arXiv:2202.14019v3 Announce Type: replace-cross Abstract: Maintaining proper form while exercising is important for preventing injuries and maximizing muscle mass gains. Detecting errors in workout form naturally requires estimating human's body pose. However, off-the-shelf pose estimators struggle to perform well on the videos recorded in gym scenarios due to factors such as camera angles, occlusion from gym equipment, illumination, and clothing. To aggravate the problem, the errors to be detected in the workouts are very subtle. To that end, we propose to learn exercise-oriented image and vi
Advances in computer vision and self-supervised learning are enabling more robust solutions for real-world scenarios like fitness tracking, improving upon previous limitations in varied environments.
Accurate, automated workout form assessment has significant implications for personal health, sports medicine, and the broader fitness industry, potentially reducing injuries and enhancing training efficacy.
The development of domain knowledge-informed AI models makes accurate pose estimation feasible in challenging gym settings, moving beyond lab conditions towards practical application.
- · Fitness app developers
- · Smart gym equipment manufacturers
- · Personal trainers (augmented)
- · Individual exercisers
- · Inferior workout tracking devices
- · Generic pose estimation models (in this domain)
Improved workout form assessment tools become more widely available and accurate, enhancing user safety and performance.
This leads to a greater adoption of AI-driven personalized fitness coaching and injury prevention platforms.
Long-term health outcomes for consistent exercisers could improve, potentially reducing healthcare burdens related to exercise-induced injuries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI