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From Human–Machine Interaction to Human–Machine Cooperation: Status and Progress, 2nd Edition

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 November 2026 | Viewed by 11658

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Editors

Dr. Tomislav Stipančić

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Guest Editor

Department of Robotics and Production System Automation, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lucica 5, 10000 Zagreb, Croatia
Interests: human-machine interaction; cognitive informatics; smart robotics; virtual agents; IoT; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Katerina Kabassi

E-Mail Website
Guest Editor

Department of Environment, Ionian University, Ionian Islands, 49132 Kerkira, Greece
Interests: software engineering; user modeling; human–computer interaction
Special Issues, Collections and Topics in MDPI journals

Dr. Yuchong Zhang

E-Mail Website
Guest Editor Assistant

Division of Robotics, Perception and Learning, The Royal Institute of Technology (KTH), Stockholm, Sweden
Interests: human-computer interaction; human-robot interaction; augmented/mixed reality; human-centered design; human perception; affective computing; interactive AI

Special Issue Information

Dear Colleagues,

This Special Issue is the second issue of “From Human–Machine Interaction to Human–Machine Cooperation: Status and Progress”; the first issue can be found at the following link: https://www.mdpi.com/journal/applsci/special_issues/ONT0Y07M7C.

Human–machine interaction is all about how people and automated systems interact and communicate with each other within virtual, augmented, or real environments. With the advances in AI and cyber–physical systems, the research fulcrum has gradually moved from interaction towards cooperation.

We are pleased to announce a Special Issue on challenging and innovative topics in the field of human–machine interaction and cooperation, including those related to theoretical aspects, methodologies, and practice.

Developing systems such as collaborative, social, or industrial robots and computers, bioinspired systems, and digital systems and devices for use with the Internet of Things (IoT), the Metaverse, and blockchain technology is highly interdisciplinary and often involves innovations and breakthroughs in many different technical areas. These include, but are not limited to, human behaviour modelling, task and motion planning, learning, activity recognition and intention prediction, novel interaction devices, user interface concepts and technologies, multimodal interaction and cooperation, evaluation methods and tools, emotions in HMI, and environments and tools.

The topics of interest include (but are not limited to) the following:

H2M and M2M interaction and cooperation theories and applications;
Cyber–physical systems;
Social and biomedical signal processing;
Learning by example;
Multimodal perception;
Human behaviour modelling;
Activity and intention recognition;
Intelligent manufacturing;
Human–machine dialogue systems;
Planning and decision making under uncertainty;
Context-aware and affective systems;
Safe navigation around humans;
Intelligent systems for training/teaching humans;
Collaborative VR, AR, and XR environments.

Dr. Tomislav Stipančić
Prof. Dr. Katerina Kabassi
Guest Editors

Dr. Yuchong Zhang
Guest Editor Assistant

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences 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 2400 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–machine interaction
cyber–physical systems
AI-enabled robotics
behaviour-based systems
human–machine interfaces
multimodal perception
affective computing

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Related Special Issue

From Human–Machine Interaction to Human–Machine Cooperation: Status and Progress in Applied Sciences (7 articles)

Published Papers (6 papers)

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Research

19 pages, 796 KB

Open AccessArticle

Proactive Artificial Intelligence: Evaluating Prompt Timing in Autonomous Driving Contexts

by Simone Piersigilli and Giandomenico Caruso

Appl. Sci. 2026, 16(12), 5755; https://doi.org/10.3390/app16125755 - 8 Jun 2026

Viewed by 315

Abstract

Proactive Artificial Intelligence systems in intelligent cockpits can initiate prompts without explicit user commands, yet when such prompts should be delivered remains underexplored. This study examines how prompt timing affects user experience in autonomous driving contexts. Using a Virtual prototype developed in Unity and deployed on Meta Quest, 28 participants experienced a tourism-oriented autonomous driving scenario in a within-subjects design, encountering proactive prompts at three temporal positions relative to driving events: Before, During, and After. User experience was assessed across four dimensions using validated scales. Repeated-measures ANOVA revealed significant effects of prompt timing on all measures (p < 0.001, η²p = 0.35–0.59). Prompts delivered before and during events were consistently rated higher than those delivered after, particularly for trust, usefulness, and satisfaction. Differences between Before and During conditions were limited to overall experience satisfaction, while During prompts were associated with higher cognitive load. These findings suggest that temporal alignment between system behavior and user cognitive processes plays a key role in shaping interaction quality. A layered timing framework is proposed that assigns anticipatory, real-time, and reflective functions to prompts before, during, and after, respectively. Further studies in real-world contexts are needed to validate these results. Full article

(This article belongs to the Special Issue From Human–Machine Interaction to Human–Machine Cooperation: Status and Progress, 2nd Edition)

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23 pages, 667 KB

Open AccessArticle

A Multimodal UX-Oriented Evaluation of Robot-Mediated Activities for Children with ASD: Implications for Teacher-Led Interaction

by Sofia Aguayo-Mauri, David Fonseca, Javier Herrero-Martín and Selene Caro-Via

Appl. Sci. 2026, 16(9), 4493; https://doi.org/10.3390/app16094493 - 3 May 2026

Cited by 1 | Viewed by 581

Abstract

This study investigates the user experience (UX) of game-based activities within a school-based social robot intervention for children with ASD and examines changes in task-related performance across robot-led and teacher-led structured communicative–linguistic activities. A multimodal methodology combines quantitative measures (accuracy, response time, and physiological signals) with qualitative teacher feedback. The results reveal limited significant differences in accuracy and other performance variables; however, response time decreased significantly across repetitions and was lower in teacher-led sessions. These findings indicate improved task-response efficiency and suggest a possible facilitation pattern in subsequent human-led interactions, although this effect cannot be disentangled from practice or order effects because of the sequential design. Rather than demonstrating broad linguistic gains, the study highlights the value of multimodal UX-oriented evaluation for identifying design limitations, refining robot-mediated educational activities, and supporting teacher involvement in ASD interventions. Full article

(This article belongs to the Special Issue From Human–Machine Interaction to Human–Machine Cooperation: Status and Progress, 2nd Edition)

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16 pages, 2595 KB

Open AccessArticle

Drone Rider: Effects of Wind Conditions on the Sense of Flight

by Hanyi Yang, Shogo Okamoto and Hong Shen

Appl. Sci. 2026, 16(7), 3544; https://doi.org/10.3390/app16073544 - 4 Apr 2026

Viewed by 605

Abstract

Recent advances in extended reality (XR) have enabled immersive virtual flight experiences for applications such as entertainment and teleoperation support. However, XR-based flight systems that rely primarily on audiovisual cues often fail to evoke a compelling sense of flight and embodied sensation. This study investigates how adaptive wind feedback enhances subjective flight perception in a virtual flight simulation system, Drone Rider. We implemented direction- and velocity-adaptive wind feedback that synchronizes airflow intensity and direction with the user’s motion in the virtual environment, focusing on perceptual effects in a controlled manner to identify key design factors, rather than reproducing aerodynamically accurate airflow. To explore flexible system configurations, two fan installation positions were compared: front-mounted and bottom-mounted. A questionnaire-based user study revealed that adaptive wind feedback significantly enhanced the sense of flight, self-location, and agency compared with the constant-wind and no-wind conditions. However, no significant differences were observed between velocity-adaptive wind and direction- and velocity-adaptive wind conditions. Furthermore, wind delivered from beneath the user yielded flight sensations comparable to those generated by front-mounted airflow. These findings suggest that temporal coupling between airflow intensity and visual motion plays a central role in XR flight perception and provide practical design insights for immersive and flexible XR-based flight simulation systems. Full article

(This article belongs to the Special Issue From Human–Machine Interaction to Human–Machine Cooperation: Status and Progress, 2nd Edition)

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26 pages, 4404 KB

Open AccessArticle

Study on Methods and a System for Real-Time Monitoring of the Remaining Useful Life of a Milling Cutter

by Shih-Ming Wang, Wan-Shing Tsou, Jian-Wei Huang, Shao-En Chen and Chia-Che Wu

Appl. Sci. 2026, 16(2), 958; https://doi.org/10.3390/app16020958 - 16 Jan 2026

Viewed by 428

Abstract

Tool wear degrades sharpness and durability, causing poor surface quality, dimensional errors, and high costs. Precise RUL prediction optimizes production, reduces rework, and prevents downtime. Conventional replacement relies on experience and risks inaccuracy. Real-time monitoring enables optimal intervals. Predictive maintenance cuts tooling costs and ensures quality. Industry 4.0 integrates sensors for intelligent wear management. This study applies GRNN to predict RUL with minimal TMD. A C#-based system with intuitive HMI was validated in real machining. Full article

(This article belongs to the Special Issue From Human–Machine Interaction to Human–Machine Cooperation: Status and Progress, 2nd Edition)

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25 pages, 1888 KB

Open AccessArticle

Maximizing Social Media User Engagement Through Predictive Analytics in Retail Tourism: Identifying Key Performance Indicators That Trigger User Interactions

by Prokopis K. Theodoridis and Dimitris C. Gkikas

Appl. Sci. 2025, 15(21), 11720; https://doi.org/10.3390/app152111720 - 3 Nov 2025

Cited by 2 | Viewed by 5544

Abstract

This study examines and evaluates key performance indicators (KPIs) that impact user engagement on social media platforms, with a primary focus on fashion retail within seasonal tourism contexts. The primary objective is to determine which engagement metrics most accurately predict user interaction levels and to enhance strategic decision-making in digital marketing. Using a dataset of 2500 Facebook photos and videos from a women’s retail store, collected between 2016 and 2024, the study employs descriptive analysis and predictive modeling. Three KPIs—such as 3 s video views, reach from organic posts, and other clicks—are examined for their impact on user engagement. The posts are categorized into engagement levels, and classification models, including Random Forests (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Naïve Bayes (NB), are evaluated. Results show that short video views and post reach are key predictors of user engagement. With XGBoost achieving a classification accuracy of 94.73%, the models perform effectively, and Cronbach’s alpha analysis confirms the consistency among the variables selected. The findings underscore the significance of KPI analysis in social media strategy and illustrate the value of data mining techniques in uncovering user behavior patterns that offer practical insights for optimizing digital marketing efforts. Full article

(This article belongs to the Special Issue From Human–Machine Interaction to Human–Machine Cooperation: Status and Progress, 2nd Edition)

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17 pages, 2701 KB

Open AccessArticle

Exploratory Research on the Potential of Human–AI Interaction for Mental Health: Building and Verifying an Experimental Environment Based on ChatGPT and Metaverse

by PuiTing Chung, Ruichen Cong, Lin Yao and Qun Jin

Appl. Sci. 2025, 15(20), 11209; https://doi.org/10.3390/app152011209 - 20 Oct 2025

Cited by 1 | Viewed by 3337

Abstract

The demand for mental health support has highlighted the potential of conversational AI and immersive metaverses. However, these technologies possess weaknesses. The AI agents are intelligent but often disembodied, while metaverse environments provide a sense of presence but typically lack dynamic and intelligent responsiveness. To address this gap, we design and verify an experimental environment integrated with a conversational AI agent, enabled by ChatGPT, into a metaverse platform. We conducted a within-subjects experiment with 15 participants who interacted with the agent in both the immersive metaverse and a standard text-chat interface to investigate user preferences and subjective experiences. After the experiment, participants are required to answer a questionnaire to assign the scores, which can represent the user preferences and subjective experiences. The results showed that the scores were slightly different between the two conditions. Especially, qualitative feedback from participants revealed that all participants subjectively reported the AI-Metaverse condition as better. This study provides an exploratory study to demonstrate the potential of human–AI interaction in mental health support that should be further investigated. Full article

(This article belongs to the Special Issue From Human–Machine Interaction to Human–Machine Cooperation: Status and Progress, 2nd Edition)

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