Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
Advances in Human–Robot Interactions Through Multimodal Sensing and Virtual Reality
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Human–Robot Interactions Through Multimodal Sensing and Virtual Reality

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2750

Special Issue Editors

Prof. Dr. Nilanjan Sarkar

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37212, USA
Interests: human–robot interactions; human–computer interactions; affective computing; physiological sensing; multimodal sensing; interventions for autism; interventions for dementia
Special Issues, Collections and Topics in MDPI journals
Dr. Miroslava Migovich

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37212, USA
Interests: human–robot interactions; human–computer interactions; affective computing; physiological sensing; multimodal sensing; interventions for autism; interventions for dementia

Special Issue Information

Dear Colleagues,

Recent advances in human–robot interactions (HRIs) increasingly draw upon the integration of multimodal sensing and immersive virtual reality (VR) technologies to support adaptive, context-aware, and user-centered robotic systems. These innovations, spanning affective computing, behavioral sensing, and immersive simulation, are driving new models of interaction that enhance communication, collaboration, and engagement between humans and robots across various settings, including healthcare, rehabilitation, education, and socially assistive contexts. Multimodal systems, incorporating visual, auditory, physiological, and motion-based inputs, provide a rich substrate for interpreting complex human states and enabling real-time, personalized responses from autonomous systems. The convergence of these sensing modalities with VR-based environments supports safe, repeatable, and ecologically valid testing grounds for the development and refinement of intelligent robotic platforms. As HRI becomes increasingly embedded in everyday contexts, the ability to reliably sense and interpret human affective and cognitive states through integrated sensor frameworks will be critical to supporting user acceptance, promoting meaningful interaction, and ensuring long-term engagement. This Special Issue seeks contributions that reflect interdisciplinary and translational approaches to advancing HRI through novel sensing frameworks, algorithmic methods, system-level design, and virtual environments grounded in human-centered principles.

Fit with the scope of Sensors: This special issue aligns with Sensors by showcasing advances in multimodal sensing technologies and algorithms that enable adaptive human–robot interactions within virtual reality environments. This Special Issue.

“Advances in Human–Robot Interactions through Multimodal Sensing and Virtual Reality”, aims to present state-of-the-art human–robot interactions, highlighting multimodal sensing and virtual reality-based frameworks that open new possibilities for enhancing user engagement, user outcomes, and real-time system adaptability.

Prof. Dr. Nilanjan Sarkar
Dr. Miroslava Migovich
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 250 words) can be sent to the Editorial Office for assessment.

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.

Keywords

  • human–robot interactions
  • multimodal sensing
  • virtual reality
  • affective computing
  • sensor integration
  • user-centered robotics
  • assistive technology
  • emotion recognition
  • intelligent and interactive systems
  • cognitive and social robotics

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 2910 KB  
Article
Physiological Impact of Chromatic-Weight Illusions in Augmented Reality: A Comparative sEMG Analysis of Muscle Fatigue and Stability
by Jun Wang, Julia Greenfield, Peter Mitrouchev, Guiqin Li and Franck Quaine
Sensors 2026, 26(9), 2575; https://doi.org/10.3390/s26092575 - 22 Apr 2026
Viewed by 358
Abstract
During manual operations, the human brain relies on mediated visual stimuli such as color to estimate an object’s weight and adjust muscle force through the central nervous system (CNS). This study examines the neuromuscular “reality gap” induced by the color–weight illusion (CWI) during [...] Read more.
During manual operations, the human brain relies on mediated visual stimuli such as color to estimate an object’s weight and adjust muscle force through the central nervous system (CNS). This study examines the neuromuscular “reality gap” induced by the color–weight illusion (CWI) during repetitive lifting tasks in an augmented reality (AR) interface. We analyzed the median frequency (MDF) and Co-Contraction Index (CCI) of the biceps and triceps muscles to quantify physiological strain under varying luminance conditions in both AR and physical environments. The results reveal that AR significantly amplifies the CWI, with black stimuli triggering an aggressive joint-stiffening strategy in the AR group (APG). Compared with the physical reality group, the AR group showed lower overall endurance (91.4 ± 22.8 vs. 100.1 ± 12.5 repetitions) and a stronger physiological response to the black stimulus. In the AR group, the black condition was associated with a terminal CCI of 84.7 ± 25.4% and an MDF decline of approximately 21.7 Hz, whereas the corresponding contrast was attenuated in the physical reality group. These findings demonstrate a critical decoupling between behavioral output and internal physiological strain, indicating that the CNS treats virtual visual cues as high-reliability signals that increase metabolic “bio-cost” despite task completion parity. This research identifies a “masking effect” where behavioral metrics hide severe ergonomic risks, providing novel approaches for managing musculoskeletal health in industrial settings and personalizing coordination training in clinical rehabilitation. Full article
(This article belongs to the Special Issue Advances in Human–Robot Interactions Through Multimodal Sensing and Virtual Reality)
►▼ Show Figures

Figure 1

25 pages, 4607 KB  
Article
Integrating EEG Sensors with Virtual Reality to Support Students with ADHD
by Juriaan Wolfers, William Hurst and Caspar Krampe
Sensors 2026, 26(3), 1017; https://doi.org/10.3390/s26031017 - 4 Feb 2026
Viewed by 848
Abstract
Students with attention deficit hyperactivity disorder (ADHD) face a continuous challenge with their attention span, putting them at a greater risk of academic or psychological difficulties compared to their peers. Innovative communication technologies are demonstrating potential to address these attention-span concerns. Virtual Reality [...] Read more.
Students with attention deficit hyperactivity disorder (ADHD) face a continuous challenge with their attention span, putting them at a greater risk of academic or psychological difficulties compared to their peers. Innovative communication technologies are demonstrating potential to address these attention-span concerns. Virtual Reality (VR) is one such example, and has the potential to address attention-span difficulties among ADHD students. Accordingly, this study presents an EEG-based multimodal sensing pipeline as a methodological contribution, focusing on sensor-based data acquisition, signal processing, and neurophysiological interpretation to assess attention in VR-based environments, simulating a university supply chain educational topic. Thus, in this paper, a sequential exploratory approach investigated how 35 participants experienced an interactive VR-learning-driven supply chain game. A Brain–Computer Interaction (BCI) sensor generated insights by quantitatively analysing electroencephalogram (EEG) data that were processed through the proposed pipeline and integrated with subjective measures to validate participant’s subjective feelings. These insights originated from questions during the experiment that followed the Spatial Presence and Technology Acceptance Model to form a multimodal assessment framework. Findings demonstrated that the experimental group experienced a higher improved attention, concentration, engagement, and focus levels compared to the control group. BCI results from the experimental group showed more dominant voltage potentials in the right frontal and prefrontal cortex of the brain in areas responsible for attention, memory, and decision-making. A high acceptance of the VR technology among neurodiverse students highlights the added benefits of multimodal learning assessment methods in an educational setting. Full article
(This article belongs to the Special Issue Advances in Human–Robot Interactions Through Multimodal Sensing and Virtual Reality)
►▼ Show Figures

Figure 1

20 pages, 4891 KB  
Article
Active Inference Modeling of Socially Shared Cognition in Virtual Reality
by Yoshiko Arima and Mahiro Okada
Sensors 2026, 26(2), 604; https://doi.org/10.3390/s26020604 - 16 Jan 2026
Viewed by 819
Abstract
This study proposes a process model for sharing ambiguous category concepts in virtual reality (VR) using an active inference framework. The model executes a dual-layer Bayesian update after observing both self and partner actions and predicts actions that minimize free energy. To incorporate [...] Read more.
This study proposes a process model for sharing ambiguous category concepts in virtual reality (VR) using an active inference framework. The model executes a dual-layer Bayesian update after observing both self and partner actions and predicts actions that minimize free energy. To incorporate agreement-seeking with others into active inference, we added disagreement in category judgments as a risk term in the free energy, weighted by gaze synchrony measured using Dynamic Time Warping (DTW), which is assumed to reflect joint attention. To validate the model, an object classification task in VR including ambiguous items was created. The experiment was conducted first under a bot avatar condition, in which ambiguous category judgments were always incorrect, and then under a human–human pair condition. This design allowed verification of the collaborative learning process by which human pairs reached agreement from the same degree of ambiguity. Analysis of experimental data from 14 participants showed that the model achieved high prediction accuracy for observed values as learning progressed. Introducing gaze synchrony weighting (γ0≥0.5) further improved prediction accuracy, yielding optimal performance. This approach provides a new framework for modeling socially shared cognition using active inference in human–robot interaction contexts. Full article
(This article belongs to the Special Issue Advances in Human–Robot Interactions Through Multimodal Sensing and Virtual Reality)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop