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© 2023 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 (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Background: Mobile phones, laptops, and computers have become an indispensable part of our lives in recent years. Workers may have an incorrect posture when using a computer for a prolonged period of time. Using these products with an incorrect posture can lead to neck pain. However, there are limited data on postures in real-life situations. Methods: In this study, we used a common camera to record images of subjects carrying out three different tasks (a typing task, a gaming task, and a video-watching task) on a computer. Different artificial intelligence (AI)-based pose estimation approaches were applied to analyze the head’s yaw, pitch, and roll and coordinate information of the eyes, nose, neck, and shoulders in the images. We used machine learning models such as random forest, XGBoost, logistic regression, and ensemble learning to build a model to predict whether a subject had neck pain by analyzing their posture when using the computer. Results: After feature selection and adjustment of the predictive models, nested cross-validation was applied to evaluate the models and fine-tune the hyperparameters. Finally, the ensemble learning approach was utilized to construct a model via bagging, which achieved a performance with 87% accuracy, 92% precision, 80.3% recall, 95.5% specificity, and an AUROC of 0.878. Conclusions: We developed a predictive model for the identification of non-specific neck pain using 2D video images without the need for costly devices, advanced environment settings, or extra sensors. This method could provide an effective way for clinically evaluating poor posture during real-world computer usage scenarios.

Details

Title
Assessing Non-Specific Neck Pain through Pose Estimation from Images Based on Ensemble Learning
Author
Kang, Jiunn-Horng 1   VIAFID ORCID Logo  ; En-Han, Hsieh 2 ; Cheng-Yang, Lee 2   VIAFID ORCID Logo  ; Yi-Ming, Sun 3 ; Tzong-Yi, Lee 4 ; Hsu, Justin Bo-Kai 5 ; Chang, Tzu-Hao 6   VIAFID ORCID Logo 

 Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei 110, Taiwan; [email protected]; Graduate Institute of Nanomedicine and Medical Engineering, Taipei Medical University, Taipei 110, Taiwan 
 Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei 110, Taiwan 
 PlexBio Co., Ltd., Taipei 114, Taiwan 
 Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan 
 Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan 
 Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei 110, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan 
First page
2292
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20751729
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2904760015
Copyright
© 2023 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 (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.