Markerless motion capture equipment for the development of a multi-centre biomechanical analysis tool

用于开发多中心生物力学分析工具的无标记动作捕捉设备

• 批准号: RTI-2022-00451
• 负责人: Deluzio, Kevin
• 金额: $ 8.35万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments
• 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=743068
• 关键词: Markerless; motion; capture; equipment; development

Human movement researchers across Canada have an opportunity to reimagine what data collection looks like with innovative new technology. Using a machine learning-based markerless motion capture system that extracts biomechanical data from synchronized videos, we are developing the next generation of biomechanical analysis tools. This equipment grant will support the purchase of the optimal video camera systems for markerless motion capture. One system will be used in the Human Mobility Research Laboratory of Dr. Kevin Deluzio, who is an experienced human motion researcher and a pioneer in the adoption of markerless technology. Two additional systems will be a set of mobile systems for our collaborator and for remote site data collection. This will allow us to take a motion capture lab to any site. The result will be the most cost-effective and least invasive way of collecting human movement data. By removing physical barriers to data collection, we can partner with our collaborator Dr. Janie Wilson at the Dynamics of Human Motion Laboratory at Dalhousie University to do multi-site biomechanics research. Markerless motion capture provides a feasible way to collect biomechanical data with a level of reliability that is not achievable by legacy technologies, unlocking the potential to collect these data at an epidemiological scale. Building biomechanical analysis tools from large datasets collected on diverse populations, including individuals with musculoskeletal diseases, will ensure the development of robust and effective tools. This type of research on vulnerable patients has been severely limited by the nature of the preceding marker-based technology and underscores the forthcoming expansion of human mobility research resulting from this new technology. This research (currently supported by NSERC-DG and NSERC-CRD grants) will be severely impacted by a delay in acquisition of equipment as the requested camera systems will replace existing equipment that is not suitable for the proposed work due to limits in the area that can be recorded. Over the next five years it is projected that 50 HQPs will use these systems in addition to the 8 current post-graduates that have research planned and ongoing working with the system. Markerless motion capture marks a major advancement in the field of biomechanics and will have an incredible impact on both the progression of fundamental research and the application of this research to musculoskeletal diseases, clinical movement disorders, injuries, and athletic performance. By supporting the purchase of markerless motion capture video camera systems, the Human Mobility Research Laboratory in partnership with Dynamics of Human Motion Laboratory will lead the way towards the development of biomechanical analysis tools that can be used for future translational research.

加拿大各地的人体运动研究人员有机会用创新的新技术重新想象数据收集的样子。使用基于机器学习的无标记动作捕捉系统，从同步视频中提取生物力学数据，我们正在开发下一代生物力学分析工具。这笔设备补助金将用于购买最佳的视频摄像系统，用于无标记运动捕捉。Kevin Deluzio博士是一位经验丰富的人体运动研究人员，也是采用无标记技术的先驱，他的人类移动研究实验室将使用一个系统。另外两个系统将是一组移动系统，用于我们的合作者和远程站点数据收集。这将使我们能够将动作捕捉实验室带到任何地点。其结果将是收集人体运动数据的最具成本效益和侵入性最小的方法。通过消除数据收集的物理障碍，我们可以与达尔豪斯大学人体运动动力学实验室的Janie Wilson博士合作，进行多地点的生物力学研究。无标记动作捕捉提供了一种可行的方法来收集生物力学数据，其可靠性是传统技术无法实现的，释放了在流行病学规模上收集这些数据的潜力。从不同人群（包括患有肌肉骨骼疾病的个体）收集的大型数据集构建生物力学分析工具，将确保开发出强大而有效的工具。这种针对脆弱患者的研究受到了先前基于标记的技术的严重限制，并强调了这项新技术所带来的人类流动性研究的即将扩展。这项研究（目前由NSERC-DG和NSERC-CRD资助）将受到设备采购延迟的严重影响，因为所要求的摄像系统将取代现有的设备，这些设备由于可以记录的区域的限制而不适合拟议的工作。在未来五年内，预计将有50名hqp使用这些系统，此外还有8名目前正在研究和正在使用该系统的研究生。无标记运动捕捉标志着生物力学领域的重大进步，将对基础研究的进展以及该研究在肌肉骨骼疾病、临床运动障碍、损伤和运动表现方面的应用产生不可思议的影响。通过支持购买无标记运动捕捉视频摄像机系统，人类移动研究实验室与人类运动动力学实验室合作，将引领生物力学分析工具的发展，这些工具可用于未来的转化研究。