SBIR Phase II: 3D Markerless Motion Capture Technology For Gait Analysis

SBIR 第二阶段：用于步态分析的 3D 无标记运动捕捉技术

• 批准号: 2153138
• 负责人: Andrew Ekelem
• 金额: $ 99.86万
• 依托单位: EVOLUTION DEVICES, INC.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153138&HistoricalAwards=false
• 关键词: SBIR; Phase; II; 3D; Markerless

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project benefitd the 70 million people struggling with gait disorders in the US. Gait analysis monitors how individuals walk to predict or prevent injuries and track rehabilitation progress. Due to the potentially high equipment costs, space requirements, and required technical expertise to collect and interpret results, detailed gait analyses are limited to a few centers and mainly used for surgical decision-making. This project uses Artificial Intelligence (AI) for gait analysis at roughly 70% less than typical costs, as well as offering reduced set-up time and improved workflow efficiency.The proposed project will address the existing technological and commercial barriers that make gait analysis an exclusive clinical tool restricted to only 300 gait labs across the country. This project will develop a cost-effective markerless motion 3D-tracking system. Technical activities include: (1) increase the motion database to a diverse population, including children and patients with mobility impairments; (2) improve the computer vision algorithms to account for multiple individuals within the camera system’s field of view; (3) integrate EMG and Force Plates; (4) implement skeletal geometry estimation from computed tomography; (5) develop a self-calibration system that can operate with HD low cost cameras; and (5) improve accuracy and usability substantially to reduce the expertise required for computational gait analysis.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.

该小企业创新研究 (SBIR) 第二阶段项目的更广泛影响/商业潜力使美国 7000 万名患有步态障碍的人受益，步态分析可监测个人的行走方式，以预测或预防受伤并跟踪康复进度。由于潜在的高设备成本、空间要求以及收集和解释结果所需的技术专业知识，详细的步态分析仅限于少数中心，并且主要用于手术决策，该项目大致使用人工智能（AI）进行步态分析。比典型成本低 70%，并减少了设置时间并提高了工作流程效率。拟议项目将解决现有的技术和商业障碍，这些障碍使步态分析成为仅限于全国仅 300 个步态实验室的独家临床工具。该项目将开发一种经济高效的无标记运动 3D 跟踪系统，技术活动包括：(1) 增加针对不同人群的运动数据库，包括儿童和行动障碍患者；(2) 改进计算机视觉算法以考虑到这一问题。内有多个个体相机系统的视野；(3) 集成肌电图和测力台；(4) 通过计算机断层扫描实现骨骼几何形状估计；(5) 开发可与高清低成本相机一起使用的自校准系统；准确性和可用性大大减少了计算步态分析所需的专业知识。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。