Abstract
Abstract The world population is facing several difficulties related to an aging society. In particular, the widespread increase of chronic and incapacitating diseases is overwhelming for traditional healthcare services. Ambient assisted living (AAL) systems can greatly improve healthcare scalability and scope while keeping people in the comfort of their home environments. This chapter focuses precisely on presenting the fundamental key aspects (cf. processing and sensing, integration and management, communication and coordination, intelligence and reasoning) to promote safety and support for outpatients living autonomously in AAL settings. Furthermore, for each key issue, a set of practical technological solutions are reported and detailed, showing real applicability of ubicomp technology to the integration and management of AAL systems specially designed for supporting daily living activities of people with progressive loss of capacities. © 2019 John Wiley & Sons, Ltd.

Abstract
Recommendation systems can play an important role in today’s digital content platforms by supporting the suggestion of relevant content in a personalised manner for each customer. Such content customisation has not been consistent across most media domains, and particularly on radio streaming and gaming aggregators, which are the two real-world application domains focused in this work. The challenges faced in these application areas are the dynamic nature of user preferences and the difficulty of generating recommendations for less popular content, due to the overwhelming choice and polarisation of available top content. We present the design and implementation of a Reinforcement Learning-based Recommendation System (RLRS) for web applications, using a Deep Deterministic Policy Gradient (DDPG) agent and, as a reward function, a weighted sum of the user Click Distribution (CD) across the recommended items and the Dwell Time (DT), a measure of the time users spend interacting with those items. Our system has been deployed in real production scenarios with preliminary but promising results. Several metrics are used to track the effectiveness of our approach, such as content coverage, category diversity, and intra-list similarity. In both scenarios tested, the system shows consistent improvement and adaptability over time, reinforcing its applicability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller quad skate variant of hockey team sports, it is of great interest to automatically track player’s movements and positions, player’s sticks and, also, making other judgments, such as being able to locate the ball. In this work, we introduce a real-time pipeline composed by an object detection model, created specifically for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and quick motions, our deep learning object detection model effectively identifies and tracks, in real-time, important visual elements such as: ball; players; sticks; referees; crowd; goalkeeper; and goal. Using a curated dataset composed by a collection of videos of rink hockey, comprising 2525 annotated frames, we trained and evaluated the algorithm performance and compare it to state of the art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80%, and presents a good performance in terms of accuracy and speed, according to our results, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected one important event type in rink hockey games, the occurrence of penalties.

Abstract
There is a widespread social awareness for the need of environment protection and sustainable systems in different areas of human activity. In particular, the catering industry is responsible for a significant share of sewage systems pollution, due to daily leaks of food remnants containing Fat, Oil and Grease (FOG). This work focuses on building a combined IoT monitoring solution to automate the remote management of industrial FOG-Separators, aiming to prevent or reduce leakage of FOG and food debris into sewer systems. The proposed solution adopted the use of custom-made in-premises sensor motes integrating two particular sensors: an in-the-house developed conductivity sensor, built specifically to distinguish levels of water and FOG in industrial FOG-Separators; an off-the-shelf turbidity sensor integrated to assess the amount of water debris. Briefly, this work has four major fold contributions: i) design and implementation of a combined IoT sensing solution; ii) most significant was the development, test, and integration of the capacity-based sensor coupled to local sensor motes, for assessing Water/FOG levels; iii) assessing and profiling edge motes energy autonomy; iv) finally, deploying the combined IoT architecture to validate the entire process of monitoring and scheduling the maintenance of industrial FOG-Separators. © 2022 IEEE.