| Autor: |
Hauth J; Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA., Jabri S; Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA., Kamran F; Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA., Feleke EW; Department of Software Engineering, School of Information Technology and Scientific Computing, Addis Ababa Institute of Technology, Addis Ababa 1000, Ethiopia., Nigusie K; Department of Software Engineering, School of Information Technology and Scientific Computing, Addis Ababa Institute of Technology, Addis Ababa 1000, Ethiopia., Ojeda LV; Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA., Handelzalts S; Department of Physical Therapy, Ben-Gurion University, Beer Sheva 8400711, Israel., Nyquist L; Division of Geriatric and Palliative Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA., Alexander NB; Division of Geriatric and Palliative Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA.; VA Ann Arbor Healthcare System Geriatric Research Education and Clinical Center, Ann Arbor, MI 48105, USA., Huan X; Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA., Wiens J; Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA., Sienko KH; Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. |
| Abstrakt: |
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. |
