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Applied Sciences
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Emotion-Aware Intelligent Environments
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Emotion-Aware Intelligent Environments

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 15649

Special Issue Editors

Dr. Carlos A. Iglesias

E-Mail Website
Guest Editor
Intelligent Systems Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: multiagent systems; social computing; linked data; natural language processing; affect technology; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Álvaro Carrera Barroso

E-Mail Website
Guest Editor
Intelligent Systems Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: machine learning; agent technology; cognitive bots; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleague,

Emotion-aware ambient intelligence extends the notion of intelligent environments to detecting, processing, and adapting intelligent environments to users’ emotional state, exploiting theories from psychology and social sciences for the analysis of human emotional context. Considering emotions in the user context can improve the customization of services in IoT scenarios and help users to improve their emotional intelligence. However, emotion technologies are rarely addressed within AmI systems, and have been frequently ignored. We are organizing the conference Intelligent Environments 2020 (https://blogs.upm.es/ie2020/), and our idea would be to recommend extended versions of selected papers of the conference for this Special Issue, in addition to papers submitted by researchers.

Dr. Carlos A. Iglesias
Dr. Álvaro Carrera
Guest Editors

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Keywords

  • emotion
  • intelligent environments
  • affect computing

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Published Papers (3 papers)

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Research

11 pages, 1152 KiB  
Article
Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection
by Gen Li and Jason J. Jung
Appl. Sci. 2020, 10(21), 7677; https://doi.org/10.3390/app10217677 - 30 Oct 2020
Cited by 13 | Viewed by 2835
Abstract
Emotion detection is an important research issue in electroencephalogram (EEG). Signal preprocessing and feature selection are parts of feature engineering, which determines the performance of emotion detection and reduces the training time of the deep learning models. To select the efficient features for [...] Read more.
Emotion detection is an important research issue in electroencephalogram (EEG). Signal preprocessing and feature selection are parts of feature engineering, which determines the performance of emotion detection and reduces the training time of the deep learning models. To select the efficient features for emotion detection, we propose a maximum marginal approach on EEG signal preprocessing. The approach selects the least similar segments between two EEG signals as features that can represent the difference between EEG signals caused by emotions. The method defines a signal similarity described as the distance between two EEG signals to find the features. The frequency domain of EEG is calculated by using a wavelet transform that exploits a wavelet to calculate EEG components in a different frequency. We have conducted experiments by using the selected feature from real EEG data recorded from 10 college students. The experimental results show that the proposed approach performs better than other feature selection methods by 17.9% on average in terms of accuracy. The maximum marginal approach-based models achieve better performance than the models without feature selection by 21% on average in terms of accuracy. Full article
(This article belongs to the Special Issue Emotion-Aware Intelligent Environments)
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31 pages, 3964 KiB  
Article
Constructing a Smart Home for Future Elders toward All-around Happiness: Taking Connectivity as the Core Element
by Yuqi Liu, Ryoichi Tamura and Yao Song
Appl. Sci. 2020, 10(16), 5690; https://doi.org/10.3390/app10165690 - 17 Aug 2020
Cited by 11 | Viewed by 5373
Abstract
Smart homes, as one of the most prosperous industries of the Internet of Things, have tremendous potential in helping the elderly aging in place and dealing with the global aging society challenges. This study takes the needs of future elderly in China as [...] Read more.
Smart homes, as one of the most prosperous industries of the Internet of Things, have tremendous potential in helping the elderly aging in place and dealing with the global aging society challenges. This study takes the needs of future elderly in China as a starting point and proposes that the core requirement of the aging group is “connectivity”, smart homes for older adults should assist them with connectivity establishment both physically and psychologically to improve their quality of life and help them live an independent, safe, and happy life in their older stage. The article defines the types of elderly connectivity needs as two main types and eight sub-connectivity, and further puts forward thirty smart home subsystems and their implementation elements. Moreover, the research applies the Kano model and questionnaire survey to provide the empirical proof of those thirty smart home subsystems based on the analysis of 371 questionnaire responses. Last but not least, we construct a five-layer architecture and abstract four principles on the connectivity building of a smart home for future Chinese elders. The research explores the possibility of building a wide range of connectivity and all-around happiness smart home environment for future elderly, which provides significant insights and an important reference for both the smart home industry and the pension industry. Full article
(This article belongs to the Special Issue Emotion-Aware Intelligent Environments)
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Graphical abstract

22 pages, 3068 KiB  
Article
Empowering Museum Experiences Applying Gamification Techniques Based on Linked Data and Smart Objects
by Alejandro López-Martínez, Álvaro Carrera and Carlos A. Iglesias
Appl. Sci. 2020, 10(16), 5419; https://doi.org/10.3390/app10165419 - 5 Aug 2020
Cited by 27 | Viewed by 6050
Abstract
Museums play a crucial role in preserving cultural heritage. However, the forms in which they display cultural heritage might not be the most effective at piquing visitors’ interest. Therefore, museums tend to integrate different technologies that aim to create engaging and memorable experiences. [...] Read more.
Museums play a crucial role in preserving cultural heritage. However, the forms in which they display cultural heritage might not be the most effective at piquing visitors’ interest. Therefore, museums tend to integrate different technologies that aim to create engaging and memorable experiences. In this context, the emerging Internet of Things (IoT) technology results particularly promising due to the possibility of implementing smart objects in museums, granting exhibits advanced interaction capabilities. Gamification techniques are also a powerful technique to draw visitors’ attention. These often rely on interactive question-based games. A drawback of such games is that questions must be periodically regenerated, and this is a time-consuming task. To confront these challenges, this paper proposes a low-maintenance gamified smart object platform that automates the creation of questions by exploiting semantic web technologies. The platform has been implemented in a real-life scenario. The results obtained encourage the use of the platform in the museum considered. Therefore, it appears to be a promising work that could be extrapolated and adapted to other kinds of museums or cultural heritage institutions. Full article
(This article belongs to the Special Issue Emotion-Aware Intelligent Environments)
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