Design of a Customized multipurpose nano-enabled implantable system for in-vivo theranostics

The first part of this paper reviews the current development and key issues on implantable multi-sensor devices for in vivo theranostics. Afterwards, the authors propose an innovative biomedical multisensory system for in vivo biomarker monitoring that could be suitable for customized theranostics applications. At this point, findings suggest that cross-cutting Key Enabling Technologies (KETs) could improve the overall performance of the system given that the convergence of technologies in nanotechnology, biotechnology, micro&nanoelectronics and advanced materials permit the development of new medical devices of small dimensions, using biocompatible materials, and embedding reliable and targeted biosensors, high speed data communication, and even energy autonomy. Therefore, this article deals with new research and market challenges of implantable sensor devices, from the point of view of the pervasive system, and time-to-market. The remote clinical monitoring approach introduced in this paper could be based on an array of biosensors to extract information from the patient. A key contribution of the authors is that the general architecture introduced in this paper would require minor modifications for the final customized bio-implantable medical device

SPATIAL AUTONOMY: Exploring Industry 4.0/5.0 Trends on Architectural Design

The rise of Industry 4.0 and 5.0 presents significant opportunities for the architecture industry to incorporate advanced technologies into its design and construction processes. However, the full potential of these technologies in architectural design has yet to be fully explored. This thesis, ‘Spatial Autonomy: Exploring Industry 4.0 and 5.0 Trends in Architectural Design,’ aims to investigate the ability of buildings to function autonomously through the integration of smart technologies. This exploration focuses on how Industry 4.0 and 5.0 trends can optimize building performance, creating more comfortable and enjoyable experiences for users, while also enhancing efficiency and sustainability. It examines the emerging questions: How to design in response to these technologies? What constitutes a design framework for integrating Industry 4.0 and 5.0 into architectural design, and how can this framework be applied to future projects? As digital, cloud, and AI computing demands increase globally, current data centers, which contribute to 0.3% of global CO2 emissions, are primarily designed to meet existing demands rather than anticipating and creating a more balanced relationship between demand and environmental sustainability. This thesis challenges this norm by proposing the design of a data center integrated within a mixed-use complex that adheres to the principles of Industry 5.0, emphasizing environmental and social health. This approach advocates for the integration of systems at the onset of the design process, proposing that early incorporation can significantly enhance the benefits of these advanced technologies. The research seeks to redefine the data center not just as a static structure but as a dynamic, responsive, and sustainable architectural form that functions as a closed feedback loop with its urban environment, dynamically interacting with and adapting to its human and ecological context. In conclusion, ‘Spatial Autonomy’ not only explores but also aims to redefine the process of designing in the digital age, setting a precedent for a more harmonious integration of cutting-edge technologies into architectural design. This thesis illustrates the potential for buildings to be not merely static structures but dynamic environments that intelligently respond to user needs and contribute actively to environmental sustainability

Internet of Things Architectures, Technologies, Applications, Challenges, and Future Directions for Enhanced Living Environments and Healthcare Systems: A Review

Internet of Things (IoT) is an evolution of the Internet and has been gaining increased attention from researchers in both academic and industrial environments. Successive technological enhancements make the development of intelligent systems with a high capacity for communication and data collection possible, providing several opportunities for numerous IoT applications, particularly healthcare systems. Despite all the advantages, there are still several open issues that represent the main challenges for IoT, e.g., accessibility, portability, interoperability, information security, and privacy. IoT provides important characteristics to healthcare systems, such as availability, mobility, and scalability, that o er an architectural basis for numerous high technological healthcare applications, such as real-time patient monitoring, environmental and indoor quality monitoring, and ubiquitous and pervasive information access that benefits health professionals and patients. The constant scientific innovations make it possible to develop IoT devices through countless services for sensing, data fusing, and logging capabilities that lead to several advancements for enhanced living environments (ELEs). This paper reviews the current state of the art on IoT architectures for ELEs and healthcare systems, with a focus on the technologies, applications, challenges, opportunities, open-source platforms, and operating systems. Furthermore, this document synthesizes the existing body of knowledge and identifies common threads and gaps that open up new significant and challenging future research directions.info:eu-repo/semantics/publishedVersio

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

Design and Implementation of a Prototype with a Standardized Interface for Transducers in Ambient Assisted Living

Solutions in the field of Ambient Assisted Living (AAL) do not generally use standards to implement a communication interface between sensors and actuators. This makes these applications isolated solutions because it is so difficult to integrate them into new or existing systems. The objective of this research was to design and implement a prototype with a standardized interface for sensors and actuators to facilitate the integration of different solutions in the field of AAL. Our work is based on the roadmap defined by AALIANCE, using motes with TinyOS telosb, 6LoWPAN, sensors, and the IEEE 21451 standard protocol. This prototype allows one to upgrade sensors to a smart status for easy integration with new applications and already existing ones. The prototype has been evaluated for autonomy and performance. As a use case, the prototype has been tested in a serious game previously designed for people with mobility problems, and its advantages and disadvantages have been analysed.Junta de Andalucía P08-TIC-363

Crptography based Lifi for Patient Privacy and Emergency Health Service Using IOT

Medical care is one such region, where WIFI is as yet not utilized as the electromagnetic waves influences patients with sicknesses like neurological problems, diseases and so forth. Accordingly, LIFI can be respected the following large thing, as it represents no gamble to patients and offers more advantages than WIFI, such as faster speeds and a larger spectrum. The only issue that hospitals have while exchanging data through it is ensuring confidentiality. The methodology proposed here leverages Secure Hash Algorithms to give maximum security as a solution to this challenge. The Secure Hash Algorithm is a bonus feature that is mostly utilised for authentication. IoT connects physical devices such as sensors and actuators to networks. The programming routines can be visualised from any location thanks to cloud storage. These algorithms can be employed in a variety of applications, including smart homes, digital technologies, and banking systems. This research presents a model that takes into account a human's heart rate, glucose level, and temperature. In the even to fan emergency, adjacent hospitals are alerted to the patient's condition, allowing them to provide timely and correct care. This will save you from having to go to the hospital. Temperature, blood pressure, heart rate, gas sensor, and fall detection are among the vital signs monitored by the system. An Arduino controller and a GSM900Amodule make up the system design. The monitored values can be supplied via mobile phones, and if an abnormal state is detected, the buzzer is activated, and the information is communicated to the concerned members via the mobile app

Plateforme informatique pour l'assistance à l'autonomie à domicile de personnes âgées

RÉSUMÉ : Ambient Assisted Living (AAL) en général et Activity Recognition (AR) en particulier sont des domaines de recherche actifs qui visent à aider les personnes dans leurs activités de la vie quotidienne (AVQ). Au cours des dernières années, nous avons constaté un intérêt accru pour leur applicabilité aux personnes âgées vivant en milieu rural qui perdent lentement leur autonomie en raison du vieillissement et aux maladies chroniques. Une avenue de recherche importante consiste à agréger et à rechercher des corrélations entre les données physiologiques qui servent à surveiller la santé des personnes âgées, leurs AVQ, leurs mouvements et toute autre donnée pouvant être recueillis sur leur environnement immédiat. Dans ce travail, nous abordons la possibilité de développer une plateforme non intrusive et abordable en raison de l'absence d'une telle plateforme. Elle est basée sur des capteurs de santé, de mouvement, d'activité et de localisation. En outre, nous discutons des principaux concepts derrière la création d'une architecture en couches, flexible et hautement modulaire qui se concentre sur la façon dont l'intégration de données de capteurs combinés peut être réalisée. À l'aide d'un prototype d'application de téléphonie mobile, nos travaux ont montré que nous pouvons intégrer de nombreuses technologies non invasives qui ne sont pas nécessairement les plus récentes, mais les plus abordables, évolutives et prêtes à être déployées dans des environnements réels. Un autre domaine de recherche découlant de ces avancées est de savoir comment la technologie et l'analyse pourraient bénéficier à la prévention et au traitement des maladies chroniques chez le nombre croissant de personnes âgées ayant des problèmes de santé. De nombreuses architectures sont proposées dans la littérature, mais elles manquent de modularité et de flexibilité pour différents types de capteurs. À cette fin, nous proposons une architecture à quatre couches et hautement modulaire pour l'analyse de la santé des personnes âgées. Finalement, nous évaluons l'approche en implémentant une partie de l'architecture sur des nœuds de brouillard et le cloud. De plus, nous déployons ces capteurs abordables, de qualité, et accessibles au grand public dans un appartement afin d'avancer vers l'utilisation du système proposé. Des données recueillies sont utilisées comme un test préliminaire pour évaluer les capacités de la plate-forme. En utilisant les données collectées lors de l'étape de validation, nous effectuons des prévisions d'une semaine dans le futur pour des séries univariées en utilisant des méthodes classiques populaires et les méthodes d'apprentissage en profondeur les plus récentes. Une comparaison de précision est présentée. -- Mot(s) clé(s) en français : IoT, suivi à distance des personnes âgées, santé intelligente et connectée, analyse, assistance à la vie ambiante, capteurs, intelligence artificielle. -- ABSTRACT : Ambient Assisted Living (AAL) in general and Activity Recognition (AR) in particular are active fields of research that aim at assisting people in their Activities of Daily Living (ADL). In recent years, we have seen an increased interest in their applicability to the rural seniors who are slowly losing their autonomy due to aging and chronic diseases. One research venue is to aggregate and seek for correlations between the physiological data that serves to monitor the health of the elderly, their ADLs, their movements and any other data that may be collected about their immediate environment. In this work, we are tackling the possibility of developing a non-intrusive and affordable platform due to the lack of such a platform. It is based on embedded health, movement, activity and location sensors. Furthermore, we discuss the main concepts behind the creation of a layered, flexible and highly modular architecture that focuses on how the integration of newly combined sensor data can be achieved. Using a mobile phone application prototype, our work has shown that we can integrate many non-invasive technologies that are not necessarily the newest, but the most affordable, scalable and ready to be deployed in real life settings. Another researched venue deriving from these advances is how the technology and analytics could benefit the prevention and treatment of chronic diseases in the escalating number of elderly people experiencing health issues. Many architectures are proposed in the literature, but they lack modularity and flexibility for different types of sensors. To that end, we propose a four layered and highly modular architecture for health analytics of elderly people. In the final analysis, we evaluate the approach by implementing part of the architecture on fog nodes and the cloud. Moreover, we deploy these affordable consumer grade sensors in an apartment in order to move toward the use of the system proposed. The data collected from this experiment is used as a preliminary test of the capabilities of the platform. We perform univariate series forecasting using a popular classical methods and the more recent deep learning methods by using the data collected in the validation stage. An accuracy comparison is presented. -- Mot(s) clé(s) en anglais : IoT, remote elderly monitoring, smart and connected Health, analytics, ambient assisted living, sensors