论文标题
从在线视频中促进运动学习的姿势估算
Pose Estimation for Facilitating Movement Learning from Online Videos
论文作者
论文摘要
存在许多在线视频教程，以教授诸如练习之类的身体运动。但是，用户在遵循此类视频时缺乏支持来验证其动作的准确性，并且必须依靠自己的看法。为了解决这个问题，我们开发了一个基于Web的应用程序，该应用程序使用在线视频和网络摄像头上的视频输入来执行人类姿势估计，然后向用户提供不同类型的视觉反馈。我们的研究表明，用户的骨骼覆盖在用户的相机供稿上，改善了用户的性能，而用户的骨骼单独使用或带有教练视频的培训师的骨架提供了有限的好处。我们认为，我们的应用程序展示了从在线视频中增强学习身体运动的潜力，并为其他指导系统设计合适的可视化提供了基础。
There exists a multitude of online video tutorials to teach physical movements such as exercises. Yet, users lack support to verify the accuracy of their movements when following such videos and have to rely on their own perception. To address this, we developed a web-based application that performs human pose estimation using both video inputs from the online video and web camera, then provides different types of visual feedback to a user. Our study suggests that the user's skeleton overlaid on the user's camera feed improved user performance, whereas the user's skeleton on its own or trainer's skeleton with the trainer video offered limited benefits. We believe that our application demonstrates the potential to enhance learning physical movements from online videos and provides a basis for other guidance systems to design suitable visualizations.
