THEMATIC SESSION #17
Intelligent Wearable Sensor Systems for AI-Driven Health and Activity Monitoring
ORGANIZED BY
Rim Barioul
Rim Barioul
Chemnitz University of Technology
THEMATIC SESSION DESCRIPTION
Recent advances in sensing technologies, embedded systems, and artificial intelligence have enabled a new generation of intelligent smart wearable devices capable of continuous and unobtrusive monitoring of human health and activity. These systems integrate multimodal biosignal acquisition, wireless communication, and on-device analytics to support personalized health assessment, early anomaly detection, and real-time feedback in everyday environments.
This thematic session aims to bring together researchers working on the design, development, and evaluation of intelligent wearable sensor systems for AI-driven health and activity monitoring. The session focuses on end-to-end solutions that combine advanced sensing hardware, robust signal processing, and machine learning–based interpretation to transform raw physiological and behavioral data into clinically relevant and human–machine interaction–oriented insights.
Contributions addressing system integration, sensor fusion, edge intelligence, data quality assurance, and user-centered design are especially encouraged. Furthermore, the session seeks to highlight approaches for validating, benchmarking, and ensuring the reliability, robustness, and explainability of AI-driven wearable technologies in health-related and human–machine interface applications.
By fostering interdisciplinary exchange between experts in sensor technology, biomedical engineering, artificial intelligence, and human-centered system design, this session aims to advance the development of trustworthy and scalable wearable monitoring solutions. The outcomes of this session are expected to support next-generation digital health services, preventive medicine, and personalized well-being management within the broader context of Industry 5.0 and Health 5.0.
ABOUT THE ORGANIZERS
Dr. Rim Barioul is a Postdoctoral Researcher and the Group Leader of the Smart Wearables research group at the Professorship of Measurement and Sensor Technology, Technische Universität Chemnitz, Germany. She completed at TU Chemnitz in 2021, where her doctoral research focused on machine learning–based methods for hand gesture recognition and human–machine interaction using myographic data. Dr. Barioul’s research interests encompass machine learning, embedded systems, gesture recognition, feature selection, and intelligent wearable sensor systems, with a particular focus on integrating advanced signal processing and machine learning methods for real-time interpretation of physiological data. She has contributed to numerous publications in these areas and plays an active role in advancing wearable technologies that support health and activity monitoring applications.
