Ambient intelligence for quality of life assessment

Abstract

We are often, consciously or unconsciously, self-assessing our quality of life in order to make decisions about our future actions. People with special needs are sometimes not able to perform this evaluation, this being the responsibility of their relatives or carers. The literature shows this to be a challenging task due to the inherent subjectivity, and the limited data collection tools and biased information available. This paper proposes that context awareness and artificial intelligence can support this task by providing digested and objective information about a person's quality of life evolution. Ambient Assisted Living continuously obtains relevant data from different sources such as sensors, the use of household appliances and interaction with user interfaces. An artificial neural network model known as self-organizing maps processes this data to monitor how the user carries out different activities of daily living (e.g. cooking or doing the washing). This information, together with statistical analysis from the said data, is automatically compiled by the system in a report to visualize trends in user behavior that might lead to the detection of a person's cognitive, physical or sensory deterioration. This report has been validated by a group of experts who considered it a tool of great usefulness and power to complement existing tools used by social workers.