背景:长时间的不当姿势会导致头部姿势(FHP),导致头痛,呼吸功能受损,和疲劳。这在久坐的情况下尤其相关,个人经常长时间保持静态姿势,这是许多人日常生活的重要组成部分。能够检测FHP的系统的开发是至关重要的,因为它不仅会提醒使用者纠正他们的姿势,而且还可以通过防止与这种情况相关的慢性损伤的进展来实现更广泛的目标,为公众健康做出贡献。然而,尽管在从标准2D图像估计人类姿势方面取得了重大进展,大多数计算姿态模型不包括颅骨角度的测量,涉及到C7椎骨,对于诊断FHP至关重要。
目的:FHP的准确诊断通常需要专用设备,如临床姿势评估或专业成像设备,但是它们的使用对于连续来说是不切实际的,在日常设置中进行实时监控。因此,开发一个可访问的,定期姿势评估的有效方法,可以轻松地集成到日常活动中,提供实时反馈,并促进纠正行动,是必要的。
方法:系统从提供的2D图像中依次估计2D和3D人体解剖关键点,使用Detectron2D和VideoPose3D算法,分别。然后使用图卷积网络(GCN),专门设计用于分析3D空间中上身解剖关键点的空间配置和对齐。这个GCN旨在隐含地学习估计的3D关键点和正确的姿势之间的复杂关系,专门用于识别FHP。
结果:当输入包括与上身关键点相对应的所有关节时,测试准确性为78.27%。GCN模型在各个班级中表现出略微优越的平衡性能,F1得分(宏)为77.54%,与基线前馈神经网络(FFNN)模型的75.88%相比。具体来说,GCN模型显示了类之间更平衡的精度和召回率,表明它有可能在不同姿势的FHP检测中更好地推广。同时,基线FFNN模型为FHP案例展示了更高的精度,但以较低的召回为代价,表明,虽然它在检测到FHP时更准确地确认,它错过了大量的实际FHP实例。通过使用t分布随机邻居嵌入对潜在特征空间的检查,进一步证实了这一主张。其中GCN模型呈现各向同性分布,与FFNN模型不同,表现出各向异性分布。
结论:基于使用3D人体姿态估计联合输入的2D图像输入,研究发现,使用提出的基于GCN的网络来开发姿势校正系统,可以学习FHP相关特征。我们通过解决当前系统的局限性并提出该领域未来工作的潜在途径来总结本文。
BACKGROUND: Prolonged improper posture can lead to forward head posture (FHP), causing headaches, impaired respiratory function, and fatigue. This is especially relevant in sedentary scenarios, where individuals often maintain static postures for extended periods-a significant part of daily life for many. The development of a system capable of detecting FHP is crucial, as it would not only alert users to correct their posture but also serve the broader goal of contributing to public health by preventing the progression of chronic injuries associated with this condition. However, despite significant advancements in estimating human poses from standard 2D images, most computational pose models do not include measurements of the craniovertebral angle, which involves the C7 vertebra, crucial for diagnosing FHP.
OBJECTIVE: Accurate diagnosis of FHP typically requires dedicated devices, such as clinical postural assessments or specialized imaging equipment, but their use is impractical for continuous, real-time monitoring in everyday settings. Therefore, developing an accessible, efficient method for regular posture assessment that can be easily integrated into daily activities, providing real-time feedback, and promoting corrective action, is necessary.
METHODS: The system sequentially estimates 2D and 3D human anatomical key points from a provided 2D image, using the Detectron2D and VideoPose3D algorithms, respectively. It then uses a graph convolutional network (GCN), explicitly crafted to analyze the spatial configuration and alignment of the upper body\'s anatomical key points in 3D space. This GCN aims to implicitly learn the intricate relationship between the estimated 3D key points and the correct posture, specifically to identify FHP.
RESULTS: The test accuracy was 78.27% when inputs included all joints corresponding to the upper body key points. The GCN model demonstrated slightly superior balanced performance across classes with an F1-score (macro) of 77.54%, compared to the baseline feedforward neural network (FFNN) model\'s 75.88%. Specifically, the GCN model showed a more balanced precision and recall between the classes, suggesting its potential for better generalization in FHP detection across diverse postures. Meanwhile, the baseline FFNN model demonstrates a higher precision for FHP cases but at the cost of lower recall, indicating that while it is more accurate in confirming FHP when detected, it misses a significant number of actual FHP instances. This assertion is further substantiated by the examination of the latent feature space using t-distributed stochastic neighbor embedding, where the GCN model presented an isotropic distribution, unlike the FFNN model, which showed an anisotropic distribution.
CONCLUSIONS: Based on 2D image input using 3D human pose estimation joint inputs, it was found that it is possible to learn FHP-related features using the proposed GCN-based network to develop a posture correction system. We conclude the paper by addressing the limitations of our current system and proposing potential avenues for future work in this area.