Non-intrusive behavior awareness for residents of a smart house
Guillermo Ramirez‐Prado, Bashar Barmada, Veronica Liesaputra · 2019
Behavior and activities awareness of people within a smart house is studied using pressure sensors on the floor. In turn, Machine Learning algorithms are used to predict unusual behavior of the house residents. The research focuses in people with supported needs trying to live a normal life in their own houses, allowing them more independence. The system relies on using pressure sensors on the floor to estimate the position of a person in some regions of interest, by measuring their presence on the sensors. System is designed as an Internet of Things (IOT) platform. An array of sensors on the floor are connected through routing devices to an Internet gateway that publishes the data to a Cloud System and into a Database. Machine learning (ML) is used to predict the activities of a person (standing, sitting, lying down), and to determine whether their behavior is common or not. IOT enables technologies that allow having remote monitoring of activities and behavior of residents. Unlike other researches done using pressure sensors that concentrates on predicting people's activities on small area of the room, we are utilizing the sensors to predict people's activities and behavior on all areas of the room.