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

An Ontology-Based Approach in Personalization of the e-Learning System

The emergence of the Semantic Web and its technologies have opened the way, over the last decade, for the development of ontologies and systems that use ontologies in various fields, including e-learning. This article presents elements that underpin the development of an e-learning system in the area of the Human Resource Management in the field of ontology health, respectively basic notions about the semantic Web, ontologies, personalization in e-learning. The article presents a personalized e-learning environment that uses new technologies, semantic Web and ontologies to improve the human resource management training process, being addressed to hospital managers. The necessity of this approach is given by the training requirements in the field of human resources management for the specialists from the medical system in Romania, as well as by the need to solve current limitations of the e-learning systems. The implementation of the concept of personalization of learning in the e-learning system is performed starting from the student model built to determine the level of knowledge and the objectives of training. Modeling the student profile using ontologies has demonstrated the possibility of re-using the models, the detailed and complete representation of the student’s knowledge as well as the reasoning process. This learning program aims to increase the performance, skills and competence of health managers, by deepening knowledge in the field of public health, healthcare management, and human resource management

Smart Pain Assessment tool for critically ill patients unable to communicate: Early stage development of a medical device

Critically ill patients often experience pain during their treatment but due to patients’ lowered ability to communicate, pain assessment may be challenging. The aim of the study was to develop the concept of the Smart Pain Assessment tool based on the Internet of Things technology for critically ill patients who are unable to communicate their pain. The study describes two phases of the early stage development of the Smart Pain Assessment tool in a medical device development framework. The initiation Phase I consists of a scoping review, conducted to explore the potentiality of the Internet of Things technology in basic nursing care. In the formulation Phase II, the prototype of the Smart Pain Assessment tool was tested and the concept was evaluated for feasibility. The prototype was tested with healthy participants (n=31) during experimental pain, measuring pain-related physiological variables and activity of five facial muscles. The variables were combined using machine learning to create a model for pain prediction. The feasibility of the concept was evaluated in focus group interviews with critical care nurses (n=20) as potential users of the device. The literature review suggests that the development of Internet of Things -based innovations in basic nursing care is diverse but still in its early stages. The prototype was able to identify experimental pain and classify its intensity as mild or moderate/severe with 83% accuracy. In addition, three of the five facial muscles tested were recognised to provide the most pain-related information. According to critical care nurses, the Smart Pain Assessment tool could be used to ensure pain assessment, but it needs to be integrated into an existing patient monitoring and information system, and the reliability of the data provided by the device needs to be assessable for nurses. Based on the results of this study, detecting and classifying experimental pain's intensity automatically using an Internet of Things -based device is possible. The prototype of the device should be further developed and tested in clinical trials, involving the users at each stage of the development to ensure clinical relevance and a user-centric design.Älykäs kipumittari kommunikoimaan kykenemättömille kriittisesti sairaille potilaille: Lääkinnällisen laitteen varhainen kehittäminen Kriittisesti sairaat potilaat kokevat usein kipua hoidon aikana, mutta potilaiden kivun arviointi on haastavaa tilanteissa, joissa potilaan kyky kommunikoida on alentunut. Tutkimuksen tavoitteena oli kehittää toimintakonsepti esineiden internet -teknologiaan perustuvalle Älykkäälle kipumittarille, joka on suunniteltu kriittisesti sairaille potilaille, jotka eivät kykene kommunikoimaan kivustaan. Tutkimuksessa kuvataan Älykkään kipumittarin varhaisia kehitysvaiheita lääkinnällisen laitteen kehitysprosessin mukaisesti. Aloitusvaiheessa I toteutettiin kartoittava kirjallisuuskatsaus, jossa selvitettiin esineiden internet -teknologian mahdollisuuksia perushoidossa. Muotoiluvaiheessa II testattiin laitteen prototyyppiä ja arvioitiin laitteen toimintakonseptin toteutettavuutta. Prototyypin testaukseen osallistui terveitä koehenkilöitä (n=31), joille tuotettiin kipua. Kipualtistuksen aikana mitattiin kipuun liittyviä fysiologisia muuttujia ja viiden kasvolihaksen aktivoitumista. Muuttujat yhdistettiin koneoppimismenetelmällä kivun ennustemalliksi. Lisäksi teho-osastolla työskentelevät sairaanhoitajat (n=20) arvioivat fokusryhmähaastatteluissa laitteen toimintakonseptin toteutettavuutta. Kirjallisuuskatsauksen tuloksista käy ilmi, että esineiden internetiin perustuvien innovaatioiden kehittäminen perushoidon tukemiseen on monipuolista mutta se on vielä alkuvaiheessa. Älykkään kipumittarin prototyyppi osoittautui lupaavaksi kokeellisen kivun tunnistamisessa ja sen voimakkuuden luokittelussa, saavuttaen 83 %:n tarkkuuden kivun luokittelussa lievään tai kohtalaiseen/voimakkaaseen. Lisäksi todettiin, että viidestä mitatusta kasvolihaksesta kolme antoi merkittävintä tietoa kivun tunnistamiseen ja voimakkuuteen liittyen. Sairaanhoitajat näkivät potentiaalia Älykkään kipumittarin käytössä potilaiden kivun arvioinnissa teho-osastolla. Laite tulisi kuitenkin integroida käytössä olevaan potilastietojärjestelmään, ja laitteen tuottamien tietojen luotettavuus tulisi olla hoitajien arvioitavissa. Tulosten perusteella esineiden internet -teknologiaan perustuvan laitteen avulla on mahdollista tunnistaa ja luokitella kokeellisen kivun voimakkuutta automaattisesti. Laitteen prototyyppiä tulee jatkokehittää ja testata kliinisissä tutkimuksissa. Tulevat käyttäjät tulee ottaa mukaan jokaiseen kehitysvaiheeseen laitteen kliinisen merkityksen ja käyttäjälähtöisen muotoilun varmistamiseksi

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

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