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Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit
Cueing and feedback training can be effective in maintaining or improving gait in individuals with Parkinson’s disease. We previously designed a rehabilitation assist device that can detect and classify a user’s gait at only the swing phase of the gait cycle, for the ease of data processing. In this...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385410/ https://www.ncbi.nlm.nih.gov/pubmed/37514932 http://dx.doi.org/10.3390/s23146638 |
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author | Kim, Hyeonjong Kim, Ji-Won Ko, Junghyuk |
author_facet | Kim, Hyeonjong Kim, Ji-Won Ko, Junghyuk |
author_sort | Kim, Hyeonjong |
collection | PubMed |
description | Cueing and feedback training can be effective in maintaining or improving gait in individuals with Parkinson’s disease. We previously designed a rehabilitation assist device that can detect and classify a user’s gait at only the swing phase of the gait cycle, for the ease of data processing. In this study, we analyzed the impact of various factors in a gait detection algorithm on the gait detection and classification rate (GDCR). We collected acceleration and angular velocity data from 25 participants (1 male and 24 females with an average age of 62 ± 6 years) using our device and analyzed the data using statistical methods. Based on these results, we developed an adaptive GDCR control algorithm using several equations and functions. We tested the algorithm under various virtual exercise scenarios using two control methods, based on acceleration and angular velocity, and found that the acceleration threshold was more effective in controlling the GDCR (average Spearman correlation −0.9996, p < 0.001) than the gyroscopic threshold. Our adaptive control algorithm was more effective in maintaining the target GDCR than the other algorithms (p < 0.001) with an average error of 0.10, while other tested methods showed average errors of 0.16 and 0.28. This algorithm has good scalability and can be adapted for future gait detection and classification applications. |
format | Online Article Text |
id | pubmed-10385410 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103854102023-07-30 Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit Kim, Hyeonjong Kim, Ji-Won Ko, Junghyuk Sensors (Basel) Article Cueing and feedback training can be effective in maintaining or improving gait in individuals with Parkinson’s disease. We previously designed a rehabilitation assist device that can detect and classify a user’s gait at only the swing phase of the gait cycle, for the ease of data processing. In this study, we analyzed the impact of various factors in a gait detection algorithm on the gait detection and classification rate (GDCR). We collected acceleration and angular velocity data from 25 participants (1 male and 24 females with an average age of 62 ± 6 years) using our device and analyzed the data using statistical methods. Based on these results, we developed an adaptive GDCR control algorithm using several equations and functions. We tested the algorithm under various virtual exercise scenarios using two control methods, based on acceleration and angular velocity, and found that the acceleration threshold was more effective in controlling the GDCR (average Spearman correlation −0.9996, p < 0.001) than the gyroscopic threshold. Our adaptive control algorithm was more effective in maintaining the target GDCR than the other algorithms (p < 0.001) with an average error of 0.10, while other tested methods showed average errors of 0.16 and 0.28. This algorithm has good scalability and can be adapted for future gait detection and classification applications. MDPI 2023-07-24 /pmc/articles/PMC10385410/ /pubmed/37514932 http://dx.doi.org/10.3390/s23146638 Text en © 2023 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 Kim, Hyeonjong Kim, Ji-Won Ko, Junghyuk Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit |
title | Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit |
title_full | Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit |
title_fullStr | Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit |
title_full_unstemmed | Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit |
title_short | Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit |
title_sort | adaptive control method for gait detection and classification devices with inertial measurement unit |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385410/ https://www.ncbi.nlm.nih.gov/pubmed/37514932 http://dx.doi.org/10.3390/s23146638 |
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