Advances in Artificial Intelligence and sensing technologies have made it possible to build intelligent solutions aimed at improving our daily life, at providing personalized recommendations in terms of physical and cognitive exercises, nutrition, and general activities promoting health and well-being. Big data analysis through cutting-edge AI algorithms is expected to provide new insights into disease patterns and contribute to the design of more efficient interventions. Such solutions can promote independent living and can become an indispensable part of integrated care technologies. Indeed, spending more time at home but, at the same time, making it possible to communicate health-related signals to professionals and caregivers, becomes more necessary – and feasible – than ever, especially given the current pandemic and its consequences on maintaining a healthy lifestyle at home.
Making sure medical and other information is passed, in a timely and personalized manner, to the right medical professionals and caregivers, is necessary for getting the right consultation, well-targeted to the needs of the individual. This information must reflect personal needs, should take into account user habits and preferences and, of course, needs to consider all health-related parameters involved (including diseases, other comorbidities and medication). The role of sensing technologies and artificial intelligence can be very crucial in such solutions, as learning from historical data or from users with similar health-related issues and needs can be beneficial in building the right AI models.
However, even if sensing devices (e.g. cameras, wearable devices, web access points) and intelligent eHealth applications have improved significantly in the last years, the most promising algorithms for human activity analysis, automated behaviour recognition and personalization are constrained by limited datasets, usage contexts, physical obstacles, personalized patterns of behaviours and individual preferences. Moreover, underlying health conditions vary significantly among individuals for promoting their health and well-being.
Another big challenge in data analysis and efficient application of artificial intelligence arises from the fact that indoor environments impose problems related to sensor noise, scene clutter and false alarms attributed to contextual constraints.
In this workshop, research papers are invited, but are not limited to, the following areas topics:
• Automated, indoors human activity and behaviour recognition for promoting healthy lifestyles.
• AI solutions for personalization and analysis of electronic health records.
• Explainable AI models for health and well-being.
• Multimodality in human behaviour recognition and Ambient Assisted Living.
• Factors related to personality and affect in interpreting human actions, behaviours and preferences in daily life activities.
• Anomaly detection in indoor activities.
• Health-related factors in analysing human daily routine and related computational models.
• AI recommender systems in health.
• Intelligent health interfaces and assistive technologies.
The scope of this workshop is to bring together researchers, developers and the industry working in the area of data and AI-driven solutions in ambient assisted living environments, focusing on the promotion of health benefits and personalization. Latest computational models and sensor signal interpretation techniques will be discussed in the form of oral presentations of peer-review papers.
The workshop will leverage results from the PROCare4Life and PROTEIN EU H2020 projects, and it will welcome research contributions from the broader research community. The workshop is jointly organized by Maastricht University (UM) and Polytechnic University of Madrid (UPM), the responsible partners in PROCare4Life to develop the sensorial ecosystem and the decision support system. The workshop is also co-organized along with a partner of another EU project CERTH – PROTEIN EU project as part of the PETRA 2021 conference.