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Evaluating the Accuracy of Virtual Reality Trackers for Computing Spatiotemporal Gait Parameters
Ageing, disease, and injuries result in movement defects that affect daily life. Gait analysis is a vital tool for understanding and evaluating these movement dysfunctions. In recent years, the use of virtual reality (VR) to observe motion and offer augmented clinical care has increased. Although VR...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8151659/ https://www.ncbi.nlm.nih.gov/pubmed/34064807 http://dx.doi.org/10.3390/s21103325 |
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author | Guaitolini, Michelangelo Petros, Fitsum E. Prado, Antonio Sabatini, Angelo M. Agrawal, Sunil K. |
author_facet | Guaitolini, Michelangelo Petros, Fitsum E. Prado, Antonio Sabatini, Angelo M. Agrawal, Sunil K. |
author_sort | Guaitolini, Michelangelo |
collection | PubMed |
description | Ageing, disease, and injuries result in movement defects that affect daily life. Gait analysis is a vital tool for understanding and evaluating these movement dysfunctions. In recent years, the use of virtual reality (VR) to observe motion and offer augmented clinical care has increased. Although VR-based methodologies have shown benefits in improving gait functions, their validity against more traditional methods (e.g., cameras or instrumented walkways) is yet to be established. In this work, we propose a procedure aimed at testing the accuracy and viability of a VIVE Virtual Reality system for gait analysis. Seven young healthy subjects were asked to walk along an instrumented walkway while wearing VR trackers. Heel strike (HS) and toe off (TO) events were assessed using the VIVE system and the instrumented walkway, along with stride length (SL), stride time (ST), stride width (SW), stride velocity (SV), and stance/swing percentage (STC, SWC%). Results from the VR were compared with the instrumented walkway in terms of detection offset for time events and root mean square error (RMSE) for gait features. An absolute offset between VR- and walkway-based data of (15.3 ± 12.8) ms for HS, (17.6 ± 14.8) ms for TOs and an RMSE of 2.6 cm for SW, 2.0 cm for SL, 17.4 ms for ST, 2.2 m/s for SV, and 2.1% for stance and swing percentage were obtained. Our findings show VR-based systems can accurately monitor gait while also offering new perspectives for VR augmented analysis. |
format | Online Article Text |
id | pubmed-8151659 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81516592021-05-27 Evaluating the Accuracy of Virtual Reality Trackers for Computing Spatiotemporal Gait Parameters Guaitolini, Michelangelo Petros, Fitsum E. Prado, Antonio Sabatini, Angelo M. Agrawal, Sunil K. Sensors (Basel) Article Ageing, disease, and injuries result in movement defects that affect daily life. Gait analysis is a vital tool for understanding and evaluating these movement dysfunctions. In recent years, the use of virtual reality (VR) to observe motion and offer augmented clinical care has increased. Although VR-based methodologies have shown benefits in improving gait functions, their validity against more traditional methods (e.g., cameras or instrumented walkways) is yet to be established. In this work, we propose a procedure aimed at testing the accuracy and viability of a VIVE Virtual Reality system for gait analysis. Seven young healthy subjects were asked to walk along an instrumented walkway while wearing VR trackers. Heel strike (HS) and toe off (TO) events were assessed using the VIVE system and the instrumented walkway, along with stride length (SL), stride time (ST), stride width (SW), stride velocity (SV), and stance/swing percentage (STC, SWC%). Results from the VR were compared with the instrumented walkway in terms of detection offset for time events and root mean square error (RMSE) for gait features. An absolute offset between VR- and walkway-based data of (15.3 ± 12.8) ms for HS, (17.6 ± 14.8) ms for TOs and an RMSE of 2.6 cm for SW, 2.0 cm for SL, 17.4 ms for ST, 2.2 m/s for SV, and 2.1% for stance and swing percentage were obtained. Our findings show VR-based systems can accurately monitor gait while also offering new perspectives for VR augmented analysis. MDPI 2021-05-11 /pmc/articles/PMC8151659/ /pubmed/34064807 http://dx.doi.org/10.3390/s21103325 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Guaitolini, Michelangelo Petros, Fitsum E. Prado, Antonio Sabatini, Angelo M. Agrawal, Sunil K. Evaluating the Accuracy of Virtual Reality Trackers for Computing Spatiotemporal Gait Parameters |
title | Evaluating the Accuracy of Virtual Reality Trackers for Computing Spatiotemporal Gait Parameters |
title_full | Evaluating the Accuracy of Virtual Reality Trackers for Computing Spatiotemporal Gait Parameters |
title_fullStr | Evaluating the Accuracy of Virtual Reality Trackers for Computing Spatiotemporal Gait Parameters |
title_full_unstemmed | Evaluating the Accuracy of Virtual Reality Trackers for Computing Spatiotemporal Gait Parameters |
title_short | Evaluating the Accuracy of Virtual Reality Trackers for Computing Spatiotemporal Gait Parameters |
title_sort | evaluating the accuracy of virtual reality trackers for computing spatiotemporal gait parameters |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8151659/ https://www.ncbi.nlm.nih.gov/pubmed/34064807 http://dx.doi.org/10.3390/s21103325 |
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