Pervasive Lying Posture Tracking

Abstract

Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

Publication
Sensors, October 2020
Parastoo Alinia
Parastoo Alinia
Research Assistant

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.