Introduction

In recent years, the rapid evolution of Artificial Intelligence (AI) has begun to reshape how we study, model, and interpret human movement and physiological processes. At the same time, advances in sensing technologies -ranging from wearable devices and motion capture systems to thermal imaging and biomedical sensors - have made it possible to collect increasingly rich and diverse streams of biomechanical data. These developments are creating new opportunities, but also new challenges, for researchers and practitioners working in healthcare, rehabilitation, and human performance science.

One of the most promising directions emerging from this convergence is the use of multimodal generative models to integrate and simulate complex biomechanical information. Rather than analyzing individual data sources in isolation, these models allow researchers to bring together heterogeneous signals-such as kinematic measurements, thermal patterns, physiological indicators, and imaging data-into coherent representations that better reflect the dynamic nature of the human body.

Beyond improving data integration, generative models are increasingly being used to address practical limitations that many researchers encounter in real-world settings. For example, they can help generate realistic synthetic datasets when clinical samples are limited, support personalized simulations for patient-specific rehabilitation planning, and enable predictive modeling of injury risk or recovery trajectories. In thermal and biomechanical applications, these capabilities are particularly valuable for monitoring physiological stress, understanding tissue responses, and designing adaptive intervention strategies.

Looking ahead, the combination of multimodal sensing, generative modeling, and biomechanical simulation is likely to become a foundational component of next-generation digital health systems. Concepts such as digital twins, continuous health monitoring, and intelligent rehabilitation platforms are gradually moving from research prototypes toward practical deployment. Achieving this transition will require not only methodological innovation but also careful attention to reliability, interpretability, and integration with clinical workflows.

This issue, “Multimodal Generative Models for Biomechanics Data Fusion and Simulation”, aims to provide a forum for researchers, engineers, clinicians, and practitioners who are exploring how multimodal generative models can support more realistic, scalable, and clinically meaningful biomechanical analysis and simulation. We particularly welcome contributions that bridge methodological development with practical implementation, and that demonstrate clear relevance to health informatics, rehabilitation, sports science, or biomedical engineering.

The Issue Topics

Multimodal Generative Modeling

• Generative AI (GenAI) models for multimodal biomechanical data integration
• Diffusion models, variational autoencoders, and Generative Adversarial Network (GAN)-based simulation
• Multimodal representation learning for physiological and motion data
• Synthetic data generation for healthcare and biomedical research
• Data-driven modeling under limited or noisy datasets
• Uncertainty-aware and probabilistic generative modeling

Healthcare and Health Informatics Applications

• AI-enabled rehabilitation and recovery monitoring
• Remote health monitoring using multimodal sensors
• Intelligent decision support systems for clinical environments
• Personalized healthcare and adaptive treatment planning
• Assistive technologies for aging populations
• Movement analysis for neurological and musculoskeletal disorders

Academic Editors

Prof. Mu-Yen Chen, Ph.D (H-Index: 44)
Email: mychen119@gs.ncku.edu.tw
Affiliation: Department of Engineering Science, National Cheng Kung University, Taiwan
Website: https://scholar.google.com/citations?user=3LR7DhgAAAAJ&hl=zh-TW

Dr. Mu-Yen Chen is a Distinguished Professor of Engineering Science at National Cheng Kung University, Taiwan. He received his Ph.D. in Information Management from the National Chiao-Tung University in Taiwan. His current research interests include Artificial Intelligence (AI), soft computing, data mining, deep learning, context awareness, machine learning, and social network mining, with more than 200 publications in these areas. He has co-edited several special issues in International Journals (e.g., Computational Economics, IEEE Transactions on Engineering Management, IEEE Access, ACM Transactions on Management Information Systems, ACM Transactions on Sensor Networks, Computers in Human Behavior, Applied Soft Computing, Soft Computing, Information Fusion, Journal of Real-Time Image Processing, Sustainable Cities and Society, Neurocomputing, Supercomputing, Enterprise Information Systems). He has served as Editor in Chief and Associate Editor of international journals (e.g., International Journal of Big Data and Analytics in Healthcare, IEEE Transactions on Engineering Management, IEEE Access, Applied Soft Computing, Innovation and Emerging Technologies, Granular Computing, Human-centric Computing and Information Sciences, Journal of Information Processing Systems, International Journal of Social and Humanistic Computing) while he is an editorial board member on several SCI journals.

Prof. Patrick C. K. Hung (H-Index: 45)
Email: patrick.hung@ontariotechu.ca
Affiliation: Faculty of Business and IT, Ontario Tech University, Canada
Website: https://scholar.google.com/citations?user=slVFuNIAAAAJ&hl=en

Prof. Patrick C. K. Hung is a Professor at Ontario Tech University, Faculty of Business and Information Technology, Canada. He is an adjunct Professor at the College of Health Sciences, VinUniversity in Vietnam. He is an Honourable Guest Professor at Shizuoka University, Hamamatsu, Japan. He was a Leverhulme Visiting Professor at Aston University, England; a Distinguished Visiting Fellow at Abertay University, Scotland and a São Paulo Research Foundation (FAPESP) Visiting Professor at the University of São Paulo, Brazil. Hung collaborated with Boeing Research and Technology in Seattle on aviation services-related research, resulting in two US patents for the mobile network dynamic workflow system. Before that, he was a Research Scientist with Australia’s Commonwealth Scientific and Industrial Research Organization. He has a Ph.D. and a Master in Computer Science from the Hong Kong University of Science and Technology, a Master in Management Sciences from the University of Waterloo, Canada, and a Bachelor in Computer Science from the University of New South Wales, Australia. His recent research interests include Artificial Intelligence (AI), Social Robots and Human-Robot Interactions (HRI) in healthcare applications and accessibility.

Important Dates

Submission due: 31 October 2026
Notification of final acceptance: 31 December 2026
Final papers: Q1, 2027
All accepted papers will be published in the earliest available issue.

Submission Guideline

To submit your manuscript, please visit the Thermal Technology and Artificial Intelligence in Health Informatics journal’s submission website:
https://sciflux.org/authors/submissions/add-submissions?journalCode=1919623584772112385
Author submission guidelines can be found at:
https://www.sciltp.com/journals/ttaihi/instructionForAuthors

There is No Article Processing Charge (APC) for all submissions and accepted papers.

All manuscripts will undergo peer review in accordance with the journal’s established policies and procedures. Final acceptance will be based on the peer-review reports and the evaluation of the Academic Editors and the Editor-in-Chief. Decisions will be made by the Editor-in-Chief or Academic Editors who have no conflicts of interest with any of the authors.