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: 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=742847
• 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，这些工具昂贵、笨重，需要训练有素的专家，而且采收率有限；因此，它们的使用仅限于实验室环境。为了克服这些限制，在过去的几十年里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。IMU虽然前景光明，但由于环境影响、校准问题和数据融合算法优化过程等因素，容易出现各种可靠性问题。因此，它们在人体运动学方面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功能的能力值得怀疑。视频和惯性数据的互补性已在机器人导航等不同领域得到强调，但其潜力尚未真正研究用于移动评估。我的研究计划的长期目标是开发新的系统和算法，以便更好地捕捉和理解流动性问题。该研究计划旨在开发基于视频和惯性数据（VIMU）组合的人体运动学评估创新系统。具体来说，将考虑两个互补的应用框架：基于观察者的VIMU系统和可穿戴的VIMU工具。两者都将使用内置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，在各种环境中动态跟踪和评估一个人的运动将成为可能，包括复杂的条件（例如，平坦/倾斜的行走路径，楼梯上升/下降）。第二种方法考虑开发一种可穿戴的VIMU工具，用于移动性评估。明确地说，VIMU系统将跟踪环境特征，并将其与惯性信息相结合，以确定姿态的变化。可穿戴方法将首先集中于半控制条件，包括临床认可的活动能力测试，向自由生活的客观活动能力评估迈进。这两种方法都将解决与校准过程（相机外在/内在参数、传感器调谐、参考帧识别）、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，本建议解决了技术问题，以便能够开发创新的VIMU系统，以评估人类的流动性，并使其使用民主化。