An IoT−based system that aids learning from human behavior: A potential application for the care of the elderly

Abstract

Abstract. The goal of this paper is to describe the way of taking advantage of the non-intrusive indoor air quality monitoring system by using data oriented modeling technologies to determine specific human behaviors. The specific goal is to determine when a human presence occurs in a specific room, while the objective is to extend the use of the existing indoor air quality monitoring system to provide a higher level aspect of the house usage. Different models have been trained by means of machine learning algorithms using the available temperature, relative humidity and CO 2 levels to determine binary occupation. The paper will discuss the overall acceptable quality provided by those classifiers when operating over new data not previously seen. Therefore, a recommendation on how to proceed is provided, as well as the confidence level regarding the new created knowledge. Such knowledge could bring additional opportunities in the care of the elderly for specific diseases that are usually accompanied by changes in patterns of behavior.

An IoT sensor network to model occupancy profiles for energy usage simulation tools

“…Indirect detection methods are less in number but are gaining attention as they make possible to detect presence reusing other variables, in this group we find detection methods based in environmental values such as temperature, humidity, CO 2 , etc. [23][24] [25].…”