11,522 research outputs found
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
Artificial neural network (ANN) enabled internet of things (IoT) architecture for music therapy
Alternative medicine techniques such as music therapy have been a recent interest of medical practitioners and researchers. Significant clinical evidence suggests that music has a positive influence over pain, stress and anxiety for the patients of cancer, pre and post surgery, insomnia, child birth, end of life care, etc. Similarly, the technologies of Internet of Things (IoT), Body Area Networks (BAN) and Artificial Neural Networks (ANN) have been playing a vital role to improve the health and safety of the population through offering continuous remote monitoring facilities and immediate medical response. In this article, we propose a novel ANN enabled IoT architecture to integrate music therapy with BAN and ANN for providing immediate assistance to patients by automating the process of music therapy. The proposed architecture comprises of monitoring the body parameters of patients using BAN, categorizing the disease using ANN and playing music of the most appropriate type over the patient’s handheld device, when required. In addition, the ANN will also exploit Music Analytics such as the type and duration of music played and its impact over patient’s body parameters to iteratively improve the process of automated music therapy. We detail development of a prototype Android app which builds a playlist and plays music according to the emotional state of the user, in real time. Data for pulse rate, blood pressure and breath rate has been generated using Node-Red, and ANN has been created using Google Colaboratory (Colab). MQTT broker has been used to send generated data to Android device. The ANN uses binary and categorical cross-entropy loss functions, Adam optimiser and ReLU activation function to predict the mood of patient and suggest the most appropriate type of music
Revolutionizing Healthcare through Health Monitoring Applications with Wearable Biomedical Devices
The Internet of Things (IoT) has revolutionized the connectivity and communication of tangible objects, and it serves as a versatile and cost-effective solution in the healthcare sector, particularly in regions with limited healthcare infrastructure. This research explores the application of sensors such as LM35, AD8232, and MAX30100 for the detection of vital health indicators, including body temperature, pulse rate, electrocardiogram (ECG), and oxygen saturation levels, with data transmission through IoT cloud, offering real-time parameter access via an Android application for non-invasive remote patient monitoring. The study aims to expand healthcare services to various settings, such as hospitals, commercial areas, educational institutions, workplaces, and residential neighborhoods. After the COVID-19 pandemic, IoT-enabled continuous monitoring of critical health metrics such as temperature and pulse rate has become increasingly crucial for early illness detection and efficient communication with healthcare providers. Our low-cost wearable device, which includes ECG monitoring, aims to bridge the accessibility gap for people with limited financial resources, with the primary goal of providing efficient healthcare solutions to underserved rural areas while also contributing valuable data to future medical research. Our proposed system is a low-cost, high-efficiency solution that outperforms existing systems in healthcare data collection and patient monitoring. It improves access to vital health data and shows economic benefits, indicating a significant advancement in healthcare technology
Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review
Animals play a profoundly important and intricate role in our lives today.
Dogs have been human companions for thousands of years, but they now work
closely with us to assist the disabled, and in combat and search and rescue
situations. Farm animals are a critical part of the global food supply chain,
and there is increasing consumer interest in organically fed and humanely
raised livestock, and how it impacts our health and environmental footprint.
Wild animals are threatened with extinction by human induced factors, and
shrinking and compromised habitat. This review sets the goal to systematically
survey the existing literature in smart computing and sensing technologies for
domestic, farm and wild animal welfare. We use the notion of \emph{animal
welfare} in broad terms, to review the technologies for assessing whether
animals are healthy, free of pain and suffering, and also positively stimulated
in their environment. Also the notion of \emph{smart computing and sensing} is
used in broad terms, to refer to computing and sensing systems that are not
isolated but interconnected with communication networks, and capable of remote
data collection, processing, exchange and analysis. We review smart
technologies for domestic animals, indoor and outdoor animal farming, as well
as animals in the wild and zoos. The findings of this review are expected to
motivate future research and contribute to data, information and communication
management as well as policy for animal welfare
Devices and Data Workflow in COPD Wearable Remote Patient Monitoring: A Systematic Review
Background: With global increase in Chronic Obstructive Pulmonary Disease (COPD)
prevalence and mortality rates, and socioeconomical burden continuing to rise, current
disease management strategies appear inadequate, paving the way for technological
solutions, namely remote patient monitoring (RPM), adoption considering its acute disease
events management benefit. One RPM’s category stands out, wearable devices, due to its
availability and apparent ease of use.
Objectives: To assess the current market and interventional solutions regarding wearable
devices in the remote monitoring of COPD patients through a systematic review design from
a device composition, data workflow, and collected parameters description standpoint.
Methods: A systematic review was conducted to identify wearable device trends in this
population through the development of a comprehensive search strategy, searching beyond
the mainstream databases, and aggregating diverse information found regarding the same
device. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis
(PRISMA) guidelines were followed, and quality appraisal of identified studies was
performed using the Critical Appraisal Skills Programme (CASP) quality appraisal
checklists.
Results: The review resulted on the identification of 1590 references, of which a final 79
were included. 56 wearable devices were analysed, with the slight majority belonging to the
wellness devices class. Substantial device heterogeneity was identified regarding device
composition type and wearing location, and data workflow regarding 4 considered
components. Clinical monitoring devices are starting to gain relevance in the market and
slightly over a third, aim to assist COPD patients and healthcare professionals in
exacerbation prediction. Compliance with validated recommendations is still lacking, with
no devices assessing the totality of recommended vital signs.
Conclusions: The identified heterogeneity, despite expected considering the relative
novelty of wearable devices, alerts for the need to regulate the development and research of
these technologies, specially from a structural and data collection and transmission
standpoints.Introdução: Com o aumento global das taxas de prevalência e mortalidade da Doença
Pulmonar Obstrutiva Crónica (DPOC) e o seu impacto socioeconómico, as atuais estratégias
de gestão da doença parecem inadequadas, abrindo caminho para soluções tecnológicas,
nomeadamente para a adoção da monitorização remota, tendo em conta o seu benefício na
gestão de exacerbações de doenças crónicas. Dentro destaca-se uma categoria, os
dispositivos wearable, pela sua disponibilidade e aparente facilidade de uso.
Objetivos: Avaliar as soluções existentes, tanto no mercado, como na área de investigação,
relativas a dispositivos wearable utilizados na monitorização remota de pacientes com
DPOC através de uma revisão sistemática, do ponto de vista da composição do dispositivo,
fluxo de dados e descrição dos parâmetros coletados.
Métodos: Uma revisão sistemática foi realizada para identificar tendências destes
dispositivos, através do desenvolvimento de uma estratégia de pesquisa abrangente,
procurando pesquisar para além das databases convencionais e agregar diversas
informações encontradas sobre o mesmo dispositivo. Para tal, foram seguidas as diretrizes
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), e a
avaliação da qualidade dos estudos identificados foi realizada utilizando a ferramenta CASP
(Critical Appraisal Skills Programme).
Resultados: A revisão resultou na identificação de 1590 referências, das quais 79 foram
incluídas. Foram analisados 56 dispositivos wearable, com a ligeira maioria a pertencer à
classe de dispositivos de wellness. Foi identificada heterogeneidade substancial nos
dispositivos em relação à sua composição, local de uso e ao fluxo de dados em relação a 4
componentes considerados. Os dispositivos de monitorização clínica já evidenciam alguma
relevância no mercado e, pouco mais de um terço, visam auxiliar pacientes com DPOC e
profissionais de saúde na previsão de exacerbações. Ainda assim, é notória a falta do
cumprimento das recomendações validadas, não estando disponíveis dispositivos que
avaliem a totalidade dos sinais vitais recomendados.
Conclusão: A heterogeneidade identificada, apesar de esperada face à relativa novidade
dos dispositivos wearable, alerta para a necessidade de regulamentação do
desenvolvimento e investigação destas tecnologias, especialmente do ponto de vista
estrutural e de recolha e transmissão de dados
Self-adjustable domain adaptation in personalized ECG monitoring integrated with IR-UWB radar
To enhance electrocardiogram (ECG) monitoring systems in personalized detections, deep neural networks (DNNs) are applied to overcome individual differences by periodical retraining. As introduced previously [4], DNNs relieve individual differences by fusing ECG with impulse radio ultra-wide band (IR-UWB) radar. However, such DNN-based ECG monitoring system tends to overfit into personal small datasets and is difficult to generalize to newly collected unlabeled data. This paper proposes a self-adjustable domain adaptation (SADA) strategy to prevent from overfitting and exploit unlabeled data. Firstly, this paper enlarges the database of ECG and radar data with actual records acquired from 28 testers and expanded by the data augmentation. Secondly, to utilize unlabeled data, SADA combines self organizing maps with the transfer learning in predicting labels. Thirdly, SADA integrates the one-class classification with domain adaptation algorithms to reduce overfitting. Based on our enlarged database and standard databases, a large dataset of 73200 records and a small one of 1849 records are built up to verify our proposal. Results show SADA\u27s effectiveness in predicting labels and increments in the sensitivity of DNNs by 14.4% compared with existing domain adaptation algorithms
ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALTH
ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALT
Postoperative Remote Automated Monitoring:Need for and State of the Science
Worldwide, more than 230 million adults have major noncardiac surgery each year. Although surgery can improve quality and duration of life, it can also precipitate major complications. Moreover, a substantial proportion of deaths occur after discharge. Current systems for monitoring patients postoperatively, on surgical wards and after transition to home, are inadequate. On the surgical ward, vital signs evaluation usually occurs only every 4-8 hours. Reduced in-hospital ward monitoring, followed by no vital signs monitoring at home, leads to thousands of cases of undetected/delayed detection of hemodynamic compromise. In this article we review work to date on postoperative remote automated monitoring on surgical wards and strategy for advancing this field. Key considerations for overcoming current barriers to implementing remote automated monitoring in Canada are also presented
Cuff-Less Methods for Blood Pressure Telemonitoring.
Blood pressure telemonitoring (BPT) is a telemedicine strategy that uses a patient\u27s self-measured blood pressure (BP) and transmits this information to healthcare providers, typically over the internet. BPT has been shown to improve BP control compared to usual care without remote monitoring. Traditionally, a cuff-based monitor with data communication capabilities has been used for BPT; however, cuff-based measurements are inconvenient and cause discomfort, which has prevented the widespread use of cuff-based monitors for BPT. The development of new technologies which allow for remote BP monitoring without the use of a cuff may aid in more extensive adoption of BPT. This would enhance patient autonomy while providing physicians with a more complete picture of their patient\u27s BP profile, potentially leading to improved BP control and better long-term clinical outcomes. This mini-review article aims to: (1) describe the fundamentals of current techniques in cuff-less BP measurement; (2) present examples of commercially available cuff-less technologies for BPT; (3) outline challenges with current methodologies; and (4) describe potential future directions in cuff-less BPT development
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