on daily basis observation on vital signs. Six participants, from Spain (n=3) and Slovenia (n=3) have been using the proposed healthcare system for eight weeks (e.g. 300+ health measurements) in their home environments to monitor their health. The sensitivity, specificity and overall accuracy of the DSS‟s classification are achieved as 90%, 97% and 96% respectively while k=2 i.e., top 2 most similar retrieved cases are considered. The initial user evaluation resultdemonstrates the feasibility and performance of the implemented system through the proposed framework.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESpijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESamsjournal
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
In the present research, the synthesis of silver nanoparticles by the green method is done using stem and leaves aqueous
extract of Tinospora cordifolia (T.C). The pathway of nanoparticles formation is by means of reduction of silver nitrate by
extracts, which act as both reducing and capping agents. The silver nanoparticles characterized by UV-Vis-spectrometer, Fourier
transform infra-red spectroscopy, X-ray diffractometer, Scanning electron microscopy, Energy dispersive spectroscopy. The sizes
of the synthesized silver nanoparticles are found to be in the range of 27- 58 nm. The energy dispersive spectrum confirmed the
presence of silver metal. The silver nanoparticles synthesized in this process have the efficient antimicrobial activity against
pathogenic bacteria like Bacillus subtilis, Escherichia coli, Klebsiella pneumonia, proteus mirabilis, Staphylococcus aureus
and Serratia marcescens using paper disc diffusion method.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A modern healthcare IoT platform with an intelligent medicine box along with sensors for health monitoring and diagnosis is proposed here. Health care services based on Internet of Things have great potential in medical field. In this paper, an intelligent home-based medicine box with wireless connectivity along with an android application (Health-IoT) that helps patients and doctors to be in a more close communication. The proposed platform has an intelligent medicine box that gives alerts for patients to take their medication at the right time. The box is wirelessly connected to internet to make timely updates about medicines which will be notified in the android application with in patient’s smartphone. The system automatically gives alarm so that the patient takes the right medicine at the right time. And if there are any vital signs noticed SMS alerts are given to the predefined guardian.
The objective of project is to improve end-users’ Healthcare experience through its IoT-based Healthcare services and to support business incubation scheme with better
regulatory support
Artificial intelligence (AI), machine learning, and data science have started to shape the delivery of health services. We see this in every critical step, from patient scheduling management to physically assisted surgery.
The worldwide network of internet of things (IOT) combined with advancements in sensor networks, FID and software platform connects objects of various application fields and technology. IOT is most commonly described as an ecosystem of technologies but it requires necessary components to enable communication between devices and objects. Components being RFID and sensors. Many organization have already implemented IOT. Healthcare industry too have adopted IOT and can be extensively used in the future for the benefit of patients, elderly people and caregivers. A new concept named 'Health Internet of Things (HIOT)' was proposed to exploit sensor technologies and wireless networks in monitoring medical conditions. Also advancements in E textile technologies make the textile multifunctional, adaptive and responsive system which combined with IOT performs functions such as communication, computation and health care benefits. Cloud is used to store, control and retrieve or transform or classify information. The use of cloud based application in healthcare industries is constantly growing to benefit patients so that they can monitor their health, store and share records. This paper aims at developing a dependable, productive, high performance and assured smart healthcare system to deliver service to patients avoiding health risks using e textile technologies
IoT and machine learning (ML) are becoming increasingly efficient in the medical and telemedicine areas all around the world. This article describes a system that employs latest technology to give a more accurate method of forecasting disease. This technology uses sensors to collect data from the body of the patient. The obtained sensor information is collected with NodeMcU before being transferred to the Cloud Platform "ThinkSpeak" through an ESP8266 Wi-Fi module. ThinkSpeak is a cloud server that provides real-time data streams in the cloud. For the best results, data currently saved in the cloud is evaluated by one of the machine learning algorithms, the KNN algorithm. Based on the findings of the analysis and compared with the data sets, the disease is predicted and a prescription for the relevant disease is issued.
E-HEALTH BIOSENSOR PLATFORM FOR NONINVASIVE HEALTH MONITORING FOR THE ELDERLY...ijbesjournal
New technologies in the field of tele-health using biosensor systems for non-invasive vital signs monitoring of patients, especially elderly people who need long-term care, and marginalized areas with hard to reach health care services are emerging. A study involving a self-care approach within the cardiac domain, where late detection increases the likelihood of patient disability or of premature death is proposed. In the
study the application of e-health biosensors platform in medical services is experimented. The study resulted into the synthesis of vital signs from various body positions with biosensors that does not require a full coupled system. A model for the prevention of cardiovascular disease management based on noninvasive personal health monitoring systems with easy access for everybody, at any time or location is designed. A personal vital sign system such as ECG sensor which contain the functionality, allows recording anywhere and at any time a diagnostic quality ECG and analyzing it “on-board” by comparing it to a reference ECG, is modelled. The model called Mobile Health for the Elderly Persons (MOHELP)
which relies on with application in estimation and control of boolean processes based on noisy and incomplete measurements is designed. This enabled a reliable recommendation from a digital artificial intelligence-based diagnosis, which can support an elderly person to take timely and correct decisions upon his (her) health status. In a case of urgency, the assistant puts the elderly person in a contact with
healthcare providers. The signal pattern sensitivity related to sensors placement is one of the issues this study addressed using e-sensor platform. Sensors displacement errors have a direct impact on the medical diagnosis, especially if the diagnostic procedure is automated. The study resulted into the formulation of a methodology for e-Health Sensor Platform, in software architecture terms, that permits use of system
biosensors to adapt to the user-specific context for self-healthcare
Predictive Data Mining for Converged Internet ofJames Kang
Kang, J. J., Adibi, S., Larkin, H., & Luan, T. (2016). Predictive data mining for Converged Internet of Things: A Mobile Health perspective. In Telecommunication Networks and Applications Conference (ITNAC), 2015 International (pp. 5-10). IEEE Xplore: IEEE. doi: http://dx.doi.org/10.1109/ATNAC.2015.7366781
Activity and health monitoring systems
This paper presents an Open Platform Activity and health monitoring systems which are also called e-Health systems. These systems measure and store parameters that reflect changes in the human body. Due to continuous monitoring (e.g. in rest state and in physical effort state), a specialist can learn about the individual's physiological parameters. Because the human body is a complex system, the examiner can notice some changes within the body by looking at the physiological parameters. Six different sensors ensure us that the patient's individual parameters are monitored. The main components of the device are: A Raspberry Pi 3 small single-board computer, an e-Health Sensor Platform by Cooking-Hacks, a Raspberry Pi to Arduino Shields Connection Bridge and a 7-inch Raspberry Pi 3 touch screen. The processing unit is the Raspberry Pi 3 board. The Raspbian operating system runs on the Raspberry Pi 3, which provides a solid base for the software. Every examination can be controlled by the touch screen. The measurements can be started with the graphical interface by pressing a button and every measured result can be represented on the GUI’s label or on the graph. The results of every examination can be stored in a database. From that database the specialist can retrieve every personalized data
An Intelligent Healthcare Serviceto Monitor Vital Signs in Daily Life – A Cas...IJERA Editor
Vital signs monitoring for elderly in daily life environment is a promising concept that efficiently can provide medical services to people at home. However, make the system self-served and functioning as personalized provision makes the challenge even larger. This paper presents a case study on a Health-IoT system where an intelligent healthcare service is developed to monitor vital signs in daily life. Here, a generic Health-IoT framework with a Clinical Decision Support System (CDSS) is presented. The generic framework is mainly focused on the supporting sensors, communication media, secure and safe data communication, cloud-based storage, and remote accesses of the data. The CDSS is used to provide a personalized report on persons‟ health condition based on daily basis observation on vital signs. Six participants, from Spain (n=3) and Slovenia (n=3) have been using the proposed healthcare system for eight weeks (e.g. 300+ health measurements) in their home environments to monitor their health. The sensitivity, specificity and overall accuracy of the DSS‟s classification are achieved as 90%, 97% and 96% respectively while k=2 i.e., top 2 most similar retrieved cases are considered. The initial user evaluation resultdemonstrates the feasibility and performance of the implemented system through the proposed framework.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESpijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESamsjournal
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
In the present research, the synthesis of silver nanoparticles by the green method is done using stem and leaves aqueous
extract of Tinospora cordifolia (T.C). The pathway of nanoparticles formation is by means of reduction of silver nitrate by
extracts, which act as both reducing and capping agents. The silver nanoparticles characterized by UV-Vis-spectrometer, Fourier
transform infra-red spectroscopy, X-ray diffractometer, Scanning electron microscopy, Energy dispersive spectroscopy. The sizes
of the synthesized silver nanoparticles are found to be in the range of 27- 58 nm. The energy dispersive spectrum confirmed the
presence of silver metal. The silver nanoparticles synthesized in this process have the efficient antimicrobial activity against
pathogenic bacteria like Bacillus subtilis, Escherichia coli, Klebsiella pneumonia, proteus mirabilis, Staphylococcus aureus
and Serratia marcescens using paper disc diffusion method.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A modern healthcare IoT platform with an intelligent medicine box along with sensors for health monitoring and diagnosis is proposed here. Health care services based on Internet of Things have great potential in medical field. In this paper, an intelligent home-based medicine box with wireless connectivity along with an android application (Health-IoT) that helps patients and doctors to be in a more close communication. The proposed platform has an intelligent medicine box that gives alerts for patients to take their medication at the right time. The box is wirelessly connected to internet to make timely updates about medicines which will be notified in the android application with in patient’s smartphone. The system automatically gives alarm so that the patient takes the right medicine at the right time. And if there are any vital signs noticed SMS alerts are given to the predefined guardian.
The objective of project is to improve end-users’ Healthcare experience through its IoT-based Healthcare services and to support business incubation scheme with better
regulatory support
Artificial intelligence (AI), machine learning, and data science have started to shape the delivery of health services. We see this in every critical step, from patient scheduling management to physically assisted surgery.
The worldwide network of internet of things (IOT) combined with advancements in sensor networks, FID and software platform connects objects of various application fields and technology. IOT is most commonly described as an ecosystem of technologies but it requires necessary components to enable communication between devices and objects. Components being RFID and sensors. Many organization have already implemented IOT. Healthcare industry too have adopted IOT and can be extensively used in the future for the benefit of patients, elderly people and caregivers. A new concept named 'Health Internet of Things (HIOT)' was proposed to exploit sensor technologies and wireless networks in monitoring medical conditions. Also advancements in E textile technologies make the textile multifunctional, adaptive and responsive system which combined with IOT performs functions such as communication, computation and health care benefits. Cloud is used to store, control and retrieve or transform or classify information. The use of cloud based application in healthcare industries is constantly growing to benefit patients so that they can monitor their health, store and share records. This paper aims at developing a dependable, productive, high performance and assured smart healthcare system to deliver service to patients avoiding health risks using e textile technologies
IoT and machine learning (ML) are becoming increasingly efficient in the medical and telemedicine areas all around the world. This article describes a system that employs latest technology to give a more accurate method of forecasting disease. This technology uses sensors to collect data from the body of the patient. The obtained sensor information is collected with NodeMcU before being transferred to the Cloud Platform "ThinkSpeak" through an ESP8266 Wi-Fi module. ThinkSpeak is a cloud server that provides real-time data streams in the cloud. For the best results, data currently saved in the cloud is evaluated by one of the machine learning algorithms, the KNN algorithm. Based on the findings of the analysis and compared with the data sets, the disease is predicted and a prescription for the relevant disease is issued.
E-HEALTH BIOSENSOR PLATFORM FOR NONINVASIVE HEALTH MONITORING FOR THE ELDERLY...ijbesjournal
New technologies in the field of tele-health using biosensor systems for non-invasive vital signs monitoring of patients, especially elderly people who need long-term care, and marginalized areas with hard to reach health care services are emerging. A study involving a self-care approach within the cardiac domain, where late detection increases the likelihood of patient disability or of premature death is proposed. In the
study the application of e-health biosensors platform in medical services is experimented. The study resulted into the synthesis of vital signs from various body positions with biosensors that does not require a full coupled system. A model for the prevention of cardiovascular disease management based on noninvasive personal health monitoring systems with easy access for everybody, at any time or location is designed. A personal vital sign system such as ECG sensor which contain the functionality, allows recording anywhere and at any time a diagnostic quality ECG and analyzing it “on-board” by comparing it to a reference ECG, is modelled. The model called Mobile Health for the Elderly Persons (MOHELP)
which relies on with application in estimation and control of boolean processes based on noisy and incomplete measurements is designed. This enabled a reliable recommendation from a digital artificial intelligence-based diagnosis, which can support an elderly person to take timely and correct decisions upon his (her) health status. In a case of urgency, the assistant puts the elderly person in a contact with
healthcare providers. The signal pattern sensitivity related to sensors placement is one of the issues this study addressed using e-sensor platform. Sensors displacement errors have a direct impact on the medical diagnosis, especially if the diagnostic procedure is automated. The study resulted into the formulation of a methodology for e-Health Sensor Platform, in software architecture terms, that permits use of system
biosensors to adapt to the user-specific context for self-healthcare
Predictive Data Mining for Converged Internet ofJames Kang
Kang, J. J., Adibi, S., Larkin, H., & Luan, T. (2016). Predictive data mining for Converged Internet of Things: A Mobile Health perspective. In Telecommunication Networks and Applications Conference (ITNAC), 2015 International (pp. 5-10). IEEE Xplore: IEEE. doi: http://dx.doi.org/10.1109/ATNAC.2015.7366781
Internet of Things IoT Based Healthcare Monitoring System using NodeMCU and A...ijtsrd
Today Internet has become one of the important parts of daily life. It has changed how people live, work, play and learn. Internet serves for many purposes educations, finance, Business, Industries, Entertainment, Social Networking, etc. The IoT is connected objects to the Internet and used to control of those objects or remote monitoring. A health care monitoring system is necessary to constantly monitor the patient's physiological parameters. The main advantage of this system is that the results can be viewed at any time and place. The doctors can be notified by using mobile phones messages if patient health is abnormal. In this system, heartbeat sensor, temperature sensor and blood pressure sensor are used. The system can analyze the signal to detect normal or abnormal conditions. In the system, the internet of things IoT is becoming a major platform for many services and applications. The IoT is generally considered as connecting objects to the Internet and using that connection for control of those objects or remote monitoring. Khin Thet Wai | Nyan Phyo Aung | Lwin Lwin Htay "Internet of Things (IoT) Based Healthcare Monitoring System using NodeMCU and Arduino UNO" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26482.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/26482/internet-of-things-iot-based-healthcare-monitoring-system-using-nodemcu-and-arduino-uno/khin-thet-wai
In the last decade the healthcare monitoring systems have drawn considerable attentions of the researchers. The prime goal was to develop a reliable patient monitoring system so that the healthcare professionals can monitor their patients, who are either hospitalized or executing their normal daily life activities. In this work we present a mobile device based wireless healthcare monitoring system that can provide real time online information about physiological conditions of a patient. Our proposed system is designed to measure and monitor important physiological data of a patient in order to accurately describe the status of her/his health and fitness. In addition the proposed system is able to send alarming message about the patient’s critical health data by text messages or by email reports. By using the information contained in the text or e-mail message the healthcare professional can provide necessary medical
advising. The system mainly consists of sensors, the data acquisition unit, microcontroller (i.e., Arduino), and software (i.e., LabVIEW). The patient’s temperature, heart beat rate, muscles, blood pressure, blood glucose level, and ECG data are monitored, displayed, and stored by our system. To ensure reliability and accuracy the proposed system has been field tested. The test results show that our system is able to measure the patient’s physiological data with a very high accuracy.
Smart Health care Monitoring using Arduino.pptxIdrisFiras
It's presentation about smart health care monitoring by using Arduino and some sensors then we will display results on remoteXY app to monitor vital signs of patient
Advance IoT-based BSN Healthcare System for Emergency Response of Patient wit...IJMTST Journal
BSN is a prior and emerging technology in the medical field .BSN care system first deploy light weight ,tinypowered
sensors on patient body which communicate with each other and the coordinator node which is on
the body.This system mainly focuses on the measurement and estimation of important parameters like
ECG,temperature,level of blood. This real time system focuses on several parameters like patient location,
data storage, motion detection and transmission of data as well as alert messages to first responder and
server of hospital.In this system we are using three types of sensors ECG sensor,temperature sensor and
level sensor which gathers patients information and sends to micro-controller via which it goes to
BTC(Bluetooth Controller) through it is transferred to an Android smart phone of patient via Wi-Fi/Internet it
goes to server and from the server data is sends to doctors Android App. We are additionally using NFC(Near
Field Communication) technology,motion detection of patient by continuous grabbing of feed from
camera.Subsequently, by using this customized algorithm we proposed IOT based healthcare system using
body sensors which efficiently accomplish requirement.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESpijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASES pijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT)
In this paper, a novel cloud-based WBAN health management system is introduced to. This system can be used for people’s health information collection, record, storage and transmission, health status monitoring and assessment, health education, telemedicine, and remote health management. Therefore it can provide health management services on-demand timely, appropriately and without boundaries.
M health an emerging trend an empirical studycsandit
The advent and advancement in technology specific to medical field has seen a migration of its
work across the globe, adapting higher and newer levels of m-health. Technology has been
successful in transforming the way traditional monitoring and alert system work to a modern
approach wherein minimizing the need for physical monitoring. Today, the field of healthcare
use varied monitoring systems to monitor the health of patients using ubiquitous and nonubiquitous
devices. These are sensor based devices that can read vital signs of patients and send
the data to the required personnel’s using mobile networks. This paper understands and
analyses how the monitoring and alert system works specific to m-health. m-health including
wearable and non-wearable devices read various vital signs and have the ability to monitor
health real-time and transfer the information collected using mobile network. m-health has
become an useful tool for elderly in this fast paced world where almost all the family members
are working or studying to keep track and maintain optimal health status. m-health alert system
involves the patient, the caretaker and medical service provider wherein the patient wears the
device and vital signs recorded are transferred the medical service provider who then analyses
the data collected and required changes in the medication are implemented. This paper
proposes a medical alert system that enlightens the capabilities of m-health making health
monitoring easy and reliable. It contains a three-level severity check and raises an alarm to the
caretaker, the physician or the ambulatory service provider.
All submitted research articles are subjected to immediate rapid screening by the editors, in consultation with the Editorial Board or others working in the field as appropriate, to ensure they are likely to be of the level of interest and importance appropriate for the journal. ijerst offers a fast publication schedule whilst maintaining rigorous peer review; the use of recommended electronic formats for article delivery expedites the process.
Similar to Ecis final paper-june2017_two way architecture between iot sensors and cloud computing for remote (20)
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
2. TWO WAY ARCHITECTURE BETWEEN IOT SENSORS
AND CLOUD COMPUTING FOR REMOTE HEALTH
CARE MONITORING APPLICATIONS
Research in Progress
Jing, Ma, Auckland University of Technology, Auckland, New Zealand, jing.ma@aut.ac.nz
Hoa, Nguyen, Auckland University of Technology, Auckland, New Zealand, h.nguyen@aut.ac.nz
Farhaan, Mirza, Auckland University of Technology, Auckland, New Zealand, farhaan.mirza@aut.ac.nz
Oliver,Neuland, Auckland University of Technology, Auckland, New Zealand, oliver.neuland@aut.ac.nz
Abstract
This research presents an intelligent two way IoT (Internet of Things) architecture that uses IoT sensors
and cloud-technology for data collection, monitoring and alerting strategies. This approach can enhance
development of support systems which are useful for patients and aging individuals who want to remain in
an independent living environment. Such an architecture can be used for early detection of anomalies and
reduce medical costs. In this paper we present a technical architecture called SMMC - Sensors, Micro
Controller, Machine to Machine Protocols and Cloud. The technical architecture proposed will firstly
collect data from IoT sensors at the point of care. Secondly, the data collected by sensors is usually an
analogue signal, this is processed by the micro controller. Thereafter the data is sent to the cloud, where
clinical decision support algorithms can be applied to check for any clinically alarming anomalies in the
data. Finally using machine to machine protocols can be used to activate sensors for feedback or alerts.
We present this architecture along with a smart bed scenario, and describe further research in progress.
Keywords: Remote Health Care Monitoring, IoT Health Care, MQTT, ThingSpeak, AAL
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães,Portugal, 2017 1
3. Ma et al. / IoT Architecture for Remote Healthcare Monitoring
1 Introduction
IoT technology is utilized widely in developing health care solutions, especially in applications such as
remote health monitoring and in-home care. IoT-based health care applications have received compelling
attention in recent years, especially for solutions that enable independent living. Existing research has
focused on the design and implementations of IoT-based health care systems that provide remote and
continuous monitoring and support early detection and timely care for those people who need special
attention such as chronic disease patients, people with disabilities, and the elderly. The motivation of these
applications is to reduce health care expenses while providing better quality of life for people.
In this paper we present a technical architecture called SMMC - Sensors, Micro Controller, Machine
to Machine Protocols and Cloud. The technical architecture proposed will firstly collect data from IoT
sensors at the point of care. Secondly, the data collected by sensors is usually an analogue signal, this is
processed by the micro controller. Thereafter the data is sent to the cloud, where clinical decision support
algorithms can be applied to check for any clinically alarming anomalies in the data. Finally using machine
to machine protocols can be used to activate sensors for feedback or alerts. For example a weighing sensor
measures weight of an elderly individual in their bed. The data is processed by a micro controller and sent
to the cloud. The individuals weight data is not being received & it is late night, therefore the sensor in
next of kin’s room is activated (led light and an alarm) prompting to check on the older patient.
The structure of the paper is as follows - we present the literature on health monitoring systems in IOT in
section 2. Then we articulate the SMMC architecture in section 3. The SMMC is applied to a scenario of
weight sensor, this is presented in section 4 followed by conclusions and plans for further work in section
5.
2 Literature Review
This section focuses on a review of smart healthcare applications based on IoT including remote health
monitoring applications and ambient assisted living (AAL) applications.
2.1 Remote Health Monitoring Applications
Numerous studies have attempted to focus on remote health monitoring, especially continuous monitoring,
which is very important in chronic diseases such as heart failure, diabetes, and asthma. Chang et al.
developed a context-aware and interactive m-health system for diabetics, consisting a blood glucose
monitor and a cloud server platform (Chang et al., 2016). Patient’s blood glucose values and measurement
scenarios are collected and sent to a cloud server by an Android-based device. Patient’s health status
obtained by analyzing the collected information is used for performing appropriate interventions such
as sending instruction messages to the patient or generating notifications to the patient’s caregivers if
necessary.
In the research developed by (Fanucci et al., 2013), H@H platform is described using biomedical sensors
for daily collecting parameters of Chronic Heart Failure (CHF) patients, including Electrocardiography
(ECG), SpO2, blood pressure and weight. The collected data are automatically sent to the hospital’s
database and monitored by physicians, allowing patients to take timely interventions in case of necessary.
A smart shirt which is a wearable ECG system has been designed and implemented by (Jeon, J. Lee, and
Choi, 2013), using an ECG sensing device for real-time monitoring and self-diagnosis for CHF patients.
The measured data are transmitted wirelessly to a smart phone using Bluetooth. In case of emergency,
the system can make an automatic call to the Emergency Rescue Center. Suh et al. developed a weight
and activity with blood pressure monitoring system called WANDA (Suh et al., 2011) which consists of
sensors, web servers, and back-end databases. The weight, blood pressure and physical activity of CHF
patients are measured and sent to the web server’s database. Simultaneously, 12 most common signs and
symptoms of CHF are daily checked through questionnaires using a smart phone-based application. When
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães,Portugal, 2017 2
4. Ma et al. / IoT Architecture for Remote Healthcare Monitoring
the obtained values are out of the acceptable range, alerts are sent to healthcare providers via text message
or e-mail.
2.2 Ambient Assisted Living (AAL) Applications
Providing personalized care for the elderly or the disabled based on their profile and the surrounding
context, which is well-known as Ambient Assisted Living (AAL), is one of the key solutions that brings
sustainable care for those people, and supports them to live more independently in their homes without
worrying about losing their independence (Al-Shaqi, Mourshed, and Rezgui, 2016).
In the research carried out by (Chuang et al., 2015), a system called SilverLink is proposed which is a
home-care solution for seniors. It uses multiple object sensors placed in-home for tracking users’ activities
as well as their health status based on their preference and lifestyle. Simultaneously, a human sensor is
attached on each users’ body all the time for recording their motions such as walking, sitting or falling.
The sensed data are transmitted to the data center and processed by the advanced analytic engine for
checking and triggering alerts to the emergency response team if there is any abnormalities in users’
activities or movement patterns.
A similar system called Help to You (H2U) is developed by (Basanta, Y.-P. Huang, and T.-T. Lee, 2016)
that makes use of wearable devices, biosensors and wireless sensor networks for providing applications
including emergency calls, medication reminders and symptom checks based on monitoring real-time
activities and health status of the old aged. Medical adherence solutions such as a drug management
system based on RFID technology (Parida et al., 2012), or an intelligent pill box (S.-C. Huang et al., 2014)
are useful for elderly people who have high risk of suffering from dementia.
In (Santos et al., 2016), authors proposed a system that integrates an IoT-based mobile gateway with an
Intelligent Personal Assistant (IPA) platform for providing services including location monitoring, heart
rate monitoring, and fall detecting. The user’s information about location, heart rate, and possible fall
detection is collected by the gateway and sent immediately to the caregiver’s IPA for generating alarms or
appropriate interventions. In addition, the capability to interact with other smart objects enables IPAs to
obtain new knowledge and awareness about their users. The experiment results indicate that the location
monitoring service is more accurate in outdoor locations than at indoor locations. In the meanwhile, the
heart rate monitoring accuracy depends on the accuracy of the used sensors. The falls detection algorithm
accuracy achieves 100% with still, walk, sit down, stand up and lay down activities, while achieves 96.7%,
86.7% and 93.3% with three activities including jump, run and fall, respectively. Another real-time falls
detection system was introduced by (Cheng, Jiang, and Shi, 2015) using wearable sensors for tracking
the motion and location of the body. The system has 96.4% of fall detection accuracy based on total 150
experiments with 10 intentional falls and 5 daily life activities.
2.3 Research Gap and Future Opportunities for IoT Health Care applications
Our review on recent IoT applications in health care found that existing studies focus on vital sign moni-
toring and activity monitoring for detecting abnormal situations, fewer of them concentrate on integrating
environmental factors for analytic and decision-making support (Al-Shaqi, Mourshed, and Rezgui, 2016).
Moreover, most of the systems are designed for tracking physical health status of individuals, while mental
health status has considerable impact on human well-being. Solutions for monitoring and detecting both
physical and mental health changes need to be further developed. For example, authors in (Mirza et al.,
2016) suggest to use a smart bed with an embedded weight sensor for collecting data and identifying any
indications in physical or mental health changes. In addition, future IoT-based health care applications
should empower effective self-care for individuals, especially for people with chronic conditions or the
old aged, to help them optimize their outcomes and quality of life. On the other hand, research performed
by (Al-Shaqi, Mourshed, and Rezgui, 2016) also indicates that IoT-based systems in AAL field experience
several limitations including: the lack of sufficient clinical evidence showing that AAL systems improve
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães,Portugal, 2017 3
5. Ma et al. / IoT Architecture for Remote Healthcare Monitoring
the patients’ quality of life; the shortage of detailed research related to users’ acceptance of AAL systems;
the deficiency in solving the needs and demands of end-users; and the slightness of information about the
implemented AAL system that users’ caregivers are educated. Such limitations should be solved in future
AAL applications in order to achieve sustainable and holistic solutions for supporting the old and disable
people to maintain their health and live independently in their own homes.
Therefore, monitoring weight is a key elment of care. “sudden elderly weigh loss” is an early indicator of
critical health changes, for instance: cancer, depression, forgetting to eat or drink. The challenge for home
care of elderly or ill compared to the hospital environment is, that even the simple task of consistent weigh
monitoring is challenging for family or care personal. They have to move the patient to the scale daily and
record data consistently. The described solution, apart from anomalies around behaviour (time spend in
bed/ absence), can help with such tasks and detect health condition changes early on. Furthermore, the
system will be able to solve the problem of consistent and accurate weigh monitoring of elderly or ill in
a home environment where there is no stringent process in place like in hospitals. Weight progression
as an early indicator of health status changes can be monitored without a care taker having to conduct
this routine daily. Warnings and patient history can be provided to clinicians instantly. This enables
independent living of patients especially such with memory issues. The system also enables occupancy,
fall and behaviour monitoring without the need of wearing a wrist band (which patients are sometimes
inclined to remove/ take off) or fitting the house with an array of PIR sensors or sensor mats. The solution
supplies one more element in the tool kit of home and health monitoring reducing worries of family
members for their independent living elderly. With an aging society and less care personal this solution
addresses the problem of efficiently monitoring people at risk and enables aging with dignity.
3 SMMC Model for IoT
Based on the gap analysis in our literature review, we focus on proposing an architecture which facilitates
design and development of IoT solutions that not only collect and monitor consistenly data, but are able
to respond, alert and inform in real time for clinical decision support. In the IoT monitoring solutions, a
typical setup consists of smart sensors, micro-controllers, networks, ubiquitous devices and underlying
software services. Thus, we name the model as SMMC, in which sensors, micro-controller, MQTT and
cloud computing are utilized to build IoT. Each component in SMMC will use the most effective hardware
and software. Each component is replaceable and can be substituted based on alternatives available.
The SMMC model being specifically considered is made up of the following components:
1. World of Sensors
Sensor nodes to measure or monitor basic vital signs of people, such as people’s weight or temperature.
On the other hand, sensors used to remind or alert, such as light sensor or buzzer.
2. Micro-controller Platform
Micro-controllers connect with sensors allowing sending and receiving of data. The micro-controller
converts the analog signal to digital values and controls all the devices and sensors. Arduino and
Raspberry Pi are two popular choices to connect with IoT sensors.
3. Machine-to-Machine Protocols
Different IoT devices are connected by special type of IoT protocols, in other words, Machine-to-
Machine (M2M) is to use a sort of IoT protocols to support real time communication. Popular M2M
protocols include MQ Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP)
are in use (Zubov, 2016). MQTT is designed as an extremely lightweight publish/subscribe messaging
transport with TCP. It is useful for connections with remote locations where a small code footprint is
required and/or network bandwidth is at a premium. CoAP is a specialized web transfer protocol for
use with constrained nodes and constrained networks in the IoT with UDP protocol.
4. Cloud Computing
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães,Portugal, 2017 4
6. Ma et al. / IoT Architecture for Remote Healthcare Monitoring
Cloud computing can be utilized for storing IoT data remotely, and this extends the scope of IoT
solutions to deal with real world things in a more distributed and dynamic manner (Botta et al., 2016).
Cloud servers can be setup using proprietary needs. However packages such as Pachube, Nimbits and
ThingSpeak offer direct APIs, can be consumed by micro controllers to post data. Data storage on
the cloud enables leveraging clinical decision support algorithms, to detect anomalies, and build in
triggers to send warnings, alerts, or information. This can occur either by using 3rd party services, or
using the M2M protocols to trigger actions on the sensors. For example using a HTTP web service - a
text message or automated phone call can be generated, to inform family members of the person at risk,
alternatively send a M2M signal back to patient’s home, reminding them about medical adherence or
any other medical activity that needs to be conducted.
4 Scenario of IoT Health Care using SMMC
In this section, a scenario of continuous weight monitoring is proposed. Figure 1 shows a visual rep-
resentation of SMMC, with a weighing sensor. The data from weight sensor is captured continuously.
Then, all data is aggregated through micro-controller by WIFI module and are stored at Cloud platform.
On the other hand, MQTT Machine-to-Machine protocol plays a significant role in IoT framework. It
can communicate with micro-controller and other IoT devices, which could be a control command to
micro-controller or a trigger to other IoT devices. Meanwhile, other IoT devices can communicate with
micro-controller directly using MQTT protocol. This example used ThingSpeak as an IoT Cloud platform
that stores, classifies and analyzes data. The following sections describe each component in the SMCC
architecture for this scenario.
Figure 1. SMMC Framework of IoT for Health Care
4.1 Sensor to Micro-controller
In terms of the SMMC IoT framework, an experiment would be verify the effectiveness of this architecture.
we first use prototype to measure weights, which includes weight load sensor and an amplifier HX711.
According to the requirement of the health care monitoring, real time analogue weight data would be read.
We used Arduino Uno as micro-controller because it can read real time data and convert analogue signals
to digital signals, as summarized in the pseudo-code below.
Input: Weights W
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães,Portugal, 2017 5
7. Ma et al. / IoT Architecture for Remote Healthcare Monitoring
Output: Weights W at Arduino
Method
1. Receive Analogue W from weight sensor
2. Convert Analogue W to Digital W
3. For accuracy, Average W after receiving five W.
4. Send W from ‘WeightSensor’ ⇒ Arduino
However, the standard Arduino Uno micro-controller does not have WIFI module. Either WIFI shield or
external WIFI module would be utilized to send Arduino data to be stored at Cloud. CC3300 WIFI Shield
is an Arduino shield which has CC3000 WIFI chipset developed by Texas Instruments. It uses SPI for
communication. It has no AP mode so that it can connect to access points but cannot be an access point.
Moreover, there is a library of CC3000 WIFI shield for Arduino. Therefore, Arduino Uno with CC3000
WIFI shield is used in the scenario to read and send weight data to Cloud in this experiment. Weights data
has to be encrypted before sending because weights data is private and personal information. In this case,
MD5 is to be used to encrypt data even though the data is not a data file.
4.2 Two Way Communication Between Micro-controller and Cloud Platform
For this scenario we used ThingSpeak, it is an IoT cloud platform that allows to collect, store, analyse,
visualize, and act on data from sensors or actuators in the cloud and develop IoT applications. Sensor
data can be sent to ThingSpeak from Arduino, Raspberry Pi, BeagleBone Black, and other hardware. The
primary element of ThingSpeak activity is the channel, which contains data fields, location fields, and a
status field. We installed ThingSpeak library from Arduino IDE and configure the parameters, such as
myChannelNumber and myWriteAPIKey variables. Next, we create a ThingSpeak channel and write data
to the channel. Finally, the weighs data is send to ThingSpeak periodically at configurable interval times.
The method is summarized in the pseudo-code below:
Input: Average Digital Weight W at Adruino
Output: Averae Digital Weight W at Cloud Platform
Method
1. Receive W
2. WIFI CONNECT
3. If WIFI is connected Then Set up HTTP
4. Send Encrypted data to Cloud
5. Else if WIFI Reconnect ⇒ Exit
4.3 Machine-to-Machine Protocol
The machine to machine protocol can be accessed via cloud or micro controller. For instance if the
anomaly in data is detected by the cloud application (line 4 in pseudo code in section 4.2) based on clinical
decision support algorithms, then it will trigger feedback or alert originating from the cloud. Figure 2
depicts a concept for setting up the escalation process visually for reminding relevant people who may
need this information. The medium for alerts can be text message, email or calls as well as turning on/off
sensors. For example, the weight of a patient dropped constantly over a week an alert is triggered to next
of kin. However another alternative is to detect these anomalies at the micro controller level and instantiate
a machine to machine request. For example the person wakes up at night, which turns on the LED lights.
MQTT is a Client/Server publish or subscribe messaging protocol on IoT health care system, and provides
an Arduino library to adapt. Specifically, MQTT client publish or subscribe message with other devices by
MQTT tool, and Arduino with WIFI module can receive message as well from publishing, which means
it can control Arduino. To use MQTT protocol transfering data, the MQTT and SMS services have been
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães,Portugal, 2017 6
8. Ma et al. / IoT Architecture for Remote Healthcare Monitoring
building in CentOS at Cloud platform as well in the experiment. The sms service is from Nexmo by sms
API and to alert users who are relevant to health care system. Figure 3 shows a free mobile app for MQTT
client, which includes subscribe and publish features. All clinical rules can be set up at decision support
web application on Cloud Server CentOS, where MQTT server, decision support web application and
other external services are installed. In the meanwhile, we use buzzer and LED light sensor for testing in
this scenario.
Figure 2. Concept of a Configurable Visual Escalation Process
Figure 3. MQTT Client Mobile Application
5 Conclusion and Future Work
This article focused on solutions using IoT for health care, and proposed a framework called SMMC. The
article also presented scenario which monitors weights and sends data to ThingSpeak and triggers alerts
via M2M and HTTP. The aim of this research is to improve patient experience when living independently,
while engaging relatives and caregivers through a low cost, commercial ‘app-cessory’ leveraging cloud
based services. However, this study was time consuming for seamless and consistent gathering the accurate
weight data. The future work involves testing this framework in an actual bed in AAL living environments,
as well as with pressure sensors, which would be measure movements. This testing will show the usability,
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães,Portugal, 2017 7
9. Ma et al. / IoT Architecture for Remote Healthcare Monitoring
utility and quality of such approach in the real world.
References
Basanta, H., Y.-P. Huang, and T.-T. Lee (2016). “Intuitive IoT-based H2U healthcare system for elderly
people.” In: 2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC).
IEEE, pp. 1–6.
Botta, A., W. de Donato, V. Persico, and A. Pescapé (2016). “Integration of cloud computing and internet
of things: a survey.” Future Generation Computer Systems 56, 684–700.
Chang, S.-H., R.-D. Chiang, S.-J. Wu, and W.-T. Chang (2016). “A Context-Aware, Interactive M-Health
System for Diabetics.” IT Professional 18 (3), 14–22.
Cheng, Y., C. Jiang, and J. Shi (2015). “A Fall Detection System based on SensorTag and Windows 10
IoT Core.”
Chuang, J., L. Maimoon, S. Yu, H. Zhu, C. Nybroe, O. Hsiao, S.-H. Li, H. Lu, and H. Chen (2015).
“SilverLink: Smart Home Health Monitoring for Senior Care.” In: International Conference on Smart
Health. Springer, pp. 3–14.
Fanucci, L., S. Saponara, T. Bacchillone, M. Donati, P. Barba, I. Sánchez-Tato, and C. Carmona (2013).
“Sensing devices and sensor signal processing for remote monitoring of vital signs in CHF patients.”
IEEE transactions on instrumentation and measurement 62 (3), 553–569.
Huang, S.-C., H.-Y. Chang, Y.-C. Jhu, and G.-Y. Chen (2014). “The intelligent pill box—Design and
implementation.” In: Consumer Electronics-Taiwan (ICCE-TW), 2014 IEEE International Conference
on. IEEE, pp. 235–236.
Jeon, B., J. Lee, and J. Choi (2013). “Design and implementation of a wearable ECG system.” International
Journal of Smart Home 7 (2), 61–69.
Mirza, F., A. Mirza, C. Y. S. Chung, and D. Sundaram (2016). “Sustainable, Holistic, Adaptable, Real-
Time, and Precise (SHARP) Approach Towards Developing Health and Wellness Systems.” In:
International Conference on Future Network Systems and Security. Springer, pp. 157–171.
Parida, M., H.-C. Yang, S.-W. Jheng, and C.-J. Kuo (2012). “Application of RFID Technology for
In-House Drug Management System.” In: 2012 15th International Conference on Network-Based
Information Systems. IEEE, pp. 577–581.
Santos, J., J. J. Rodrigues, B. M. Silva, J. Casal, K. Saleem, and V. Denisov (2016). “An IoT-based mobile
gateway for intelligent personal assistants on mobile health environments.” Journal of Network and
Computer Applications.
Al-Shaqi, R., M. Mourshed, and Y. Rezgui (2016). “Progress in ambient assisted systems for independent
living by the elderly.” SpringerPlus 5 (1), 1.
Suh, M.-k., C.-A. Chen, J. Woodbridge, M. K. Tu, J. I. Kim, A. Nahapetian, L. S. Evangelista, and M.
Sarrafzadeh (2011). “A remote patient monitoring system for congestive heart failure.” Journal of
medical systems 35 (5), 1165–1179.
Zubov, D. (2016). “An IoT Concept of the Small Virtual Power Plant Based on Arduino Platform and
MQTT Protocol.”
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães,Portugal, 2017 8