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Despite significant advances, the practical deployment of wearable IoT systems faces key challenges related to energy eƯiciency, real-time processing, heterogeneous sensor integration, and scalable AI implementation. These issues are compounded by the need for lightweight, low-power solutions capable of delivering robust performance in resource-constrained environments.\u003C/p>\u003Cp style=\"text-align:justify;\">\u003Cbr>Edge AI represents a paradigm shift, by enabling on-device data processing and decision-making. This in turn as potential to reduce latency, conserve bandwidth, and enhance privacy. This session explores cutting-edge research in Edge AI for low-power IoT systems, emphasising fundamental research challenges, algorithmic innovation, and system-level optimization.\u003C/p>\u003Cp style=\"text-align:justify;\">Contributions can be application-agnostic and are invited on the following themes:\u003C/p>\u003Cul>\u003Cli style=\"text-align:justify;\">EƯicient Edge Learning Architectures: Development of novel optimised approaches for deployment of models onto low-power devices, including federated learning, knowledge distillation, etc.\u003C/li>\u003Cli style=\"text-align:justify;\">Sensor Fusion and Multimodal Analysis: Research on integrating heterogeneous sensors for robust data interpretation and advanced context-awareness.\u003C/li>\u003Cli style=\"text-align:justify;\">Adaptive AI for Edge Devices: Exploration of dynamic reconfiguration techniques that balance energy, accuracy, and computational load in real-time applications.\u003C/li>\u003Cli style=\"text-align:justify;\">Edge AI Methodologies: Novel approaches in human activity recognition, time series analysis, physics-informed AI, and real-time edge analytics for resource-constrained\u003Cbr>devices.\u003C/li>\u003Cli style=\"text-align:justify;\">Energy-Aware Hardware-Software Co-Design: Collaborative strategies for creating ultra-low-power devices with tailored AI accelerators and optimized communication protocols.\u003C/li>\u003Cli style=\"text-align:justify;\">Scalability and Generalisation: Techniques for deploying IoT devices across diverse\u003Cbr>scenarios while maintaining reliability and interpretability.\u003C/li>\u003C/ul>\u003Cp style=\"text-align:justify;\">This session aims bridge theoretical innovation with practical implementation across multiple domains, fostering collaborations that advance the state of Edge AI for wearable and IoT devices.\u003C/p>",{"id":155,"__component":734,"componentVariation":735,"styles":16,"header":16,"contactGroup":736},"content.contacts","Contacts Flex Card Image Cover",[737],{"id":285,"groupTitle":738,"contacts":739},"Session Organizers",[740,748],{"id":311,"contact":741},{"id":298,"name":742,"committee":16,"position":16,"affiliation":16,"email":16,"biography":743,"createdAt":744,"updatedAt":744,"url_path_id":745,"contactPhoto":16,"socialLinks":746,"url_path":747},"Salvatore Tedesco","\u003Cp style=\"text-align:justify;\">Salvatore Tedesco (Member, IEEE) received the B.Sc. degree (Hons.) in information technology engineering and the M.Sc. degree (Hons.) in telecommunications engineering from the University of Salento, Lecce, Italy, and the Ph.D. degree in electrical and electronic engineering from University College Cork (UCC), Cork, Ireland. Since April 2012, he has been with the Wireless\u003Cbr>Sensor Networks Group, Tyndall National Institute, UCC, where he is currently a Senior Researcher II and a Team Leader responsible for leading a research team in the data analytics area. He has authored more than 100 articles in international journals and conference proceedings. He has received over 1.5 million euro in grant funding as a Principal Investigator and a Co-Principal Investigator. Since joining the Tyndall National Institute, he has managed and successfully led over 30 industrial and research-oriented projects focused on his main research interests on wearable technologies for health and well-being, motion analysis in sports and\u003Cbr>clinical populations, digital health, physiological monitoring, image and signal processing, edge analytics, and machine learning. Further contributions deal with radio frequency identification (RFID) technology, ultrawideband localization systems for indoor applications, sensor calibration, animal monitoring, and industry 4.0.\u003C/p>","2025-01-28T00:13:14.879Z","79",[],"-49",{"id":569,"contact":749},{"id":433,"name":750,"committee":16,"position":16,"affiliation":16,"email":16,"biography":751,"createdAt":752,"updatedAt":752,"url_path_id":753,"contactPhoto":16,"socialLinks":754,"url_path":755},"Ian Cleland","\u003Cp style=\"text-align:justify;\">Dr Ian Cleland is a Senior Lecturer within the School of Computing at Ulster University, Belfast Northern Ireland, where he leads the research theme of Human Computer Interaction within the Pervasive Computing Research Centre. He received his BSc in Biomedical Engineering in 2009 and a PhD in Computer Science in 2012, both from Ulster University. Ian been the Research Director for the School of Computing since February 2024. He was previously Postgraduate Tutor\u003Cbr>for the School from 2018-2024 where he overseen the academic processes for around 55 PhD Researchers each year. His research combines wearable, pervasive, and mobile computing with data science and artificial intelligence to produce innovative digital solutions, mainly in the healthcare domain. Areas of particular interest include sensor-based activity recognition and the use of synthetic data to support data driven modelling. He has published his research in over 100\u003Cbr>peer reviewed scientific publications in top forums in digital health and health informatics. Ian has secured externally funded research, as both Principal Investigator and Co-Investigator, to the value of over £7.89M. Most notably he is a Co-I on the Connected Health Innovation Centre (CHIC-Phase 2 £3.36M) and the PwC Advanced Engineering and Research Centre (ARC £3.13M). Ian was Co-Chair for the International Conference of Ambient Intelligence (UCAmI2024). He is\u003Cbr>currently Vice Chair of the Executive Committee for the Alzheimer’s Association Technology and Dementia Professional Interest Area.\u003C/p>","2025-01-28T00:22:07.223Z","80",[],"-50",{"data":757,"meta":763},{"id":721,"heading":722,"createdAt":758,"updatedAt":759,"publishedAt":760,"url_path_id":761,"url_path":762,"contentType":118},"2025-01-28T01:20:26.837Z","2025-01-28T01:41:45.574Z","2025-01-28T01:20:56.342Z","92","/special-sessions/special-session-5",{},1778853381645]