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The present work seizes the opportunity to counter IoT technology fragmentation that the World Wide Web Consortium is offering with the upcoming standard Web Of Things. A gateway service based on this standard and an air quality manager sensor were designed and implemented in a Smart Home with the aim to develop a tool to manage this critical parameter and improve people´s quality of life. The system proved to be able to report and manage the Smart Home devices to reduce Fine Particulate Matter concentration on demand and in real time, also allowing to record data to be consulted by the Smart Home users. The technology produced is agnostic, scalable and replicable in other environments and contexts such as industry, healthcare and Smart Cities among others.

Utilizing context-aware tools in smart homes (SH) helps to incorporate higher quality interaction paradigms between the house and specific groups of users such as people with Alzheimer’s disease (AD). One method of delivering these interaction paradigms acceptably and efficiently is through context processing the behavior of the residents within the SH. Predicting human behavior and uncertain events is crucial in the prevention of upcoming missteps and confusion when people with AD perform their daily activities. Modelling human behavior and mental states using cognitive architectures produces computational models capable of replicating real use case scenarios. In this way, SHs can reinforce the execution of daily activities effectively once they acquire adequate awareness about the missteps, interruptions, memory problems, and unpredictable events that can arise during the daily life of a person living with cognitive deterioration. This paper presents a conceptual computational framework for the modelling of daily living activities of people with AD and their progression through different stages of AD. Simulations and initial results demonstrate that it is feasible to effectively estimate and predict common errors and behaviors in the execution of daily activities under specific assessment tests.

Assistive systems and emerging technologies are capable of supporting individuals with specific needs and diseases effectively. Nonetheless, in the domain of Smart Homes (SH), the interactions tend to become more complex and difficult to adapt to the users, creating a bottleneck for the efficient use and the acceptability of the interventions. This paper presents best practices on personalization and adaptive interaction techniques in order to homogenize different solutions within the SH, ease the interaction acceptability by adaptation to specific user needs and implement better healthcare interventions able to improve the Quality of Life (QoL). The recommendations arise as a result of previous research studies conducted within the MSCA-ITN project ACROSSING.