Autonomous Training of Activity Recognition Algorithms in Mobile Sensors: A Transfer Learning Approach in Context-Invariant Views

Abstract

Wearable technologies play a central role in human-centered Internet-of-Things applications. Wearables leverage machine learning algorithms to detect events of interest such as physical activities and medical complications. A major obstacle in large-scale utilization of current wearables is that their computational algorithms need to be re-built from scratch upon any changes in the configuration. Retraining of these algorithms requires significant amount of labeled training data, a process that is labor-intensive and time-consuming. We propose an approach for automatic retraining of the machine learning algorithms in real-time without need for any labeled training data. We measure the inherent correlation between observations made by an old sensor view for which trained algorithms exist and the new sensor view for which an algorithm needs to be developed. Our multi-view learning approach can be used in both online and batch modes. By applying the autonomous multi-view learning in the batch mode, we achieve an accuracy of 83.7 percent in activity recognition which is an improvement of 9.3 percent due to the automatic labeling of the data in the new sensor node. In addition to gain the less computation advantage of incremental training, the online learning algorithm results in an accuracy of 82.2 percent in activity recognition.

Publication
IEEE Transactions on Mobile Computing (TMC), vol. 17, no. 8, pp. 1764-1777, August 2018 (Featured Article of August 2018 Issue)
Ali Rokni
Ali Rokni
Graduate Alumni

Graduate Research Assistant.

Hassan Ghasemzadeh
Hassan Ghasemzadeh
Director

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.