Pervasive Computing for E-health

  • Posted on: 21 March 2017
  • By: Lan Chunyuan
Wednesday, March 22, 2017
Prof.Yiqiang Chen
The current aging of the population in our society, of the empty nest, loss of energy of the increasingly serious, plus all kinds of chronic diseases have become the main cause of health effects, leading to universal health care needs are very urgent. Studies have shown that the causes of chronic diseases and unhealthy lifestyles largely related to activities of daily living such as some older people have significantly lower activity and prolonged low activity state often exhibit different degrees of depression and other emotional states , thus speeding up the older body of the function, awareness of cognitive function decline, and even lead to severe Alzheimer’s, Parkinson’s and other high-risk disease, so the user behavior perception becomes the key to universal health care. With the rapid development of microelectromechanical systems. A lot of sensors to be developed and embedded into a variety of intelligent wearable devices (including wearable equipment, portable equipment, etc.), especially smart phones, smart watches, smart glasses and other people in their daily life can be easy to carry wearable and portable smart mobile devices, both largely non-interference, sustainable conduct perceived user behavior, mining and infrastructure to provide convenient conditions, but also facilitated the user behavior around wearable or portable device sensing system research and deployment. Thus, we based wearable device, universal health care and in the daily management, user behavior perception were related to some research work. With the objective of uncovering the diversity perception of behavior data, a multi-source data based unobtrusive activity sensing method was proposed. In order to achieve personalized activity recognition, an integrated incremental transfer learning model and method were proposed. Aiming to study the complex relation between cognition and behavior, an effective iterative feature selection method using unbalance relevance was proposed to uncover objective internal relations between cognitive functions and motor behaviors. So as to achieve behavior and cognitive function based healthcare.

Prof. Yiqiang Chen is now the Director of Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences. he is the group leader of Wearable Standard Group which belongs to National Digital Audio and Video Standards Working Group, co-sponsor of IEEE Interactive Computing and Equipment Technology Committee, the supervisor of China Computer Federation(CCF), the vice leader of CCF Pervasive computing Specialized Committee, honorary member of CCF Young Computer Scientists & Engineers Forum(YOCSEF). He obtained Ph.D. Degree in Institute of Computer Technology Chinese Academy of Sciences in 2003, and he did research in Hong Kong University Science & Technology and Nanyang Technology University Singapore as visiting scholar. His research area is Computer interaction and pervasive computing, focusing on human-computer interaction, wearable computing, activity recognition, context-aware and immersive interaction. He has authored or co-authored 34 SCI index journals and 11 IEEE/ACM Transactions papers. His papers have received 2035 Google Scholar citations, with the highest single article cited 198 times.