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首页> 外文会议>Conference on Image Processing: Algorithms and Systems III; 20040119-20040121; San Jose,CA; US >Posture Kinematics Reconstruction and Body Model Creation
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Posture Kinematics Reconstruction and Body Model Creation

机译：姿势运动学重建和人体模型创建

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The application of the proposed method allows to analyse images recorded during orthostatic posture trials. The possibility to put in evidence mechanisms that govern postural control has been demonstrated evaluating the values of hip rotation over time. The algorithm, furthermore, produces a human body model. In particular, the method is able to estimate the trajectory of the typical kinematic variable, the Centre of Mass, which gives valuable informations to clinicians. In order to quantify the movement ability of the patient, the exploitation of image recording techniques inside movement analysis laboratories has become necessary in clinical practice: their spreading out and thus their routinely use is, however, prevented by the non-soberness in economical and timing terms, caused by the use of "ad hoc" motion analysis systems, and the setting up of external devices that have to be applied to the patient. In this context, the use of commercially available video systems, together with the development of techniques granting the reconstruction of the kinematics in absence of markers, reduces costs and times, and therefore favours their diffusion in clinical environments. However, novel methods for image processing must be used in order to determine the position of body segments during orthostatic exercises with the required accuracy. The method proposed in [3] has been used as the kernel for the motion estimation process into video sequences. It has been tested both in simulated motions, and in real video sequences, and applied to the tracking of body points during orthostatic postural tests, and has been demonstrated able to reliably represent the kinematics of the stabilometric fluctuations in the sagittal direction; in particular, the method is capable to discriminate between mechanisms that govern the human plant in postural tests, and then to give useful information for the clinician. As an extension of the technique to other Human Motion Analysis applications, the technological development facilitates the extension of the limits for frame rate acquisition: during the recordings of biomechanical tasks, if the motion process is temporally oversampled, the disparity between time-adjacent frames will be smaller, and the approximation driven by simple translational motion ensured if the dynamical update of the reference is used. Correspondingly, the motion estimation process must be more accurate, as the relative motion of the selected elements is minor; in this context the interpolation process to subpixel resolution is the key for every estimation process in human movement analysis.
机译：所提出的方法的应用允许分析在体位姿势试验中记录的图像。在评估随时间变化的髋关节旋转值方面，已经证明可以引入控制姿势控制的证据机制。此外，该算法产生人体模型。特别地，该方法能够估计典型运动变量质量中心的轨迹，从而为临床医生提供有价值的信息。为了量化患者的运动能力，在临床实践中必须使用运动分析实验室中的图像记录技术：然而，它们的散布以及因此的日常使用被经济和时间上的不清醒所阻止。这是由于使用“临时”运动分析系统以及必须安装到患者身上的外部设备引起的。在这种情况下，使用市场上可买到的视频系统，以及在没有标记的情况下允许进行运动学重建的技术的发展，降低了成本和时间，因此有利于它们在临床环境中的传播。但是，必须使用新颖的图像处理方法来确定体位锻炼期间身体段的位置，并达到所需的精度。 [3]中提出的方法已被用作视频序列运动估计过程的核心。它已在模拟运动和真实视频序列中进行了测试，并已在体位姿势测试中用于跟踪人体点，并已被证明能够可靠地表示矢状方向稳定波动的运动学。特别地，该方法能够在姿势测试中区分控制人类植物的机制，然后为临床医生提供有用的信息。作为该技术对其他人体运动分析应用程序的扩展，技术发展促进了帧速率获取限制的扩展：在记录生物力学任务期间，如果运动过程在时间上被过采样，则相邻时间帧之间的差异将如果使用参考的动态更新，则近似值较小，并且可以确保由简单平移运动驱动的近似值。相应地，运动估计过程必须更加精确，因为所选元素的相对运动较小。在这种情况下，子像素分辨率的插值过程是人体运动分析中每个估计过程的关键。

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