The ultimate goal of communication technologies is to support people. Recent advances in sensing, artificial social intelligence, affective computing, and machine learning allows to provide effective support in learning processes, helping elderly toward healthy and independent living. Also, when things are not that important such as making people comfortable in their smart homes, helping them to enjoy their favorite food preparation etc.
Our lab addressed the problem of elderly independent living supported by technology. Care for the elderly is an indicator of how humane our society is. According to today’s needs and demands, technology is a crucial contributor in integrated care for elderly. Bringing technology support to a friendly, nonintrusive, and effective care of elderly is a challenging task. A personalized, contextualized and social signal-aware communication among elderly and smart systems is the response to this challenge.
One of the challenges is a long-term measurement of impact of the smart technology on elderly person. Our response is a development of a domain-specific metrics if healthy aging and independent living in collaboration with Anton Trstenjak gerontology institute. This metric will allow us to quantitatively estimate healthy aging and independent living applying machine learning model on real world behavior data of an individual elderly person.
The next challenge is an effective, user-friendly communication of this technical support to elderly person. In this regard, we are developing a personalized contextualized recommender system for daily activities. The aim is to optimize these recommendations not according to standard computer science metrics such as precision and recall but according to the healthy aging metrics (see previous section).