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引用次数: 7

摘要

在医疗保健领域,使用Kinect传感器监测并向进行干预或康复训练的患者提供反馈是一种即将到来的趋势。然而,Kinect传感器测量的关节位置通常是不可靠的,特别是对于被身体其他部位遮挡的关节。运动捕捉(MOCAP)系统使用来自不同视角的多个摄像头,可以准确地跟踪患者身上的标记位置。但是这样的系统既昂贵又不方便病人。在这项工作中,我们在训练阶段同时使用Kinect传感器和MOCAP系统捕获关节位置,并训练高斯过程回归模型将有噪声的Kinect测量映射到更准确的MOCAP测量。针对不同人在肢体长度和身体姿势上的固有差异,我们进一步提出了关节标准化方法,将不同人的原始关节位置转换为标准坐标,其中每对相邻关节之间的距离保持在参考距离。我们的实验表明,采用该方法的去噪Kinect测量值比几种基准方法更准确。
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Denoising of Joint Tracking Data by Kinect Sensors Using Clustered Gaussian Process Regression.

Using Kinect sensors to monitor and provide feedback to patients performing intervention or rehabilitation exercises is an upcoming trend in healthcare. However, the joint positions measured by the Kinect sensor are often unreliable, especially for joints that are occluded by other parts of the body. Motion capture (MOCAP) systems using multiple cameras from different view angles can accurately track marker positions on the patient. But such systems are costly and inconvenient to patients. In this work, we simultaneously capture the joint positions using both a Kinect sensor and a MOCAP system during a training stage and train a Gaussian Process regression model to map the noisy Kinect measurements to the more accurate MOCAP measurements. To deal with the inherent variations in limb lengths and body postures among different people, we further propose a joint standardization method, which translates the raw joint positions of different people into a standard coordinate, where the distance between each pair of adjacent joints is kept at a reference distance. Our experiments show that the denoised Kinect measurements by the proposed method are more accurate than several benchmark methods.

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