Keynote Speaker_ICIET 2026

Fellow of the AAAS, ACM,and IEEE

the Chinese University of Hong Kong, Hong Kong, China

Biography: Benjamin W. Wah is currently the Provost and Wei Lun Professor ofComputer Science and Engineering of the Chinese University of Hong Kong.He also serves as the Chair of the Research Grants Council of Hong Kong.Before then, he served as the Director of the Advanced Digital SciencesCenter in Singapore, as well as the Franklin W. Woeltge Endowed Professor ofElectrical and Computer Engineering and Professor of the CoordinatedScience Laboratory of the University of Illinois, Urbana-Champaign, IL. Hereceived his Ph.D. degree in computer science from the University ofCalifornia, Berkeley, CA, in 1979. He has received a number of awards for hisresearch contributions, which include the IEEE CS Technical AchievementAward (1998), the IEEE Millennium Medal (2000), the IEEE-CS W. WallaceMcDowell Award (2006), the Pan Wen-Yuan Outstanding Research Award(2006), the IEEE-CS Richard E. Merwin Award (2007), the IEEE-CS TsutomuKanai Award (2009), and the Distinguished Alumni Award in ComputerScience of the University of California, Berkeley (2011). Wah's current research interests are in the areas of bigdata applications and multimedia signal processing. Wah cofounded the IEEE Transactions on Knowledge and Data Engineering in 1988 and served as its Editor-in-Chiefbetween 1993 and 1996, and is the Honorary Editor-in-Chief of Knowledge and Information Systems. He currentlyserves on the editorial boards of Information Sciences, International Journal on Artificial Intelligence Tools, Journalof VLSI Signal Processing, and World Wide Web. He has served the IEEE Computer Society in various capacities,including Vice President for Publications (1998 and 1999) and President (2001). He is a Fellow of the AAAS, ACM,and IEEE.

Talk title: Machine Learning to Improve Perceptual Quality for Multimedia in Education

University of Madrid, Spain

Biography: Baltasar Fernández-Manjón is a Full Professor in Computer Science and Serious Games the Department of Software Engineering and Artificial Intelligence at the Complutense University of Madrid (UCM), where he leads the e-UCM research group. He served as Vice Dean for Research and External Relations at the Faculty of Computer Science of UCM, where he was responsible for fostering partnerships with industry and research institutions, as well as promoting innovation and international collaboration. He has also held a position as Visiting Associate Professor at Harvard University (2010–2011), where he collaborated on projects related to educational technology and health training simulations. His research lies at the intersection of educational technologies, serious games, learning analytics, and AI-driven systems for personalized, data-informed learning. He has led and collaborated on numerous national and international projects in these áreas (e.g. EU Horizon 2020, Erasmus+), often with academic, corporate, and institutional partners. A Senior Member of IEEE, he actively contributes to the research community as a reviewer, editor, and program committee member for leading journals and conferences. He is also a key developer of SIMVA, an open platform for the scientific validation of serious games through learning analytics.

Talk title: Serious Games in the Classroom: From Fun to Evidence to Validation

Absract: Serious games are no longer limited to motivation or engagement, but are becoming powerful tools for learning and assessment in different fields (e.g., health, military, and business). However, serious games have not yet become widespread in classrooms. This talk will explore how serious games can be effectively integrated into the educational environment, beyond entertainment, to generate meaningful learning outcomes. We will analyze how learning analytics can provide evidence of student progress and how platforms such as SIMVA integrate multimodal information to support the scientific validation of serious games. Real-world examples will illustrate how educators and researchers can use data to ensure that game-based learning is not only motivating, but also effective and measurable.

National Chung Hsing University, Taiwan

Biography: Chih-Peng Fan received the B.S., M.S., and, and Ph.D. degrees, all in electrical engineering, from National Cheng Kung University, Taiwan, in 1991, 1993 and 1998, respectively. During October 1998 to January 2003, he was a design engineer at Computer and Communications Research Laboratories (CCL), Industrial Technology Research Institute (ITRI), Hsinchu, Taiwan. In 2003, he joined the Department of Electrical Engineering at National Chung Hsing University in Taiwan as an Assistant Professor. He became a full Professor in 2013. He has more than 110 publications, including technical journals, technical reports, book chapters, and conference papers. His teaching and research interests include deep-learning based digital image processing and pattern recognition, digital video coding and processing, digital baseband transceiver design, VLSI design for digital signal processing, and fast prototype of DSP systems with FPGA and embedded SOC platform. He served and is serving as: General Chair of ICCE-TW 2018; Executive Conference Chair of ICCE 2024; IEEE Transactions on Consumer Electronics -Associate Editor (2022-now); Member of Editorial Board of Journal of Real-Time Image Processing; IEEE CTSoc Representative at the IEEE Systems Council's AdCom (2020-2021); IEEE CTSoc Representative at the IEEE Sensors Council's AdCom (2023-Now); Chair of IEEE CTSoc Sensors and Actuators (SEA) TC (2025-Now); He is a member of Taiwan IC Design Society (TICD), IEICE, and IEEE.

Talk title: OpenPose Based Yoga Exercise Learning Assistant Design with User–Instructor Synchronization and Pose Difficulty Evaluation Technologies for Dynamic and Static Yoga Self-Practice Assistant System on the GPU-Based Platform

Absract: Yoga is popular across all age groups because of its benefits for physical and mental health. To assist beginners with yoga self-practice. In this keynote talk, the OpenPose based yoga self-practice assistance system for dynamic and static yoga by angle-based poses matching and pose difficulty estimation on the NVIDIA Jetson Nano platform is introduced. The developed system uses the OpenPose Body25 model to extract the important information related to body key joints by evaluating the accuracy of users’ poses against yoga instructor demonstrations with user-instructor synchronization on the basis of joint angle differences and total distance between adjacent video frames, and then the user’s feedback with fuzzy based scoring strategy will be processed simultaneously. To prevent overly difficult yoga poses from causing injuries to beginners, the developed system includes a difficulty assessment feature that allows users to select poses according to their ability. The proposed system evaluates pose difficulty from the front and side views by estimating angular velocity, body area, body bending direction, flexibility requirements, and range of motion on the basis of joint angles and vectors. Then the information of developed Yoga difficulty estimation levels is integrated into the developed yoga self-practice system. The developed system’s effectiveness was validated on over 100 yoga pose images obtained from different sources. Strong correlations were observed between the ratings provided by the system and instructor, confirming the accuracy of the system and its potential for improving safety and adaptability in yoga self-practice.