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Automated and accurate assessment for postural abnormalities in patients with Parkinson’s disease based on Kinect and machine learning

BACKGROUND: Automated and accurate assessment for postural abnormalities is necessary to monitor the clinical progress of Parkinson’s disease (PD). The combination of depth camera and machine learning makes this purpose possible. METHODS: Kinect was used to collect the postural images from 70 PD pat...

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Autores principales: Zhang, Zhuoyu, Hong, Ronghua, Lin, Ao, Su, Xiaoyun, Jin, Yue, Gao, Yichen, Peng, Kangwen, Li, Yudi, Zhang, Tianyu, Zhi, Hongping, Guan, Qiang, Jin, LingJing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8643004/
https://www.ncbi.nlm.nih.gov/pubmed/34863184
http://dx.doi.org/10.1186/s12984-021-00959-4
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author Zhang, Zhuoyu
Hong, Ronghua
Lin, Ao
Su, Xiaoyun
Jin, Yue
Gao, Yichen
Peng, Kangwen
Li, Yudi
Zhang, Tianyu
Zhi, Hongping
Guan, Qiang
Jin, LingJing
author_facet Zhang, Zhuoyu
Hong, Ronghua
Lin, Ao
Su, Xiaoyun
Jin, Yue
Gao, Yichen
Peng, Kangwen
Li, Yudi
Zhang, Tianyu
Zhi, Hongping
Guan, Qiang
Jin, LingJing
author_sort Zhang, Zhuoyu
collection PubMed
description BACKGROUND: Automated and accurate assessment for postural abnormalities is necessary to monitor the clinical progress of Parkinson’s disease (PD). The combination of depth camera and machine learning makes this purpose possible. METHODS: Kinect was used to collect the postural images from 70 PD patients. The collected images were processed to extract three-dimensional body joints, which were then converted to two-dimensional body joints to obtain eight quantified coronal and sagittal features (F1-F8) of the trunk. The decision tree classifier was carried out over a data set established by the collected features and the corresponding doctors’ MDS-UPDRS-III 3.13 (the 13th item of the third part of Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale) scores. An objective function was implanted to further improve the human–machine consistency. RESULTS: The automated grading of postural abnormalities for PD patients was realized with only six selected features. The intraclass correlation coefficient (ICC) between the machine’s and doctors’ score was 0.940 (95%CI, 0.905–0.962), meaning the machine was highly consistent with the doctors’ judgement. Besides, the decision tree classifier performed outstandingly, reaching 90.0% of accuracy, 95.7% of specificity and 89.1% of sensitivity in rating postural severity. CONCLUSIONS: We developed an intelligent evaluation system to provide accurate and automated assessment of trunk postural abnormalities in PD patients. This study demonstrates the practicability of our proposed method in the clinical scenario to help making the medical decision about PD.
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spelling pubmed-86430042021-12-06 Automated and accurate assessment for postural abnormalities in patients with Parkinson’s disease based on Kinect and machine learning Zhang, Zhuoyu Hong, Ronghua Lin, Ao Su, Xiaoyun Jin, Yue Gao, Yichen Peng, Kangwen Li, Yudi Zhang, Tianyu Zhi, Hongping Guan, Qiang Jin, LingJing J Neuroeng Rehabil Methodology BACKGROUND: Automated and accurate assessment for postural abnormalities is necessary to monitor the clinical progress of Parkinson’s disease (PD). The combination of depth camera and machine learning makes this purpose possible. METHODS: Kinect was used to collect the postural images from 70 PD patients. The collected images were processed to extract three-dimensional body joints, which were then converted to two-dimensional body joints to obtain eight quantified coronal and sagittal features (F1-F8) of the trunk. The decision tree classifier was carried out over a data set established by the collected features and the corresponding doctors’ MDS-UPDRS-III 3.13 (the 13th item of the third part of Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale) scores. An objective function was implanted to further improve the human–machine consistency. RESULTS: The automated grading of postural abnormalities for PD patients was realized with only six selected features. The intraclass correlation coefficient (ICC) between the machine’s and doctors’ score was 0.940 (95%CI, 0.905–0.962), meaning the machine was highly consistent with the doctors’ judgement. Besides, the decision tree classifier performed outstandingly, reaching 90.0% of accuracy, 95.7% of specificity and 89.1% of sensitivity in rating postural severity. CONCLUSIONS: We developed an intelligent evaluation system to provide accurate and automated assessment of trunk postural abnormalities in PD patients. This study demonstrates the practicability of our proposed method in the clinical scenario to help making the medical decision about PD. BioMed Central 2021-12-04 /pmc/articles/PMC8643004/ /pubmed/34863184 http://dx.doi.org/10.1186/s12984-021-00959-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Methodology
Zhang, Zhuoyu
Hong, Ronghua
Lin, Ao
Su, Xiaoyun
Jin, Yue
Gao, Yichen
Peng, Kangwen
Li, Yudi
Zhang, Tianyu
Zhi, Hongping
Guan, Qiang
Jin, LingJing
Automated and accurate assessment for postural abnormalities in patients with Parkinson’s disease based on Kinect and machine learning
title Automated and accurate assessment for postural abnormalities in patients with Parkinson’s disease based on Kinect and machine learning
title_full Automated and accurate assessment for postural abnormalities in patients with Parkinson’s disease based on Kinect and machine learning
title_fullStr Automated and accurate assessment for postural abnormalities in patients with Parkinson’s disease based on Kinect and machine learning
title_full_unstemmed Automated and accurate assessment for postural abnormalities in patients with Parkinson’s disease based on Kinect and machine learning
title_short Automated and accurate assessment for postural abnormalities in patients with Parkinson’s disease based on Kinect and machine learning
title_sort automated and accurate assessment for postural abnormalities in patients with parkinson’s disease based on kinect and machine learning
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8643004/
https://www.ncbi.nlm.nih.gov/pubmed/34863184
http://dx.doi.org/10.1186/s12984-021-00959-4
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