Power-Aware Computing in Wearable Sensor Networks: An Optimal Feature Selection


Wearable sensory devices are becoming the enabling technology for many applications in healthcare and well-being, where computational elements are tightly coupled with the human body to monitor specific events about their subjects. Classification algorithms are the most commonly used machine learning modules that detect events of interest in these systems. The use of accurate and resource-efficient classification algorithms is of key importance because wearable nodes operate on limited resources on one hand and intend to recognize critical events (e.g., falls) on the other hand. These algorithms are used to map statistical features extracted from physiological signals onto different states such as health status of a patient or type of activity performed by a subject. Conventionally selected features may lead to rapid battery depletion, mainly due to the absence of computing complexity criterion while selecting prominent features. In this paper, we introduce the notion of power-aware feature selection, which aims at minimizing energy consumption of the data processing for classification applications such as action recognition. Our approach takes into consideration the energy cost of individual features that are calculated in real-time. A graph model is introduced to represent correlation and computing complexity of the features. The problem is formulated using integer programming and a greedy approximation is presented to select the features in a power-efficient manner. Experimental results on thirty channels of activity data collected from real subjects demonstrate that our approach can significantly reduce energy consumption of the computing module, resulting in more than 30 percent energy savings while achieving 96.7 percent classification accuracy.

IEEE Transactions on Mobile Computing (TMC), 14(4), pp.800-812, 2016
Hassan Ghasemzadeh
Hassan Ghasemzadeh

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.