Smartphone Application-Based Markerless Motion Capture and Biomechanical Analysis of Athletes using a Deep Learning Model

使用深度学习模型对运动员进行基于智能手机应用的无标记动作捕捉和生物力学分析

• 批准号: 486618
• 负责人: Attias Cedric
• 金额: $ 1.27万
• 依托单位: University of Waterloo (Ontario)
• 依托单位国家: 加拿大
• 项目类别: Studentship Programs
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-12-01 至 2023-12-01
• 项目状态: 已结题
• 来源: https://webapps.cihr-irsc.gc.ca/decisions/p/project_details.html?applId=486618&lang=en
• 关键词: Athletic Performance; Biomechanical Analysis; Deep Convolutional Neural Networks; Deep Learning; Injury Prevention; Keypoint Estimation; Markerless Motion Capture; Motion Tracking; Smartphone Application; Technique; Athletic Performance; Biomechanical Analysis; Deep Convolutional Neural Networks; Deep Learning; Injury Prevention; Keypoint Estimation; Markerless Motion Capture; Motion Tracking; Smartphone Application; Technique

To quantitatively assess athletic performance, motion capture (MC) technology can be leveraged to provide biomechanical feedback to athletes, coaches, and sports engineers. MC is the process of digitally encoding three-dimensional movements of objects, tr

为了定量评估运动表现，可以利用运动捕获（MC）技术为运动员，教练和体育工程师提供生物力学反馈。 MC是数字编码对象的三维运动的过程