Toward Ultra-Low-Power Remote Health Monitoring: An Optimal and Adaptive Compressed Sensing Framework for Activity Recognition

Abstract

Activity recognition, as an important component of behavioral monitoring and intervention, has attracted enormous attention, especially in Mobile Cloud Computing (MCC) and Remote Health Monitoring (RHM) paradigms. While recently resource constrained wearable devices have been gaining popularity, their battery life is limited and constrained by the frequent wireless transmission of data to more computationally powerful back-ends. This paper proposes an ultra-low power activity recognition system using a novel adaptive compressed sensing technique that aims to minimize transmission costs. Coarse-grained on-body sensor localization and unsupervised clustering modules are devised to autonomously reconfigure the compressed sensing module for further power saving. We perform a thorough heuristic optimization using Grammatical Evolution (GE) to ensure minimal computation overhead of the proposed methodology. Our evaluation on a real-world dataset and a low power wearable sensing node demonstrates that our approach can reduce the energy consumption of the wireless data transmission up to 81.2 and 61.5 percent, with up to 60.6 and 35.0 percent overall power savings in comparison with baseline and a naive state-of-the-art approaches, respectively. These solutions lead to an average activity recognition accuracy of 89.0 percent-only 4.8 percent less than the baseline accuracy-while having a negligible energy overhead of on-node computation.

Publication
IEEE Transactions on Mobile Computing (TMC)
Ramin Fallahzadeh
Ramin Fallahzadeh
Graduate Alumni

Graduate Research Assistant.

Mahdi Pedram
Mahdi Pedram
Research Assistant

I am a fourth year PhD student at Washington State University. I work as a research assistant with Professor Hassan Ghasemzadeh. My research topics include embedded systems, health monitoring systems, wearable sensor development, sensor data mining, power optimization, and machine learning. I received my B.S. degree in Computer Cngineering from Amirkabir University of Technology, Tehran, Iran in 2014.

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.