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Deep Learning for Daily Monitoring of Parkinson’s Disease Outside the Clinic Using Wearable Sensors

Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson’s disease (PD). However, the unsupervised and “open world” nature of this type...

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Detalles Bibliográficos
Autores principales: Atri, Roozbeh, Urban, Kevin, Marebwa, Barbara, Simuni, Tanya, Tanner, Caroline, Siderowf, Andrew, Frasier, Mark, Haas, Magali, Lancashire, Lee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502239/
https://www.ncbi.nlm.nih.gov/pubmed/36146181
http://dx.doi.org/10.3390/s22186831
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author Atri, Roozbeh
Urban, Kevin
Marebwa, Barbara
Simuni, Tanya
Tanner, Caroline
Siderowf, Andrew
Frasier, Mark
Haas, Magali
Lancashire, Lee
author_facet Atri, Roozbeh
Urban, Kevin
Marebwa, Barbara
Simuni, Tanya
Tanner, Caroline
Siderowf, Andrew
Frasier, Mark
Haas, Magali
Lancashire, Lee
author_sort Atri, Roozbeh
collection PubMed
description Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson’s disease (PD). However, the unsupervised and “open world” nature of this type of data collection make such applications difficult to develop. In this proof-of-concept study, we used inertial sensor data from Verily Study Watches worn by individuals for up to 23 h per day over several months to distinguish between seven subjects with PD and four without. Since motor-related PD symptoms such as bradykinesia and gait abnormalities typically present when a PD subject is walking, we initially used human activity recognition (HAR) techniques to identify walk-like activity in the unconstrained, unlabeled data. We then used these “walk-like” events to train one-dimensional convolutional neural networks (1D-CNNs) to determine the presence of PD. We report classification accuracies near 90% on single 5-s walk-like events and 100% accuracy when taking the majority vote over single-event classifications that span a duration of one day. Though based on a small cohort, this study shows the feasibility of leveraging unconstrained wearable sensor data to accurately detect the presence or absence of PD.
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spelling pubmed-95022392022-09-24 Deep Learning for Daily Monitoring of Parkinson’s Disease Outside the Clinic Using Wearable Sensors Atri, Roozbeh Urban, Kevin Marebwa, Barbara Simuni, Tanya Tanner, Caroline Siderowf, Andrew Frasier, Mark Haas, Magali Lancashire, Lee Sensors (Basel) Article Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson’s disease (PD). However, the unsupervised and “open world” nature of this type of data collection make such applications difficult to develop. In this proof-of-concept study, we used inertial sensor data from Verily Study Watches worn by individuals for up to 23 h per day over several months to distinguish between seven subjects with PD and four without. Since motor-related PD symptoms such as bradykinesia and gait abnormalities typically present when a PD subject is walking, we initially used human activity recognition (HAR) techniques to identify walk-like activity in the unconstrained, unlabeled data. We then used these “walk-like” events to train one-dimensional convolutional neural networks (1D-CNNs) to determine the presence of PD. We report classification accuracies near 90% on single 5-s walk-like events and 100% accuracy when taking the majority vote over single-event classifications that span a duration of one day. Though based on a small cohort, this study shows the feasibility of leveraging unconstrained wearable sensor data to accurately detect the presence or absence of PD. MDPI 2022-09-09 /pmc/articles/PMC9502239/ /pubmed/36146181 http://dx.doi.org/10.3390/s22186831 Text en © 2022 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
Atri, Roozbeh
Urban, Kevin
Marebwa, Barbara
Simuni, Tanya
Tanner, Caroline
Siderowf, Andrew
Frasier, Mark
Haas, Magali
Lancashire, Lee
Deep Learning for Daily Monitoring of Parkinson’s Disease Outside the Clinic Using Wearable Sensors
title Deep Learning for Daily Monitoring of Parkinson’s Disease Outside the Clinic Using Wearable Sensors
title_full Deep Learning for Daily Monitoring of Parkinson’s Disease Outside the Clinic Using Wearable Sensors
title_fullStr Deep Learning for Daily Monitoring of Parkinson’s Disease Outside the Clinic Using Wearable Sensors
title_full_unstemmed Deep Learning for Daily Monitoring of Parkinson’s Disease Outside the Clinic Using Wearable Sensors
title_short Deep Learning for Daily Monitoring of Parkinson’s Disease Outside the Clinic Using Wearable Sensors
title_sort deep learning for daily monitoring of parkinson’s disease outside the clinic using wearable sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502239/
https://www.ncbi.nlm.nih.gov/pubmed/36146181
http://dx.doi.org/10.3390/s22186831
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