A Review on Data-Driven Methods for Human Activity Recognition in Smart Homes

Abstract

The smart home is one application of intelligent environments, where sensors are equipped to detect the status inside the domestic home. With the development of sensing technologies, more signals can be obtained with heterogenous statistical properties with faster processing speed. To make good use of the technical advantages, data-driven methods are becoming popular in intelligent environments. On the other hand, to recognize human activity is one essential target to understand the status inside a smart home. In this chapter, the authors focus on the human activity recognition (HAR) problem, which is the recognition of lower levels of activities, using data-driven models.