An Early Warning System for Ambient Assisted Living

Daily-life activities at home can generate dangers that may lead to accidents. Risky situations may be difficult to notice by people with a cognitive or physical impairment. Therefore, recognizing dangers is very important so as to assist users in preventing accidents, and ensure their health, safety and wellbeing. The present thesis aims to design a system that, given a representation of the environment as input, learns how to evaluate states according to their danger level, and is able to alert and prevent users from getting too close to a potential danger. We explore the search space for disclosing dangers and finding a safe path leading to the goal. The project led to the implementation of a working prototype, which is able to suggest the best action to perform, and reports the level of danger and an evaluation of the last performed action. Also, it is able to warn the user when the level of danger exceeds a given threshold. We offer a general solution, as the system is able to play arbitrary games described with the Game Description Language, and perform on-line planning by means of the Q(λ) algorithm. For this purpose, we implemented a Java library for implementing TD-learning agents. In addition, we defined the concept of sphere of protection and we disclose dangers by using a variant of breadth-first search. Finally, we exploited virtual environments as a general testbed for simulating effects of warning notifications and we conducted informal user testing for evaluating the effects of warning notifications on actual users. To my family and all people living away from home