Biomac3D: 2D-to-3D Human Pose Analysis Model for Tele-Rehabilitation Based on Pareto Optimized Deep-Learning Architecture

The research introduces a unique deep-learning-based technique for remote rehabilitative analysis of image-captured human movements and postures. We present ploninomial Pareto-optimized deep-learning architecture processing inverse kinematics sorting out rearranging skeleton joints generated by RGB-based two-dimensional (2D) recognition algorithms, with the goal producing full 3D model as final result. suggested method extracts entire humanoid character motion curve, which is then connected to three-dimensional (3D) mesh real-time preview. Our maintains high joint mapping accuracy smooth frames while ensuring anthropometric regularity, mean average precision (mAP) 0.950 task predicting position single subject. Furthermore, system, trained on MoVi dataset, enables seamless evaluation posture in environment, allowing participants be examined from numerous perspectives using recorded camera feed. results our own self-collected dataset videos cross-validation benchmark MPII KIMORE datasets are presented.