Correct Execution of Weightlifting Exercises using Pose Estimation


This thesis is a technical analysis of the weightlifting exercise deadlift, using pose detection and machine learning. Weightlifting is an increasingly popular exercise method with substantial benefits. However, if done incorrectly could lead to injuries. The project aims to research whether machine learning technologies help create a solution that is an alternative to hiring a personal trainer. The technical goal is to recognize correct and incorrect movements from video input by running videos through Google MediaPipe pose detection to gather x, y, and z coordinates. The dataset contains either correct or incorrect video labels to feed the machine learning prediction model to predict whether or not a particular video was correct or incorrect. By doing this, the trained model can clear distinct between correct and incorrect movement, resulting in an alternative to hiring a personal trainer.