论文标题
从运动学到动态：估计压力的估计中心和人类运动视频框架的支持基础
From Kinematics To Dynamics: Estimating Center of Pressure and Base of Support from Video Frames of Human Motion
论文作者
论文摘要
为了了解给定的人类姿势图像与人类受试者的相应物理脚压力之间的关系，我们提出并验证两个端到端的深度学习体系结构PressNet和PressNet-Simple，以从视频帧中得出2D人类姿势（Kinematics）的脚压力热图（动力学）。一个独特的视频和脚压力数据集，由813,050对，由6个受试者的5分钟长的编排太极拳序列组成，并用于留下一个受试者的交叉验证。我们的最初实验结果表明，从单个图像中证明了可靠且可重复的脚压力预测，这为计算机视觉中这种复杂的跨模态映射问题设定了第一个基线。此外，我们从预测的脚压力分布中计算和定量验证压力中心（COP）和支撑基底（BOS），从具有运动生物学，医学，体育和机器人技术中潜在应用的图像中获得姿势稳定性分析中的关键成分。
To gain an understanding of the relation between a given human pose image and the corresponding physical foot pressure of the human subject, we propose and validate two end-to-end deep learning architectures, PressNet and PressNet-Simple, to regress foot pressure heatmaps (dynamics) from 2D human pose (kinematics) derived from a video frame. A unique video and foot pressure data set of 813,050 synchronized pairs, composed of 5-minute long choreographed Taiji movement sequences of 6 subjects, is collected and used for leaving-one-subject-out cross validation. Our initial experimental results demonstrate reliable and repeatable foot pressure prediction from a single image, setting the first baseline for such a complex cross modality mapping problem in computer vision. Furthermore, we compute and quantitatively validate the Center of Pressure (CoP) and Base of Support (BoS) from predicted foot pressure distribution, obtaining key components in pose stability analysis from images with potential applications in kinesiology, medicine, sports and robotics.
