FACE Project Presented at AIIoT 2026 in Seattle

Research from Siegen in the International Arena: At the 2026 IEEE World AI IoT Congress (AIIoT) in Seattle, the University of Siegen presented the latest research findings from the FACE project on the efficient processing and analysis of ECG data on edge systems. 

As part of the 2026 IEEE World AI IoT Congress (AIIoT) in Seattle, the FACE research project was presented to an international audience of experts. The conference brings together researchers from the fields of artificial intelligence, the Internet of Things, edge and cloud computing, and data-driven applications, thereby providing an international forum for current developments at the intersection of AI and connected systems. AIIoT 2026 took place from May 20 to 22, 2026, in Seattle, Washington. 

The University of Siegen’s presentation focused on the paper“Benchmarking Framework for ECG Preprocessing Algorithms: Method and Comparison for Edge Computing.” The paper deals with the systematic investigation and comparability of methods for preprocessing electrocardiograms (ECG) with regard to their use on resource-constrained edge devices. The paper was developed as part of the research conducted within the FACE project and was published as part of AIIoT 2026 with the DOI“https://doi.org/10.1109/AIIoT68874.2026.11569553.”

The preprocessing of ECG signals is a key component of AI-based analysis chains. Particularly when processing directly on edge devices, technical requirements such as computational complexity, available resources, and suitability for real-time processing must be taken into account in addition to the quality of signal processing. Systematic benchmarking makes it possible to evaluate different algorithms under comparable conditions and thus select suitable methods for specific edge computing scenarios. 

This issue is of particular importance for the FACE project, which is developing AI-based methods for the automated analysis of large volumes of long-term ECG data. The goal is to detect anomalies more reliably and support medical diagnostic processes. In particular, the project is investigating approaches that combine edge and cloud computing to enable efficient processing of ECG data. 

The presentation at AIIoT 2026 provided an opportunity to discuss the FACE project’s research to date with an international audience of experts and to bring together different perspectives on edge AI, medical signal processing, and resource-efficient AI systems. At the same time, the professional exchange provided new impetus for the further development of efficient and practical methods for AI-supported ECG analysis.