  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.
