Extending 3D markerless tracking for biomechanical analysis of human gait

扩展 3D 无标记跟踪以用于人类步态的生物力学分析

• 批准号: 543855-2019
• 负责人: Deluzio, Kevin
• 金额: $ 5.83万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=720802
• 关键词: Extending; 3D; markerless; tracking; biomechanical

The research conducted in our laboratory uses mathematical models for assessing movement adaptations or compensations associated with osteoarthritis (OA). An important goal is to predict the progression of OA, and to predict the outcomes of surgical interventions for OA for an individual. Achieving this goal of personalized care is challenged by the observation that OA symptoms, progression and clinical outcomes from surgical and non surgical interventions are unique to an individual. Ironically patient specific models require that research studies be conducted with large patient populations in order to classify a broad selection of attributes. For our research, accurate measures of three dimensional human motion, and estimation of the underlying forces that generated such motion, are derived using expensive cameras that track the location of markers placed on the body combined with sensors that measure foot-ground contact forces. The data collection volumes are small, and experimental protocols are time consuming and manpower intensive. More importantly for our research the repeatability of measures is poor because the repeatability and reliability of marker placement is poor, which precludes the large population studies of OA that we need to achieve our goals. HAS-Motion (our industrial partner) has developed a markerless 3D motion capture system that uses an array of standard video cameras and deep neural networks to record human motion consistently and quickly across different environments. This system has the potential to allow us to conduct longitudinal and multicentre studies of OA. Before we can adopt this system we must verify the accuracy, repeatability, and reliability of this system in the context of the class of movements that are important to our research, and to analyze and interpret the data in the context of the signals and models that are commonly used in our research. We also propose to collaborate with HAS-Motion to integrate musculoskeletal models and environmental constraints into their solution. This three year project will support one PDF, one PhD student, two Master's students, one lab technician, and three undergraduate summer students.

我们实验室进行的研究使用数学模型来评估与骨关节炎 (OA) 相关的运动适应或补偿。一个重要的目标是预测 OA 的进展，并预测个体 OA 手术干预的结果。观察到手术和非手术干预的 OA 症状、进展和临床结果对个体而言是独特的，因此实现个性化护理目标面临挑战。具有讽刺意味的是，患者特定模型要求对大量患者群体进行研究，以便对广泛的属性选择进行分类。在我们的研究中，通过使用昂贵的相机来精确测量三维人体运动，并估计产生这种运动的潜在力，这些相机跟踪放置在身体上的标记的位置，并结合测量脚与地面接触力的传感器。数据收集量小，实验方案耗时耗力。对于我们的研究来说，更重要的是，测量的可重复性很差，因为标记放置的可重复性和可靠性很差，这妨碍了我们实现目标所需的 OA 的大规模人群研究。 HAS-Motion（我们的工业合作伙伴）开发了一种无标记 3D 动作捕捉系统，该系统使用一系列标准摄像机和深度神经网络在不同环境中一致、快速地记录人体动作。该系统有潜力让我们能够对 OA 进行纵向和多中心研究。在采用该系统之前，我们必须在对我们的研究很重要的运动类别中验证该系统的准确性、可重复性和可靠性，并在信号和模型的背景下分析和解释数据。我们的研究中经常使用。我们还建议与 HAS-Motion 合作，将肌肉骨骼模型和环境约束集成到他们的解决方案中。这个为期三年的项目将支持一名 PDF 学生、一名博士生、两名硕士生、一名实验室技术员和三名本科生暑期学生。