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Virtual Reality and Sensing Techniques for Human
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Virtual Reality and Sensing Techniques for Human

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1685

Special Issue Editors

Dr. Răzvan Gabriel Boboc

E-Mail
Guest Editor
Department of Automotive and Transport Engineering, Transilvania University of Brașov, 500036 Brașov, Romania
Interests: robotics; virtual reality; artificial intelligence; mechanics
Special Issues, Collections and Topics in MDPI journals
Dr. Ali Vatankhah Barenji

E-Mail Website
Guest Editor
Georgia Institute of Technology, Atlanta, GA, USA
Interests: blockchain; multi-agent systems; real time control and scheduling; virtual reality; robotics; IoT

Special Issue Information

Dear Colleagues,

Virtual reality (VR) has evolved as a transformative platform for developing immersive and interactive experiences, enabling new ways for people to interact with digital content, environments, and one another. Expertise in computer graphics, human–computer interaction, cognitive psychology, sensor technology, and other fields is combined in this multidisciplinary field. With the goal of improving the quality, realism, and efficacy of human interaction in virtual environments, this Special Issue is looking for ground-breaking research, cutting-edge methodologies, and real-world applications that explore the relationship between VR and sensing techniques. Topics of interest include, but are not limited to:

  • VR-based interaction design;
  • Multisensory experiences;
  • Sensor fusion for VR;
  • Embodiment and presence;
  • Social interaction in VR;
  • Ethical and privacy considerations;
  • Health and well-being applications.

Dr. Răzvan Gabriel Boboc
Dr. Ali Vatankhah Barenji
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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 submissions that pass pre-check are 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 2600 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.

Published Papers (2 papers)

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Research

13 pages, 1757 KiB  
Article
A Comparison of Head Movement Classification Methods
by Chloe Callahan-Flintoft, Emily Jensen, Jasim Naeem, Michael W. Nonte, Anna M. Madison and Anthony J. Ries
Sensors 2024, 24(4), 1260; https://doi.org/10.3390/s24041260 - 16 Feb 2024
Viewed by 480
Abstract
To understand human behavior, it is essential to study it in the context of natural movement in immersive, three-dimensional environments. Virtual reality (VR), with head-mounted displays, offers an unprecedented compromise between ecological validity and experimental control. However, such technological advancements mean that new [...] Read more.
To understand human behavior, it is essential to study it in the context of natural movement in immersive, three-dimensional environments. Virtual reality (VR), with head-mounted displays, offers an unprecedented compromise between ecological validity and experimental control. However, such technological advancements mean that new data streams will become more widely available, and therefore, a need arises to standardize methodologies by which these streams are analyzed. One such data stream is that of head position and rotation tracking, now made easily available from head-mounted systems. The current study presents five candidate algorithms of varying complexity for classifying head movements. Each algorithm is compared against human rater classifications and graded based on the overall agreement as well as biases in metrics such as movement onset/offset time and movement amplitude. Finally, we conclude this article by offering recommendations for the best practices and considerations for VR researchers looking to incorporate head movement analysis in their future studies. Full article
(This article belongs to the Special Issue Virtual Reality and Sensing Techniques for Human)
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12 pages, 6624 KiB  
Article
A Study on E-Nose System in Terms of the Learning Efficiency and Accuracy of Boosting Approaches
by Il-Sik Chang, Sung-Woo Byun, Tae-Beom Lim and Goo-Man Park
Sensors 2024, 24(1), 302; https://doi.org/10.3390/s24010302 - 04 Jan 2024
Viewed by 828
Abstract
With the development of the field of e-nose research, the potential for application is increasing in various fields, such as leak measurement, environmental monitoring, and virtual reality. In this study, we characterize electronic nose data as structured data and investigate and analyze the [...] Read more.
With the development of the field of e-nose research, the potential for application is increasing in various fields, such as leak measurement, environmental monitoring, and virtual reality. In this study, we characterize electronic nose data as structured data and investigate and analyze the learning efficiency and accuracy of deep learning models that use unstructured data. For this purpose, we use the MOX sensor dataset collected in a wind tunnel, which is one of the most popular public datasets in electronic nose research. Additionally, a gas detection platform was constructed using commercial sensors and embedded boards, and experimental data were collected in a hood environment such as used in chemical experiments. We investigated the accuracy and learning efficiency of deep learning models such as deep learning networks, convolutional neural networks, and long short-term memory, as well as boosting models, which are robust models on structured data, using both public and specially collected datasets. The results showed that the boosting models had a faster and more robust performance than deep learning series models. Full article
(This article belongs to the Special Issue Virtual Reality and Sensing Techniques for Human)
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