Human activity recognition is the core technology that enables a wide variety of applications such as health care, smart homes, fitness tracking, and building surveillance. We recognize human activities using signals from commercial WiFi devices. Human bodies reflect wireless signals as they are mostly made of water. Different human activities cause different changes on wireless signals. Thus, by analyzing the changes in wireless signals, we can recognize the corresponding human activities that cause the changes. We classify human activities into macro activities, which involve mostly arm, leg, or body scale movements, and micro activities, which involve mostly finger or hand scale movements. Human activity recognition and monitoring is the enabling technology for various applications such as elderly/health care, building surveillance, human-computer interaction, health care, smart homes, and fitness tracking. In this talk, I will present our research results on this topic.
Alex X. Liu received his Ph.D. degree in Computer Science from The University of Texas at Austin in 2006, and is a professor at the Department of Computer Science and Engineering, Michigan State University. He received the IEEE & IFIP William C. Carter Award in 2004, a National Science Foundation CAREER award in 2009, and the Michigan State University Withrow Distinguished Scholar Award in 2011. He has served as an Editor for IEEE/ACM Transactions on Networking, and he is currently an Associate Editor for IEEE Transactions on Dependable and Secure Computing and IEEE Transactions on Mobile Computing, and an Area Editor for Computer Communications. He has served as the TPC Co-Chair for ICNP 2014 and IFIP Networking 2019. He received Best Paper Awards from ICNP-2012, SRDS-2012, and LISA-2010. His research interests focus on networking and security. He is a Fellow of the IEEE.（文/贺峰）