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Special Issue "Human-Computer Interaction in Pervasive Computing Environments"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 15 February 2021.

Special Issue Editors

Dr. Alicia García-Holgado
Website1 Website2 SciProfiles
Guest Editor
Department of Computer Science, University of Salamanca, 37008 Salamanca, Spain
Interests: technological ecosystems; knowledge management; information systems; software engineering; model-driven engineering; model-driven architecture; human–computer interaction; human factors in software engineering; social responsibility and inclusion
Dr. Brij B Gupta
Guest Editor
National Institute of Technology Kurukshetra, 136119 Haryana, India
Interests: information security; cybersecurity; cloud computing; web security; sensor networks; multimedia; healthcare
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Sensors are widely used in everyday life today. They are present in a wide variety of areas, offering an excellent opportunity to face challenges related to medicine and healthcare, smart cities, smart homes, smart learning, and entertainement, among others. Sensors bring technology closer to humans in an increasingly transparent and natural approach, building genuine technological ecosystems in which human–computer interaction plays a key role.

Despite the penetration of the sensors, though, it is necessary to continue improving their design, implementation, and use to improve usability, accessibility, and user experience in smart environments.

The aim of this Special Issue is to highlight recent advances and trends in human–computer interaction in pervasive computing environments. It will address a broad range of topics related to smart environments, including (but not limited) to the following:

  • Usability, accessibility, and sustainability;
  • User experience;
  • Natural user interfaces;
  • Sensors networks;
  • Haptic computing;
  • Ambient-assisted living;
  • Healthcare environments;
  • Smart cities design;
  • Multimodal systems and interfaces;
  • IoT dashboards and platforms;
  • Technological ecosystems for smart enviroments;
  • Service-oriented information visualization;
  • Smart interfaces for learning;
  • Ambient and pervasive interactions.

Dr. Alicia García-Holgado
Dr. Brij B. Gupta
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Smart environment
  • Smart spaces
  • Smart cities
  • Smart interfaces for learning
  • Human–computer Interaction
  • Usability, accessibility and sustainability
  • User experience
  • Natural user interfaces
  • Sensors networks
  • Haptic computing
  • Ambient assisted living
  • Healthcare Environments
  • Multimodal systems
  • Multimodal interfaces
  • IoT dashboards
  • IoT platforms
  • Technological ecosystems
  • Service-oriented information visualization
  • Ambient and pervasive interactions

Published Papers (1 paper)

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Open AccessArticle
Gaze in the Dark: Gaze Estimation in a Low-Light Environment with Generative Adversarial Networks
Sensors 2020, 20(17), 4935; - 31 Aug 2020
In smart interactive environments, such as digital museums or digital exhibition halls, it is important to accurately understand the user’s intent to ensure successful and natural interaction with the exhibition. In the context of predicting user intent, gaze estimation technology has been considered [...] Read more.
In smart interactive environments, such as digital museums or digital exhibition halls, it is important to accurately understand the user’s intent to ensure successful and natural interaction with the exhibition. In the context of predicting user intent, gaze estimation technology has been considered one of the most effective indicators among recently developed interaction techniques (e.g., face orientation estimation, body tracking, and gesture recognition). Previous gaze estimation techniques, however, are known to be effective only in a controlled lab environment under normal lighting conditions. In this study, we propose a novel deep learning-based approach to achieve a successful gaze estimation under various low-light conditions, which is anticipated to be more practical for smart interaction scenarios. The proposed approach utilizes a generative adversarial network (GAN) to enhance users’ eye images captured under low-light conditions, thereby restoring missing information for gaze estimation. Afterward, the GAN-recovered images are fed into the convolutional neural network architecture as input data to estimate the direction of the user gaze. Our experimental results on the modified MPIIGaze dataset demonstrate that the proposed approach achieves an average performance improvement of 4.53%–8.9% under low and dark light conditions, which is a promising step toward further research. Full article
(This article belongs to the Special Issue Human-Computer Interaction in Pervasive Computing Environments)
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