Machine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data

The goal of this study was to develop a framework to classify dependence in ambulation by employing a deep model in a 3D convolutional neural network (3D-CNN) using video data recorded by a smartphone during inpatient rehabilitation therapy in stroke patients. Among 2311 video clips, 1218 walk actio...

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Autores principales:	Jong Taek Lee, Eunhee Park, Tae-Du Jung
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning stroke rehabilitation dependent ambulation postural balance Medicine R
Acceso en línea:	https://doaj.org/article/47618b5aad364084a4feec85ccb1f94c
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spelling	oai:doaj.org-article:47618b5aad364084a4feec85ccb1f94c2021-11-25T18:06:58ZMachine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data10.3390/jpm111110802075-4426https://doaj.org/article/47618b5aad364084a4feec85ccb1f94c2021-10-01T00:00:00Zhttps://www.mdpi.com/2075-4426/11/11/1080https://doaj.org/toc/2075-4426The goal of this study was to develop a framework to classify dependence in ambulation by employing a deep model in a 3D convolutional neural network (3D-CNN) using video data recorded by a smartphone during inpatient rehabilitation therapy in stroke patients. Among 2311 video clips, 1218 walk action cases were collected from 206 stroke patients receiving inpatient rehabilitation therapy (63.24 ± 14.36 years old). As ground truth, the dependence in ambulation was assessed and labeled using the functional ambulatory categories (FACs) and Berg balance scale (BBS). The dependent ambulation was defined as a FAC score less than 4 or a BBS score less than 45. We extracted patient-centered video and patient-centered pose of the target from the tracked target’s posture keypoint location information. Then, the extracted patient-centered video was input in the 3D-CNN, and the extracted patient-centered pose was used to measure swing time asymmetry. Finally, we evaluated the classification of dependence in ambulation using video data via fivefold cross-validation. When training the 3D-CNN based on FACs and BBS, the model performed with 86.3% accuracy, 87.4% precision, 94.0% recall, and 90.5% F1 score. When the 3D-CNN based on FACs and BBS was combined with swing time asymmetry, the model exhibited improved performance (88.7% accuracy, 89.1% precision, 95.7% recall, and 92.2% F1 score). The proposed framework for dependence in ambulation can be useful, as it alerts clinicians or caregivers when stroke patients with dependent ambulatory move alone without assistance. In addition, monitoring dependence in ambulation can facilitate the design of individualized rehabilitation strategies for stroke patients with impaired mobility and balance function.Jong Taek LeeEunhee ParkTae-Du JungMDPI AGarticlemachine learningstrokerehabilitationdependent ambulationpostural balanceMedicineRENJournal of Personalized Medicine, Vol 11, Iss 1080, p 1080 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning stroke rehabilitation dependent ambulation postural balance Medicine R
spellingShingle	machine learning stroke rehabilitation dependent ambulation postural balance Medicine R Jong Taek Lee Eunhee Park Tae-Du Jung Machine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data
description	The goal of this study was to develop a framework to classify dependence in ambulation by employing a deep model in a 3D convolutional neural network (3D-CNN) using video data recorded by a smartphone during inpatient rehabilitation therapy in stroke patients. Among 2311 video clips, 1218 walk action cases were collected from 206 stroke patients receiving inpatient rehabilitation therapy (63.24 ± 14.36 years old). As ground truth, the dependence in ambulation was assessed and labeled using the functional ambulatory categories (FACs) and Berg balance scale (BBS). The dependent ambulation was defined as a FAC score less than 4 or a BBS score less than 45. We extracted patient-centered video and patient-centered pose of the target from the tracked target’s posture keypoint location information. Then, the extracted patient-centered video was input in the 3D-CNN, and the extracted patient-centered pose was used to measure swing time asymmetry. Finally, we evaluated the classification of dependence in ambulation using video data via fivefold cross-validation. When training the 3D-CNN based on FACs and BBS, the model performed with 86.3% accuracy, 87.4% precision, 94.0% recall, and 90.5% F1 score. When the 3D-CNN based on FACs and BBS was combined with swing time asymmetry, the model exhibited improved performance (88.7% accuracy, 89.1% precision, 95.7% recall, and 92.2% F1 score). The proposed framework for dependence in ambulation can be useful, as it alerts clinicians or caregivers when stroke patients with dependent ambulatory move alone without assistance. In addition, monitoring dependence in ambulation can facilitate the design of individualized rehabilitation strategies for stroke patients with impaired mobility and balance function.
format	article
author	Jong Taek Lee Eunhee Park Tae-Du Jung
author_facet	Jong Taek Lee Eunhee Park Tae-Du Jung
author_sort	Jong Taek Lee
title	Machine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data
title_short	Machine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data
title_full	Machine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data
title_fullStr	Machine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data
title_full_unstemmed	Machine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data
title_sort	machine learning-based classification of dependence in ambulation in stroke patients using smartphone video data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/47618b5aad364084a4feec85ccb1f94c
work_keys_str_mv	AT jongtaeklee machinelearningbasedclassificationofdependenceinambulationinstrokepatientsusingsmartphonevideodata AT eunheepark machinelearningbasedclassificationofdependenceinambulationinstrokepatientsusingsmartphonevideodata AT taedujung machinelearningbasedclassificationofdependenceinambulationinstrokepatientsusingsmartphonevideodata
_version_	1718411613990551552

Machine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data

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