Real-Time Feedback and Benchmark Dataset for Isometric Pose Evaluation
This addresses the need for expert-level corrective feedback in home fitness training, with potential applications in rehabilitation and physiotherapy, though it is incremental as it builds on existing models and datasets.
The paper tackles the problem of unreliable digital media for isometric exercises by presenting a real-time feedback system, resulting in the release of the largest multiclass isometric exercise video dataset with over 3,600 clips and a novel three-part metric for evaluation.
Isometric exercises appeal to individuals seeking convenience, privacy, and minimal dependence on equipments. However, such fitness training is often overdependent on unreliable digital media content instead of expert supervision, introducing serious risks, including incorrect posture, injury, and disengagement due to lack of corrective feedback. To address these challenges, we present a real-time feedback system for assessing isometric poses. Our contributions include the release of the largest multiclass isometric exercise video dataset to date, comprising over 3,600 clips across six poses with correct and incorrect variations. To support robust evaluation, we benchmark state-of-the-art models-including graph-based networks-on this dataset and introduce a novel three-part metric that captures classification accuracy, mistake localization, and model confidence. Our results enhance the feasibility of intelligent and personalized exercise training systems for home workouts. This expert-level diagnosis, delivered directly to the users, also expands the potential applications of these systems to rehabilitation, physiotherapy, and various other fitness disciplines that involve physical motion.