4,880 research outputs found
IOT BASED HEALTH MONITORING SYSTEM
Health monitoring is a major issue in today’s world. Due to the lack of health monitoring, patientssuffer from serious health problems. Health experts are also taking advantage of these smart devices to keepan eye on their projects. Here in this project, we will make an IOT based health monitoring system whichrecords the patient heartbeat rate, body temperature and skin pressure. Heartbeat rate, body temperatureand skin pressure values are recorded over thingspeak and Google sheets so that patient health can bemonitored from anywhere in the world over the internet. We will use Thinspeak to monitor patient heartbeat,temperature and skin pressure online using internet. We will also use IFTTT platform to connect thingspeakto SMS so that alert message can be sent whenever the patient is in critical state
Comparison of Collaborative and Cooperative Schemes in Sensor Networks for Non-Invasive Monitoring of People at Home
This paper looks at wireless sensor networks (WSNs) in healthcare, where they can monitor patients remotely. WSNs are considered one of the most promising technologies due to their flexibility and autonomy in communication. However, routing protocols in WSNs must be energy-efficient, with a minimal quality of service, so as not to compromise patient care. The main objective of this work is to compare two work schemes in the routing protocol algorithm in WSNs (cooperative and collaborative) in a home environment for monitoring the conditions of the elderly. The study aims to optimize the performance of the algorithm and the ease of use for people while analyzing the impact of the sensor network on the analysis of vital signs daily using medical equipment. We found relationships between vital sign metrics that have a more significant impact in the presence of a monitoring system. Finally, we conduct a performance analysis of both schemes proposed for the home tracking application and study their usability from the user’s point of view
Unobtrusive Health Monitoring in Private Spaces: The Smart Home
With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking
Algorithms design for improving homecare using Electrocardiogram (ECG) signals and Internet of Things (IoT)
Due to the fast growing of population, a lot of hospitals get crowded from the huge amount of
patients visits. Moreover, during COVID-19 a lot of patients prefer staying at home to minimize
the spread of the virus. The need for providing care to patients at home is essential. Internet
of Things (IoT) is widely known and used by different fields. IoT based homecare will help
in reducing the burden upon hospitals. IoT with homecare bring up several benefits such as
minimizing human exertions, economical savings and improved efficiency and effectiveness. One
of the important requirement on homecare system is the accuracy because those systems are
dealing with human health which is sensitive and need high amount of accuracy. Moreover,
those systems deal with huge amount of data due to the continues sensing that need to be
processed well to provide fast response regarding the diagnosis with minimum cost requirements.
Heart is one of the most important organ in the human body that requires high level of caring.
Monitoring heart status can diagnose disease from the early stage and find the best medication
plan by health experts. Continues monitoring and diagnosis of heart could exhaust caregivers
efforts. Having an IoT heart monitoring model at home is the solution to this problem. Electrocardiogram
(ECG) signals are used to track heart condition using waves and peaks. Accurate
and efficient IoT ECG monitoring at home can detect heart diseases and save human lives.
As a consequence, an IoT ECG homecare monitoring model is designed in this thesis for detecting
Cardiac Arrhythmia and diagnosing heart diseases. Two databases of ECG signals are used;
one online which is old and limited, and another huge, unique and special from real patients
in hospital. The raw ECG signal for each patient is passed through the implemented Low
Pass filter and Savitzky Golay filter signal processing techniques to remove the noise and any
external interference. The clear signal in this model is passed through feature extraction stage
to extract number of features based on some metrics and medical information along with feature extraction algorithm to find peaks and waves. Those features are saved in the local database to
apply classification on them. For the diagnosis purpose a classification stage is made using three
classification ways; threshold values, machine learning and deep learning to increase the accuracy.
Threshold values classification technique worked based on medical values and boarder lines. In
case any feature goes above or beyond these ranges, a warning message appeared with expected
heart disease. The second type of classification is by using machine learning to minimize the
human efforts. A Support Vector Machine (SVM) algorithm is proposed by running the algorithm
on the features extracted from both databases. The classification accuracy for online and hospital
databases was 91.67% and 94% respectively. Due to the non-linearity of the decision boundary, a
third way of classification using deep learning is presented. A full Multilayer Perceptron (MLP)
Neural Network is implemented to improve the accuracy and reduce the errors. The number of
errors reduced to 0.019 and 0.006 using online and hospital databases.
While using hospital database which is huge, there is a need for a technique to reduce the amount
of data. Furthermore, a novel adaptive amplitude threshold compression algorithm is proposed.
This algorithm is able to make diagnosis of heart disease from the reduced size using compressed
ECG signals with high level of accuracy and low cost. The extracted features from compressed
and original are similar with only slight differences of 1%, 2% and 3% with no effects on machine
learning and deep learning classification accuracy without the need for any reconstructions. The
throughput is improved by 43% with reduced storage space of 57% when using data compression.
Moreover, to achieve fast response, the amount of data should be reduced further to provide
fast data transmission. A compressive sensing based cardiac homecare system is presented.
It gives the channel between sender and receiver the ability to carry small amount of data.
Experiment results reveal that the proposed models are more accurate in the classification of
Cardiac Arrhythmia and in the diagnosis of heart diseases. The proposed models ensure fast
diagnosis and minimum cost requirements. Based on the experiments on classification accuracy,
number of errors and false alarms, the dictionary of the compressive sensing selected to be 900.
As a result, this thesis provided three different scenarios that achieved IoT homecare Cardiac
monitoring to assist in further research for designing homecare Cardiac monitoring systems. The experiment results reveal that those scenarios produced better results with high level of accuracy
in addition to minimizing data and cost requirements
East Midlands Research into Ageing Network (EMRAN) Discussion Paper Series
Academic geriatric medicine in Leicester
.
There has never been a better time to consider joining us. We have recently appointed a
Professor in Geriatric Medicine, alongside Tom Robinson in stroke and Victoria Haunton,
who has just joined as a Senior Lecturer in Geriatric Medicine. We have fantastic
opportunities to support students in their academic pursuits through a well-established
intercalated BSc programme, and routes on through such as ACF posts, and a successful
track-record in delivering higher degrees leading to ACL post. We collaborate strongly
with Health Sciences, including academic primary care. See below for more detail on our
existing academic set-up.
Leicester Academy for the Study of Ageing
We are also collaborating on a grander scale, through a joint academic venture focusing
on ageing, the ‘Leicester Academy for the Study of Ageing’ (LASA), which involves the
local health service providers (acute and community), De Montfort University; University
of Leicester; Leicester City Council; Leicestershire County Council and Leicester Age UK.
Professors Jayne Brown and Simon Conroy jointly Chair LASA and have recently been
joined by two further Chairs, Professors Kay de Vries and Bertha Ochieng. Karen
Harrison Dening has also recently been appointed an Honorary Chair.
LASA aims to improve outcomes for older people and those that care for them that takes
a person-centred, whole system perspective. Our research will take a global perspective,
but will seek to maximise benefits for the people of Leicester, Leicestershire and Rutland,
including building capacity. We are undertaking applied, translational, interdisciplinary
research, focused on older people, which will deliver research outcomes that address
domains from: physical/medical; functional ability, cognitive/psychological; social or
environmental factors. LASA also seeks to support commissioners and providers alike for
advice on how to improve care for older people, whether by research, education or
service delivery. Examples of recent research projects include: ‘Local History Café’
project specifically undertaking an evaluation on loneliness and social isolation; ‘Better
Visits’ project focused on improving visiting for family members of people with dementia
resident in care homes; and a study on health issues for older LGBT people in Leicester.
Clinical Geriatric Medicine in Leicester
We have developed a service which recognises the complexity of managing frail older
people at the interface (acute care, emergency care and links with community services).
There are presently 17 consultant geriatricians supported by existing multidisciplinary
teams, including the largest complement of Advance Nurse Practitioners in the country.
Together we deliver Comprehensive Geriatric Assessment to frail older people with
urgent care needs in acute and community settings.
The acute and emergency frailty units – Leicester Royal Infirmary
This development aims at delivering Comprehensive Geriatric Assessment to frail older
people in the acute setting. Patients are screened for frailty in the Emergency
Department and then undergo a multidisciplinary assessment including a consultant
geriatrician, before being triaged to the most appropriate setting. This might include
admission to in-patient care in the acute or community setting, intermediate care
(residential or home based), or occasionally other specialist care (e.g. cardiorespiratory).
Our new emergency department is the county’s first frail friendly build and includes
fantastic facilities aimed at promoting early recovering and reducing the risk of hospital
associated harms.
There is also a daily liaison service jointly run with the psychogeriatricians (FOPAL); we
have been examining geriatric outreach to oncology and surgery as part of an NIHR
funded study.
We are home to the Acute Frailty Network, and those interested in service developments
at the national scale would be welcome to get involved.
Orthogeriatrics
There are now dedicated hip fracture wards and joint care with anaesthetists,
orthopaedic surgeons and geriatricians. There are also consultants in metabolic bone
disease that run clinics.
Community work
Community work will consist of reviewing patients in clinic who have been triaged to
return to the community setting following an acute assessment described above.
Additionally, primary care colleagues refer to outpatients for sub-acute reviews. You will
work closely with local GPs with support from consultants to deliver post-acute, subacute,
intermediate and rehabilitation care services.
Stroke Medicine
24/7 thrombolysis and TIA services. The latter is considered one of the best in the UK
and along with the high standard of vascular surgery locally means one of the best
performances regarding carotid intervention
Proceeding: 3rd Java International Nursing Conference 2015 “Harmony of Caring and Healing Inquiry for Holistic Nursing Practice; Enhancing Quality of Care”, Semarang, 20-21 August 2015
This is the proceeding of the 3rd Java International Nursing Conference 2015 organized by School of Nursing, Faculty of Medicine, Diponegoro University, in collaboration with STIKES Kendal. The conference was held on 20-21 August 2015 in Semarang, Indonesia.
The conference aims to enable educators, students, practitioners and researchers from nursing, medicine, midwifery and other health sciences to disseminate and discuss evidence of nursing education, research, and practices to improve the quality of care. This conference also provides participants opportunities to develop their professional networks, learn from other colleagues and meet leading personalities in nursing and health sciences.
The 3rd JINC 2015 was comprised of keynote lectures and concurrent submitted oral presentations and poster sessions.
The following themes have been chosen to be the focus of the conference: (a) Multicenter Science: Physiology, Biology, Chemistry, etc. in Holistic Nursing Practice, (b) Complementary Therapy in Nursing and Complementary, Alternative Medicine: Alternative Medicine (Herbal Medicine), Complementary Therapy (Cupping, Acupuncture, Yoga, Aromatherapy, Music Therapy, etc.), (c) Application of Inter-professional Collaboration and Education: Education Development in Holistic Nursing, Competencies of Holistic Nursing, Learning Methods and Assessments, and (d) Application of Holistic Nursing: Leadership & Management, Entrepreneurship in Holistic Nursing, Application of Holistic Nursing in Clinical and Community Settings
Wearable technology: role in respiratory health and disease
In the future, diagnostic devices will be able to monitor a patient's physiological or biochemical parameters continuously, under natural physiological conditions and in any environment through wearable biomedical sensors. Together with apps that capture and interpret data, and integrated enterprise and cloud data repositories, the networks of wearable devices and body area networks will constitute the healthcare's Internet of Things. In this review, four main areas of interest for respiratory healthcare are described: pulse oximetry, pulmonary ventilation, activity tracking and air quality assessment. Although several issues still need to be solved, smart wearable technologies will provide unique opportunities for the future or personalised respiratory medicine
Smart home technology for aging
The majority of the growing population, in the US and the rest of the world requires some degree of formal and or informal care either due to the loss of function or failing health as a result of aging and most of them suffer from chronic disorders. The cost and burden of caring for elders is steadily increasing. This thesis focuses on providing the analysis of the technologies with which a Smart Home is built to improve the quality of life of the elderly. A great deal of emphasis is given to the sensor technologies that are the back bone of these Smart Homes. In addition to the Analysis of these technologies a survey of commercial sensor products and products in research that are concerned with monitoring the health of the occupants of the Smart Home is presented. A brief analysis on the communication technologies which form the communication infrastructure for the Smart Home is also illustrated. Finally, System Architecture for the Smart Home is proposed describing the functionality and users of the system. The feasibility of the system is also discussed. A scenario measuring the blood glucose level of the occupant in a Smart Home is presented as to support the system architecture presented
Internet of Things (IoT) for Automated and Smart Applications
Internet of Things (IoT) is a recent technology paradigm that creates a global network of machines and devices that are capable of communicating with each other. Security cameras, sensors, vehicles, buildings, and software are examples of devices that can exchange data between each other. IoT is recognized as one of the most important areas of future technologies and is gaining vast recognition in a wide range of applications and fields related to smart homes and cities, military, education, hospitals, homeland security systems, transportation and autonomous connected cars, agriculture, intelligent shopping systems, and other modern technologies. This book explores the most important IoT automated and smart applications to help the reader understand the principle of using IoT in such applications
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