RI: Medium: From Vision to Dynamics

在健康和体育领域，对人体运动的详细分析为个性化的成绩评估、训练和健康评估提供指导。用于人体运动的生物力学分析的数据收集通常使用昂贵的专门硬件来执行，这需要在受限的实验室环境中进行实验。使用摄像机进行数据采集的计算机视觉算法能够以最低的成本在更自然的环境中进行观察，但目前基于视觉的人体姿势和运动估计在生物力学上并不准确，并且缺乏人类运动背后的物理力的量化模型。这项研究将训练现代机器学习系统，以对生物力学上准确的身体姿势、运动和力量进行基于图像的估计，将非正式的计算机视觉转化为科学测量工具，使用现成的低成本摄像机在不受限制的日常环境中研究人类的运动和稳定性。这些结果将使个性化培训和预防医学方面的新应用成为可能，并导致提高老年人的灵活性和稳定性、降低跌倒导致的死亡率和提高生活质量的战略，服务于NSF促进科学进步以促进国民健康的使命。一个涵盖人类和机器人稳定性主题的推广计划将通过结合运动、增强现实和类人机器人的演示来激励K-12学生。该项目团队将收集一个独特的、多模式的人类运动数据集，并使用它来训练一个新的深度学习系统，该系统将多任务回归与生物力学人类运动模型相结合，仅从视觉数据就可以估计准确的姿势和运动以及动力学，如肌肉扭矩和地面反作用力。这些结果将填补运动学视觉观察和人体动力学推理之间的现有空白，极大地提高计算机视觉对需要动力学信息的平衡和稳定性进行推理的能力。数据驱动的发现与稳定性相关或有因果关系的歧视性特征将导致识别运动序列中最能增强稳定性的姿势/动作，以及估计运动中人的稳定性的新措施。在科学研究的指导下，将探索四个应用：1)开发基于视觉的稳定性监控系统，可以量化和监控稳定性的变化；2)开发基于视觉的个人教练，用于教学增强稳定性的练习；3)分析历史体育视频，以揭示表演风格和动力学的变化；以及4)研究在保持稳定性的同时将人类运动重新定位到类人机器人的方法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

In the realms of health and sports, detailed analysis of human movement provides guidance for individualized performance assessment, training, and health evaluation. Data collection for biomechanical analysis of human motion is typically performed using expensive, specialized hardware that requires experiments to be carried out in a constrained lab setting. Computer vision algorithms that use video cameras for data collection enable observation in more natural environments with minimal cost, but current vision-based estimates of body pose and motion are not biomechanically accurate and lack a quantitative model of the physical forces underlying human movement. This research effort will train a modern machine learning system to perform imagery-based estimation of biomechanically accurate body pose, motion and force, turning informal computer vision into a scientific measurement tool for human movement and stability research in unconstrained, everyday settings using readily available low-cost video cameras. The results will enable novel applications in personalized training and preventative medicine, and lead to strategies for improving flexibility and stability among the elderly, reducing fall-induced mortality and improving quality of life, serving NSF's mission to promote progress of science to advance the national health. An outreach program covering topics in human and robot stability will inspire K-12 students with demonstrations that combine exercise, augmented reality, and humanoid robots.The project team will collect a unique, multi-modal human motion dataset and use it to train a novel deep learning system that combines multi-task regression with a biomechanical human motion model to estimate, from visual data alone, accurate pose and motion as well as dynamics such as muscle torques and ground reaction forces. The results will fill an existing gap between visual observation of kinematics and the inference of body dynamics, greatly improving the ability of computer vision to make inferences about balance and stability that require dynamics information. Data-driven discovery of discriminative features that are causal or correlated to stability will lead to identifying the most stability-enhancing poses/moves for a movement sequence and new measures for estimating stability of a person in motion. Guided by the scientific research, four applications will be explored: 1) Developing a vision-based stability surveillance system that can quantify and monitor changes in stability; 2) Developing a vision-based personal trainer for teaching stability-enhancing exercises; 3) Analyzing historical sports videos to reveal changes in performance styles and dynamics; and 4) Studying methods for retargeting human movements to a humanoid robot while maintaining stability.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.