Motivation and scope

Increased life expectancy and decreased birth rates are challenging developed countries worldwide, with a rapidly growing elderly population, and a declining workforce. These two contextual phenomena are generating new needs, as ageing increases the probability of developing physical and cognitive impairments, requiring new and more effective ways to provide support and care. According to the United Nations, the elderly, defined as people over the age of 60, will make up 37% of the European population by 2050, from 20% in 2000: this will put a dramatic pressure on public welfare systems, and new ways of assistance and care delivery have to be designed. The Internet of Things may provide significant enhancement to the quality of life for the elderly and, in general, people in need, bringing into Active and Assisted Living (AAL) new algorithms, architectures and platforms, to develop new and innovative approaches.

A peculiar aspect of IoT relies in enabling the collection of massive amounts of data, generated from physical and virtual sensors, things, smart objects and users. As such, IoT is totally functional to AAL, with the pervasive collection of environmental and personal data related to the monitored subject, and their processing, aimed at generating information to be used in different types of applications, from behavioral analysis, to gesture and action recognition, to fall prevention and detection, to human-machine interaction, just to mention a few. This workshop aims at investigating the close relationship between IoT and AAL, in a data-oriented perspective, and at different layers: from the technologies enabling sensor data collection, to the communication architectures needed to support data transfer, to the reasoning techniques applied to data, in order to generate the information upon which AAL services and functionalities are built.

Among the topics of interest, a non-exhaustive list includes: use of emerging networking, sensing and tracking technologies for AAL; low power communication technologies; cloud and mobile cloud architectures supporting AAL through IoT; privacy-preserving data processing techniques; big data, analytics, and signal processing for AAL enabled by an IoT approach; machine learning to enable AAL services (behavioral analysis, gesture and action recognition, fall prevention and detection, intake monitoring, anomalous patterns detection, sleep monitoring, etc…); datasets to enable benchmarking of machine learning algorithms for AAL applications; new design paradigms in human-machine interaction for AAL; smart and sustainable IoT solutions for AAL; application of context awareness and adaptive interfaces for AAL; and implementation of Social IoT for AAL. IoTAAL will bring experts and researchers from the IoT field and the AAL field together, to foster a better common understanding, to exchange visions and latest research results addressing IoT specialization for AAL, to discuss promising new technologies and to highlight open research challenges.