论文标题
3D基于姿势的体育锻炼反馈
3D Pose Based Feedback for Physical Exercises
论文作者
论文摘要
如果执行不正确，则无监督的自我锻炼练习和体育训练可能会造成严重伤害。我们介绍了一个基于学习的框架，该框架可以识别用户犯的错误，并提出纠正措施，以更轻松，更安全的个人培训。我们的框架不依赖于硬编码的启发式规则。取而代之的是，它从数据中学习，这有助于其适应特定用户需求。为此，我们使用作用于用户姿势序列的图形卷积网络（GCN）体系结构来建模身体关节轨迹之间的关系。为了评估我们的方法，我们介绍了一个具有3种不同体育锻炼的数据集。我们的方法产生了90.9％的错误识别精度，并成功纠正了94.2％的错误。
Unsupervised self-rehabilitation exercises and physical training can cause serious injuries if performed incorrectly. We introduce a learning-based framework that identifies the mistakes made by a user and proposes corrective measures for easier and safer individual training. Our framework does not rely on hard-coded, heuristic rules. Instead, it learns them from data, which facilitates its adaptation to specific user needs. To this end, we use a Graph Convolutional Network (GCN) architecture acting on the user's pose sequence to model the relationship between the body joints trajectories. To evaluate our approach, we introduce a dataset with 3 different physical exercises. Our approach yields 90.9% mistake identification accuracy and successfully corrects 94.2% of the mistakes.
