Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery

Abstract The need to develop patient-specific interventions is apparent when one considers that clinical studies often report satisfactory motor gains only in a portion of participants. This observation provides the foundation for “precision rehabilitation”. Tracking and predicting outcomes defining...

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Autores principales:	Catherine Adans-Dester, Nicolas Hankov, Anne O’Brien, Gloria Vergara-Diaz, Randie Black-Schaffer, Ross Zafonte, Jennifer Dy, Sunghoon I. Lee, Paolo Bonato
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/60e218763d274b808b3008102b700226
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spelling	oai:doaj.org-article:60e218763d274b808b3008102b7002262021-12-02T18:14:29ZEnabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery10.1038/s41746-020-00328-w2398-6352https://doaj.org/article/60e218763d274b808b3008102b7002262020-09-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-00328-whttps://doaj.org/toc/2398-6352Abstract The need to develop patient-specific interventions is apparent when one considers that clinical studies often report satisfactory motor gains only in a portion of participants. This observation provides the foundation for “precision rehabilitation”. Tracking and predicting outcomes defining the recovery trajectory is key in this context. Data collected using wearable sensors provide clinicians with the opportunity to do so with little burden on clinicians and patients. The approach proposed in this paper relies on machine learning-based algorithms to derive clinical score estimates from wearable sensor data collected during functional motor tasks. Sensor-based score estimates showed strong agreement with those generated by clinicians. Score estimates of upper-limb impairment severity and movement quality were marked by a coefficient of determination of 0.86 and 0.79, respectively. The application of the proposed approach to monitoring patients’ responsiveness to rehabilitation is expected to contribute to the development of patient-specific interventions, aiming to maximize motor gains.Catherine Adans-DesterNicolas HankovAnne O’BrienGloria Vergara-DiazRandie Black-SchafferRoss ZafonteJennifer DySunghoon I. LeePaolo BonatoNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-10 (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 Catherine Adans-Dester Nicolas Hankov Anne O’Brien Gloria Vergara-Diaz Randie Black-Schaffer Ross Zafonte Jennifer Dy Sunghoon I. Lee Paolo Bonato Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery
description	Abstract The need to develop patient-specific interventions is apparent when one considers that clinical studies often report satisfactory motor gains only in a portion of participants. This observation provides the foundation for “precision rehabilitation”. Tracking and predicting outcomes defining the recovery trajectory is key in this context. Data collected using wearable sensors provide clinicians with the opportunity to do so with little burden on clinicians and patients. The approach proposed in this paper relies on machine learning-based algorithms to derive clinical score estimates from wearable sensor data collected during functional motor tasks. Sensor-based score estimates showed strong agreement with those generated by clinicians. Score estimates of upper-limb impairment severity and movement quality were marked by a coefficient of determination of 0.86 and 0.79, respectively. The application of the proposed approach to monitoring patients’ responsiveness to rehabilitation is expected to contribute to the development of patient-specific interventions, aiming to maximize motor gains.
format	article
author	Catherine Adans-Dester Nicolas Hankov Anne O’Brien Gloria Vergara-Diaz Randie Black-Schaffer Ross Zafonte Jennifer Dy Sunghoon I. Lee Paolo Bonato
author_facet	Catherine Adans-Dester Nicolas Hankov Anne O’Brien Gloria Vergara-Diaz Randie Black-Schaffer Ross Zafonte Jennifer Dy Sunghoon I. Lee Paolo Bonato
author_sort	Catherine Adans-Dester
title	Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery
title_short	Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery
title_full	Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery
title_fullStr	Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery
title_full_unstemmed	Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery
title_sort	enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/60e218763d274b808b3008102b700226
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Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery

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