Assessing Non-Specific Neck Pain through Pose Estimation from Images Based on Ensemble Learning
Author:
Kang Jiunn-Horng12ORCID, Hsieh En-Han3, Lee Cheng-Yang3ORCID, Sun Yi-Ming4, Lee Tzong-Yi5, Hsu Justin Bo-Kai6, Chang Tzu-Hao37ORCID
Affiliation:
1. Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei 110, Taiwan 2. Graduate Institute of Nanomedicine and Medical Engineering, Taipei Medical University, Taipei 110, Taiwan 3. Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei 110, Taiwan 4. PlexBio Co., Ltd., Taipei 114, Taiwan 5. Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan 6. Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan 7. Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
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.
Funder
Taipei Medical University
Subject
Paleontology,Space and Planetary Science,General Biochemistry, Genetics and Molecular Biology,Ecology, Evolution, Behavior and Systematics
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