Real-Time Feedback and Benchmark Dataset for Isometric Pose Evaluation
Journal:
arXiv
Published Date:
Jun 13, 2025
Abstract
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.
