It is feasible to see how communication and information technology have advanced at a rapid pace in today’s world. The introduction of wearable technology is one aspect contributing to this progress and has the potential to be an innovative solution to healthcare challenges since it may be utilized for illness prevention and maintenance, such as physical monitoring, as well as patient management. In order to solve some of the healthcare challenges, this paper proposes the development of an intelligent health monitoring system with alerts and continuous monitoring using wearable devices capable of collecting biometric data on human health. The concept was then proven by the development of a prototype using sensors connected to a micro-controller which transmits its information via MQTT to a Node-RED powered dashboard that handles the health metrics monitoring. The designed prototype has proven satisfactory to provide evidences that support the developed research questions.

The evolution of technologies in recent years has allowed many advances in various areas, such as robotics, the Internet of Things, Big Data, Blockchain, among others. These advances have allowed to solve many of the problems we face nowadays, such as today, for example, they have been used recently in the fight against Covid-19 which has affected and continues to affect people and organizations around the world. Taking advantage of the advancement of these technologies, the circular economy concept has been successfully implemented in various spheres of society and in various countries, with the aim of transition from linear economy ideas (produce, consume, and dispose) to more sustainable models (recycle, reuse, and reduce), keeping products and raw materials in circulation if possible, while adding value for society and business. In this paper we will address how Blockchain can leverage the ideals of the circular economy, analysing and implementing a prototype Blockchain network in the used car parts market. The structure of this work is based on bibliographical research that served as a basis to collect data and information relevant to the themes in question, as well as the practical implementation of the concepts in the case of the reuse of used parts.

Health Remote Monitoring Systems (HRMS) offer the ability to address health-care human resource concerns. In developing nations, where pervasive mobile networks and device access are linking people like never before, HRMS are of special relevance. A fundamental aim of this research work is the realization of technological-based solution to triage and follow-up people living with dementias so as to reduce pressure on busy staff while doing this from home so as to avoid all unnecessary visits to hospital facilities, increasingly perceived as dangerous due to COVID-19 but also raising nosocomial infections, raising alerts for abnormal values. Sensing approaches are complemented by advanced predictive models based on Machine Learning (ML) and Artificial Intelligence (AI), thus being able to explore novel ways of demonstrating patient-centered predictive measures. Low-cost IoT devices composing a network of sensors and actuators aggregated to create a digital experience that will be used and exposure to people to simultaneously conduct several tests and obtain health data that can allow screening of early onset dementia and to aid in the follow-up of selected cases. The best ML for predicting AD was logistic regression with an accuracy of 86.9%. This application as demonstrated to be essential for caregivers once they can monitor multiple patients in real-time and actuate when abnormal values occur.

Energy consumption in buildings depends on the local climate, building characteristics, and user behavior. Focusing on user interaction, this research work developed a novel approach to monitoring and interaction with local users by providing in situ context information through graphic descriptions of energy consumption and indoor/outdoor environment parameters: temperature, luminosity, and humidity, which are routinely measured in real-time and stored to identify consumption patterns and other savings actions. To involve local users, collected data are represented in 3D color representation using building 3d models. A simplified color scale depicts environmental comfort (low/comfortable/high temperature/relative humidity) and energy consumption (above/below usual patterns). We found that these indices induced user commitment and increased their engagement and participation in saving actions like turning off lights and better management of air conditioning systems.