  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.
