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Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment

Advanced sensor technologies have been applied to support frozen shoulder assessment. Sensor-based assessment tools provide objective, continuous and quantitative information for evaluation and diagnosis. However, the current tools for assessment of functional shoulder tasks mainly rely on manual op...

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Autores principales: Chang, Chih-Ya, Hsieh, Chia-Yeh, Huang, Hsiang-Yun, Wu, Yung-Tsan, Chen, Liang-Cheng, Chan, Chia-Tai, Liu, Kai-Chun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795360/
https://www.ncbi.nlm.nih.gov/pubmed/33375341
http://dx.doi.org/10.3390/s21010106
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author Chang, Chih-Ya
Hsieh, Chia-Yeh
Huang, Hsiang-Yun
Wu, Yung-Tsan
Chen, Liang-Cheng
Chan, Chia-Tai
Liu, Kai-Chun
author_facet Chang, Chih-Ya
Hsieh, Chia-Yeh
Huang, Hsiang-Yun
Wu, Yung-Tsan
Chen, Liang-Cheng
Chan, Chia-Tai
Liu, Kai-Chun
author_sort Chang, Chih-Ya
collection PubMed
description Advanced sensor technologies have been applied to support frozen shoulder assessment. Sensor-based assessment tools provide objective, continuous and quantitative information for evaluation and diagnosis. However, the current tools for assessment of functional shoulder tasks mainly rely on manual operation. It may cause several technical issues to the reliability and usability of the assessment tool, including manual bias during the recording and additional efforts for data labeling. To tackle these issues, this pilot study aims to propose an automatic functional shoulder task identification and sub-task segmentation system using inertial measurement units to provide reliable shoulder task labeling and sub-task information for clinical professionals. The proposed method combines machine learning models and rule-based modification to identify shoulder tasks and segment sub-tasks accurately. A hierarchical design is applied to enhance the efficiency and performance of the proposed approach. Nine healthy subjects and nine frozen shoulder patients are invited to perform five common shoulder tasks in the lab-based and clinical environments, respectively. The experimental results show that the proposed method can achieve 87.11% F-score for shoulder task identification, and 83.23% F-score and 427 mean absolute time errors (milliseconds) for sub-task segmentation. The proposed approach demonstrates the feasibility of the proposed method to support reliable evaluation for clinical assessment.
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spelling pubmed-77953602021-01-10 Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment Chang, Chih-Ya Hsieh, Chia-Yeh Huang, Hsiang-Yun Wu, Yung-Tsan Chen, Liang-Cheng Chan, Chia-Tai Liu, Kai-Chun Sensors (Basel) Article Advanced sensor technologies have been applied to support frozen shoulder assessment. Sensor-based assessment tools provide objective, continuous and quantitative information for evaluation and diagnosis. However, the current tools for assessment of functional shoulder tasks mainly rely on manual operation. It may cause several technical issues to the reliability and usability of the assessment tool, including manual bias during the recording and additional efforts for data labeling. To tackle these issues, this pilot study aims to propose an automatic functional shoulder task identification and sub-task segmentation system using inertial measurement units to provide reliable shoulder task labeling and sub-task information for clinical professionals. The proposed method combines machine learning models and rule-based modification to identify shoulder tasks and segment sub-tasks accurately. A hierarchical design is applied to enhance the efficiency and performance of the proposed approach. Nine healthy subjects and nine frozen shoulder patients are invited to perform five common shoulder tasks in the lab-based and clinical environments, respectively. The experimental results show that the proposed method can achieve 87.11% F-score for shoulder task identification, and 83.23% F-score and 427 mean absolute time errors (milliseconds) for sub-task segmentation. The proposed approach demonstrates the feasibility of the proposed method to support reliable evaluation for clinical assessment. MDPI 2020-12-26 /pmc/articles/PMC7795360/ /pubmed/33375341 http://dx.doi.org/10.3390/s21010106 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
Chang, Chih-Ya
Hsieh, Chia-Yeh
Huang, Hsiang-Yun
Wu, Yung-Tsan
Chen, Liang-Cheng
Chan, Chia-Tai
Liu, Kai-Chun
Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment
title Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment
title_full Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment
title_fullStr Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment
title_full_unstemmed Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment
title_short Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment
title_sort automatic functional shoulder task identification and sub-task segmentation using wearable inertial measurement units for frozen shoulder assessment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795360/
https://www.ncbi.nlm.nih.gov/pubmed/33375341
http://dx.doi.org/10.3390/s21010106
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