Pose estimation and correction are multidomain tasks that involve identification of body keypoints, tracking these keypoints via multimodal analysis, continuous recommendations, and pose improvements. This review paper provides an overview of the recent advances in such pose correction estimation methods for human subjects. Pose estimation is a critical task for various applications, including fitness tracking, motion analysis, and virtual reality scenarios. The paper discusses the state-of-the-art methods used for human pose estimation, such as deep learning-based approaches, multi-camera systems, and bioinspired models. Additionally, the paper covers various applications of human pose estimation, including gait analysis, rehabilitation, and sports analysis. The challenges in human pose estimation, including occlusions, limited training data, and variations in body shape and size, are also discussed for different scenarios. This paper also compares existing models in terms of different metrics including accuracy, precision, computational complexity, delay, and scalability levels. This will assist readers to identify optimal models for different performance-specific use cases. This text also proposes calculation of a novel Pose Correction Rank Metric (PCRM), which combines all these metrics for individual models. Evaluation of this metric will assist readers to identify optimal models that can be suited for high-performance scenarios. The paper concludes by identifying future research recommendations and potential applications of human pose correction estimations.