Video Based Exercise Recognition Using GCN

Researchers frequently employ artificial intelligence models to estimate human pose. Pose recognition can be used to monitor and supervise personal workout sessions. Exercises for self-rehabilitation without supervision and improper physical training can result in significant injury if the procedure is not followed correctly. The majority of the work that has formerly been done on exercise classification depends on sensors that are either worn or externally attached. However, these sensors frequently are unable to discern between similar workouts. Additionally, they are rarely ideal due to a variety of other issues, including connectivity, defective sensors, excessive sweating, expense, and improper alignment. This study classifies each person’s workouts before predicting whether or not the posture that corresponds with each exercise is correct. The person’s pose sequence and ten other novel features are used to train a Graph Convolutional Network (GCN) architecture to capture the relationship between the 2D coordinates of the body joints, their angles with key limbs, and a specific workout. We introduce a dataset with 2 different physical exercises (squats and lunges) to assess our methodology. Our method produces recognition accuracy for the proper workout postures for lunges and squats of 94.44% and 98.65% respectively.