Integrated system for measuring multijoint movements

描述(申请人提供)：这个项目的目标是开发一个简单易用的数据分析系统，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在人类和其他物种的基础运动控制研究方面继续取得进展的先决条件。运动捕捉硬件已经变得广泛使用，并允许快速和合理准确地测量连接到身体上的各种制造者的位置、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；估计标记非刚性连接到的多关节机构的结构仍然是一个具有挑战性的问题。具体地说，a)现有方法假定固定标记，并且不提供对不可避免的软组织变形和错误校准所造成的误差的估计；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长时间的设置过程；c)依赖传感器特定的估计方法使得难以使用新的传感器模式或放置；d)由于身体结构导致的传感器数据中的冗余很少被利用来处理丢失数据、标记错误识别和一般的噪声；e)运动学估计是与动力学分开进行的，因此可以产生动态上不可能的轨迹；F)使用更通用的迭代最小化技术的少数现有系统不能保证实时性能；g)大多数现有系统是为计算机动画产业的需求量身定做的，甚至不试图满足研究和临床工具的精度要求；h)需要这些工具的研究人员面临着开发他们自己的艰巨任务。我们建议开发一个综合系统来解决所有上述问题。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。我们认为，开发这种解决方案所需的多种核心方法已经存在，尽管有不同的文献，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令人满意的解决方案，并将这里提出的系统整合到南加州大学目前正在开发的更大的生物力学分析和模拟软件工具套件中。拟议的系统不仅将用于我们自己的研究，而且将被记录下来，并提供给对复杂的多自由度运动感兴趣的其他研究人员。

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.