Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units
Jeremiah Hauth,
Safa Jabri,
Fahad Kamran,
Eyoel W. Feleke,
Kaleab Nigusie,
Lauro V. Ojeda,
Shirley Handelzalts,
Linda Nyquist,
Neil B. Alexander,
Xun Huan,
Jenna Wiens,
Kathleen H. Sienko
Affiliations
Jeremiah Hauth
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Safa Jabri
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Fahad Kamran
Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Eyoel W. Feleke
Department of Software Engineering, School of Information Technology and Scientific Computing, Addis Ababa Institute of Technology, Addis Ababa 1000, Ethiopia
Kaleab Nigusie
Department of Software Engineering, School of Information Technology and Scientific Computing, Addis Ababa Institute of Technology, Addis Ababa 1000, Ethiopia
Lauro V. Ojeda
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Shirley Handelzalts
Department of Physical Therapy, Ben-Gurion University, Beer Sheva 8400711, Israel
Linda Nyquist
Division of Geriatric and Palliative Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
Neil B. Alexander
Division of Geriatric and Palliative Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
Xun Huan
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Jenna Wiens
Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Kathleen H. Sienko
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
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.
