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Analysis of institutional authors

Yu J.AuthorDe Antonio A.Corresponding AuthorVillalba-Mora E.Author

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

Publicated to:Computers. 11 (2): 26- - 2022-02-01 11(2), DOI: 10.3390/computers11020026

Authors: Yu, Jiyeon; de Antonio, Angelica; Villalba-Mora, Elena

Affiliations

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.

Keywords

artartificial-intelligencechallengesdeep learningdeep neural network (dnn)long short-term memory (lstm)recurrent neural network (rnn)smart homesstatesystematic reviewwearable sensorConvolutional neural network (cnn)Deep learningDeep neural network (dnn)Human activity recognitionLong short-term memory (lstm)Recurrent neural network (rnn)Smart homesSystematic review

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Computers due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2022, it was in position , thus managing to position itself as a Q2 (Segundo Cuartil), in the category Computer Networks and Communications.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 2.75. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 4.82 (source consulted: FECYT Feb 2024)
  • Field Citation Ratio (FCR) from Dimensions: 20.73 (source consulted: Dimensions Jun 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-06-07, the following number of citations:

  • WoS: 30
  • Scopus: 54
  • OpenCitations: 33

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-07:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 139.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 137 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 0.75.
  • The number of mentions on the social network X (formerly Twitter): 2 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (YU, JI YEON) and Last Author (VILLALBA MORA, ELENA).

the author responsible for correspondence tasks has been ANTONIO JIMENEZ, ANGELICA DE.