Transfer Learning for Activity Recognition in Mobile Health


While activity recognition from inertial sensors holds potential for mobile health, differences in sensing platforms and user movement patterns cause performance degradation. Aiming to address these challenges, we propose a transfer learning framework, TransFall1 ,for sensor-based activity recognition. TransFall’s design contains a two-tier data transformation, a label estimation layer, and a model generation layer to recognize activities for the new scenario. We validate TransFall analytically and empirically.

KDD 2020 Workshop on on Applied Data Science for Healthcare
Graduate Alumni

Graduate Research Assistant.


Hassan Ghasemzadeh (Zadeh) is an Associate Professor of Computer Science in the School of Electrical Engineering and Computer Science at Washington State University (WSU). Prior to joining WSU in 2014, he was a Research Manager at the UCLA Wireless Health Institute and an Adjunct Professor of Biomedical Informatics at San Diego State University. He received his Ph.D. in Computer Engineering from the University of Texas at Dallas in 2010, and spent the academic year 2010-2011 as a Postdoctoral Fellow at the West Health Institute. He was Founding Chair of Computer Science and Engineering Department at Azad University, Damavand, 2003-2006. He received his M.S. degree in Computer Engineering from University of Tehran, Tehran, Iran, in 2001 and his B.S. degree in Computer Engineering from Sharif University of Technology, Tehran, Iran in 1998. He received the 2019 WSU GPSA Academic Advisor Excellence Award, 2018 NSF CAREER Award, 2018 WSU EECS Early Career Award, 2018 WSU VCEA Outstanding Communication, Connection, and Engagement Award, 2016 NSF CRII Award, and 2011 IEEE RTAS Best Paper Award.