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

A Review on Data-Driven Methods for Human Activity Recognition in Smart Homes
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Author(s): Jiancong Ye (South China University of Technology, China) and Junpei Zhong (The Hong Kong Polytechnic University, China)
Copyright: 2022
Pages: 20
Source title: Cases on Virtual Reality Modeling in Healthcare
Source Author(s)/Editor(s): Yuk Ming Tang (Hong Kong Polytechnic University, China), Ho Ho Lun (Hong Kong Polytechnic University, China) and Ka Yin Chau (City University of Macau, China)
DOI: 10.4018/978-1-7998-8790-4.ch002

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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.

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