Towards fog-driven IoT eHealth:Promises and challenges of IoT in medicine and healthcare

Internet of Things (IoT) offers a seamless platform to connect people and objects to one another for enriching and making our lives easier. This vision carries us from compute-based centralized schemes to a more distributed environment offering a vast amount of applications such as smart wearables, smart home, smart mobility, and smart cities. In this paper we discuss applicability of IoT in healthcare and medicine by presenting a holistic architecture of IoT eHealth ecosystem. Healthcare is becoming increasingly difficult to manage due to insufficient and less effective healthcare services to meet the increasing demands of rising aging population with chronic diseases. We propose that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other. This patient-centric IoT eHealth ecosystem needs a multi-layer architecture: (1) device, (2) fog computing and (3) cloud to empower handling of complex data in terms of its variety, speed, and latency. This fog-driven IoT architecture is followed by various case examples of services and applications that are implemented on those layers. Those examples range from mobile health, assisted living, e-medicine, implants, early warning systems, to population monitoring in smart cities. We then finally address the challenges of IoT eHealth such as data management, scalability, regulations, interoperability, device–network–human interfaces, security, and privacy

An overview of internet of things

The internet of things is an emerging technology that is currently present in most processes and devices, allowing to improve the quality of life of people and facilitating the access to specific information and services. The main purpose of the present article is to offer a general overview of internet of things, based on the analysis of recently published work. The added value of this article lies in the analysis of the main recent publications and the diversity of applications of internet of things technology. As a result of the analysis of the current literature, internet of things technology stands out as a facilitator in business and industrial performance but above all in improving the quality of life. As a conclusion to this document, the internet of things is a technology that can overcome the challenges in terms of security, processing capacity and data mobility, as long as the development related to other technologies follows its expected course

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

IOT Service Utilisation in Healthcare

Utilising the new trend technologies in healthcare sector could offer alternative ways in managing the patients’ health records and also improve the healthcare quality. As such, this chapter provides an overview of utilising the Internet of Things (IoT) technology in healthcare sector as an emerging research and practical trend nowadays. The main benefits and advantages have been discussed in this chapter. On the other hand, it has been found that most of the hospitals in different countries are still facing many issues regarding their health information exchange. Recently, various studies in the area of healthcare information system mentioned that the fragmentations of the health information are one of the most important challenges with the distribution of patient information records. Therefore, in this chapter, we gave an in detail overview regarding the current issues facing the health sector in line with the IoT technologies. Additionally, a full description of advantages and disadvantages has been highlighted for using IoT in healthcare that can be considered as solutions for the mentioned issues

No soldiers left behind: An IoT-based low-power military mobile health system design

© 2013 IEEE. There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-Time actuation of IoT equipment, and activation of emergency alarms through the inference of a user\u27s situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to conserve battery power for sensors and equipment which transmit data to a central server. An inference system can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption, however this could result in compromising accuracy. This paper presents a framework for secure automated messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the battery power of devices such as wearables and sensor devices. The results for this system showed a data reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods. Authentication accuracy can be further enhanced with additional biometrics and health data information

Personalized data analytics for internet-of-things-based health monitoring

The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months

Radio Frequency Identification (RFID) in health care: where are we? A scoping review

Purpose (RFID) is a technology that uses radio waves for data collection and transfer, so data is captured efficiently, automatically and in real time without human intervention. This technology, alone or in addition to other technologies has been considered as a possible solution to reduce problems that endanger public health or to improve its management. This scoping review aims to provide readers with an up-to-date picture of the use of this technology in health care settings. Methods This scoping review examines the state of RFID technology in the healthcare area for the period 2017-2022, specifically addressing RFID versatility and investigating how this technology can contribute to radically change the management of public health. The guidelines of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) have been followed. Literature reviews or surveys were excluded. Only articles describing technologies implemented on a real environment or on prototypes were included. Results The search returned 366 results. After screening, based on title and abstract, 58 articles were considered suitable for this work. 11 articles were reviewed because they met the qualifying requirements. The study of the selected articles highlighted six matters that can be profitably impacted by this technology Conclusion The selected papers show that this technology can improve patient safety by reducing medical errors, that can occur within operating rooms. It can also be the solution to overcome the problem of the black market in counterfeiting drugs, or as a prevention tool. Further research is needed, especially on data management, security, and privacy, given the sensitive nature of medical information

Data processing of physiological sensor data and alarm determination utilising activity recognition

Current physiological sensors are passive and transmit sensed data to Monitoring centre (MC) through wireless body area network (WBAN) without processing data intelligently. We propose a solution to discern data requestors for prioritising and inferring data to reduce transactions and conserve battery power, which is important requirements of mobile health (mHealth). However, there is a problem for alarm determination without knowing the activity of the user. For example, 170 beats per minute of heart rate can be normal during exercising, however an alarm should be raised if this figure has been sensed during sleep. To solve this problem, we suggest utilising the existing activity recognition (AR) applications. Most of health related wearable devices include accelerometers along with physiological sensors. This paper presents a novel approach and solution to utilise physiological data with AR so that they can provide not only improved and efficient services such as alarm determination but also provide richer health information which may provide content for new markets as well as additional application services such as converged mobile health with aged care services. This has been verified by experimented tests using vital signs such as heart pulse rate, respiration rate and body temperature with a demonstrated outcome of AR accelerometer sensors integrated with an Android app

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain