Video and inertial (VIMU) motion capture systems for human movement assessment

用于人体运动评估的视频和惯性 (VIMU) 运动捕捉系统

• 批准号: RGPIN-2021-04059
• 负责人: Lebel, Karina
• 金额: $ 2.04万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=754132
• 关键词: Video; inertial; VIMU; motion; capture

For years now, optoelectronic systems have been considered the ground truth for human kinematics assessment. Yet, these tools are expensive, cumbersome, require trained specialists, and provide a restricted volume of acquisition; their use is thus limited to laboratory settings. To overcome these limitations, new approaches for kinematics assessment arose in the past decades, including wearable inertial measurement units (IMU). Composed of accelerometers, gyroscopes and magnetometers, IMU allow movement to be assessed in a variety of environments such as lab, clinic, and home. Although promising, IMU are prone to various reliability issues due to environmental effects, calibration problems, and data fusion algorithms optimization process. Hence, their true potential for human kinematics is currently limited. Human pose can also be estimated from videos, though these methods were mainly developed for animation purposes; their ability to provide accurate and reliable mobility features from a clinical perspective is thus questionable. The complementarity of video and inertial data has been highlighted in different fields such as robotic navigation, but its potential has not truly been investigated for mobility assessment yet. The long-term objective of my research program is to develop new systems and algorithms to allow better capture and understanding of mobility issues. This research proposal aims at developing innovative systems for human kinematics assessment based on a combination of video and inertial data (VIMU). Specifically, two complementary frameworks of applications will be considered: an observer-based VIMU system and a wearable VIMU tool. Both will make use of action sports cameras with embedded IMU. The first approach will tackle the challenges associated with VIMU movement assessment from an observer's standpoint. Thus, dynamic tracking and assessment of a person's motion in various environments will be enabled, including complexed conditions (e.g., flat/inclined walking paths, stair ascent/descent). The second approach considers the development of a wearable VIMU tool for mobility assessment. Explicitly, the VIMU system will track environmental features and combine it with inertial information to determine changes in posture. The wearable approach will first concentrate on semi-controlled conditions, including clinically recognized mobility tests, moving towards free-living objective mobility assessment. Both approaches will tackle technological challenges associated with calibration process (cameras extrinsic/intrinsic parameters, sensors tuning, reference frame identification), human movement assessment algorithms (human recognition, task identification and segmentation, kinematics parameters assessment), and include development of autonomous quality feedback algorithms. This proposal thus addresses the technical issues to enable development of innovative VIMU systems for human mobility assessment, and to democratize their use.

多年来，光电系统一直被认为是人体运动学评估的基础。然而，这些工具是昂贵的，笨重的，需要训练有素的专家，并提供有限的采集量;因此，它们的使用仅限于实验室环境。为了克服这些限制，在过去的几十年中出现了新的运动学评估方法，包括可穿戴惯性测量单元（IMU）。IMU由加速度计、陀螺仪和磁力计组成，可以在实验室、诊所和家庭等各种环境中评估运动。惯性测量组合虽然前景广阔，但由于环境影响、标定问题和数据融合算法优化过程等原因，容易出现各种可靠性问题。因此，它们对人类运动学的真正潜力目前是有限的。人体姿势也可以从视频中估计，尽管这些方法主要是为了动画目的而开发的;因此，从临床角度来看，它们提供准确可靠的移动性特征的能力是值得怀疑的。视频和惯性数据的互补性已经在不同的领域，如机器人导航中得到了强调，但其潜力还没有真正被调查的流动性评估。我的研究计划的长期目标是开发新的系统和算法，以更好地捕捉和理解移动性问题。本研究旨在开发基于视频和惯性数据（VIMU）相结合的人体运动学评估创新系统。具体而言，将考虑两个互补的应用框架：基于嵌入式的VIMU系统和可穿戴VIMU工具。两者都将使用带有嵌入式IMU的运动相机。第一种方法将从观察者的角度解决与VIMU运动评估相关的挑战。因此，将实现对人在各种环境中的运动的动态跟踪和评估，包括复杂条件（例如，平坦/倾斜的步行路径、楼梯上升/下降）。第二种方法考虑开发可穿戴VIMU工具进行移动性评估。VIMU系统将跟踪环境特征，并将其与惯性信息联合收割机结合起来，以确定姿态的变化。可穿戴方法将首先集中在半受控条件下，包括临床认可的移动性测试，转向自由生活的客观移动性评估。这两种方法都将解决与校准过程（相机外部/内部参数，传感器调整，参考系识别），人体运动评估算法（人体识别，任务识别和分割，运动学参数评估）相关的技术挑战，并包括自主质量反馈算法的开发。因此，该提案解决了技术问题，以开发用于人类移动性评估的创新VIMU系统，并使其使用民主化。