PDF(Pubmed)
Abstract：
Noninvasive tracking devices are widely used to monitor real-time posture. Yet significant potential exists to enhance postural control quantification through walking videos. This study advances computational science by integrating OpenPose with a Support Vector Machine (SVM) to perform highly accurate and robust postural analysis, marking a substantial improvement over traditional methods which often rely on invasive sensors. Utilizing OpenPose-based deep learning, we generated Dynamic Joint Nodes Plots (DJNP) and iso-block postural identity images for 35 young adults in controlled walking experiments. Through Temporal and Spatial Regression (TSR) models, key features were extracted for SVM classification, enabling the distinction between various walking behaviors. This approach resulted in an overall accuracy of 0.990 and a Kappa index of 0.985. Cutting points for the ratio of top angles (TAR) and the ratio of bottom angles (BAR) effectively differentiated between left and right skews with AUC values of 0.772 and 0.775, respectively. These results demonstrate the efficacy of integrating OpenPose with SVM, providing more precise, real-time analysis without invasive sensors. Future work will focus on expanding this method to a broader demographic, including individuals with gait abnormalities, to validate its effectiveness across diverse clinical conditions. Furthermore, we plan to explore the integration of alternative machine learning models, such as deep neural networks, enhancing the system\'s robustness and adaptability for complex dynamic environments. This research opens new avenues for clinical applications, particularly in rehabilitation and sports science, promising to revolutionize noninvasive postural analysis.
摘要：
无创跟踪设备被广泛用于监测实时姿势。然而，通过步行视频增强姿势控制量化的巨大潜力。这项研究通过将OpenPose与支持向量机（SVM）集成来执行高度准确和强大的姿势分析来推进计算科学。标志着比通常依赖于侵入式传感器的传统方法有了实质性的改进。利用基于OpenPose的深度学习，在受控步行实验中，我们为35名年轻人生成了动态关节节点图（DJNP）和等块姿势身份图像。通过时间和空间回归(TSR)模型，为SVM分类提取关键特征,能够区分各种行走行为。该方法导致0.990的总体准确度和0.985的Kappa指数。顶角比率（TAR）和底角比率（BAR）的切割点在左右偏斜之间有效区分，AUC值分别为0.772和0.775。这些结果证明了OpenPose与SVM集成的有效性，提供更精确的，无需侵入式传感器的实时分析。未来的工作将集中在将这种方法扩展到更广泛的人群中，包括步态异常的个体,以验证其在不同临床条件下的有效性。此外，我们计划探索替代机器学习模型的集成，比如深度神经网络，增强系统对复杂动态环境的鲁棒性和适应性。这项研究为临床应用开辟了新的途径，特别是在康复和运动科学方面，有望彻底改变无创姿势分析。
