Pose Trainer: Correcting Exercise Posture using Pose Estimation

TL;DR

Pose Trainer is an application that leverages pose estimation and machine learning to detect exercise form errors and provide personalized feedback, aiming to improve workout effectiveness and safety.

Contribution

The paper introduces a novel system combining pose estimation with geometric heuristics and machine learning to evaluate and correct exercise postures.

Findings

Successfully detects correct and incorrect exercise poses

Provides personalized feedback for four common exercises

Operates on Windows and Linux with GPU support

Abstract

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.