论文标题
基于图像的稳定性量化
Image-based Stability Quantification
论文作者
论文摘要
使用脚压力/力量测量硬件和运动捕获（MOCAP）技术对人类稳定的定量评估昂贵，耗时，并且仅限于实验室。我们提出了一种基于图像的新方法来估计稳定计算的三个关键组成部分：质量中心（COM），支持基础（BOS）和压力中心（COP）。此外，我们通过使用公开可用的多模式（MOCAP，脚压力，两视频视频），十种对象人类运动数据集对直接从基于实验室的传感器输出（地面真相）直接生成的值来定量验证我们基于图像的方法，以计算两种经典稳定性措施，即comtocop和comtobos距离。实验结果使用一个主题（LOSO）交叉验证，表明：1）我们基于图像的COM估计方法（COMNET）始终优于最先进的基于惯性传感器的COM估计技术； 2）通过我们的基于图像的方法与鞋垫脚压力传感器数据相结合计算的稳定性与地面真相稳定性措施产生一致，强且具有统计学意义的相关性（comtocop r = 0.79 p <0.001，commtobos r = 0.75 p <0.001）; 3）我们对稳定性的完全基于图像的估计在两个稳定性指标上产生一致，正统计和具有统计学意义的相关性（comtocop r = 0.31 p <0.001，comtobos r = 0.22 p <0.043）。我们的研究为自然环境中基于图像的稳定性评估的可行性提供了有希望的定量证据。
Quantitative evaluation of human stability using foot pressure/force measurement hardware and motion capture (mocap) technology is expensive, time consuming, and restricted to the laboratory. We propose a novel image-based method to estimate three key components for stability computation: Center of Mass (CoM), Base of Support (BoS), and Center of Pressure (CoP). Furthermore, we quantitatively validate our image-based methods for computing two classic stability measures, CoMtoCoP and CoMtoBoS distances, against values generated directly from laboratory-based sensor output (ground truth) using a publicly available, multi-modality (mocap, foot pressure, two-view videos), ten-subject human motion dataset. Using Leave One Subject Out (LOSO) cross-validation, experimental results show: 1) our image-based CoM estimation method (CoMNet) consistently outperforms state-of-the-art inertial sensor-based CoM estimation techniques; 2) stability computed by our image-based method combined with insole foot pressure sensor data produces consistent, strong, and statistically significant correlation with ground truth stability measures (CoMtoCoP r = 0.79 p < 0.001, CoMtoBoS r = 0.75 p < 0.001); 3) our fully image-based estimation of stability produces consistent, positive, and statistically significant correlation on the two stability metrics (CoMtoCoP r = 0.31 p < 0.001, CoMtoBoS r = 0.22 p < 0.043). Our study provides promising quantitative evidence for the feasibility of image-based stability evaluation in natural environments.
