Cargando…

Using Wearable Sensors to Assess Freezing of Gait in the Real World

Freezing of gait (FOG) is a debilitating symptom of Parkinson’s disease (PD) that remains difficult to assess. Wearable movement sensors and associated algorithms can be used to quantify FOG in laboratory settings, but the utility of such methods for real world use is unclear. We aimed to determine...

Descripción completa

Detalles Bibliográficos
Autores principales: May, David S., Tueth, Lauren E., Earhart, Gammon M., Mazzoni, Pietro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045234/
https://www.ncbi.nlm.nih.gov/pubmed/36978680
http://dx.doi.org/10.3390/bioengineering10030289
_version_ 1784913551594356736
author May, David S.
Tueth, Lauren E.
Earhart, Gammon M.
Mazzoni, Pietro
author_facet May, David S.
Tueth, Lauren E.
Earhart, Gammon M.
Mazzoni, Pietro
author_sort May, David S.
collection PubMed
description Freezing of gait (FOG) is a debilitating symptom of Parkinson’s disease (PD) that remains difficult to assess. Wearable movement sensors and associated algorithms can be used to quantify FOG in laboratory settings, but the utility of such methods for real world use is unclear. We aimed to determine the suitability of our wearable sensor-based FOG assessment method for real world use by assessing its performance during in-clinic simulated real world activities. Accuracy of the sensor-based method during simulated real-world tasks was calculated using expert rated video as the gold standard. To determine feasibility for unsupervised home use, we also determined correlations between the percent of active time spent freezing (%ATSF) during unsupervised home use and in-clinic activities. Nineteen people with PD and FOG participated in this study. Results from our sensor-based method demonstrated an accuracy above 90% compared to gold-standard expert review during simulated real-world tasks. Additionally, %ATSF from our sensor-based method during unsupervised home use correlated strongly with %ATSF from our sensor-based method during in-clinic simulated real-world activities (ρ = 0.73). Accuracy values and correlation patterns suggest our method may be useful for FOG assessment in the real world.
format Online
Article
Text
id pubmed-10045234
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-100452342023-03-29 Using Wearable Sensors to Assess Freezing of Gait in the Real World May, David S. Tueth, Lauren E. Earhart, Gammon M. Mazzoni, Pietro Bioengineering (Basel) Article Freezing of gait (FOG) is a debilitating symptom of Parkinson’s disease (PD) that remains difficult to assess. Wearable movement sensors and associated algorithms can be used to quantify FOG in laboratory settings, but the utility of such methods for real world use is unclear. We aimed to determine the suitability of our wearable sensor-based FOG assessment method for real world use by assessing its performance during in-clinic simulated real world activities. Accuracy of the sensor-based method during simulated real-world tasks was calculated using expert rated video as the gold standard. To determine feasibility for unsupervised home use, we also determined correlations between the percent of active time spent freezing (%ATSF) during unsupervised home use and in-clinic activities. Nineteen people with PD and FOG participated in this study. Results from our sensor-based method demonstrated an accuracy above 90% compared to gold-standard expert review during simulated real-world tasks. Additionally, %ATSF from our sensor-based method during unsupervised home use correlated strongly with %ATSF from our sensor-based method during in-clinic simulated real-world activities (ρ = 0.73). Accuracy values and correlation patterns suggest our method may be useful for FOG assessment in the real world. MDPI 2023-02-23 /pmc/articles/PMC10045234/ /pubmed/36978680 http://dx.doi.org/10.3390/bioengineering10030289 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
May, David S.
Tueth, Lauren E.
Earhart, Gammon M.
Mazzoni, Pietro
Using Wearable Sensors to Assess Freezing of Gait in the Real World
title Using Wearable Sensors to Assess Freezing of Gait in the Real World
title_full Using Wearable Sensors to Assess Freezing of Gait in the Real World
title_fullStr Using Wearable Sensors to Assess Freezing of Gait in the Real World
title_full_unstemmed Using Wearable Sensors to Assess Freezing of Gait in the Real World
title_short Using Wearable Sensors to Assess Freezing of Gait in the Real World
title_sort using wearable sensors to assess freezing of gait in the real world
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045234/
https://www.ncbi.nlm.nih.gov/pubmed/36978680
http://dx.doi.org/10.3390/bioengineering10030289
work_keys_str_mv AT maydavids usingwearablesensorstoassessfreezingofgaitintherealworld
AT tuethlaurene usingwearablesensorstoassessfreezingofgaitintherealworld
AT earhartgammonm usingwearablesensorstoassessfreezingofgaitintherealworld
AT mazzonipietro usingwearablesensorstoassessfreezingofgaitintherealworld