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Instrumented Gait Classification Using Meaningful Features in Patients with Impaired Coordination

Early onset ataxia (EOA) and developmental coordination disorder (DCD) both affect cerebellar functioning in children, making the clinical distinction challenging. We here aim to derive meaningful features from quantitative SARA-gait data (i.e., the gait test of the scale for the assessment and rati...

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Autores principales: Dominguez-Vega, Zeus T., de Quiros, Mariano Bernaldo, Elting, Jan Willem J., Sival, Deborah A., Maurits, Natasha M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611111/
https://www.ncbi.nlm.nih.gov/pubmed/37896504
http://dx.doi.org/10.3390/s23208410
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author Dominguez-Vega, Zeus T.
de Quiros, Mariano Bernaldo
Elting, Jan Willem J.
Sival, Deborah A.
Maurits, Natasha M.
author_facet Dominguez-Vega, Zeus T.
de Quiros, Mariano Bernaldo
Elting, Jan Willem J.
Sival, Deborah A.
Maurits, Natasha M.
author_sort Dominguez-Vega, Zeus T.
collection PubMed
description Early onset ataxia (EOA) and developmental coordination disorder (DCD) both affect cerebellar functioning in children, making the clinical distinction challenging. We here aim to derive meaningful features from quantitative SARA-gait data (i.e., the gait test of the scale for the assessment and rating of ataxia (SARA)) to classify EOA and DCD patients and typically developing (CTRL) children with better explainability than previous classification approaches. We collected data from 18 EOA, 14 DCD and 29 CTRL children, while executing both SARA gait tests. Inertial measurement units were used to acquire movement data, and a gait model was employed to derive meaningful features. We used a random forest classifier on 36 extracted features, leave-one-out-cross-validation and a synthetic oversampling technique to distinguish between the three groups. Classification accuracy, probabilities of classification and feature relevance were obtained. The mean classification accuracy was 62.9% for EOA, 85.5% for DCD and 94.5% for CTRL participants. Overall, the random forest algorithm correctly classified 82.0% of the participants, which was slightly better than clinical assessment (73.0%). The classification resulted in a mean precision of 0.78, mean recall of 0.70 and mean F1 score of 0.74. The most relevant features were related to the range of the hip flexion–extension angle for gait, and to movement variability for tandem gait. Our results suggest that classification, employing features representing different aspects of movement during gait and tandem gait, may provide an insightful tool for the differential diagnoses of EOA, DCD and typically developing children.
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spelling pubmed-106111112023-10-28 Instrumented Gait Classification Using Meaningful Features in Patients with Impaired Coordination Dominguez-Vega, Zeus T. de Quiros, Mariano Bernaldo Elting, Jan Willem J. Sival, Deborah A. Maurits, Natasha M. Sensors (Basel) Article Early onset ataxia (EOA) and developmental coordination disorder (DCD) both affect cerebellar functioning in children, making the clinical distinction challenging. We here aim to derive meaningful features from quantitative SARA-gait data (i.e., the gait test of the scale for the assessment and rating of ataxia (SARA)) to classify EOA and DCD patients and typically developing (CTRL) children with better explainability than previous classification approaches. We collected data from 18 EOA, 14 DCD and 29 CTRL children, while executing both SARA gait tests. Inertial measurement units were used to acquire movement data, and a gait model was employed to derive meaningful features. We used a random forest classifier on 36 extracted features, leave-one-out-cross-validation and a synthetic oversampling technique to distinguish between the three groups. Classification accuracy, probabilities of classification and feature relevance were obtained. The mean classification accuracy was 62.9% for EOA, 85.5% for DCD and 94.5% for CTRL participants. Overall, the random forest algorithm correctly classified 82.0% of the participants, which was slightly better than clinical assessment (73.0%). The classification resulted in a mean precision of 0.78, mean recall of 0.70 and mean F1 score of 0.74. The most relevant features were related to the range of the hip flexion–extension angle for gait, and to movement variability for tandem gait. Our results suggest that classification, employing features representing different aspects of movement during gait and tandem gait, may provide an insightful tool for the differential diagnoses of EOA, DCD and typically developing children. MDPI 2023-10-12 /pmc/articles/PMC10611111/ /pubmed/37896504 http://dx.doi.org/10.3390/s23208410 Text en © 2023 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
Dominguez-Vega, Zeus T.
de Quiros, Mariano Bernaldo
Elting, Jan Willem J.
Sival, Deborah A.
Maurits, Natasha M.
Instrumented Gait Classification Using Meaningful Features in Patients with Impaired Coordination
title Instrumented Gait Classification Using Meaningful Features in Patients with Impaired Coordination
title_full Instrumented Gait Classification Using Meaningful Features in Patients with Impaired Coordination
title_fullStr Instrumented Gait Classification Using Meaningful Features in Patients with Impaired Coordination
title_full_unstemmed Instrumented Gait Classification Using Meaningful Features in Patients with Impaired Coordination
title_short Instrumented Gait Classification Using Meaningful Features in Patients with Impaired Coordination
title_sort instrumented gait classification using meaningful features in patients with impaired coordination
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611111/
https://www.ncbi.nlm.nih.gov/pubmed/37896504
http://dx.doi.org/10.3390/s23208410
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