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Sensors 2017, 17(3), 466; doi:10.3390/s17030466

Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models

Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, USA
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Author to whom correspondence should be addressed.
Academic Editors: Edward Sazonov and Subhas Chandra Mukhopadhyay
Received: 12 January 2017 / Revised: 17 February 2017 / Accepted: 21 February 2017 / Published: 25 February 2017
(This article belongs to the Special Issue Wearable and Ambient Sensors for Healthcare and Wellness Applications)
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Abstract

The emerging technology of wearable inertial sensors has shown its advantages in collecting continuous longitudinal gait data outside laboratories. This freedom also presents challenges in collecting high-fidelity gait data. In the free-living environment, without constant supervision from researchers, sensor-based gait features are susceptible to variation from confounding factors such as gait speed and mounting uncertainty, which are challenging to control or estimate. This paper is one of the first attempts in the field to tackle such challenges using statistical modeling. By accepting the uncertainties and variation associated with wearable sensor-based gait data, we shift our efforts from detecting and correcting those variations to modeling them statistically. From gait data collected on one healthy, non-elderly subject during 48 full-factorial trials, we identified four major sources of variation, and quantified their impact on one gait outcome—range per cycle—using a random effects model and a fixed effects model. The methodology developed in this paper lays the groundwork for a statistical framework to account for sources of variation in wearable gait data, thus facilitating informative statistical inference for free-living gait analysis. View Full-Text
Keywords: pervasive gait analysis; mounting location uncertainty; gait speed variation; gait data quality; sources of variation; gyroscope; accelerometer; statistical characterization; random effects models; fixed effects models pervasive gait analysis; mounting location uncertainty; gait speed variation; gait data quality; sources of variation; gyroscope; accelerometer; statistical characterization; random effects models; fixed effects models
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Cresswell, K.G.; Shin, Y.; Chen, S. Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models. Sensors 2017, 17, 466.

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