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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: 31 August 2025 | Viewed by 10676

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
Milton Stewart School of Industrial and Systems Engineering, 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.

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Published Papers (7 papers)

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Research

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22 pages, 4213 KiB  
Article
User Experience of Virtual Human and Immersive Virtual Reality Role-Playing in Psychological Testing and Assessment: A Case Study of ‘EmpathyVR’
by Sunny Thapa Magar, Haejung Suk and Teemu H. Laine
Sensors 2025, 25(9), 2719; https://doi.org/10.3390/s25092719 - 25 Apr 2025
Viewed by 146
Abstract
Recent immersive virtual reality (IVR) technologies provide users with an enhanced sense of spatial and social presence by integrating various modern technologies into virtual spaces and virtual humans (VHs). Researchers and practitioners in psychology are attempting to understand the psychological processes underlying human [...] Read more.
Recent immersive virtual reality (IVR) technologies provide users with an enhanced sense of spatial and social presence by integrating various modern technologies into virtual spaces and virtual humans (VHs). Researchers and practitioners in psychology are attempting to understand the psychological processes underlying human behavior by allowing users to engage in realistic experiences within illusions enabled by IVR technologies. This study examined the user experience of role-playing with VHs in the context of IVR-based psychological testing and assessment (PTA) with a focus on EmpathyVR, an IVR-based empathy-type assessment tool developed in an interdisciplinary project. This study aimed to evaluate the advantages and disadvantages of integrating IVR-based role-playing with VHs into PTA by examining user immersion, embodiment, and satisfaction. A mixed-method approach was used to collect data from 99 Korean adolescents. While the participants reported high levels of immersion and satisfaction, the sense of embodiment varied across the correspondents, suggesting that users may have had disparate experiences in terms of their connection to the virtual body. This study highlights the potential of IVR-based role-playing with VHs to enhance PTA, particularly in empathy-related assessments, while underscoring areas for improvement in user adaptation and VH realism. The results suggest that IVR experiences based on role-playing with VHs may be feasible for PTA to advance user experience and engagement. Full article
(This article belongs to the Special Issue Virtual Reality and Sensing Techniques for Human)
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17 pages, 3900 KiB  
Article
A Deep Learning Approach for Mental Fatigue State Assessment
by Jiaxing Fan, Lin Dong, Gang Sun and Zhize Zhou
Sensors 2025, 25(2), 555; https://doi.org/10.3390/s25020555 - 19 Jan 2025
Viewed by 1021
Abstract
This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) [...] Read more.
This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.29% in identifying fatigue from original ECG data, 2D spectral characteristics and physiological information of subjects. In comparison to traditional methods, such as Support Vector Machines (SVMs) and Random Forests (RFs), as well as other deep learning methods, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), the proposed approach demonstrates significantly improved experimental outcomes. Overall, this study offers a promising solution for accurately recognizing fatigue through the analysis of physiological signals, with potential applications in sports and physical fitness training contexts. Full article
(This article belongs to the Special Issue Virtual Reality and Sensing Techniques for Human)
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26 pages, 559 KiB  
Article
A Petri Net and LSTM Hybrid Approach for Intrusion Detection Systems in Enterprise Networks
by Gaetano Volpe, Marco Fiore, Annabella la Grasta, Francesca Albano, Sergio Stefanizzi, Marina Mongiello and Agostino Marcello Mangini
Sensors 2024, 24(24), 7924; https://doi.org/10.3390/s24247924 - 11 Dec 2024
Cited by 1 | Viewed by 1129
Abstract
Intrusion Detection Systems (IDSs) are a crucial component of modern corporate firewalls. The ability of IDS to identify malicious traffic is a powerful tool to prevent potential attacks and keep a corporate network secure. In this context, Machine Learning (ML)-based methods have proven [...] Read more.
Intrusion Detection Systems (IDSs) are a crucial component of modern corporate firewalls. The ability of IDS to identify malicious traffic is a powerful tool to prevent potential attacks and keep a corporate network secure. In this context, Machine Learning (ML)-based methods have proven to be very effective for attack identification. However, traditional approaches are not always applicable in a real-time environment as they do not integrate concrete traffic management after a malicious packet pattern has been identified. In this paper, a novel combined approach to both identify and discard potential malicious traffic in a real-time fashion is proposed. In detail, a Long Short-Term Memory (LSTM) supervised artificial neural network model is provided in which consecutive packet groups are considered as they flow through the corporate network. Moreover, the whole IDS architecture is modeled by a Petri Net (PN) that either blocks or allows packet flow throughout the network based on the LSTM model output. The novel hybrid approach combining LSTM with Petri Nets achieves a 99.71% detection accuracy—a notable improvement over traditional LSTM-only methods, which averaged around 97%. The LSTM–Petri Net approach is an innovative solution combining machine learning with formal network modeling for enhanced threat detection, offering improved accuracy and real-time adaptability to meet the rapid security needs of virtual environments and CPS. Moreover, the approach emphasizes the innovative role of the Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) as a form of “virtual sensing technology” applied to advanced network security. An extensive case study with promising results is provided by training the model with the popular IDS 2018 dataset. Full article
(This article belongs to the Special Issue Virtual Reality and Sensing Techniques for Human)
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16 pages, 4662 KiB  
Article
How to Make the Skin Contact Area Controllable by Optical Calibration in Wearable Tactile Displays of Softness
by Gabriele Frediani and Federico Carpi
Sensors 2024, 24(20), 6770; https://doi.org/10.3390/s24206770 - 21 Oct 2024
Viewed by 1181
Abstract
Virtual reality systems may benefit from wearable (fingertip-mounted) haptic displays capable of rendering the softness of virtual objects. According to neurophysiological evidence, the easiest reliable way to render a virtual softness is to generate purely tactile (as opposed to kinaesthetic) feedback to be [...] Read more.
Virtual reality systems may benefit from wearable (fingertip-mounted) haptic displays capable of rendering the softness of virtual objects. According to neurophysiological evidence, the easiest reliable way to render a virtual softness is to generate purely tactile (as opposed to kinaesthetic) feedback to be delivered via a finger-pulp-interfaced deformable surface. Moreover, it is necessary to control not only the skin indentation depth by applying quasi-static (non-vibratory) contact pressures, but also the skin contact area. This is typically impossible with available devices, even with those that can vary the contact area, because the latter cannot be controlled due to the complexity of sensing it at high resolutions. This causes indetermination on an important tactile cue to render softness. Here, we present a technology that allows the contact area to be open-loop controlled via personalised optical calibrations. We demonstrate the solution on a modified, pneumatic wearable tactile display of softness previously described by us, consisting of a small chamber containing a transparent membrane inflated against the finger pulp. A window on the device allowed for monitoring the skin contact area with a camera from an external unit to generate a calibration curve by processing photos of the skin membrane interface at different pressures. The solution was validated by comparisons with an ink-stain-based method. Moreover, to avoid manual calibrations, a preliminary automated procedure was developed. This calibration strategy may be applied also to other kinds of displays where finger pulps are in contact with transparent deformable structures. Full article
(This article belongs to the Special Issue Virtual Reality and Sensing Techniques for Human)
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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
Cited by 1 | Viewed by 1677
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 - 4 Jan 2024
Cited by 3 | Viewed by 2533
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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Review

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22 pages, 2874 KiB  
Review
Leveraging Wearable Sensors in Virtual Reality Driving Simulators: A Review of Techniques and Applications
by Răzvan Gabriel Boboc, Eugen Valentin Butilă and Silviu Butnariu
Sensors 2024, 24(13), 4417; https://doi.org/10.3390/s24134417 - 8 Jul 2024
Cited by 1 | Viewed by 2004
Abstract
Virtual reality (VR) driving simulators are very promising tools for driver assessment since they provide a controlled and adaptable setting for behavior analysis. At the same time, wearable sensor technology provides a well-suited and valuable approach to evaluating the behavior of drivers and [...] Read more.
Virtual reality (VR) driving simulators are very promising tools for driver assessment since they provide a controlled and adaptable setting for behavior analysis. At the same time, wearable sensor technology provides a well-suited and valuable approach to evaluating the behavior of drivers and their physiological or psychological state. This review paper investigates the potential of wearable sensors in VR driving simulators. Methods: A literature search was performed on four databases (Scopus, Web of Science, Science Direct, and IEEE Xplore) using appropriate search terms to retrieve scientific articles from a period of eleven years, from 2013 to 2023. Results: After removing duplicates and irrelevant papers, 44 studies were selected for analysis. Some important aspects were extracted and presented: the number of publications per year, countries of publication, the source of publications, study aims, characteristics of the participants, and types of wearable sensors. Moreover, an analysis and discussion of different aspects are provided. To improve car simulators that use virtual reality technologies and boost the effectiveness of particular driver training programs, data from the studies included in this systematic review and those scheduled for the upcoming years may be of interest. Full article
(This article belongs to the Special Issue Virtual Reality and Sensing Techniques for Human)
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