GYMetricPose: A light-weight angle-based graph adaptation for action quality assessment

Improving the overall quality of exercise is crucial for achieving effective and safe techniques during gym workouts. Moreover, identifying errors during workouts can optimize training benefits and minimize the risk of injury. In this paper, we propose a cross-domain method to exploit angle information in human pose skeletons, aiming to detect fine-grained posture problems in complex real-world environments. Specifically, we integrate the Geometric Representation Extraction (GRE) module along with transformer-based pose estimation. Our approach demonstrates efficacy on the Fitness-AQA dataset, which comprises authentic exercise samples captured in real-world gym settings. This performance is achieved after pose estimation with approximately 164k parameters in its base configuration. The experimental results highlight that our method is a competitive approach compared to self-supervised video/image approaches in complex environments. In the Back Squat exercise, our method outperforms Motion Disentangling (MD) in detecting Knee Inward Error (KIE) with an F1-score of 0.4398. For Static Shallow Squat Error, it achieved the second-best F1-score of 0.8677, just 0.0017 below Cross-View Cross-Subject Pose Contrastive Learning (CVCSPC). In the Overhead Press exercise, the method significantly improved the detection of Knee error, achieving an Fl-score of 0.8160, surpassing CVCSPC and other methods. Overall, these results demonstrate that the proposed method provides competitive performance compared to the state-of-the-art models while using 187x fewer parameters than the model with the highest performance in the AQA dataset, the Motion Disentangling (MD) approach. Code will be available at: https://github.com/CaroFernando/G3inPose