报告题目：Lifelogging Everything I Do
报告人：Dr. Cathal Gurrin
主持人：王长波 教授
报告时间：3月20日14:00-15:00
报告地点：中北校区数学馆201室
报告人简介：  

       Cathal Gurrin is a senior lecturer at the School of Computing, at Dublin City University, Ireland and he is a Principal investigator at the Insight Centre for Data Analytics. His research interests are personal analytics and lifelogging. Lifelogging integrates personal sensing, computer science, cognitive science and data-driven healthcare analytics to realize the next-generation of digital records for the individual. He is especially interested in how wearable sensors can be used to infer knowledge about the real-world activities of the individual and how lifelogs can be used to enhance the life experience of the individual. He regularly speaks at Quantified Self events and his research been featured internationally on Discovery Channel, BBC, NHK, as well as in the Economist magazine, New York Times, among many others. He has been the General Chair of ECIR 2011, MMM 2014, MB2016 and MMM2017. He was also the PC co-chair of ECIR 2010.

报告摘要：

        Technology advances have brought society to the point where it is becoming possible to passively capture rich multimedia traces of life activity with minimal cost.  We refer to such multimedia traces as lifelogs. However, unlike many other sources of personal data, there is as of yet not a clear understanding of how this data can be used to provide a new range of assistive technologies for the individual and employed with positive societal impact.  In this talk, I will explore the potential and challenges of ubiquitous lifelogging. This requires the exploration of the different types of lifelog data available now and in the coming years. Following that, the challenge of indexing and retrieval will be discussed before examining the different applications of the first generation of lifelog prototypes. Finally, we will explore the various efforts underway to drive the community forward by practitioners through real-world applications and  by researchers through benchmarking exercises, such a NTCIR13-Lifelog or ImageCLEF-Lifelog.