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."
