A Solution based on semantic IoT and machine learning to improve the lives of people with disabilities

Abstract

The technology is in a remarkable and continuous development field, it took a part in all other fields such as medicine which makes it indispensable because of the facilities it brings the doctors and also the patients especially to persons with disabilities. Because of its inter-connectivity, the Internet of Things (IoT) technology has received a lot of attention in recent years. The need for semantics comes from the different sources and sensors we depend on for information collection. In this project we also use Semantic Web technologies (Ontologies, SWRL, SPARQL) to make a combination with database data to ensure better interoperability and thus make the Internet of Things semantic. The large size of the IoT data and the considerable number of SWRL rules requires optimization techniques, so we used machine learning to optimize this process and reduce the number of rules. From this combination, a system based on an enriched ontological model containing knowledge about the target person (such as: vital signs, type of disability, their goals, the obstacles that can be found, etc.) is realized in order to provide a better life to handicaps as well as to give them a little more autonomy in their movements.