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.
翻译:未经监督的自我康复练习和身体训练如果执行不当,可能会造成严重伤害。 我们引入一个基于学习的框架, 确定用户的错误, 并提出更简单、更安全的个人培训的纠正措施。 我们的框架并不依赖于硬编码、 疲劳主义的规则。 相反, 它从数据中学习它们, 从而便于根据用户的具体需要进行调整。 为此, 我们使用一个图形革命网络(GCN)架构, 以用户的姿势顺序来模拟身体联合轨迹之间的关系。 为了评估我们的方法, 我们引入了一个包含3种不同物理练习的数据集。 我们的方法得出了90.9%的识别错误准确度,并成功纠正了94.2%的错误。