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Using force data to self-pace an instrumented treadmill and measure self-selected walking speed

BACKGROUND: Self-selected speed is an important functional index of walking. A self-pacing controller that reliably matches walking speed without additional hardware can be useful for measuring self-selected speed in a treadmill-based laboratory. METHODS: We adapted a previously proposed self-pacing...

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Autores principales: Song, Seungmoon, Choi, Hojung, Collins, Steven H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7268460/
https://www.ncbi.nlm.nih.gov/pubmed/32493426
http://dx.doi.org/10.1186/s12984-020-00683-5
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author Song, Seungmoon
Choi, Hojung
Collins, Steven H.
author_facet Song, Seungmoon
Choi, Hojung
Collins, Steven H.
author_sort Song, Seungmoon
collection PubMed
description BACKGROUND: Self-selected speed is an important functional index of walking. A self-pacing controller that reliably matches walking speed without additional hardware can be useful for measuring self-selected speed in a treadmill-based laboratory. METHODS: We adapted a previously proposed self-pacing controller for force-instrumented treadmills and validated its use for measuring self-selected speeds. We first evaluated the controller’s estimation of subject speed and position from the force-plates by comparing it to those from motion capture data. We then compared five tests of self-selected speed. Ten healthy adults completed a standard 10-meter walk test, a 150-meter walk test, a commonly used manual treadmill speed selection test, a two-minute self-paced treadmill test, and a 150-meter self-paced treadmill test. In each case, subjects were instructed to walk at or select their comfortable speed. We also assessed the time taken for a trial and a survey on comfort and ease of choosing a speed in all the tests. RESULTS: The self-pacing algorithm estimated subject speed and position accurately, with root mean square differences compared to motion capture of 0.023 m s (−1) and 0.014 m, respectively. Self-selected speeds from both self-paced treadmill tests correlated well with those from the 10-meter walk test (R>0.93,p<1×10(−13)). Subjects walked slower on average in the self-paced treadmill tests (1.23±0.27 ms(−1)) than in the 10-meter walk test (1.32±0.18 ms(−1)) but the speed differences within subjects were consistent. These correlations and walking speeds are comparable to those from the manual treadmill speed selection test (R=0.89,p=3×10(−11);1.18±0.24 ms(−1)). Comfort and ease of speed selection were similar in the self-paced tests and the manual speed selection test, but the self-paced tests required only about a third of the time to complete. Our results demonstrate that these self-paced treadmill tests can be a strong alternative to the commonly used manual treadmill speed selection test. CONCLUSIONS: The self-paced force-instrumented treadmill well adapts to subject walking speed and reliably measures self-selected walking speeds. We provide the self-pacing software to facilitate use by gait researchers and clinicians.
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spelling pubmed-72684602020-06-07 Using force data to self-pace an instrumented treadmill and measure self-selected walking speed Song, Seungmoon Choi, Hojung Collins, Steven H. J Neuroeng Rehabil Research BACKGROUND: Self-selected speed is an important functional index of walking. A self-pacing controller that reliably matches walking speed without additional hardware can be useful for measuring self-selected speed in a treadmill-based laboratory. METHODS: We adapted a previously proposed self-pacing controller for force-instrumented treadmills and validated its use for measuring self-selected speeds. We first evaluated the controller’s estimation of subject speed and position from the force-plates by comparing it to those from motion capture data. We then compared five tests of self-selected speed. Ten healthy adults completed a standard 10-meter walk test, a 150-meter walk test, a commonly used manual treadmill speed selection test, a two-minute self-paced treadmill test, and a 150-meter self-paced treadmill test. In each case, subjects were instructed to walk at or select their comfortable speed. We also assessed the time taken for a trial and a survey on comfort and ease of choosing a speed in all the tests. RESULTS: The self-pacing algorithm estimated subject speed and position accurately, with root mean square differences compared to motion capture of 0.023 m s (−1) and 0.014 m, respectively. Self-selected speeds from both self-paced treadmill tests correlated well with those from the 10-meter walk test (R>0.93,p<1×10(−13)). Subjects walked slower on average in the self-paced treadmill tests (1.23±0.27 ms(−1)) than in the 10-meter walk test (1.32±0.18 ms(−1)) but the speed differences within subjects were consistent. These correlations and walking speeds are comparable to those from the manual treadmill speed selection test (R=0.89,p=3×10(−11);1.18±0.24 ms(−1)). Comfort and ease of speed selection were similar in the self-paced tests and the manual speed selection test, but the self-paced tests required only about a third of the time to complete. Our results demonstrate that these self-paced treadmill tests can be a strong alternative to the commonly used manual treadmill speed selection test. CONCLUSIONS: The self-paced force-instrumented treadmill well adapts to subject walking speed and reliably measures self-selected walking speeds. We provide the self-pacing software to facilitate use by gait researchers and clinicians. BioMed Central 2020-06-03 /pmc/articles/PMC7268460/ /pubmed/32493426 http://dx.doi.org/10.1186/s12984-020-00683-5 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Song, Seungmoon
Choi, Hojung
Collins, Steven H.
Using force data to self-pace an instrumented treadmill and measure self-selected walking speed
title Using force data to self-pace an instrumented treadmill and measure self-selected walking speed
title_full Using force data to self-pace an instrumented treadmill and measure self-selected walking speed
title_fullStr Using force data to self-pace an instrumented treadmill and measure self-selected walking speed
title_full_unstemmed Using force data to self-pace an instrumented treadmill and measure self-selected walking speed
title_short Using force data to self-pace an instrumented treadmill and measure self-selected walking speed
title_sort using force data to self-pace an instrumented treadmill and measure self-selected walking speed
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7268460/
https://www.ncbi.nlm.nih.gov/pubmed/32493426
http://dx.doi.org/10.1186/s12984-020-00683-5
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