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DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units
In recent years, there has been a growing interest in developing and evaluating gait analysis algorithms based on inertial measurement unit (IMU) data, which has important implications, including sports, assessment of diseases, and rehabilitation. Multi-tasking and physical fatigue are two relevant...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442385/ https://www.ncbi.nlm.nih.gov/pubmed/37604913 http://dx.doi.org/10.1038/s41597-023-02391-w |
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author | Zhou, Lin Fischer, Eric Brahms, Clemens Markus Granacher, Urs Arnrich, Bert |
author_facet | Zhou, Lin Fischer, Eric Brahms, Clemens Markus Granacher, Urs Arnrich, Bert |
author_sort | Zhou, Lin |
collection | PubMed |
description | In recent years, there has been a growing interest in developing and evaluating gait analysis algorithms based on inertial measurement unit (IMU) data, which has important implications, including sports, assessment of diseases, and rehabilitation. Multi-tasking and physical fatigue are two relevant aspects of daily life gait monitoring, but there is a lack of publicly available datasets to support the development and testing of methods using a mobile IMU setup. We present a dataset consisting of 6-minute walks under single- (only walking) and dual-task (walking while performing a cognitive task) conditions in unfatigued and fatigued states from sixteen healthy adults. Especially, nine IMUs were placed on the head, chest, lower back, wrists, legs, and feet to record under each of the above-mentioned conditions. The dataset also includes a rich set of spatio-temporal gait parameters that capture the aspects of pace, symmetry, and variability, as well as additional study-related information to support further analysis. This dataset can serve as a foundation for future research on gait monitoring in free-living environments. |
format | Online Article Text |
id | pubmed-10442385 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104423852023-08-23 DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units Zhou, Lin Fischer, Eric Brahms, Clemens Markus Granacher, Urs Arnrich, Bert Sci Data Data Descriptor In recent years, there has been a growing interest in developing and evaluating gait analysis algorithms based on inertial measurement unit (IMU) data, which has important implications, including sports, assessment of diseases, and rehabilitation. Multi-tasking and physical fatigue are two relevant aspects of daily life gait monitoring, but there is a lack of publicly available datasets to support the development and testing of methods using a mobile IMU setup. We present a dataset consisting of 6-minute walks under single- (only walking) and dual-task (walking while performing a cognitive task) conditions in unfatigued and fatigued states from sixteen healthy adults. Especially, nine IMUs were placed on the head, chest, lower back, wrists, legs, and feet to record under each of the above-mentioned conditions. The dataset also includes a rich set of spatio-temporal gait parameters that capture the aspects of pace, symmetry, and variability, as well as additional study-related information to support further analysis. This dataset can serve as a foundation for future research on gait monitoring in free-living environments. Nature Publishing Group UK 2023-08-21 /pmc/articles/PMC10442385/ /pubmed/37604913 http://dx.doi.org/10.1038/s41597-023-02391-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Zhou, Lin Fischer, Eric Brahms, Clemens Markus Granacher, Urs Arnrich, Bert DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units |
title | DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units |
title_full | DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units |
title_fullStr | DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units |
title_full_unstemmed | DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units |
title_short | DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units |
title_sort | duo-gait: a gait dataset for walking under dual-task and fatigue conditions with inertial measurement units |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442385/ https://www.ncbi.nlm.nih.gov/pubmed/37604913 http://dx.doi.org/10.1038/s41597-023-02391-w |
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