Abstract:

The recent trends of information technology and immersive media is reshaping the educational landscape, with Virtual Reality (VR) and Augmented Reality (AR) emerging as transformative tools in higher education.

These technologies offer interactive, engaging, and highly visual learning experiences that significantly enhance the delivery and comprehension of complex subject matter. Moreover, they provide safe, cost-effective, and widely accessible alternatives to traditional learning environments, such as physical laboratories. Our research has explored the use of immersive technologies across diverse fields, including medical, biomedical, and accounting study programs, to support deeper understanding and active learning. In anatomy education, for example, Hanamy (Heart Anatomy) and Gama Cardiac AR, which use augmented reality and 3D visuals to focus on the heart’s anatomy. Anaries (Anatomy Stories) application, leverages VR and AR to simulate cranial anatomy and includes a mini quiz designed to boost learning interactivity. Whilst, Kadavee models the human skeleton, including the head, torso, arms, and legs, in 3D visualization and virtual reality. This application enhances spatial awareness beyond what traditional cadaver-based methods typically offer. Augmented reality further enriches learning by overlaying digital content on physical models, creating interactive, and hands-on experiences.

Biography:

Adhistya Erna Permanasari is an Associate Professor in the Department of Electrical and Information Engineering at Universitas Gadjah Mada (UGM) in Yogyakarta, Indonesia. She earned her B.S. in Electrical Engineering at UGM in 2002 and her M.Tech in Electrical Engineering from the same university in 2006, before completing her Ph.D. in Computer and Information Science at Universiti Teknologi PETRONAS, Malaysia, in 2010. Her research interests encompass decision support systems, forecasting, health informatics, educational informatics, artificial intelligence, and immersive technologies. She has published extensively over 160 works on topics ranging from forecasting disease incidence and network management to AR based learning tools and healthcare information systems. She has led and co-led several interdisciplinary projects, particularly on immersive technology initiatives. At UGM, she is an active member of the Intelligent Systems Research Group. She supervises graduate students working on projects in health informatics, immersive learning environments, decision-support systems, and more.

Abstract:

Technologies for accurately recognizing the positions of people and objects are essential for realizing applications such as the Internet of Things (IoT), cyber-physical systems (CPS), augmented reality (AR), and digital twins. While Global Navigation Satellite System (GNSS) is the standard positioning technology in outdoor environments, there is still no such universally accepted technology for indoor settings, and various approaches have been proposed so far. Some market research reports predict that the market related to indoor positioning technologies is expected to grow at a compound annual growth rate (CAGR) exceeding 40%, reaching USD 150 billion by 2030. In light of the social background, this talk will first introduce recent research trends in indoor positioning technologies. Then, some of the research achievements from the speaker group indoor positioning research project will be presented. The talk will describe indoor positioning systems realized using smartphone built-in sensors and provide examples of their applications. Finally, future prospects for research in indoor positioning will be discussed.

Biography:

Masanori Sugimoto received the B.E., M.E., and D.E. degrees in aeronautics and astronautics from the University of Tokyo, Tokyo, Japan, in 1990, 1992 and 1995, respectively. He is currently a Professor with the Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan. His research interests include acoustic engineering, signal processing, artificial intelligence, and human-computer interaction technologies for designing smart systems and environments

Abstract:

Heart rate variability (HRV) has long been used as a non-invasive indicator of autonomic nervous system activity, and it has become widely adopted in fields ranging from human interface design to human-robot interaction. However, misinterpretations and methodological pitfalls in HRV analysis remain widespread, often leading to erroneous conclusions about autonomic function. Our landmark paper, “Pitfalls of assessment of autonomic function by heart rate variability” (2019), has been cited more than 370 times in just five years, reflecting the growing concern and interest in improving the scientific rigor of HRV-based assessments.

In this talk, I will discuss the physiological basis of HRV, clarify common misunderstandings in its interpretation, and demonstrate why certain HRV metrics fail to reflect autonomic balance under conditions involving speech, motion, or cognitive load. Using evidence from both experimental and clinical studies, I will outline appropriate methods for extracting meaningful physiological information from HRV and related bio-signals. Furthermore, I will introduce practical applications of refined HRV analysis in the context of real-world systems, such as driver monitoring, fatigue detection, and affective computing. Emphasis will be placed on aligning signal processing techniques with physiological principles to ensure robust and interpretable outcomes. This presentation aims to promote more accurate and effective use of bio-signal analysis in modern human-centered technologies.

Biography:

Prof. Dr. Emi Yuda is a professor specializing in biomedical signal processing and bio-medical big data analysis. Her obtained her PhD in Engineering from Niigata University, and served as an assistant professor and associate professor at Tohoku University, before becoming a professor at Mie University in 2024. Her research interests span a wide range of fields, from biomedical engineering to health sciences, including autonomic nerve interpretation using heart rate variability (HRV) analysis extracted from electrocardiograms (ECGs), as well as multimodal analysis centered on time series data from wearable sensors. She has contributed to the development of advanced algorithms for detecting human cardiac diseases, sleep apnea, fatigue, drowsiness, and posture changes. Recent research has utilized bio-signal analysis for ensuring the safety of elderly people and drivers. In biomedical big data analysis, she integrates the analysis using machine learning. She has published numerous papers in peer-reviewed journals and international conferences, and is actively engaged in collaborative research with industry and medical institutions.