Deep Learning (CNN, RNN) Applications for Smart Homes: A Systematic Review

Computers

Elena Villalba-Mora¹  Angelica de Antonio²  Jiyeon Yu² 

[1] Centre for Biomedical Technology (CTB), Universidad Politécnica de Madrid (UPM), 28223 Pozuelo de Alarcon, Spain;Madrid HCI Lab, Research Group on Human-Computer Interaction and Advanced Interactive Systems, Universidad Politécnica de Madrid (UPM), 28660 Boadilla del Monte, Spain;

关键词: convolutional neural network (CNN);    deep learning;    deep neural network (DNN);    long short-term memory (LSTM);    recurrent neural network (RNN);    smart homes;   

DOI  :  10.3390/computers11020026

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown