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Measuring freezing of gait during daily-life: an open-source, wearable sensors approach

BACKGROUND: Although a growing number of studies focus on the measurement and detection of freezing of gait (FoG) in laboratory settings, only a few studies have attempted to measure FoG during daily life with body-worn sensors. Here, we presented a novel algorithm to detect FoG in a group of people...

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Autores principales: Mancini, Martina, Shah, Vrutangkumar V., Stuart, Samuel, Curtze, Carolin, Horak, Fay B., Safarpour, Delaram, Nutt, John G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7784003/
https://www.ncbi.nlm.nih.gov/pubmed/33397401
http://dx.doi.org/10.1186/s12984-020-00774-3
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author Mancini, Martina
Shah, Vrutangkumar V.
Stuart, Samuel
Curtze, Carolin
Horak, Fay B.
Safarpour, Delaram
Nutt, John G.
author_facet Mancini, Martina
Shah, Vrutangkumar V.
Stuart, Samuel
Curtze, Carolin
Horak, Fay B.
Safarpour, Delaram
Nutt, John G.
author_sort Mancini, Martina
collection PubMed
description BACKGROUND: Although a growing number of studies focus on the measurement and detection of freezing of gait (FoG) in laboratory settings, only a few studies have attempted to measure FoG during daily life with body-worn sensors. Here, we presented a novel algorithm to detect FoG in a group of people with Parkinson’s disease (PD) in the laboratory (Study I) and extended the algorithm in a second cohort of people with PD at home during daily life (Study II). METHODS: In Study I, we described of our novel FoG detection algorithm based on five inertial sensors attached to the feet, shins and lumbar region while walking in 40 participants with PD. We compared the performance of the algorithm with two expert clinical raters who scored the number of FoG episodes from video recordings of walking and turning based on duration of the episodes: very short (< 1 s), short (2–5 s), and long (> 5 s). In Study II, a different cohort of 48 people with PD (with and without FoG) wore 3 wearable sensors on their feet and lumbar region for 7 days. Our primary outcome measures for freezing were the % time spent freezing and its variability. RESULTS: We showed moderate to good agreement in the number of FoG episodes detected in the laboratory (Study I) between clinical raters and the algorithm (if wearable sensors were placed on the feet) for short and long FoG episodes, but not for very short FoG episodes. When extending this methodology to unsupervised home monitoring (Study II), we found that percent time spent freezing and the variability of time spent freezing differentiated between people with and without FoG (p < 0.05), and that short FoG episodes account for 69% of the total FoG episodes. CONCLUSION: Our findings showed that objective measures of freezing in PD using inertial sensors on the feet in the laboratory are matching well with clinical scores. Although results found during daily life are promising, they need to be validated. Objective measures of FoG with wearable technology during community-living would be useful for managing this distressing feature of mobility disability in PD.
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spelling pubmed-77840032021-01-14 Measuring freezing of gait during daily-life: an open-source, wearable sensors approach Mancini, Martina Shah, Vrutangkumar V. Stuart, Samuel Curtze, Carolin Horak, Fay B. Safarpour, Delaram Nutt, John G. J Neuroeng Rehabil Research BACKGROUND: Although a growing number of studies focus on the measurement and detection of freezing of gait (FoG) in laboratory settings, only a few studies have attempted to measure FoG during daily life with body-worn sensors. Here, we presented a novel algorithm to detect FoG in a group of people with Parkinson’s disease (PD) in the laboratory (Study I) and extended the algorithm in a second cohort of people with PD at home during daily life (Study II). METHODS: In Study I, we described of our novel FoG detection algorithm based on five inertial sensors attached to the feet, shins and lumbar region while walking in 40 participants with PD. We compared the performance of the algorithm with two expert clinical raters who scored the number of FoG episodes from video recordings of walking and turning based on duration of the episodes: very short (< 1 s), short (2–5 s), and long (> 5 s). In Study II, a different cohort of 48 people with PD (with and without FoG) wore 3 wearable sensors on their feet and lumbar region for 7 days. Our primary outcome measures for freezing were the % time spent freezing and its variability. RESULTS: We showed moderate to good agreement in the number of FoG episodes detected in the laboratory (Study I) between clinical raters and the algorithm (if wearable sensors were placed on the feet) for short and long FoG episodes, but not for very short FoG episodes. When extending this methodology to unsupervised home monitoring (Study II), we found that percent time spent freezing and the variability of time spent freezing differentiated between people with and without FoG (p < 0.05), and that short FoG episodes account for 69% of the total FoG episodes. CONCLUSION: Our findings showed that objective measures of freezing in PD using inertial sensors on the feet in the laboratory are matching well with clinical scores. Although results found during daily life are promising, they need to be validated. Objective measures of FoG with wearable technology during community-living would be useful for managing this distressing feature of mobility disability in PD. BioMed Central 2021-01-04 /pmc/articles/PMC7784003/ /pubmed/33397401 http://dx.doi.org/10.1186/s12984-020-00774-3 Text en © The Author(s) 2020 Open AccessThis 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
Mancini, Martina
Shah, Vrutangkumar V.
Stuart, Samuel
Curtze, Carolin
Horak, Fay B.
Safarpour, Delaram
Nutt, John G.
Measuring freezing of gait during daily-life: an open-source, wearable sensors approach
title Measuring freezing of gait during daily-life: an open-source, wearable sensors approach
title_full Measuring freezing of gait during daily-life: an open-source, wearable sensors approach
title_fullStr Measuring freezing of gait during daily-life: an open-source, wearable sensors approach
title_full_unstemmed Measuring freezing of gait during daily-life: an open-source, wearable sensors approach
title_short Measuring freezing of gait during daily-life: an open-source, wearable sensors approach
title_sort measuring freezing of gait during daily-life: an open-source, wearable sensors approach
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7784003/
https://www.ncbi.nlm.nih.gov/pubmed/33397401
http://dx.doi.org/10.1186/s12984-020-00774-3
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