Human pose detection for exercise assistance: A comprehensive survey

Human pose detection plays a pivotal role in various applications, particularly in the context of exercise and fitness monitoring. This survey paper provides an in-depth analysis of the state-of-the-art techniques and advancements in human pose detection for exercise-related activities. We categorize and review key methodologies and evaluation metrics used in this domain. Beginning with an overview of the importance of human pose detection in exercise, we delve into the evolution of pose estimation methods, from traditional computer vision techniques to the rise of deep learning-based approaches. We discuss the challenges specific to exercise scenarios, such as real-time performance, multi-person tracking, and robustness in varied environments. This survey not only summarizes the current state of the art but also identifies emerging trends and future directions in human pose detection for exercise. We discuss the proposed methodology for human pose detection for exercises. By providing a comprehensive overview of the field, this survey paper aims to serve as a valuable resource for researchers, practitioners, and fitness technology developers interested in leveraging human pose detection for exercise monitoring and improvement.