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Keynote Lectures

From Signals to Meaning: Biosignals as a Language for Health, Emotion, and Connection
Antonio Lanatà, Department of Information Engineering, University of Florence, Italy, Italy

Advanced Materials and Technologies for Biomedical Application and Translational Research
He Huacheng, Oujiang Laboratory, China, China

Extended Reality in Healthcare
Kavitha Anandan, SSN College of Engineering, India, India

AI in Medicine: Progress, Perils, and Perspectives
Tim Hahn, University of Münster, Germany, Germany

From Signals to Meaning: Biosignals as a Language for Health, Emotion, and Connection

Antonio Lanatà
Department of Information Engineering, University of Florence, Italy

Brief Bio
Antonio Lanatà, PhD, is an Associate Professor of Bioengineering at the University of Florence, where he leads the Biosignals and Bioinstrumentations Laboratory and the Computational Physiology and Biomedical Systems research group. His work focuses on wearable systems and advanced biosignal processing, with applications ranging from affective computing and mental health to human–animal interaction. He has played leading roles in several European collaborative projects, including POTION, NEVERMIND, PSYCHE, CEEDS and PROETEX, and has authored more than 170 peer-reviewed scientific contributions. He serves as an Associate Editor of several journals such as IEEE Transactions on Affective Computing and Frontiers in Bioelectronics, and is a regular member of the scientific committees for major conferences, such as IEEE EMBC. His vision is to consider biosignals as a “language of life” that connects physiology, emotion, and interaction across humans, animals, and complex environments.

Abstract
Biosignals are not just measurements of physiology; they could represent a language of life, a continuous dialogue between body, mind, and environment. Over the past two decades, advances in sensing technologies, wearable systems, and computational intelligence have transformed biosignals into powerful tools for monitoring health, decoding emotion, and enabling novel forms of interaction.
In this keynote, I will trace a research journey from the design of wearable and textile-integrated systems for physiological monitoring, through the development of advanced signal processing and AI-based models, to the exploration of biosignals as markers of emotional and cognitive states. I will highlight results from large European projects that bridged engineering and human sciences, addressing topics such as mental health monitoring, immersive environments, and emotion-driven human–machine interaction.
A new frontier is now emerging, the use of biosignals to study synchronization and coupling not only between humans, but also across species. Recent work on human–animal interaction and interspecies emotional transfer demonstrates how biosignals can be leveraged to investigate empathy, welfare, and cross-kingdom communication. This expands the scope of biosignal research from individual monitoring to collective and ecological perspectives.
Looking toward the future, biosignals research must confront critical challenges, including ensuring reproducibility and data quality, integrating multimodal information streams, and embedding ethical and transparent AI into decision-making processes. At the same time, new opportunities are arising through hyperscanning, living labs, and real-world validation of biosignal-based systems in health, education, and society at large.
By reframing biosignals as a universal language that is quantitative, interpretable, and integrative, we can move beyond monitoring toward creating intelligent, empathetic, and sustainable systems that connect humans, animals, and their environments.
Keywords: Biosignals, Wearables, AI for Health, Emotion Recognition, Hyperscanning, Human–Animal Interaction, Interspecies Communication

Advanced Materials and Technologies for Biomedical Application and Translational Research

He Huacheng
Oujiang Laboratory, China

Brief Bio
Dr. Huacheng He is a Professor at Oujiang Laboratory in Wenzhou of China. He obtained his Ph.D. degree at College of Pharmacy of University of South Carolina at Columbia in USA in 2016. After then, he joined the faculty of College of Chemistry and Materials Engineering at Wenzhou University. In 2023, he moved to Oujiang Laboratory as a principal investigator. His research interests mainly focus on biomaterials, sensors and flexible devices for targeted wound healing. He is also the co-founder for several medical companies which emphasize on the development of medical devices. He has authored over 40 peer-reviewed papers and granted more than 16 patents. He used to serve as the guest editor for Frontiers in Bioengineering and Biotechnology, and Frontiers in Materials.

Abstract
Chronic wounds are most common diseases in daily life. Dressings have been extensively used for the treatments, yet the efficacy of these dressings is still quite limited in promoting wound repair, especially in chronic wounds (e.g., diabetic foot ulcer, pressure ulcer, burns and infected wounds), due to the lack of functionalities which fail to accommodate the complicated healing stages. Therefore, exploring novel wound dressing with advanced functions to expedite wound repair is highly demanded. Herein, we devise several facile strategies and technologies to fabricate various wound dressings with excellent bioactivities including anti-inflammation, antibacterial and antioxidative effects to accelerate wound regeneration. Both in vitro and in vivo investigations demonstrate that these dressings enhanced wound healing by promoting M2 macrophage polarization, epithelial-mesenchymal transition (EMT), angiogenesis, granulation tissue formation, collagen deposition, and re-epithelialization. Now we are optimizing the manufacturing conditions of these dressings and endeavoring to launch them onto the market soon.

Extended Reality in Healthcare

Kavitha Anandan
SSN College of Engineering, India
https://www.ssn.edu.in/bio-medical-engineering/faculty/dr-a-kavitha-professor-and-head-of-the-department/

Brief Bio
Professor Kavitha Anandan is the Head of the Department of Biomedical Engineering at SSN COllege of Engineering, Tamilnadu, India. She is the Coordinator of Centre for Healthcare Technologies (CHT), an interdisciplinary research initiative of SSN College of Engineering and is a passionate researcher of Brain dynamics in Cognition, learning and memory..She has over 25 years of research experience in the field of Medical Image processing, Bio Signal processing and Extended Reality. She received her B.E (EEE) degree from the University of Madras, M.S. (By Research) and Ph. D. both specializing in Biomedical Instrumentation from Anna University, Chennai. Her research interests include Cognitive neuroscience, Mental imagery, Neuroimaging, Bio Signal Processing, Bone biomechanics and Virtual/Augmented reality. She has published various articles in international journals and conferences in the areas of biomedical signal and image processing, respiratory mechanics, machine learning techniques, Extended reality and cognitive neuroscience. She works on various externally projects funded by Department of Science and Technology, Science and Engineering Research Board, National Agricultural and Science Foundation, Shiv Nadar Foundation etc. She is the Program Director of the SSN SACE – DREXEL Direct Pathway MSBME program (2023-till date). She has been mentoring summer interns from the Drexel School of Biomedical Engineering, Philadelphia, USA under the international summer internship iSTAR research program every year (2017 – Till date). She has various international research collaborations with University of Bologna, Italy, University of Delft and University of Twente, The Netherlands, Drexel University, USA, She has been selected as the Top 20 most influential women scientists in Tamilnadu, India (2019). She was an invited member of the Top 15 Woman Scientists to represent Indian women in STEM research at the ASEAN – INDIA Women’s conclave at Singapore (2024) and the FHED Conference at NCKU, Taiwan.

Abstract
Extended Reality (XR), is an immersive platform that encompasses Virtual Reality (VR) and Augmented Reality (AR) techniques. It provides interactive experiences by merging physical and virtual worlds, thereby allowing interaction with digital objects, enhancing learning, training, and overall understanding. XR has been extensively used in various domains and has seen applications in healthcare. XR applications in healthcare include patient treatment, surgical training, and medical education. XR offers immersive experiences that can enhance patient engagement, aid in rehabilitation, and provide new ways to train healthcare professionals. This talk intends to unfold the possibilities of employing XR in various healthcare applications. A few case based applications on using Augmented reality for spine and liver surgical systems will be discussed. Some interesting VR based environments developed for enhancing learning in children with neurodevelopmental disorders will also be explained.

AI in Medicine: Progress, Perils, and Perspectives

Tim Hahn
University of Münster, Germany

Brief Bio
Tim is a machine learning specialist and neuroscientist. He heads the Medical Machine Learning Lab at the Institute for Translational Psychiatry in Münster, Germany (www.mmll.uni-muenster.de). Tim and his team focus on translational machine learning research for patient care, machine learning software development, and infrastructure for artificial intelligence in medicine with a focus on mental health. Their aim is to bring state-of-the-art machine learning tools and predictive models for personalized medicine into clinical practice.

Abstract
AI is driving a paradigm shift in medicine, enabling thus-far unseen data analysis capabilities and improved patient care. Here, we will provide an overview of recent projects and discuss a high-level framework for translation into clinical practice.
In the area of MRI imaging, for example, projects such as deepmriprep demonstrate how deep learning can accelerate and standardize MRI preprocessing, reducing variability and enabling large-scale studies. Multimodal MRI analyses are being used to investigate brain–behavior associations, while Bayesian frameworks add uncertainty estimates to predictions of disease trajectories, for instance in multiple sclerosis.
Within medical image segmentation, AI methods are developed to automate the delineation of complex anatomical and pathological structures. These approaches reduce reliance on manual labeling and improve reproducibility across sites, addressing a central bottleneck for clinical integration. As an example beyond classical imaging, we use WiFi-based imaging to explore how ambient signals can be used to monitor mobility and behavior continuously in a non-invasive way. This provides ecologically valid, real-world data that enrich imaging-derived biomarkers. A further line of work investigates dynamical system identification using neural networks, enabling the extraction of underlying system dynamics from biomedical time series, with applications ranging from physiology to brain activity modeling.
Challenges remain with small and heterogeneous datasets, variability in pipelines, and the ethical and regulatory complexities of translation into practice. Looking ahead, standardized workflows, uncertainty-aware modeling, and multimodal integration will be crucial. By embedding ethical and legal safeguards into technical innovation, AI in medical bioimaging can evolve responsibly and contribute to more reliable, patient-centered care.