Integrated system for measuring multijoint movements

用于测量多关节运动的集成系统

• 批准号: 6605925
• 负责人: Emanuel Todorov
• 金额: $ 26.53万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-04-01 至 2006-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6605925
• 关键词: bioengineering /biomedical engineering; biomechanics; biomedical automation; biomedical equipment development; body movement; clinical research; computer data analysis; computer human interaction; computer program /software; computer system design /evaluation; human subject; joints; mathematical model; measurement; statistics /biometry

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.

描述（由申请人提供）：该项目的目的是开发一个易于使用的数据分析系统，以快速，准确且可靠地估计人体的多关运动轨迹。这种技术具有许多医疗应用，包括临床步态分析，物理医学和康复，运动医学和预防损伤，运动功能障碍的定量评估，矫形器和假体的设计和拟合，神经肌肉刺激剂的反馈控制，可植入传感器的校准。获取可靠的多期估计工具也是对人类和其他物种的基本运动控制研究的持续进展的先决条件。运动捕获硬件已广泛可用，可以快速且合理地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。但是，可用的数据分析工具落后于这些硬件进步；估计标记不符合标记的多型物体的配置仍然是一个具有挑战性的问题。特别是，a）现有方法假定刚性标记的附着，并且不提供因不可避免的软组织变形和误解而导致的错误； b）将标记放在每个受试者的预定位置和测量肢体大小上需要延长的设置会话； c）对传感器特异性估计方法的依赖使得很难使用新的传感器模式或位置； d）由于身体结构而导致的传感器数据中的冗余性很少被利用以处理缺失的数据，标记错误识别和噪声； e）运动估计与动态分开进行，因此可以产生动态不可能的轨迹； f）少数使用更通用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根据计算机动画行业的需求量身定制的，甚至不尝试满足研究和临床工具的准确性要求； h）需要此类工具的调查人员面临着开发自己的艰巨任务。我们建议开发一个解决上述所有问题的集成系统。我们的方法基于一种一般的概率公式，它使我们能够将现代统计估计，数值优化和软件工程技术的组合结合在一起。我们认为，开发这种解决方案所需的多种核心方法已经可以使用，尽管在不同的文献中，并且将它们融合在一起的时候了。我们的长期目标是为基于标记的多期估计问题提供一个令人满意的解决方案，并将此处提出的系统纳入一套更大的软件工具中，以用于生物力学分析和仿真，该工具目前正在USC开发。拟议的系统不仅将在我们自己的研究中使用，而且将记录并提供给对复杂的多项自由运动运动感兴趣的其他调查人员。