CHS: Medium: Data Driven Biomechanically Accurate Modeling of Human Gait on Unconstrained Terrain

CHS：中：数据驱动的无约束地形上人类步态的生物力学精确建模

• 批准号: 1703883
• 负责人: Dimitris Metaxas
• 金额: $ 118.25万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1703883&HistoricalAwards=false
• 关键词: CHS; Medium; Data; Driven; Biomechanically

Modeling human gait parametrically, efficiently and accurately is an open and challenging problem with many applications such as ergonomics, animation, biomechanics, rehabilitation, physical therapy, virtual reality and entertainment. Gait is a complex process, because numerous human joint degrees-of-freedom have to be simultaneously coordinated and adapted to varying types of terrain, types of gait and related kinematics and dynamics. As a consequence, no general-purpose models of gait kinematics on unconstrained complex terrain exist, and even basic kinematic gait databases and related ground forces that can form the basis for developing such models do not provide accurate information. Most gait data are collected by the commonly used surface marker systems, which suffer from soft tissue artifacts and are not sufficient for a full understanding of gait kinematics. Developing a parameterized human gait for unconstrained terrain will have broad impact on the fields of virtual reality, next generation shoe design, computer animation, workplace safety, ergonomics, sports medicine, biomedical and clinical research to aid people with gait abnormalities. Project outcomes, including algorithms and datasets, will ultimately be incorporated in the curricula of many fields including computer science and biomedical engineering, and will result in a better informed community of people working on gait modeling and its applications.Using recently purchased novel all-terrain gait treadmills, motion capture, video cameras, ground reaction forces based on force plates, and high speed ultrasonic images of knee and ankle joints, the PI and his team have collected gait data from multiple people. The first goal of this project is to integrate all of this data by exploiting novel computer vision and multimodal optimization algorithms to produce an anatomically correct human skeleton with human specific joints, human shape, ground reaction forces and kinematic data. The second goal is to develop an efficient, accurate, and general purpose parameterized model of human gait kinematics and biomechanics capable of reproducing the data, and more importantly, predicting and generalizing human specific gait for new types of terrain. The gait variations to be modeled range from very slow shuffling to normal walking on a variety of terrain conditions such as inclined and declined slope, left and right cross-slope, and up and down stairs.

对于许多应用，诸如人体工程学，动画，生物力学，康复，物理疗法，虚拟现实和娱乐等许多应用，对人类步态进行参数，有效和精确的建模是一个开放而充满挑战的问题。 步态是一个复杂的过程，因为必须同时协调许多人类联合自由度，并适应各种类型的地形，步态类型和相关的运动学和动力学。 结果，没有在不受限制的复杂地形上的步态运动学的通用模型，甚至基本的运动运动步态数据库和相关基础力也可以构成开发此类模型的基础的基础。 大多数步态数据是由常用的表面标记系统收集的，这些标记系统患有软组织伪像，并且不足以完全了解步态运动学。 为无限制地形开发参数化的人体步态将对虚拟现实，下一代鞋类设计，计算机动画，工作场所安全，人体工程学，运动医学，生物医学和临床研究的领域产生广泛的影响，以帮助步态异常的人。 项目成果（包括算法和数据集）最终将最终纳入许多领域的课程中，包括计算机科学和生物医学工程，并将导致一个更好地了解步态建模及其应用的人的知情社区。最近购买了新的全地形步态步态跑步机，基于摄像机，高度且高度的高速公路和高速倍增的全地形步态跑步机，并具有较高的图像Pi和他的团队从多个人那里收集了步态数据。 该项目的第一个目标是通过利用新颖的计算机视觉和多模式优化算法来整合所有这些数据，以产生带有人类特异性关节，人形，地面反应力和运动学数据的解剖上正确的人类骨架。 第二个目标是开发一种能够再现数据的人类步态运动学和生物力学的有效，准确和通用的参数化模型，更重要的是，预测和推广了针对新型Terrain的人类特定步态。 在各种地形条件下，斜坡，左右交叉斜坡以及上下楼梯等各种地形条件下，要建模的步态变化范围从非常缓慢的改组到正常行走。

项目成果

A Social Distancing Index: Evaluating Navigational Policies on Human Proximity using Crowd Simulations

社交距离指数：使用人群模拟评估人类接近度的导航政策

• DOI: 10.1145/3424636.3426905
• 发表时间: 2020
• 期刊: ACM SIGGRAPH Motion Interaction and Games
• 作者: Usman, Muhammad;Lee, Tien-Chi;Moghe, Ryhan;Zhang, Xun;Faloutsos, Petros;Kapadia, Mubbasir
• 通讯作者: Kapadia, Mubbasir

Effective 3D humerus and scapula extraction using low-contrast and high-shape-variability MR data

使用低对比度和高形状变异性 MR 数据有效提取 3D 肱骨和肩胛骨

• DOI: 10.1117/12.2513107
• 发表时间: 2019
• 期刊: and Functional Imaging
• 作者: He, Xiaoxiao;Tan, Chaowei;Qiao, Yuting;Tan, Virak;Metaxas, Dimitris;Li, Kang;Gimi, Barjor;Krol, Andrzej
• 通讯作者: Krol, Andrzej

Assessing crowd management strategies for the 2010 Love Parade disaster using computer simulations and virtual reality

• DOI: 10.1098/rsif.2020.0116
• 发表时间: 2020-06
• 期刊: Journal of the Royal Society Interface
• 影响因子: 3.9
• 作者: Hantao Zhao;Tyler Thrash;M. Kapadia;Katja Wolff;C. Hölscher;D. Helbing;Victor R. Schinazi

Knowledge As Priors: Cross-Modal Knowledge Generalization for Datasets Without Superior Knowledge

• DOI: 10.1109/cvpr42600.2020.00656
• 发表时间: 2020-04
• 期刊: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• 作者: Long Zhao;Xi Peng;Yuxiao Chen;M. Kapadia;Dimitris N. Metaxas

Generation of crowd arrival and destination locations/times in complex transit facilities

生成复杂交通设施中的人群到达和目的地位置/时间

• DOI: 10.1007/s00371-019-01761-z
• 发表时间: 2019
• 期刊: The Visual Computer
• 作者: Ricks, Brian;Dobson, Andrew;Krontiris, Athanasios;Bekris, Kostas;Kapadia, Mubbasir;Roberts, Fred
• 通讯作者: Roberts, Fred