Enhancing Device-Goal-Norm Modeling for Ambient Assisted Living with Large Language Models

In the rapidly evolving landscape of elderly care, designing personalized Ambient Assisted Living (AAL) applications relying on IoT-enabled devices presents complex challenges at the intersection of technology, human needs, and ethical considerations. This paper presents an innovative approach that enhances the design process of these applications by integrating large language models (LLMs) to automatically generate interaction patterns between IoT devices and users' needs and preferences. These patterns are used to select the more appropriate devices and their interaction modalities for each user at run-time. The LLM-enhanced process not only reduces analysts' workload but also may reveal nuanced interactions between IoT devices and user needs that might be missed in traditional methods, where the knowledge of the characteristics of available devices may be incomplete or not updated. The proposed approach, on the one hand, facilitates the design of highly adaptive and personalized assistive technologies and, on the other hand, demonstrates the potential of combining artificial intelligence with human expertise to improve software system design.