Markerless estimation of 3D Kinematics has the great potential to clinically diagnose and monitor movement disorders without referrals to expensive motion capture labs; however, current approaches are limited by performing multiple de-coupled steps to estimate the kinematics of a person from videos. Most current techniques work in a multi-step approach by first detecting the pose of the body and then fitting a musculoskeletal model to the data for accurate kinematic estimation. Errors in training data of the pose detection algorithms, model scaling, as well the requirement of multiple cameras limit the use of these techniques in a clinical setting. Our goal is to pave the way toward fast, easily applicable and accurate 3D kinematic estimation \xdeleted{in a clinical setting}. To this end, we propose a novel approach for direct 3D human kinematic estimation D3KE from videos using deep neural networks. Our experiments demonstrate that the proposed end-to-end training is robust and outperforms 2D and 3D markerless motion capture based kinematic estimation pipelines in terms of joint angles error by a large margin (35\% from 5.44 to 3.54 degrees). We show that D3KE is superior to the multi-step approach and can run at video framerate speeds. This technology shows the potential for clinical analysis from mobile devices in the future.
翻译:对 3D 元素数学 的无标记估计极有可能在不转诊到昂贵的运动抓取实验室的情况下临床诊断和监测运动障碍;然而,目前的方法有限,因为要从视频中评估一个人的动脉学。目前大多数技术在多步方法中发挥作用,先先检测身体的外形,然后将肌肉骨骼模型与精确的运动估计数据相匹配。在配置检测算法的培训数据、模型缩放以及多摄像头的要求方面的错误限制了临床环境中使用这些技术。我们的目标是为快速、容易适用和准确的 3D 运动学估计提供快速、易应用和准确的步骤。为此,我们提出一种新颖的方法,即利用深神经网络将3D3KE从视频中直接进行感动估计。我们的实验表明,拟议的端对端培训是稳健的,超过了2D和3D无标记动作捕获基于运动在临床环境中使用这些技术限制这些技术。我们的目标是在大边缘为快速、易应用和准确的 3D- 运动估计3D 方向分析铺平道路,从5.44 到从高距离显示高速度的图像技术。