| Title |
Biomac3D: 2D-to-3D human pose analysis model for tele-rehabilitation based on pareto optimized deep-learning architecture / |
| Authors |
Maskeliūnas, Rytis ; Kulikajevas, Audrius ; Damaševičius, Robertas ; Griškevičius, Julius ; Adomavičienė, Aušra |
| DOI |
10.3390/app13021116 |
| Full Text |
|
| Is Part of |
Applied sciences.. Basel : MDPI. 2023, vol. 13, iss. 2, art. no. 1116, p. 1-32.. ISSN 2076-3417 |
| Keywords [eng] |
Pareto optimization ; 2D to 3D ; human posture analysis ; remote rehabilitation ; telehealth |
| Abstract [eng] |
The research introduces a unique deep-learning-based technique for remote rehabilitative analysis of image-captured human movements and postures. We present a ploninomial Pareto-optimized deep-learning architecture for processing inverse kinematics for sorting out and rearranging human skeleton joints generated by RGB-based two-dimensional (2D) skeleton recognition algorithms, with the goal of producing a full 3D model as a final result. The suggested method extracts the entire humanoid character motion curve, which is then connected to a three-dimensional (3D) mesh for real-time preview. Our method maintains high joint mapping accuracy with smooth motion frames while ensuring anthropometric regularity, producing a mean average precision (mAP) of 0.950 for the task of predicting the joint position of a single subject. Furthermore, the suggested system, trained on the MoVi dataset, enables a seamless evaluation of posture in a 3D environment, allowing participants to be examined from numerous perspectives using a single recorded camera feed. The results of evaluation on our own self-collected dataset of human posture videos and cross-validation on the benchmark MPII and KIMORE datasets are presented. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2023 |
| CC license |
|
