Integrated system for measuring multijoint movements

描述（由申请人提供）：该项目的目标是开发一个易于使用的数据分析系统，用于快速，准确和稳健地估计人体的多关节运动轨迹。这项技术在医学上有许多应用，包括临床步态分析、物理医学和康复、运动医学和损伤预防、运动功能障碍的定量评估、矫形器和假肢的设计和装配、神经肌肉刺激器的反馈控制、植入式传感器的校准。获得可靠的多关节估计工具也是人类和其他物种基础运动控制研究继续取得进展的先决条件。动作捕捉硬件已经广泛使用，并且允许快速和合理准确地测量附着在身体上的各种制造商的位置，方向，弯曲，加速度等。然而，现有的数据分析工具落后于这些硬件的进步；估计非刚性附着标记的多关节体的结构仍然是一个具有挑战性的问题。特别是，a)现有的方法假设了刚性的标记物附着，并且没有提供对不可避免的软组织变形和校准错误造成的误差的估计；B)在预定位置放置标记并测量每个受试者的肢体尺寸需要延长设置时间；C)对传感器特定估计方法的依赖使得难以利用新的传感器模式或位置；D)由于车身结构导致的传感器数据冗余很少被用于处理丢失数据、标记错误识别和一般噪声；E)运动学估计与动力学分开进行，因此可以产生动态上不可能的轨迹；F)少数使用更一般迭代最小化技术的现有系统不能保证实时性能；G)大多数现有系统都是针对计算机动画行业的需求量身定制的，甚至没有尝试满足研究和临床工具的准确性要求；H)需要这些工具的研究人员面临着开发自己工具的艰巨任务。我们建议开发一个综合系统来解决上述所有问题。我们的方法是基于一般的概率公式，它允许我们应用现代统计估计、数值优化和软件工程技术的组合。我们相信，开发这样一个解决方案所需的多个核心方法已经可用，尽管在不同的文献中，并且将它们结合在一起的时机已经成熟。我们的长期目标是为基于标记的多关节估计问题提供一个令人满意的解决方案，并将这里提出的系统整合到一个更大的软件工具套件中，用于生物力学分析和模拟，目前正在南加州大学开发。所提出的系统不仅将用于我们自己的研究，而且将被记录并提供给其他对复杂的多自由度运动感兴趣的研究者。

DESCRIPTION (provided by applicant): The goal of this project is to develop an easy-to-use data analysis system for fast, accurate, and robust estimation of the multijoint movement trajectories of the human body. Such technology has a number of medical applications, including clinical gait analysis, physical medicine and rehabilitation, sports medicine and injury prevention, quantitative assessment of motor dysfunction, design and fitting of orthoses and prostheses, feedback control of neuromuscular stimulators, calibration of implantable sensors. Access to reliable multijoint estimation tools is also a prerequisite for continued progress in basic motor control research on both humans and other species. Motion capture hardware has become widely available, and allows fast and reasonably accurate measurement of the position, orientation, bending, acceleration, etc. of various makers attached to the body. The available data analysis tools, however, lag behind these hardware advances; estimating the configuration of a multiarticulate body to which markers are non-rigidly attached remains a challenging problem. In particular, a) existing methods assume rigid marker attachment and provide no estimate of the errors resulting from unavoidable soft tissue deformation and miscalibration; b) placing markers at predetermined locations and measuring limb sizes for each subject requires prolonged setup sessions; c) the reliance on sensor-specific estimation methods makes it difficult to utilize new sensor modalities or placements; d) the redundancy in the sensor data due to the body structure is rarely exploited to handle missing data, marker misidentification, and noise in general; e) kinematic estimation is performed separate from dynamics and therefore can produce dynamically impossible trajectories; f) the few existing systems that utilize more general iterative minimization techniques do not guarantee real-time performance; g) most existing systems are tailored to the needs of the computer animation industry and do not even attempt to meet the accuracy requirements for research and clinical tools; h) investigators who need such tools are faced with the daunting task of developing their own. We propose to develop an integrated system that addresses all of the above problems. Our approach is based on a general probabilistic formulation, which allows us to apply a combination of modern statistical estimation, numerical optimization, and software engineering techniques. We believe that the multiple core methodologies needed to develop such a solution are already available, albeit in different literatures, and the time is ripe to bring them together. Our longterm goal is to provide a satisfying solution to the problem of marker-based multijoint estimation, as well as to incorporate the system proposed here into a larger suite of software tools for biomechanical analysis and simulation that is currently being developed at USC. The proposed system will not only be used in our own research, but will be documented and made available to other investigators interested in complex many-degree-of-freedom movements.