@article{abc5c0db68464f1dbb604ea65db028bb,
title = "Automated loss-of-balance event identification in older adults at risk of falls during real-world walking using wearable inertial measurement units",
abstract = "Loss-of-balance (LOB) events, such as trips and slips, are frequent among community-dwelling older adults and are an indicator of increased fall risk. In a preliminary study, eight community-dwelling older adults with a history of falls were asked to perform everyday tasks in the real world while donning a set of three inertial measurement sensors (IMUs) and report LOB events via a voice-recording device. Over 290 h of real-world kinematic data were collected and used to build and evaluate classification models to detect the occurrence of LOB events. Spatiotemporal gait metrics were calculated, and time stamps for when LOB events occurred were identified. Using these data and machine learning approaches, we built classifiers to detect LOB events. Through a leave-one-participant-out validation scheme, performance was assessed in terms of the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR). The best model achieved an AUROC ≥ 0.87 for every held-out participant and an AUPR 4-20 times the incidence rate of LOB events. Such models could be used to filter large datasets prior to manual classification by a trained healthcare provider. In this context, the models filtered out at least 65.7% of the data, while detecting ≥ 87.0% of events on average. Based on the demonstrated discriminative ability to separate LOBs and normal walking segments, such models could be applied retrospectively to track the occurrence of LOBs over an extended period of time.",
keywords = "Activity recognition, Body sensor networks, Event detection, Gait recognition, Loss of balance, Machine learning, Wearable sensors",
author = "Jeremiah Hauth and Safa Jabri and Fahad Kamran and Feleke, {Eyoel W.} and Kaleab Nigusie and Ojeda, {Lauro V.} and Shirley Handelzalts and Linda Nyquist and Alexander, {Neil B.} and Xun Huan and Jenna Wiens and Sienko, {Kathleen H.}",
note = "Funding Information: Funding: Research reported herein was supported by a grant to the University of Michigan Injury Prevention Center by the Centers for Disease Control and Prevention (award number R49-CE-002099) with secondary support from the National Institute of Aging (AG024824), the National Center for Research (UL1TR002240), and the University of Michigan African Undergraduate Research Adventure program. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Centers for Disease Control and Prevention or the Department of Health and Human Services. Funding Information: Research reported herein was supported by a grant to the University of Michigan Injury Prevention Center by the Centers for Disease Control and Prevention (award number R49-CE-002099) with secondary support from the National Institute of Aging (AG024824), the National Center for Research (UL1TR002240), and the University of Michigan African Undergraduate Research Adventure program. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Centers for Disease Control and Prevention or the Department of Health and Human Services. Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2021",
month = jul,
day = "2",
doi = "10.3390/s21144661",
language = "English",
volume = "21",
journal = "Sensors",
issn = "1424-3210",
publisher = "MDPI Multidisciplinary Digital Publishing Institute",
number = "14",
}