CEE Students Yiwen Dong and Jingxiao Liu win Best Paper Award at 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Associate Professor Haeyoung Noh's PhD students Yiwen Dong (1st year) and Jingxiao Liu (3rd year), along with Stanford Postdoctoral Scholar Mostafa Mirshekari won the Best Paper Award for their paper on the Nurse Care Activity Recognition Challenge held as part of 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing.
The conference, UbiComp, is a premier interdisciplinary venue in which leading international researchers, designers, developers, and practitioners in the field present and discuss novel results in all aspects of ubiquitous and pervasive computing. This includes the design, development, and deployment of ubiquitous and pervasive computing technologies and the understanding of human experiences and social impacts that these technologies facilitate.
The paper titled “A Window-Based Sequence-to-One Approach with Dynamic Voting for Nurse Care Activity Recognition Using Acceleration-Based Wearable Sensor” was authored by Yiwen Dong, Jingxiao Liu, Yitao Gao, Sulagna Sarkar, Zhizhang Hu, Jonathon Fagert, Shijia Pan, Pei Zhang, Hae Young Noh, Mostafa Mirshekari.
This was the second year that the Nurse Care Activity Recognition Challenge has been held. Activity recognition is the task of classifying daily life activities performed by a human on sensor observations. Using mobile phones accelerometer for this observation is one of the cheapest and most convenient methods. Activity recognition is specially gaining attention in care facilities for remote monitoring of daily activities for elders living alone and automatic record creation for nurses in hospitals with a goal of providing better care to patients. One challenge for these applications is bridging the gap between models created within the lab and models for real life. In this year's challenge, participants created methods for recognizing 12 activities conducted by nurses in both lab and in real-life settings.