IoT Software Infrastructure for Remote Monitoring of Patients with Chronic Metabolic Disorders

Novel Information and Communication Technologies, such as Internet-of-Things (IoT), middleware and cloud computing, are providing innovative solutions ranging in different contexts. Smart health is one of these scenarios. Indeed, there is a rising interest in developing new healthcare services for remote patient assistance and monitoring. Among all, the main promised benefits consist on improving the patients’ quality of life, speeding up therapeutic interventions and reducing hospitalizations’ costs. This is also known as Telemedicine. In this paper, we present a novel distributed software infrastructure for remote monitoring of patients with chronic metabolic disorders: i) it collects and and makes available information coming from IoT devices, ii) it performs analysis to help medical diagnosis and iii) it promotes a bidirectional communication among the end-users (i.e. medical personnel and patients). In this paper, we also present our experimental results performed in a laboratory test environment to validate the proposed solution

Insomnia analysis based on internet of things using electrocardiography and electromyography

Insomnia is a disorder to start, maintain, and wake up from sleep, has many sufferers in the world. For patients in remote locations who suffer from insomnia, which requires testing, the gold standard performed requires patients to take the time and travel to the health care center. By making alternatives to remote sleep insomnia testing using electrocardiography and electromyography connected to the internet of things can solve the problem of patients' access to treatment. Delivery of patient data to the server is done to make observations from the visualization of patient data in real-time. Furthermore, using artificial neural networks was used to classify EMG, ECG, and combine patient data to determine patients who have Insomnia get resulted in patient classification errors around 0.2% to 2.7%

Modelling of Internet of Things (IoT) for Healthcare

Purpose: Information technology benefits the world, and it’s required for health care system, such as electronic medical records (EMR). We have proposed systematic model to study hoe IoT with 5g network has potential to benefit various healthcare services. For example, telemedicine may have some usage restrictions in rural areas and physicians may find it difficult to provide continuous monitoring to patients from such area. There are higher chances that the calls or video conferences getting significantly affected by poor networks and signals as well as non-compatible devices and patient may not get the treatment on time. 5G networking with IoT devices are believed to be the game changer for communication technology. The IoT model assists in attaining information by measuring its benefits through criteria which include 5G and IoT features along with a healthcare service requirement. Purpose of this paper is to present a model using Internet of Things (IoT) and 5G technology which will help to understand improved efficiency and efficacy of healthcare services. Our main research methodologies are literature review and modeling. The obtained results can be used for information technology applications in healthcare for various healthcare services and assist in increasing health quality in the healthcare industry.Method: Created a model to set the standard for incorporating 5G IoT devices health related technology and services. Reviewed through several models that serve as potential model to involve key factors that are unique certain healthcare services. We picked one model that can be easily incorporated in the system and can be revised to fit within the requirements using 5G IoT devices. Gathering of related literature served as a foundation in understanding the benefits of 5G IoT in the healthcare systems and parameters were pooled from it to revise the IoT model. Results: Incorporating 5G IoT features into a chosen model gave an overview of various determinants that can help understanding how IoT can influence any healthcare service and improve the quality of health. There are no rules and restrictions for use and utilization of this technology for health management yet in developing stage however, healthcare systems can rely on the 5G IoT devices for quality betterment. Conclusion: IoT with 5G has potential to improve healthcare management. The 5G world with an IoT will allow us to enter an era where real-time health services will become the part of the daily routine rather than the exception. However, further research needs to be done about its usage within any kind of specific health technology. Future research directions can utilize our model for other lesser known healthcare services

Smart vest for respiratory rate monitoring of COPD patients based on non-contact capacitive sensing

In this paper, a first approach to the design of a portable device for non-contact monitoring of respiratory rate by capacitive sensing is presented. The sensing system is integrated into a smart vest for an untethered, low-cost and comfortable breathing monitoring of Chronic Obstructive Pulmonary Disease (COPD) patients during the rest period between respiratory rehabilitation exercises at home. To provide an extensible solution to the remote monitoring using this sensor and other devices, the design and preliminary development of an e-Health platform based on the Internet of Medical Things (IoMT) paradigm is also presented. In order to validate the proposed solution, two quasi-experimental studies have been developed, comparing the estimations with respect to the golden standard. In a first study with healthy subjects, the mean value of the respiratory rate error, the standard deviation of the error and the correlation coefficient were 0.01 breaths per minute (bpm), 0.97 bpm and 0.995 (p < 0.00001), respectively. In a second study with COPD patients, the values were -0.14 bpm, 0.28 bpm and 0.9988 (p < 0.0000001), respectively. The results for the rest period show the technical and functional feasibility of the prototype and serve as a preliminary validation of the device for respiratory rate monitoring of patients with COPD.Ministerio de Ciencia e Innovación PI15/00306Ministerio de Ciencia e Innovación DTS15/00195Junta de Andalucía PI-0010-2013Junta de Andalucía PI-0041-2014Junta de Andalucía PIN-0394-201

The Role of the Internet of Things in Health Care: A Systematic and Comprehensive Study

The Internet of Things (IoT) is becoming an emerging trend and has significant potential to replace other technologies, where researchers consider it as the future of the internet. It has given tremendous support and become the building blocks in the development of important cyber-physical systems and it is being severed in a variety of application domains, including healthcare. A methodological evolution of the Internet of Things, enabled it to extend to the physical world beyond the electronic world by connecting miscellaneous devices through the internet, thus making everything is connected. In recent years it has gained higher attention for its potential to alleviate the strain on the healthcare sector caused by the rising and aging population along with the increase in chronic diseases and global pandemics. This paper surveys about various usages of IoT healthcare technologies and reviews the state of the art services and applications, recent trends in IoT based healthcare solutions, and various challenges posed including security and privacy issues, which researchers, service providers and end users need to pay higher attention. Further, this paper discusses how innovative IoT enabled technologies like cloud computing, fog computing, blockchain, and big data can be used to leverage modern healthcare facilities and mitigate the burden on healthcare resources

Information and communication technology-based interventions for chronic diseases consultation: Scoping review

Background: Medical consultations are often critical meetings between patients and health personnel to provide treatment, health-management advice, and exchange of information, especially for people living with chronic diseases. The adoption of patient-operated Information and Communication Technologies (ICTs) allows the patients to actively participate in their consultation and treatment. The consultation can be divided into three different phases: before, during, and after the meeting. The difference is identified by the activities in preparation (before), the meeting, conducted either physically or in other forms of non-face-to-face interaction (during), and the follow-up activities after the meeting (after). Consultations can be supported by various ICT-based interventions, often referred to as eHealth, mHealth, telehealth, or telemedicine. Nevertheless, the use of ICTs in healthcare settings is often accompanied by security and privacy challenges due to the sensitive nature of health information and the regulatory requirements associated with storing and processing sensitive information. Objective: This scoping review aims to map the existing knowledge and identify gaps in research about ICT-based interventions for chronic diseases consultations. The review objective is guided by three research questions: (1) which ICTs are used by people with chronic diseases, health personnel, and others before, during, and after consultations; (2) which type of information is managed by these ICTs; and (3) how are security and privacy issues addressed? Methods: We performed a literature search in ACM, IEEE, PubMed, Scopus, and Web of Science and included primary studies published between January 2015 and June 2020 that used ICT before, during, and/or after a consultation for chronic diseases. This review presents and discusses the findings from the included publications structured around the three research questions. Results: Twenty-four studies met the inclusion criteria. Only five studies reported the use of ICTs in all three phases: before, during, and after consultations. The main ICTs identified were smartphone applications, webbased portals, cloud-based infrastructures, and electronic health record systems. Different devices like sensors and wearable devices were used in 23 studies to gather diverse information. Regarding the type of information managed by these ICTs, we identified nine categories: physiological data, treatment information, medical history, consultation media like images or videos, laboratory results, reminders, lifestyle parameters, symptoms, and patient identification. Security issues were addressed in 20 studies, while only eight of the included studies addressed privacy issues. Conclusions: This scoping review highlights the potential for a new model of consultation for patients with chronic diseases. Furthermore, it emphasizes the possibilities for consultations besides physical and remote meetings

Intelligent IoT Framework for Indoor Healthcare Monitoring of Parkinson's Disease Patient

Parkinson’s disease is associated with high treatment costs, primarily attributed to the needs of hospitalization and frequent care services. A study reveals annual per-person healthcare costs for Parkinson’s patients to be 21,482,withanadditional29,695 burden to society. Due to the high stakes and rapidly rising Parkinson’s patients’ count, it is imperative to introduce intelligent monitoring and analysis systems. In this paper, an Internet of Things (IoT) based framework is proposed to enable remote monitoring, administration, and analysis of patient’s conditions in a typical indoor environment. The proposed infrastructure offers both static and dynamic routing, along with delay analysis and priority enabled communications. The scheme also introduces machine learning techniques to detect the progression of Parkinson’s over six months using auditory inputs. The proposed IoT infrastructure and machine learning algorithm are thoroughly evaluated and a detailed analysis is performed. The results show that the proposed scheme offers efficient communication scheduling, facilitating a high number of users with low latency. The proposed machine learning scheme also outperforms state-of-the-art techniques in accurately predicting the Parkinson’s progression

Applications of the Internet of Medical Things to Type 1 Diabetes Mellitus

Type 1 Diabetes Mellitus (DM1) is a condition of the metabolism typified by persistent hyperglycemia as a result of insufficient pancreatic insulin synthesis. This requires patients to be aware of their blood glucose level oscillations every day to deduce a pattern and anticipate future glycemia, and hence, decide the amount of insulin that must be exogenously injected to maintain glycemia within the target range. This approach often suffers from a relatively high imprecision, which can be dangerous. Nevertheless, current developments in Information and Communication Technologies (ICT) and innovative sensors for biological signals that might enable a continuous, complete assessment of the patient’s health provide a fresh viewpoint on treating DM1. With this, we observe that current biomonitoring devices and Continuous Glucose Monitoring (CGM) units can easily obtain data that allow us to know at all times the state of glycemia and other variables that influence its oscillations. A complete review has been made of the variables that influence glycemia in a T1DM patient and that can be measured by the above means. The communications systems necessary to transfer the information collected to a more powerful computational environment, which can adequately handle the amounts of data collected, have also been described. From this point, intelligent data analysis extracts knowledge from the data and allows predictions to be made in order to anticipate risk situations. With all of the above, it is necessary to build a holistic proposal that allows the complete and smart management of T1DM. This approach evaluates a potential shortage of such suggestions and the obstacles that future intelligent IoMT-DM1 management systems must surmount. Lastly, we provide an outline of a comprehensive IoMT-based proposal for DM1 management that aims to address the limits of prior studies while also using the disruptive technologies highlighted beforePartial funding for open access charge: Universidad de Málag

Heart failure patients monitoring using IoT-based remote monitoring system

Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana