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

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 April 2026 | Viewed by 3386

Special Issue Editors


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Guest Editor
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

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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-blind 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

Published Papers (2 papers)

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Research

25 pages, 1888 KB  
Article
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
Viewed by 1633
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 [...] Read more.
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
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17 pages, 2701 KB  
Article
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
Viewed by 1521
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 [...] Read more.
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
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