@article{e6265500b7d147489a694eb06748fe58,
title = "Wearable sensor-based prediction model of timed up and go test in older adults",
abstract = "The Timed Up and Go (TUG) test has been frequently used to assess the risk of falls in older adults because it is an easy, fast, and simple method of examining functional mobility and balance without special equipment. The purpose of this study is to develop a model that predicts the TUG test using three-dimensional acceleration data collected from wearable sensors during normal walking. We recruited 37 older adults for an outdoor walking task, and seven inertial measurement unit (IMU)-based sensors were attached to each participant. The elastic net and ridge regression methods were used to reduce gait feature sets and build a predictive model. The proposed predictive model reliably estimated the participants{\textquoteright} TUG scores with a small margin of prediction errors. Although the prediction accuracies with two foot-sensors were slightly better than those of other configurations (e.g., MAPE: foot (0.865 s) > foot and pelvis (0.918 s) > pelvis (0.921 s)), we recommend the use of a single IMU sensor at the pelvis since it would provide wearing comfort while avoiding the disturbance of daily activities. The proposed predictive model can enable clini-cians to assess older adults{\textquoteright} fall risks remotely through the evaluation of the TUG score during their daily walking.",
keywords = "Accelerometer, Elastic net, Gait analysis, Ridge regression, Timed up and go (TUG), Wearable sensor",
author = "Jungyeon Choi and Parker, {Sheridan M.} and Knarr, {Brian A.} and Yeongjin Gwon and Youn, {Jong Hoon}",
note = "Funding Information: Author Contributions: Conceptualization, J.C. and J.-H.Y.; methodology, J.C.; software, J.C.; validation, J.C., B.A.K., Y.G. and J.-H.Y.; formal analysis, J.C. and Y.G.; investigation, J.C. and S.M.P.; data curation, J.C. and S.M.P.; writing—original draft preparation, J.C. and S.M.P.; writing—review and editing, S.M.P., B.A.K., Y.G. and J.-H.Y.; visualization, J.C. ; supervision, B.A.K. and J.-H.Y.; funding acquisition, B.A.K., Y.G. and J.-H.Y. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the NSF, grant number CNS-1711386; the NIH, grant numbers R15 HD094194, R01 NS114282, and P20 GM109090; the NIGMS, grant number 5U54GM115458. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Institutional Review Board of the University of Nebraska Medical Center (protocol codes 654-16-EP and 242-18-EP, approved 8 November 2016 and 26 April 2018, respectively). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Written informed consent was obtained from the subjects to publish this paper. Acknowledgments: The authors thank the Writing Center of the University of Nebraska at Omaha for proofreading the manuscript. Conflicts of Interest: The authors declare no conflict of interest. Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2021",
month = oct,
day = "1",
doi = "10.3390/s21206831",
language = "English (US)",
volume = "21",
journal = "Sensors (Switzerland)",
issn = "1424-3210",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "20",
}