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DataSpoon: Validation of an Instrumented Spoon for Assessment of Self-Feeding

Clinically feasible assessment of self-feeding is important for adults and children with motor impairments such as stroke or cerebral palsy. However, no validated assessment tool for self-feeding kinematics exists. This work presents an initial validation of an instrumented spoon (DataSpoon) develop...

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Autores principales: Krasovsky, Tal, Weiss, Patrice L., Zuckerman, Oren, Bar, Avihay, Keren-Capelovitch, Tal, Friedman, Jason
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180859/
https://www.ncbi.nlm.nih.gov/pubmed/32283624
http://dx.doi.org/10.3390/s20072114
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author Krasovsky, Tal
Weiss, Patrice L.
Zuckerman, Oren
Bar, Avihay
Keren-Capelovitch, Tal
Friedman, Jason
author_facet Krasovsky, Tal
Weiss, Patrice L.
Zuckerman, Oren
Bar, Avihay
Keren-Capelovitch, Tal
Friedman, Jason
author_sort Krasovsky, Tal
collection PubMed
description Clinically feasible assessment of self-feeding is important for adults and children with motor impairments such as stroke or cerebral palsy. However, no validated assessment tool for self-feeding kinematics exists. This work presents an initial validation of an instrumented spoon (DataSpoon) developed as an evaluation tool for self-feeding kinematics. Ten young, healthy adults (three male; age 27.2 ± 6.6 years) used DataSpoon at three movement speeds (slow, comfortable, fast) and with three different grips: “natural”, power and rotated power grip. Movement kinematics were recorded concurrently using DataSpoon and a magnetic motion capture system (trakSTAR). Eating events were automatically identified for both systems and kinematic measures were extracted from yaw, pitch and roll (YPR) data as well as from acceleration and tangential velocity profiles. Two-way, mixed model Intraclass correlation coefficients (ICC) and 95% limits of agreement (LOA) were computed to determine agreement between the systems for each kinematic variable. Most variables demonstrated fair to excellent agreement. Agreement for measures of duration, pitch and roll exceeded 0.8 (excellent agreement) for >80% of speed and grip conditions, whereas lower agreement (ICC < 0.46) was measured for tangential velocity and acceleration. A bias of 0.01–0.07 s (95% LOA [−0.54, 0.53] to [−0.63, 0.48]) was calculated for measures of duration. DataSpoon enables automatic detection of self-feeding using simple, affordable movement sensors. Using movement kinematics, variables associated with self-feeding can be identified and aid clinical reasoning for adults and children with motor impairments.
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spelling pubmed-71808592020-05-01 DataSpoon: Validation of an Instrumented Spoon for Assessment of Self-Feeding Krasovsky, Tal Weiss, Patrice L. Zuckerman, Oren Bar, Avihay Keren-Capelovitch, Tal Friedman, Jason Sensors (Basel) Article Clinically feasible assessment of self-feeding is important for adults and children with motor impairments such as stroke or cerebral palsy. However, no validated assessment tool for self-feeding kinematics exists. This work presents an initial validation of an instrumented spoon (DataSpoon) developed as an evaluation tool for self-feeding kinematics. Ten young, healthy adults (three male; age 27.2 ± 6.6 years) used DataSpoon at three movement speeds (slow, comfortable, fast) and with three different grips: “natural”, power and rotated power grip. Movement kinematics were recorded concurrently using DataSpoon and a magnetic motion capture system (trakSTAR). Eating events were automatically identified for both systems and kinematic measures were extracted from yaw, pitch and roll (YPR) data as well as from acceleration and tangential velocity profiles. Two-way, mixed model Intraclass correlation coefficients (ICC) and 95% limits of agreement (LOA) were computed to determine agreement between the systems for each kinematic variable. Most variables demonstrated fair to excellent agreement. Agreement for measures of duration, pitch and roll exceeded 0.8 (excellent agreement) for >80% of speed and grip conditions, whereas lower agreement (ICC < 0.46) was measured for tangential velocity and acceleration. A bias of 0.01–0.07 s (95% LOA [−0.54, 0.53] to [−0.63, 0.48]) was calculated for measures of duration. DataSpoon enables automatic detection of self-feeding using simple, affordable movement sensors. Using movement kinematics, variables associated with self-feeding can be identified and aid clinical reasoning for adults and children with motor impairments. MDPI 2020-04-09 /pmc/articles/PMC7180859/ /pubmed/32283624 http://dx.doi.org/10.3390/s20072114 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Krasovsky, Tal
Weiss, Patrice L.
Zuckerman, Oren
Bar, Avihay
Keren-Capelovitch, Tal
Friedman, Jason
DataSpoon: Validation of an Instrumented Spoon for Assessment of Self-Feeding
title DataSpoon: Validation of an Instrumented Spoon for Assessment of Self-Feeding
title_full DataSpoon: Validation of an Instrumented Spoon for Assessment of Self-Feeding
title_fullStr DataSpoon: Validation of an Instrumented Spoon for Assessment of Self-Feeding
title_full_unstemmed DataSpoon: Validation of an Instrumented Spoon for Assessment of Self-Feeding
title_short DataSpoon: Validation of an Instrumented Spoon for Assessment of Self-Feeding
title_sort dataspoon: validation of an instrumented spoon for assessment of self-feeding
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180859/
https://www.ncbi.nlm.nih.gov/pubmed/32283624
http://dx.doi.org/10.3390/s20072114
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