论文标题
通过视频分析和机器学习从步态特征中预测拖线得分
Predicting TUG score from gait characteristics with video analysis and machine learning
论文作者
论文摘要
跌倒是遭受老年人和社会的死亡的主要原因。定时进行（拖线）测试是秋季风险评估的常见工具。在本文中，我们提出了一种方法，可以从用计算机视觉和机器学习技术从视频中提取的步态特征中预测拖线得分。首先，从人类运动期间用2D和3D摄像机捕获的视频估算了3D姿势，然后从3D姿势系列中计算出一组步态特征。之后，使用Copula熵来选择主要与拖线得分相关的那些特征。最后，选定的特征被馈入预测模型以预测拖线得分。现实世界数据的实验证明了该方法的有效性。作为副产品，发现了拖线得分与几个步态特征之间的关联，这奠定了所提出方法的科学基础，并使预测模型构建了这些可解释的临床用户。
Fall is a leading cause of death which suffers the elderly and society. Timed Up and Go (TUG) test is a common tool for fall risk assessment. In this paper, we propose a method for predicting TUG score from gait characteristics extracted from video with computer vision and machine learning technologies. First, 3D pose is estimated from video captured with 2D and 3D cameras during human motion and then a group of gait characteristics are computed from 3D pose series. After that, copula entropy is used to select those characteristics which are mostly associated with TUG score. Finally, the selected characteristics are fed into the predictive models to predict TUG score. Experiments on real world data demonstrated the effectiveness of the proposed method. As a byproduct, the associations between TUG score and several gait characteristics are discovered, which laid the scientific foundation of the proposed method and make the predictive models such built interpretable to clinical users.
