Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks

Abstract Levodopa-induced dyskinesias are abnormal involuntary movements experienced by the majority of persons with Parkinson’s disease (PwP) at some point over the course of the disease. Choreiform as the most common phenomenology of levodopa-induced dyskinesias can be managed by adjusting the dos...

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Autores principales:	Murtadha D. Hssayeni, Joohi Jimenez-Shahed, Michelle A. Burack, Behnaz Ghoraani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b7c642fb8afd488192b4410882dba281
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spelling	oai:doaj.org-article:b7c642fb8afd488192b4410882dba2812021-12-02T14:26:12ZDyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks10.1038/s41598-021-86705-12045-2322https://doaj.org/article/b7c642fb8afd488192b4410882dba2812021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86705-1https://doaj.org/toc/2045-2322Abstract Levodopa-induced dyskinesias are abnormal involuntary movements experienced by the majority of persons with Parkinson’s disease (PwP) at some point over the course of the disease. Choreiform as the most common phenomenology of levodopa-induced dyskinesias can be managed by adjusting the dose/frequency of PD medication(s) based on a PwP’s motor fluctuations over a typical day. We developed a sensor-based assessment system to provide such information. We used movement data collected from the upper and lower extremities of 15 PwPs along with a deep recurrent model to estimate dyskinesia severity as they perform different activities of daily living (ADL). Subjects performed a variety of ADLs during a 4-h period while their dyskinesia severity was rated by the movement disorder experts. The estimated dyskinesia severity scores from our model correlated highly with the expert-rated scores (r = 0.87 (p < 0.001)), which was higher than the performance of linear regression that is commonly used for dyskinesia estimation (r = 0.81 (p < 0.001)). Our model provided consistent performance at different ADLs with minimum r = 0.70 (during walking) to maximum r = 0.84 (drinking) in comparison to linear regression with r = 0.00 (walking) to r = 0.76 (cutting food). These findings suggest that when our model is applied to at-home sensor data, it can provide an accurate picture of changes of dyskinesia severity facilitating effective medication adjustments.Murtadha D. HssayeniJoohi Jimenez-ShahedMichelle A. BurackBehnaz GhoraaniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Murtadha D. Hssayeni Joohi Jimenez-Shahed Michelle A. Burack Behnaz Ghoraani Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks
description	Abstract Levodopa-induced dyskinesias are abnormal involuntary movements experienced by the majority of persons with Parkinson’s disease (PwP) at some point over the course of the disease. Choreiform as the most common phenomenology of levodopa-induced dyskinesias can be managed by adjusting the dose/frequency of PD medication(s) based on a PwP’s motor fluctuations over a typical day. We developed a sensor-based assessment system to provide such information. We used movement data collected from the upper and lower extremities of 15 PwPs along with a deep recurrent model to estimate dyskinesia severity as they perform different activities of daily living (ADL). Subjects performed a variety of ADLs during a 4-h period while their dyskinesia severity was rated by the movement disorder experts. The estimated dyskinesia severity scores from our model correlated highly with the expert-rated scores (r = 0.87 (p < 0.001)), which was higher than the performance of linear regression that is commonly used for dyskinesia estimation (r = 0.81 (p < 0.001)). Our model provided consistent performance at different ADLs with minimum r = 0.70 (during walking) to maximum r = 0.84 (drinking) in comparison to linear regression with r = 0.00 (walking) to r = 0.76 (cutting food). These findings suggest that when our model is applied to at-home sensor data, it can provide an accurate picture of changes of dyskinesia severity facilitating effective medication adjustments.
format	article
author	Murtadha D. Hssayeni Joohi Jimenez-Shahed Michelle A. Burack Behnaz Ghoraani
author_facet	Murtadha D. Hssayeni Joohi Jimenez-Shahed Michelle A. Burack Behnaz Ghoraani
author_sort	Murtadha D. Hssayeni
title	Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks
title_short	Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks
title_full	Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks
title_fullStr	Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks
title_full_unstemmed	Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks
title_sort	dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b7c642fb8afd488192b4410882dba281
work_keys_str_mv	AT murtadhadhssayeni dyskinesiaestimationduringactivitiesofdailylivingusingwearablemotionsensorsanddeeprecurrentnetworks AT joohijimenezshahed dyskinesiaestimationduringactivitiesofdailylivingusingwearablemotionsensorsanddeeprecurrentnetworks AT michelleaburack dyskinesiaestimationduringactivitiesofdailylivingusingwearablemotionsensorsanddeeprecurrentnetworks AT behnazghoraani dyskinesiaestimationduringactivitiesofdailylivingusingwearablemotionsensorsanddeeprecurrentnetworks
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Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks

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