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@article{197216,
        author = {Anirudh},
        title = {A Comprehensive Review on Human Posture Analysis Using Deep Learning and Computer Vision},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {16205-16212},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197216},
        abstract = {Human posture analysis is a fast-growing area within computer vision that focuses on the automatic detection of body joints, recognition of human movement patterns, and decoding of full-body posture from images or videos. It is essential for rehabilitation, physiotherapy, and work ergonomic assessments, besides sports performance evaluation, safety monitoring, and intelligent interactive systems. With the evolution of deep learning, state-of-the-art methodologies have emerged in pose estimation, including keypoint detection using CNNs, transformer architectures, and 3D reconstruction techniques. Practical frameworks have accelerated the adoption of real-time posture tracking, such as Google’s MediaPipe Holistic, which offers pose, face, and hand landmarks in a single lightweight pipeline that can run on both desktop and mobile. This paper summarizes major algorithms, benchmark datasets, and evaluation metrics that define the state of the art in posture estimation and discusses the strengths and weaknesses of existing methods. Despite great improvements in accuracy and robustness, challenges related to real-world performance remain due to factors such as occlusion, depth ambiguity, fast motion and dataset limitations. This paper provides a detailed overview of current progress and describes future research directions toward more adaptive, efficient, and usable systems in real-world scenarios. Ready posture estimation systems are available.},
        keywords = {Posture Estimation, Computer Vision, Deep Learning, Media Pipe, Human Pose Analysis.},
        month = {April},
        }