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Pose Trainer: Correcting Exercise Posture using Pose Estimation
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Fitness exercises are very beneficial to personal health and fitness; however, they can also be ineffective and potentially dangerous if performed incorrectly by the user. Exercise mistakes are made when the user does not use the proper form, or pose. In our work, we introduce Pose Trainer, an application that detects the user's exercise pose and provides personalized, detailed recommendations on how the user can improve their form. Pose Trainer uses the state of the art in pose estimation to detect a user's pose, then evaluates the vector geometry of the pose through an exercise to provide useful feedback. We record a dataset of over 100 exercise videos of correct and incorrect form, based on personal training guidelines, and build geometric-heuristic and machine learning algorithms for evaluation. Pose Trainer works on four common exercises and supports any Windows or Linux computer with a GPU.
Forward citations
Cited by 3 Pith papers
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Real-Time Feedback and Benchmark Dataset for Isometric Pose Evaluation
A new multiclass isometric exercise video dataset and benchmark, with a three-part metric that favors a simple angle-based classifier over graph networks for reliable mistake detection.
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HuMoCon: Concept Discovery for Human Motion Understanding
A framework that combines explicit video-motion feature alignment with velocity-aware masked autoencoding to improve LLM-based human motion and video question answering.
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PosePilot: An Edge-AI Solution for Posture Correction in Physical Exercises
A yoga posture correction system that combines LSTM pose recognition with BiLSTM angle forecasting to flag deviations in real time on edge hardware.
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