Pose estimation for a deadlifting workout using Machine Learning

Abstract

" Due to the difficulties in precisely estimating position, fitness technology continues to suffer difficulties in accurately predicting stance during deadlift workouts. The subtle differences in deadlift stances are difficult for conventional computer vision algorithms to capture, which frequently results in erroneous assessments. Moreover, current fitness trackers are unable to deliver accurate data for the dynamic motions involved in deadlifting exercises. The incorporation of advanced machine learning techniques that can comprehend the nuances of human movement is necessary to overcome these constraints. Therefore, using advances in machine learning and visual analysis, this project aims to create a reliable and real-time position estimate system specifically designed for deadlifting workouts. With an emphasis on accurate deadlift posture estimation, this project aims to enhance exercise effectiveness and minimize the risk of injury associated with improper form.

The present research uses a multidisciplinary method that integrates computer vision, machine learning, and fitness technology to address the difficulties in stance prediction for deadlifts. It aims to offer an automatic and precise evaluation of the lifter's stance in real-time by utilizing machine learning algorithms, allowing for prompt feedback on form and technique. The goal is to develop a comprehensive system that can provide actionable insights to improve performance and reduce injury risks, in addition to spotting deviations from optimal form. By addressing a key need in the field of deadlift exercise monitoring and coaching, this research intends to advance fitness technology through the merging of cutting edge technologies and domain-specific expertise."