Integrated system for measuring multijoint movements

用于测量多关节运动的集成系统

• 批准号: 6711104
• 负责人: Emanuel Todorov
• 金额: $ 20.4万
• 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/6711104
• 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）：该项目的目标是开发一种易于使用的数据分析系统，用于快速、准确和鲁棒地估计人体的多关节运动轨迹。此类技术具有多种医学应用，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安装、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的基础运动控制研究持续取得进展的先决条件。运动捕捉硬件已变得广泛使用，并且允许快速且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，可用的数据分析工具落后于这些硬件的进步；估计标记非刚性附着的多关节体的配置仍然是一个具有挑战性的问题。特别是，a）现有方法假设刚性标记附着，并且没有提供对不可避免的软组织变形和误校准造成的误差的估计； b) 将标记放置在预定位置并测量每个受试者的肢体尺寸需要长时间的设置过程； 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放置； d) 由于身体结构而导致的传感器数据冗余很少被用来处理丢失数据、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) 少数现有系统使用更通用的迭代最小化技术，但不能保证实时性能； 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，甚至没有试图满足研究和临床工具的准确性要求； h) 需要此类工具的研究人员面临着开发自己的工具的艰巨任务。我们建议开发一个解决上述所有问题的集成系统。我们的方法基于一般概率公式，这使我们能够应用现代统计估计、数值优化和软件工程技术的组合。我们相信，开发这种解决方案所需的多种核心方法已经存在，尽管文献不同，而且将它们整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，并将此处提出的系统纳入南加州大学目前正在开发的用于生物力学分析和模拟的更大的软件工具套件中。所提出的系统不仅将用于我们自己的研究，还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。