Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device

Abstract Objective assessment of Parkinson’s disease symptoms during daily life can help improve disease management and accelerate the development of new therapies. However, many current approaches require the use of multiple devices, or performance of prescribed motor activities, which makes them i...

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Autores principales:	Nikhil Mahadevan, Charmaine Demanuele, Hao Zhang, Dmitri Volfson, Bryan Ho, Michael Kelley Erb, Shyamal Patel
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/870165e8401a411f8ef815626bfc3a7f
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spelling	oai:doaj.org-article:870165e8401a411f8ef815626bfc3a7f2021-12-02T10:59:14ZDevelopment of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device10.1038/s41746-019-0217-72398-6352https://doaj.org/article/870165e8401a411f8ef815626bfc3a7f2020-01-01T00:00:00Zhttps://doi.org/10.1038/s41746-019-0217-7https://doaj.org/toc/2398-6352Abstract Objective assessment of Parkinson’s disease symptoms during daily life can help improve disease management and accelerate the development of new therapies. However, many current approaches require the use of multiple devices, or performance of prescribed motor activities, which makes them ill-suited for free-living conditions. Furthermore, there is a lack of open methods that have demonstrated both criterion and discriminative validity for continuous objective assessment of motor symptoms in this population. Hence, there is a need for systems that can reduce patient burden by using a minimal sensor setup while continuously capturing clinically meaningful measures of motor symptom severity under free-living conditions. We propose a method that sequentially processes epochs of raw sensor data from a single wrist-worn accelerometer by using heuristic and machine learning models in a hierarchical framework to provide continuous monitoring of tremor and bradykinesia. Results show that sensor derived continuous measures of resting tremor and bradykinesia achieve good to strong agreement with clinical assessment of symptom severity and are able to discriminate between treatment-related changes in motor states.Nikhil MahadevanCharmaine DemanueleHao ZhangDmitri VolfsonBryan HoMichael Kelley ErbShyamal PatelNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Nikhil Mahadevan Charmaine Demanuele Hao Zhang Dmitri Volfson Bryan Ho Michael Kelley Erb Shyamal Patel Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device
description	Abstract Objective assessment of Parkinson’s disease symptoms during daily life can help improve disease management and accelerate the development of new therapies. However, many current approaches require the use of multiple devices, or performance of prescribed motor activities, which makes them ill-suited for free-living conditions. Furthermore, there is a lack of open methods that have demonstrated both criterion and discriminative validity for continuous objective assessment of motor symptoms in this population. Hence, there is a need for systems that can reduce patient burden by using a minimal sensor setup while continuously capturing clinically meaningful measures of motor symptom severity under free-living conditions. We propose a method that sequentially processes epochs of raw sensor data from a single wrist-worn accelerometer by using heuristic and machine learning models in a hierarchical framework to provide continuous monitoring of tremor and bradykinesia. Results show that sensor derived continuous measures of resting tremor and bradykinesia achieve good to strong agreement with clinical assessment of symptom severity and are able to discriminate between treatment-related changes in motor states.
format	article
author	Nikhil Mahadevan Charmaine Demanuele Hao Zhang Dmitri Volfson Bryan Ho Michael Kelley Erb Shyamal Patel
author_facet	Nikhil Mahadevan Charmaine Demanuele Hao Zhang Dmitri Volfson Bryan Ho Michael Kelley Erb Shyamal Patel
author_sort	Nikhil Mahadevan
title	Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device
title_short	Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device
title_full	Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device
title_fullStr	Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device
title_full_unstemmed	Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device
title_sort	development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/870165e8401a411f8ef815626bfc3a7f
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Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device

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