2nd AI and Sensor-Supported Integrated care Solutions (ASSIST) workshop


Advances in Artificial Intelligence and sensing technologies have made it possible to build intelligent solutions aimed at improving our daily life, aiming at personalized care items in various areas promoting health and well-being. Moreover, 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 with mobility restrictions. Moreover, making sure medical and other information is passed, in a timely and personalized manner, to the right healthcare professionals and caregivers is necessary for getting the right consultation and well-targeted to the needs of the individual. Such 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 disease, other comorbidities and medication, amongst others). The role of sensing technologies and artificial intelligence can be very crucial in such solutions, as learning form historical data and 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, movement tracking sensors, etc.) and intelligent eHealth applications have improved significantly in the last years, the most promising algorithms for human activity analysis, automated behavior recognition and personalization are constrained by the limited existence of available datasets, usage context, physical obstacles, individual behavior patterns and 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.

The 2nd ASSIST workshop invites contributions that seek to advance the state of the art in using AI, eHealth and sensor-driven solutions and applications for promoting healthy lifestyles in indoor environments and for the building of summarizing results in structured datasets of personalized profiles, in order to facilitate further processing, and adding to the body of existing datasets for data-driven research, development and integration in existing integrated care solutions.


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 the most recent results from the PROCare4Life project, and it will welcome research contributions from the broader research community, following up on the steps of the previous ASSIST workshop in 2021.

Topics of interest include, but are not limited to

  • Automated, indoors human activity and behavior recognition.
  • Activities and selected events and anomalies detection related to health.
  • AI solutions for personalization and analysis of electronic health records.
  • Explainable and trustworthy AI models for health and well-being solutions and applications.
  • Multimodality in human behavior recognition and Ambient Assisted living.
  • Affective computing – Factors related to personality and affects in interpreting human actions, behaviors and preferences in daily life activities.
  • AI-based recommender systems for health and well-being applications.
  • Intelligent and personalized health user interfaces and assistive technologies.

Workshop Organizers

Jorge Alfonso Kurano
Universidad Politécnica de Madrid (UPM)
jak@gatv.ssr.upm.es, Jorge.alfonso@upm.es

Alberto Belmonte Hernández
Universidad Politécnica de Madrid (UPM)
abh@gatv.ssr.upm.es, Alberto.belmonte@upm.es

Stylianos (Stelios) Asteriadis
University of Maastrichtno

Yusuf Can Semerci
University of Maastricht