Wearable Inertial Measurement Units for Assessing Gait in Real-World Environments.

Autor:	Renggli D; Product Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland., Graf C; Product Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland., Tachatos N; Product Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland., Singh N; Institute for Biomechanics, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland., Meboldt M; Product Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland., Taylor WR; Institute for Biomechanics, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland., Stieglitz L; Department of Neurosurgery, University Hospital Zurich, Zurich, Switzerland., Schmid Daners M; Product Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland.
Jazyk:	angličtina
Zdroj:	Frontiers in physiology [Front Physiol] 2020 Feb 20; Vol. 11, pp. 90. Date of Electronic Publication: 2020 Feb 20 (Print Publication: 2020).
DOI:	10.3389/fphys.2020.00090
Abstrakt:	Background: Walking patterns can provide important indications of a person's health status and be beneficial in the early diagnosis of individuals with a potential walking disorder. For appropriate gait analysis, it is critical that natural functional walking characteristics are captured, rather than those experienced in artificial or observed settings. To better understand the extent to which setting influences gait patterns, and particularly whether observation plays a varying role on subjects of different ages, the current study investigates to what extent people walk differently in lab versus real-world environments and whether age dependencies exist. Methods: The walking patterns of 20 young and 20 elderly healthy subjects were recorded with five wearable inertial measurement units (ZurichMOVE sensors) attached to both ankles, both wrists and the chest. An automated detection process based on dynamic time warping was developed to efficiently identify the relevant sequences. From the ZurichMOVE recordings, 15 spatio-temporal gait parameters were extracted, analyzed and compared between motion patterns captured in a controlled lab environment (10 m walking test) and the non-controlled ecologically valid real-world environment (72 h recording) in both groups. Results: Several parameters ( Cluster A ) showed significant differences between the two environments for both groups, including an increased outward foot rotation, step width, number of steps per 180° turn, stance to swing ratio, and cycle time deviation in the real-world. A number of parameters ( Cluster B ) showed only significant differences between the two environments for elderly subjects, including a decreased gait velocity ( p = 0.0072), decreased cadence ( p = 0.0051) and increased cycle time ( p = 0.0051) in real-world settings. Importantly, the real-world environment increased the differences in several parameters between the young and elderly groups. Conclusion: Elderly test subjects walked differently in controlled lab settings compared to their real-world environments, which indicates the need to better understand natural walking patterns under ecologically valid conditions before clinically relevant conclusions can be drawn on a subject's functional status. Moreover, the greater inter-group differences in real-world environments seem promising regarding the sensitive identification of subjects with indications of a walking disorder. (Copyright © 2020 Renggli, Graf, Tachatos, Singh, Meboldt, Taylor, Stieglitz and Schmid Daners.)
Databáze:	MEDLINE
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