Portable, open-source solutions for estimating wrist position during reaching in people with stroke

Abstract Arm movement kinematics may provide a more sensitive way to assess neurorehabilitation outcomes than existing metrics. However, measuring arm kinematics in people with stroke can be challenging for traditional optical tracking systems due to non-ideal environments, expense, and difficulty p...

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Autores principales:	Jeffrey Z. Nie, James W. Nie, Na-Teng Hung, R. James Cotton, Marc W. Slutzky
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c3e25c8b0f6345409157174ed243b587
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spelling	oai:doaj.org-article:c3e25c8b0f6345409157174ed243b5872021-11-21T12:20:43ZPortable, open-source solutions for estimating wrist position during reaching in people with stroke10.1038/s41598-021-01805-22045-2322https://doaj.org/article/c3e25c8b0f6345409157174ed243b5872021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01805-2https://doaj.org/toc/2045-2322Abstract Arm movement kinematics may provide a more sensitive way to assess neurorehabilitation outcomes than existing metrics. However, measuring arm kinematics in people with stroke can be challenging for traditional optical tracking systems due to non-ideal environments, expense, and difficulty performing required calibration. Here, we present two open-source methods, one using inertial measurement units (IMUs) and another using virtual reality (Vive) sensors, for accurate measurements of wrist position with respect to the shoulder during reaching movements in people with stroke. We assessed the accuracy of each method during a 3D reaching task. We also demonstrated each method’s ability to track two metrics derived from kinematics-sweep area and smoothness-in people with chronic stroke. We computed correlation coefficients between the kinematics estimated by each method when appropriate. Compared to a traditional optical tracking system, both methods accurately tracked the wrist during reaching, with mean signed errors of 0.09 ± 1.81 cm and 0.48 ± 1.58 cm for the IMUs and Vive, respectively. Furthermore, both methods’ estimated kinematics were highly correlated with each other (p < 0.01). By using relatively inexpensive wearable sensors, these methods may be useful for developing kinematic metrics to evaluate stroke rehabilitation outcomes in both laboratory and clinical environments.Jeffrey Z. NieJames W. NieNa-Teng HungR. James CottonMarc W. SlutzkyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jeffrey Z. Nie James W. Nie Na-Teng Hung R. James Cotton Marc W. Slutzky Portable, open-source solutions for estimating wrist position during reaching in people with stroke
description	Abstract Arm movement kinematics may provide a more sensitive way to assess neurorehabilitation outcomes than existing metrics. However, measuring arm kinematics in people with stroke can be challenging for traditional optical tracking systems due to non-ideal environments, expense, and difficulty performing required calibration. Here, we present two open-source methods, one using inertial measurement units (IMUs) and another using virtual reality (Vive) sensors, for accurate measurements of wrist position with respect to the shoulder during reaching movements in people with stroke. We assessed the accuracy of each method during a 3D reaching task. We also demonstrated each method’s ability to track two metrics derived from kinematics-sweep area and smoothness-in people with chronic stroke. We computed correlation coefficients between the kinematics estimated by each method when appropriate. Compared to a traditional optical tracking system, both methods accurately tracked the wrist during reaching, with mean signed errors of 0.09 ± 1.81 cm and 0.48 ± 1.58 cm for the IMUs and Vive, respectively. Furthermore, both methods’ estimated kinematics were highly correlated with each other (p < 0.01). By using relatively inexpensive wearable sensors, these methods may be useful for developing kinematic metrics to evaluate stroke rehabilitation outcomes in both laboratory and clinical environments.
format	article
author	Jeffrey Z. Nie James W. Nie Na-Teng Hung R. James Cotton Marc W. Slutzky
author_facet	Jeffrey Z. Nie James W. Nie Na-Teng Hung R. James Cotton Marc W. Slutzky
author_sort	Jeffrey Z. Nie
title	Portable, open-source solutions for estimating wrist position during reaching in people with stroke
title_short	Portable, open-source solutions for estimating wrist position during reaching in people with stroke
title_full	Portable, open-source solutions for estimating wrist position during reaching in people with stroke
title_fullStr	Portable, open-source solutions for estimating wrist position during reaching in people with stroke
title_full_unstemmed	Portable, open-source solutions for estimating wrist position during reaching in people with stroke
title_sort	portable, open-source solutions for estimating wrist position during reaching in people with stroke
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c3e25c8b0f6345409157174ed243b587
work_keys_str_mv	AT jeffreyznie portableopensourcesolutionsforestimatingwristpositionduringreachinginpeoplewithstroke AT jameswnie portableopensourcesolutionsforestimatingwristpositionduringreachinginpeoplewithstroke AT natenghung portableopensourcesolutionsforestimatingwristpositionduringreachinginpeoplewithstroke AT rjamescotton portableopensourcesolutionsforestimatingwristpositionduringreachinginpeoplewithstroke AT marcwslutzky portableopensourcesolutionsforestimatingwristpositionduringreachinginpeoplewithstroke
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Portable, open-source solutions for estimating wrist position during reaching in people with stroke

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