TY  - EJOU
AU  - Bataineh, Ahmad Mwfaq 
AU  - Mohamed, Ahmad Sufril Azlan 

TI  - Monocular 3D Human Pose Estimation for REBA Ergonomics: A Critical Review of Recent Advances
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 84
IS  - 1
SN  - 1546-2226

AB  - Advancements in deep learning have considerably enhanced techniques for Rapid Entire Body Assessment (REBA) pose estimation by leveraging progress in three-dimensional human modeling. This survey provides an extensive overview of recent advancements, particularly emphasizing monocular image-based methodologies and their incorporation into ergonomic risk assessment frameworks. By reviewing literature from 2016 to 2024, this study offers a current and comprehensive analysis of techniques, existing challenges, and emerging trends in three-dimensional human pose estimation. In contrast to traditional reviews organized by learning paradigms, this survey examines how three-dimensional pose estimation is effectively utilized within musculoskeletal disorder (MSD) assessments, focusing on essential advancements, comparative analyses, and ergonomic implications. We extend existing image-based classification schemes by examining state-of-the-art two-dimensional models that enhance monocular three-dimensional prediction accuracy and analyze skeleton representations by evaluating joint connectivity and spatial configuration, offering insights into how structural variability influences model robustness. A core contribution of this work is the identification of a critical research gap: the limited exploration of estimating REBA scores directly from single RGB images using monocular three-dimensional pose estimation. Most existing studies depend on depth sensors or sequential inputs, limiting applicability in real-time and resource-constrained environments. Our review emphasizes this gap and proposes future research directions to develop accurate, lightweight, and generalizable models suitable for practical deployment. This survey is a valuable resource for researchers and practitioners in computer vision, ergonomics, and related disciplines, offering a structured understanding of current methodologies and guidance for future innovation in three-dimensional human pose estimation for REBA-based ergonomic risk assessment.
KW  - Human posture estimation; deep neural networks; three-dimensional analysis; benchmark datasets; rapid entire body assessment (REBA)

DO  - 10.32604/cmc.2025.064250