Intelligent Systems and Immersive Technologies for Human–Machine Interaction and Collaboration

Topic Information

Dear Colleagues,

This Topic invites contributions on intelligent systems that support effective, adaptive, and trustworthy interaction among humans, machines, and smart environments. The Topic focuses on applied research at the intersection of artificial intelligence, human–computer interaction, immersive technologies, robotics, the Internet of Things, and cyber–physical systems.

We welcome studies on AI-enabled interfaces, multimodal and affective interaction, human–AI and human–robot collaboration, extended reality (XR), context-aware and personalized systems, digital twins, and intelligent environments. Submissions may address methodological advances, component-level innovations, or integrated end-to-end systems, provided that they offer clear technical novelty, application relevance, or rigorous evaluation.

Particular interest is given to systems that combine sensing, inference, interaction, and actuation across physical and digital settings. Relevant contributions include adaptive interfaces, predictive and anticipatory models, collaborative intelligent agents, wearable and physiological computing, conversational and multimodal AI, and immersive systems for training, decision support, and operational assistance. Research on machine-to-machine coordination is also welcome when it strengthens or extends human–machine interaction and collaboration.

This Topic welcomes original research articles, review articles, and application-driven case studies. Contributions should demonstrate strong methodological quality and appropriate validation through experiments, comparative analysis, user studies, simulations, benchmark evaluations, or real-world deployments. Application domains may include healthcare, manufacturing, education, assistive technologies, transportation, industrial automation, and smart environments.

The aim of this Topic is to advance the design, integration, and evaluation of intelligent interactive systems that improve collaboration across human, digital, and physical contexts.

Topics of interest include, but are not limited to, the following:

• AI-enabled interfaces and intelligent user interaction;
• Verification and validation of intelligent systems;
• Multimodal interaction using speech, gesture, gaze, touch, and physiological signals;
• Context-aware, adaptive, and personalized human–machine systems;
• Human–AI collaboration and human-in-the-loop intelligent systems;
• Human–robot interaction and shared autonomy;
• Extended reality (AR, VR, MR) for training, assistance, and decision support;
• Conversational agents, multimodal foundation models, and natural language interaction;
• Affective computing, wearable sensing, and physiologically aware systems;
• IoT-enabled smart environments and cyber–physical interaction;
• Multi-agent systems and machine-to-machine coordination in human-centered settings
• Digital twins and simulation-based support for interactive systems;
• Trustworthy AI, explainability, privacy, safety, and ethics in human-facing systems;
• Evaluation methods, benchmarks, datasets, and real-world deployments.

Prof. Dr. Vassilis Charissis
Prof. Dr. Dimitris Drikakis
Dr. Mónica Ballesta Galdeano
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Computation computation	2.6	5.2	2013	13.6 Days	CHF 1800	Submit
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
Modelling modelling	1.8	2.4	2020	22.7 Days	CHF 1200	Submit
Robotics robotics	3.6	7.4	2012	20 Days	CHF 1800	Submit
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600	Submit
Virtual Worlds virtualworlds	-	7.6	2022	17.4 Days	CHF 1200	Submit

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Published Papers (4 papers)

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All Electronics Modelling Robotics Sensors Virtual Worlds Applied Sciences Computation

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23 pages, 2139 KB  

Open AccessArticle

Physiological Signal-Guided Uncertainty Management for Autonomous UAVs in Human–UAV Supervisory Control

by Jun Che, Feng Zhu and Hanbin Xiao

Computation 2026, 14(8), 192; https://doi.org/10.3390/computation14080192 - 20 Aug 2026

Abstract

This paper presents a physiological signal-guided uncertainty-management framework that integrates real-time indicators of the operator’s supervisory state into UAV autonomy to improve obstacle avoidance and risk-aware decision-making under uncertain conditions. During UAV supervisory control, multimodal physiological signals, including heart rate variability, blood pressure, [...] Read more.

This paper presents a physiological signal-guided uncertainty-management framework that integrates real-time indicators of the operator’s supervisory state into UAV autonomy to improve obstacle avoidance and risk-aware decision-making under uncertain conditions. During UAV supervisory control, multimodal physiological signals, including heart rate variability, blood pressure, electrodermal activity, and other cardiovascular or stress-related measures, are time-synchronized with UAV telemetry, perceived obstacle fields, planner confidence, environmental uncertainty estimates, and operator intervention logs, including waypoint edits, overrides, and replanning commands. These heterogeneous data streams are fused using a Bayesian hierarchical state-space framework to estimate latent supervisory states representing trust miscalibration, risk sensitivity, and situational-awareness degradation. The estimated states are then incorporated into the UAV decision-making stack as bounded uncertainty-management parameters that regulate safety margins, replanning priority, and risk preference without relaxing hard safety constraints. The framework was evaluated in a simulation-based human-in-the-loop study involving 24 operators and 180 UAV obstacle avoidance missions. Performance was assessed using held-out-operator mission success AUROC, proxy-state RMSE, mission success rate, mean minimum obstacle clearance, mission risk index, operator override rate, and replanning latency. The results support the feasibility of using physiological, behavioral, vehicle, and environmental information to adapt UAV supervisory control to uncertainty in both the operating environment and human supervisory readiness. Full article

(This article belongs to the Topic Intelligent Systems and Immersive Technologies for Human–Machine Interaction and Collaboration)

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16 pages, 8503 KB  

Open AccessArticle

An Airflow-Based Thermal–Tactile–Olfactory Display: Performance Evaluation Under AC and DC Airflow Conditions

by Rıza Ilhan

Sensors 2026, 26(16), 5139; https://doi.org/10.3390/s26165139 - 14 Aug 2026

Viewed by 258

Abstract

Developing a multimodal tactile display is a key area of interest for haptic scientists, with researchers continuously exploring new methods to achieve this goal. This study introduces a tactile–olfactory display capable of providing touch, temperature, and odor feedback. The display utilizes airflow to [...] Read more.

Developing a multimodal tactile display is a key area of interest for haptic scientists, with researchers continuously exploring new methods to achieve this goal. This study introduces a tactile–olfactory display capable of providing touch, temperature, and odor feedback. The display utilizes airflow to deliver feedback to the user, incorporating two air sources and thermoelectric components (Peltier elements). Unlike traditional technologies, it employs convection-based temperature stimulation, where air passing over the thermoelectric modules cools or warms, resulting in temperature modulation. The system was tested under steady airflow conditions (DC airflow) and frequency-modulated airflow conditions (AC airflow). First, a finite element simulation was conducted in Ansys to gain insights into the system parameters. This was followed by experimental evaluations to extract its characteristics and assess its performance. The results indicate that not only the type of airflow but also its rate and frequency play significant roles in rendering surface parameters. Additionally, this airflow-based tactile display has potential applications in both contact and noncontact haptic technologies. Full article

(This article belongs to the Topic Intelligent Systems and Immersive Technologies for Human–Machine Interaction and Collaboration)

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46 pages, 1947 KB  

Open AccessReview

Towards the Regulation of Standardised External Human–Machine Interfaces for Automated Vehicles: A Systematic Literature Review of Achievements from 2016 to 2026

by Ru Li, Jonas Bix, Derrick G. Watson and Tran Quoc Khanh

Appl. Sci. 2026, 16(16), 8095; https://doi.org/10.3390/app16168095 - 13 Aug 2026

Viewed by 340

Abstract

The upcoming proliferation of Automated Vehicles (AVs) above Level 3 fundamentally disrupts established communicative conventions between road users and vehicles, and external Human–Machine Interface (eHMI) emerges as the primary channel for conveying vehicle intent to pedestrians, cyclists, and other drivers. Despite the standardisation [...] Read more.

The upcoming proliferation of Automated Vehicles (AVs) above Level 3 fundamentally disrupts established communicative conventions between road users and vehicles, and external Human–Machine Interface (eHMI) emerges as the primary channel for conveying vehicle intent to pedestrians, cyclists, and other drivers. Despite the standardisation of the marker lamp of AVs and the growing research activity since 2016, no internationally harmonized eHMI standard has been established as of early 2026. This Systematic Literature Review (SLR), following the PRISMA 2020 protocol, synthesizes empirical and technical findings from 149 peer-reviewed English journal articles spanning the decade from 2016 to 2026, from Web of Science, Scopus, IEEE Xplore, and Google Scholar. The review covers scenario design, test methodologies, participant demographics, eHMI modalities, and performance evaluation measures by mapping, coding, and presenting in a structured framework the reviewed papers. The key findings are as follows: (1) 13 use cases (50%, out of 26) still need to be further studied; (2) vehicle kinematics remain a primary determinant of crossing decisions, with eHMI signals serving as confirmatory rather than primary cues in many scenarios; and (3) cross-cultural and demographic diversity in empirical studies remains critically insufficient for global regulatory purposes. We synthesize these findings into a structured framework of evidence-based recommendations for international eHMI standardisation organizations and identify critical research gaps requiring resolution before comprehensive standards can be enacted. Full article

(This article belongs to the Topic Intelligent Systems and Immersive Technologies for Human–Machine Interaction and Collaboration)

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25 pages, 2437 KB  

Open AccessArticle

Immersive Teleoperation of Adaptive Mobile Robots: Evaluating Human Factors and Network Resilience in Mixed Reality

by Alikhan Khamidulla, Gourav Devappa Moger and Huseyin Atakan Varol

Robotics 2026, 15(8), 149; https://doi.org/10.3390/robotics15080149 - 6 Aug 2026

Viewed by 252

Abstract

Efficient robotic teleoperation for industrial inspection requires interfaces that maximize intuitive control and situational awareness. While traditional mixed reality (MR) systems offer alternatives, standard controller-based methods lack environment customization and demand external peripheral hardware. This study introduces a peripheral-hardware-free, customizable 3D spatial virtual [...] Read more.

Efficient robotic teleoperation for industrial inspection requires interfaces that maximize intuitive control and situational awareness. While traditional mixed reality (MR) systems offer alternatives, standard controller-based methods lack environment customization and demand external peripheral hardware. This study introduces a peripheral-hardware-free, customizable 3D spatial virtual cockpit application deployed on a Meta Quest 3 headset for long-distance, non-line-of-sight teleoperation of the Improbability Roller-2, a mobile robotic platform that dynamically adjusts its wheel geometry to traverse varied terrain over a Virtual Private Network (VPN). The system’s novel spatial cockpit architecture allows operators to scale multi-channel parameters dynamically without relying on physical hardware controllers. The framework was evaluated using transmission-quality benchmarks, an active industrial machine shop deployment, and a 20-participant user study assessing usability and cognitive load. Experimental results yielded a mean System Usability Scale (SUS) score of 82.75, corresponding to an excellent usability rating, and a low mean operator workload, with a NASA Task Load Index (NASA-TLX) score of 5.19 out of 21. Participants also rated the workspace customization feature positively, assigning it a rating of 4.6 out of 5. In terms of communication performance, the system successfully completed all inspection tasks even under severely degraded VPN conditions. These findings demonstrate that the proposed customizable 3D spatial interface provides a robust, scalable alternative to traditional controller-based setups for long-distance remote robotic inspection in real-world industrial settings. Full article

(This article belongs to the Topic Intelligent Systems and Immersive Technologies for Human–Machine Interaction and Collaboration)

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