Intelligent System for Learning of Comfort Preferences to Help People with Mobility Limitations

It is essential to provide better living conditions for vulnerable sectors of society using technology and it is important to consider that the technology must be friendly with users, and even adapt to their needs and desires. We can see in current systems the user has to learn to use the devices or services, but with an intelligence system, the technology is a very effective way to determine the needs of users. In this research, we present the physical implementation of a system to assist users and patients in daily activities or duties. The system include a architecture of agents where a Deliberative agent learns from the interaction with the user, in this way the system detects thermal comfort preferences for give an automatic assistance. We propose an algorithm with a proactive stage and learning stage adapting a classification algorithm. We select the classification algorithm with the best performance using cross validation. The algorithms of pattern recognition was Back Propagation neural network, Naive Bayes, Minimum Distance and KNN (k near neighbor). Our motivation of this work was to help people with motor difficulties or people who use wheelchairs, for this reason it was essential to use a wireless controller and use a friendly interface. The system was implemented in a testbed at the Leon Institute of Technology in Guanajuato, Mexico, and include sensors of humidity and temperature, windows actuators, wireless agents and other devices. Experimental tests were performed with data collected during a time period and using use cases. The results were satisfactory because it was not only possible remote assistance by the user but it was possible to obtain user information to learn comfort preferences using vector features proposed and selecting the classification algorithm with better performance.