Analysis of Sequential Events for the Recognition of Human Behavior Patterns in Home Automation Systems

Abstract

Learning users’ frequent patterns is very useful to develop real human-centered environments. By analyzing the occurrences of events over time in a home automation system, it’s possible to find periodic patterns based on action-time relationships. However, the human behavior could be better defined if it’s related to chained actions, creating action-action relationships. This work presents IntelliDomo’s learning layer, a data mining approach based on ontologies and production rules that aims to achieve those objectives. This module is able to acquire users’ habits and automatically generate production rules for behavior patterns to anticipate the user’s periodic actions. The learning layer includes new features looking for the adaptability and personification of the environment.