## 5 Discussion and Conclusions
Additionally, the extracted joints are then passed to the IK prediction network where the predicted joint rotations can be bound to a human skeleton for further visualization. Naturally, a number of other issues exist that must be addressed in the near future, in order to further improve the robustness of the suggested approach. For example, models may generate pose candidates with symmetrical limbs or lose hands if they are concealed behind the back. Although the model output might still provide a realistic 3D pose, it may not be appropriate for thoroughmedical assessment. Finally, occlusions and self-occlusions provide the greatest challenge in pose estimation; however, they may be addressed in part by using a specialized model or semantic segmentation and data restoration [112; 113] or by using a partial UV map to depict an object-occluded human shape [114]. One significant shortcoming of such evaluation systems is that they still need users to conduct exercises while standing (or sitting) in front of a smartphone selfie camera. Musculoskeletal sufferers may find it challenging to operate the equipment and do exercises in such a restricted environment. In such circumstances, real patients' compliance may be low. The accuracy level of all marker-less human posture estimate systems, including BIOMAC, is a crucial concern. A potential decline in accuracy is possible in uncontrolled real-world home usage, which can be large due to the broad and vastly different expressions of aberrant or impaired human motions recuperating from major health issues. Furthermore, the system cannot forecast what will happen when the user's hands are behind his back or his torso is twisted at an angle that is not visible to the camera, and the AI technique lacks data to adjust and recompensate within the recovered 3D model, for example when measuring the angle of the knee joint, where a person may stand up with his or her side towards the camera. The model would first calculate the coordinates of the hip, knee, and ankle joints. These coordinates form a triangular construct, and after computing the Euclidean distances between all coordinates, the rule of cosines is used to estimate all corners. The accuracy with which such joint coordinates are computed is critical, since it might affect the overall representation and analysis of rehabilitation performance. In the near future, additional in-depth research into the movement and function of more sophisticated joints, such as hips, wrists, and shoulders, will be required to increase the accuracy of 3D posture estimate in this respect. There are also certain unresolved issues and gaps between research and actual applications, such as the impact of body part occlusion and congested backgrounds of several people. Future studies ought to look at both global and local settings for more prejudicial human body characteristics, while also including human body elements into the deep learning network for antecedent limitations. Finally, there is the issue of data variety. It is possible to collect more data for certain complicated circumstances, and there are various ways to enhance current datasets. While there is an attribute divide between artificial data and real data, GANs may potentially produce an infinite amount of data. Cross-dataset augmentation, particularly supplementing 3D datasets with 2D datasets, has the potential to alleviate the problem of inadequate diversity of training data and this is another aim on our investigation list. Conceptualization, J.G. and A.A.; Data curation, A.K., R.D. and A.A.; Formal analysis, R.M., R.D. and J.G. ; Funding acquisition, R.M. ; Investigation, R.M. and J.G. ; Methodology, R.M., R.D. and A.A.; Project administration, R.M. and J.G. ; Resources, A.K. ; Software, A.K. ; Supervision, R.M. ; Validation, R.M., A.K. and A.A.; Visualization, A.K. and R.D. ; Writing--original draft, R.M. ; Writing--review and editing, R.M., R.D. and A.A. All authors have read and agreed to the published version of the manuscript.