China Academy of Science, China
Dr Yiqiang Chen is a professor and Director of the Research Center for Ubiquitous Computing Systems, Institute of Computing Technology (ICT), the Chinese Academy of Sciences. He received his PhD degree from ICT, Chinese Academy of Sciences in 2002. In 2004, he was a Post-Doctoral Research Fellow in the Department of Computer Science, Hong Kong University of Science and Technology (HKUST). He was the visiting professor in the Joint NTU-UBC Research Center of Excellence in Active Living for the Elderly (LiLy), Nanyang Technological University. His research focuses on intelligent human computer interaction and pervasive computing, especially on learning and understanding users’ daily activity patterns in unobtrusive ways. He has published over 100 papers in reputable International Journals such as IEEE TKDE, IEEE TMC, IEEE TNN, IEEE TCSVT, Scientific Reports and Science (Advances in Computational Psychophysiology), as well as top tier International conferences such as IJCAI, AAAI, ACM MM, Ubicomp etc. He got Best Application paper award from PRICAI2005 and Best Paper Award from Gamenets2014. He received the National Science and Technology Award (2004) and Beijing Science and Technology Award (2015,2016) and been selected as a top young scientist of Beijing in 2005.
Title: Wearable AI: From E-Health to C-Health
Abstract: “Healthy China” rises to national strategy and leads the medical services transferring from after-disease treatment to preventive healthcare. The personalized healthcare needs to focus on the monitoring and analysis of individual lifestyle and behavior patterns. The real-time behavior data, which is automatically collected via wearable devices and IoT devices, enables all-round recording of individual lifestyle and behavior patterns and thus can be exploited for personalized health management. There are some key issues we need to solve before building up this kind of system. First of all, how we can acquire the real-time behavior data from wearable devices in an unobtrusive way. Second, how we can guarantee the dependable detection when the abnormal behavior occurs. Third, how to designe the intelligent quantitative ADL assessment system to effectively evaluate the elderly's motor and cognitive capability based on long-term daily behavior in the home environment. In this talk, I will discuss some solutions to solve the issues, including but not limited to, unobtrusive and dependable health data intelligent perception, heterogeneous health data structuring, standardization for the data format of wearable device, disease association pattern mining from dynamic and static health data.