CIF: Small: Collaborative Research: Geometrical and Statistical Modeling of Space-Time symmetries for Human Action Analysis and Retraining

CIF：小型：协作研究：用于人类行为分析和再训练的时空对称性的几何和统计建模

• 批准号: 1617397
• 负责人: Anuj Srivastava
• 金额: $ 21.69万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617397&HistoricalAwards=false
• 关键词: CIF; Small; Collaborative; Research; Geometrical

This interdisciplinary research aims to advance current understanding and utilization of space-time symmetries in analyzing human movements, using fundamental tools from engineering, geometry, and statistics. Broader applications of this research include home or workplace-based self-reflection of daily activities, promotion of higher efficiency of human movements, and long-term management and/or prevention of movement disorders. While the need for comprehensively and statistically analyzing human kinematics is well chronicled, the current measures are often limited to simplistic quantities such as speeds and acceleration profiles of individual limbs. This project will focus on both spatial and temporal symmetries of limb movements, full body shapes, and complete dynamical actions, for assessment of movements ranging from daily activities to physiotherapeutic exercises. Symmetry has been used in the past, in clinical biomechanics, but in a limited way. This project will develop a comprehensive theory, built on fundamental tools from differential geometry and statistical analysis of geometric objects, to represent, quantify, analyze, and classify motions according to their level of symmetry. The specific forms of symmetry will include spatial reflection, temporal reflection, and space-time glide symmetries. This formulation will incorporate data from various sensing modalities and features, including point trajectories and stick figures from motion capture systems, to shape silhouettes and dynamic textures obtained from video sensors. The project outcomes also include the development of a real-time media-system for movement re-training and reflection of common actions, such as sitting to standing (STS). The proposal brings together a strong and inter-disciplinary team of researchers with expertise in computer vision and action recognition (Turaga), differential geometry and statistics (Srivastava), and somatics and kinesiology (Coleman).

这项跨学科研究旨在利用工程学、几何学和统计学的基本工具，促进当前对时空对称性的理解和利用，以分析人类运动。这项研究的更广泛应用包括基于家庭或工作场所的日常活动自我反思、提高人体运动效率以及运动障碍的长期管理和/或预防。虽然对人体运动学进行全面和统计分析的需求已被详细记录，但当前的测量通常仅限于简单的数量，例如各个肢体的速度和加速度曲线。该项目将重点关注肢体运动的空间和时间对称性、全身形状和完整的动态动作，以评估从日常活动到理疗练习的运动。对称性过去曾用于临床生物力学，但用途有限。该项目将开发一种基于微分几何和几何对象统计分析的基本工具的综合理论，根据运动的对称程度来表示、量化、分析和分类。对称性的具体形式包括空间反射、时间反射和时空滑移对称性。该公式将整合来自各种传感模式和特征的数据，包括来自运动捕捉系统的点轨迹和简笔画，以塑造从视频传感器获得的轮廓和动态纹理。该项目成果还包括开发实时媒体系统，用于运动再训练和常见动作的反映，例如从坐到站（STS）。该提案汇集了一支强大的跨学科研究人员团队，他们拥有计算机视觉和动作识别（Turaga）、微分几何和统计学（Srivastava）以及躯体学和运动机能学（Coleman）方面的专业知识。