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Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6962225/ https://www.ncbi.nlm.nih.gov/pubmed/31970290 http://dx.doi.org/10.1038/s41746-019-0217-7 |
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author | Mahadevan, Nikhil Demanuele, Charmaine Zhang, Hao Volfson, Dmitri Ho, Bryan Erb, Michael Kelley Patel, Shyamal |
author_facet | Mahadevan, Nikhil Demanuele, Charmaine Zhang, Hao Volfson, Dmitri Ho, Bryan Erb, Michael Kelley Patel, Shyamal |
author_sort | Mahadevan, Nikhil |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-6962225 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-69622252020-01-22 Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device Mahadevan, Nikhil Demanuele, Charmaine Zhang, Hao Volfson, Dmitri Ho, Bryan Erb, Michael Kelley Patel, Shyamal NPJ Digit Med Article 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. Nature Publishing Group UK 2020-01-15 /pmc/articles/PMC6962225/ /pubmed/31970290 http://dx.doi.org/10.1038/s41746-019-0217-7 Text en © The Author(s) 2020 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Mahadevan, Nikhil Demanuele, Charmaine Zhang, Hao Volfson, Dmitri Ho, Bryan Erb, Michael Kelley Patel, Shyamal Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6962225/ https://www.ncbi.nlm.nih.gov/pubmed/31970290 http://dx.doi.org/10.1038/s41746-019-0217-7 |
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