Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson's disease.

Freezing of gait (FOG) is an intermittent walking disturbance experienced by people with Parkinson's disease (PD). Wearable FOG identification systems can improve gait and reduce the risk of falling due to FOG by detecting FOG in real-time and providing a cue to reduce freeze duration. However,...

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Autores principales:	Scott Pardoel, Gaurav Shalin, Edward D Lemaire, Jonathan Kofman, Julie Nantel
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5c9c891e58804b359e0dd8806f94c1b8
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spelling	oai:doaj.org-article:5c9c891e58804b359e0dd8806f94c1b82021-12-02T20:19:18ZGrouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson's disease.1932-620310.1371/journal.pone.0258544https://doaj.org/article/5c9c891e58804b359e0dd8806f94c1b82021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258544https://doaj.org/toc/1932-6203Freezing of gait (FOG) is an intermittent walking disturbance experienced by people with Parkinson's disease (PD). Wearable FOG identification systems can improve gait and reduce the risk of falling due to FOG by detecting FOG in real-time and providing a cue to reduce freeze duration. However, FOG prediction and prevention is desirable. Datasets used to train machine learning models often generate ground truth FOG labels based on visual observation of specific lower limb movements (event-based definition) or an overall inability to walk effectively (period of gait disruption based definition). FOG definition ambiguity may affect model performance, especially with respect to multiple FOG in rapid succession. This research examined whether merging multiple freezes that occurred in rapid succession could improve FOG detection and prediction model performance. Plantar pressure and lower limb acceleration data were used to extract a feature set and train decision tree ensembles. FOG was labeled using an event-based definition. Additional datasets were then produced by merging FOG that occurred in rapid succession. A merging threshold was introduced where FOG that were separated by less than the merging threshold were merged into one episode. FOG detection and prediction models were trained for merging thresholds of 0, 1, 2, and 3 s. Merging slightly improved FOG detection model performance; however, for the prediction model, merging resulted in slightly later FOG identification and lower precision. FOG prediction models may benefit from using event-based FOG definitions and avoiding merging multiple FOG in rapid succession.Scott PardoelGaurav ShalinEdward D LemaireJonathan KofmanJulie NantelPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258544 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Scott Pardoel Gaurav Shalin Edward D Lemaire Jonathan Kofman Julie Nantel Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson's disease.
description	Freezing of gait (FOG) is an intermittent walking disturbance experienced by people with Parkinson's disease (PD). Wearable FOG identification systems can improve gait and reduce the risk of falling due to FOG by detecting FOG in real-time and providing a cue to reduce freeze duration. However, FOG prediction and prevention is desirable. Datasets used to train machine learning models often generate ground truth FOG labels based on visual observation of specific lower limb movements (event-based definition) or an overall inability to walk effectively (period of gait disruption based definition). FOG definition ambiguity may affect model performance, especially with respect to multiple FOG in rapid succession. This research examined whether merging multiple freezes that occurred in rapid succession could improve FOG detection and prediction model performance. Plantar pressure and lower limb acceleration data were used to extract a feature set and train decision tree ensembles. FOG was labeled using an event-based definition. Additional datasets were then produced by merging FOG that occurred in rapid succession. A merging threshold was introduced where FOG that were separated by less than the merging threshold were merged into one episode. FOG detection and prediction models were trained for merging thresholds of 0, 1, 2, and 3 s. Merging slightly improved FOG detection model performance; however, for the prediction model, merging resulted in slightly later FOG identification and lower precision. FOG prediction models may benefit from using event-based FOG definitions and avoiding merging multiple FOG in rapid succession.
format	article
author	Scott Pardoel Gaurav Shalin Edward D Lemaire Jonathan Kofman Julie Nantel
author_facet	Scott Pardoel Gaurav Shalin Edward D Lemaire Jonathan Kofman Julie Nantel
author_sort	Scott Pardoel
title	Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson's disease.
title_short	Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson's disease.
title_full	Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson's disease.
title_fullStr	Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson's disease.
title_full_unstemmed	Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson's disease.
title_sort	grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in parkinson's disease.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/5c9c891e58804b359e0dd8806f94c1b8
work_keys_str_mv	AT scottpardoel groupingsuccessivefreezingofgaitepisodeshasneutraltodetrimentaleffectonfreezedetectionandpredictioninparkinsonsdisease AT gauravshalin groupingsuccessivefreezingofgaitepisodeshasneutraltodetrimentaleffectonfreezedetectionandpredictioninparkinsonsdisease AT edwarddlemaire groupingsuccessivefreezingofgaitepisodeshasneutraltodetrimentaleffectonfreezedetectionandpredictioninparkinsonsdisease AT jonathankofman groupingsuccessivefreezingofgaitepisodeshasneutraltodetrimentaleffectonfreezedetectionandpredictioninparkinsonsdisease AT julienantel groupingsuccessivefreezingofgaitepisodeshasneutraltodetrimentaleffectonfreezedetectionandpredictioninparkinsonsdisease
_version_	1718374225812652032

Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson's disease.

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