Abstract
In recent years, research on convolutional neural networks (CNN) and recurrent neural networks (RNN) in deep learning has been actively conducted. In order to provide more personalized and advanced functions in smart home services, studies on deep learning applications are becoming more frequent, and deep learning is acknowledged as an efficient method for recognizing the voices and activities of users. In this context, this study aims to systematically review the smart home studies that apply CNN and RNN/LSTM as their main solution. Of the 632 studies retrieved from the Web of Science, Scopus, IEEE Explore, and PubMed databases, 43 studies were selected and analyzed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. In this paper, we examine which smart home applications CNN and RNN/LSTM are applied to and compare how they were implemented and evaluated. The selected studies dealt with a total of 15 application areas for smart homes, where activity recognition was covered the most. This study provides essential data for all researchers who want to apply deep learning for smart homes, identifies the main trends, and can help to guide design and evaluation decisions for particular smart home services.
Highlights
Daily data from smart home devices and help provide the most appropriate functions for the users’ needs [1]. Since this is a technology that will be actively researched in the future of smart home services, we considered that an updated systematic analysis of the use of deep learning (DL) for smart homes was, and continues to be, necessary
In addition to the terms of convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), and Smart homes, the keywords related to the functions of smart homes such as activity recognition, energy management, automation, identification, fall detection, security, and sensors appear repeatedly
It was clearly reviewed whether the specified technologies (CNN, RNN/LSTM) were used as the main solution of the study and whether this study contributed to the development of smart home services
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. This study aims to establish a basic reference resource for researchers investigating deep learning (DL) for smart homes. Development efforts to apply DL to smart homes have been continuously increasing. This is because DL can learn users’
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