A Statistical Examination of Pose Correction Strategies for Multidomain Applications

Abstract

Pose estimation and correction is a multidomain problem that includes identifying body key points, tracking these key points via multimodal analysis, making continuous recommendations, and improving poses. This review article presents an overview of recent improvements in posture correction estimating algorithms for human beings. Pose estimate is an important problem in many applications, including fitness tracking, motion analysis, and virtual reality scenarios. The study explores cutting-edge methods for estimating human poses, including deep learning-based approaches, multi-camera systems, and bioinspired models. Furthermore, the study discusses numerous applications of human posture estimation, such as gait analysis, rehabilitation, and sports analysis. The problems of human posture estimation, such as occlusions, limited training data, and differences in body form and size, are also examined for various circumstances.