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Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models
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 researche...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5375752/ https://www.ncbi.nlm.nih.gov/pubmed/28245602 http://dx.doi.org/10.3390/s17030466 |
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author | Cresswell, Kellen Garrison Shin, Yongyun Chen, Shanshan |
author_facet | Cresswell, Kellen Garrison Shin, Yongyun Chen, Shanshan |
author_sort | Cresswell, Kellen Garrison |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5375752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-53757522017-04-10 Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models Cresswell, Kellen Garrison Shin, Yongyun Chen, Shanshan Sensors (Basel) Article 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. MDPI 2017-02-25 /pmc/articles/PMC5375752/ /pubmed/28245602 http://dx.doi.org/10.3390/s17030466 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cresswell, Kellen Garrison Shin, Yongyun Chen, Shanshan Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models |
title | Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models |
title_full | Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models |
title_fullStr | Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models |
title_full_unstemmed | Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models |
title_short | Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models |
title_sort | quantifying variation in gait features from wearable inertial sensors using mixed effects models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5375752/ https://www.ncbi.nlm.nih.gov/pubmed/28245602 http://dx.doi.org/10.3390/s17030466 |
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