题目：Intelligent Health Monitoring

时间：2024年7月22日（周一）， 上午10:30-11:30

地点：玉泉校区，行政楼208

报告人：Prof. Xi Long，Eindhoven University of Technology 

专家介绍

        Xi Long is an Associate Professor in the Department of Electrical Engineering, Eindhoven University of Technology (TU/e), and a management team member of the Eindhoven MedTech Innovation Center (e/MTIC), the Netherlands. He received his BEng from the department of ISEE, Zhejiang University in 2006, and the MSc and PhD (cum laude) in electrical engineering from the TU/e, in 2009 and 2015, respectively. He was a Senior Scientist and AI Lead at Philips Research where he has more than ten years of experience in healthcare research and innovation. He has been a project leader or consortium member of many research projects, such as EU-Horizon, EU-ITEA, and Dutch-NWO. His research interests include signal processing and machine learning for medical applications such as unobtrusive sensing, sleep, neonatal & health, neuroscience, cardiology, patient monitoring, and intensive care medicine. He has authored over 150 scientific articles and nearly 20 patents (Google citations: 4000+, H-index: 32). He is a Senior Member of IEEE, and serves as Associate Editor for top scientific journals including Nature npj Digital Medicine and Health Informatics Journal.

报告内容

        Intelligent health monitoring (IHM) is an evolving technology to keep track of a person’s health condition or diseases, both in a hospital or at home, including using advanced sensing technologies and telehealth for ubiquitous health data collection, as well as automatic algorithms for predicting or detecting health problems from those data. Breakthroughs in artificial intelligence promise the achievement of reliable models for disease detection/prediction, which is an essential pillar of implementing IHM. The talk will introduce the healthcare innovation ecosystem in TU Eindhoven and Brainport Eindhoven, as well as our past research and valorization in IHM with physiological signals, video/audio data, and medical records covering a wide spectrum of applications such as sleep tracking, neonatal monitoring, seizure detection, and deterioration prediction. I will also present more details of our unobtrusive sleep monitoring project as an example to showcase the pathway of developing a dedicated IHM solution.