## 5 Results  
This study utilized three distinct datasets for training, validation, and testing. The training dataset consisted of 23,741 images for the proposed model, in comparison to 118,287 images from the COCO 2017 dataset. The validation dataset comprised a total of 2224 images across three angles (0, 45, and 135 degrees) for performance evaluation, with 5012 images from the COCO 2017 dataset serving as the comparative evaluation. Finally, in order to compute the MPJPE for these two models, we acquired the inference results with the additional trial of three subjects, which corresponded to the 2510 images. The two trained models, one trained with the proposed dataset and the other trained with COCO 2017 dataset, were evaluated using the validation datasets that were set aside during the training of each respective model. The sample inference results from the model trained with the proposed dataset are depicted in Figure 5.  
The model trained with the COCO 2017 dataset achieved an average precision (AP) of 0.547 and an average recall (AR) of 0.471 for all object sizes, as well as IoU (intersection over union) thresholds between 0.50 and 0.95. This resulting value was significantly lower than what was achieved with the model trained with the proposed dataset. The second model trained with a proposed dataset achieved a higher overall AP of 0.565 and AR of 0.617 for all object sizes and IoU thresholds between 0.50 and 0.95. Furthermore, for individual IoUs at 0.5 and 0.75, the model trained with the proposed dataset recorded noticeably higher results than the COCO-trained dataset, except for the AP at the 0.75 IoU threshold, which was slightly higher for the COCO-trained dataset (0.692) than the proposed dataset (0.617). The evaluation results of two trained models are depicted in Table 1.  
Figure 5: Inference results from the BPNET trained with the proposed dataset. Secondly, the two trained models were tested using data collected for additional three subjects performing normal gait inside the VICON workspace. The subjects performed normal gait for 2-3 s, while the three different views camera recorded images of the subject. The ground truth data were also simultaneously recorded using the VICON motion capture system for comparison. The inference results from the model trained with the proposed dataset are described in Figure 5.  
For both the trained models, the Euclidean distance between the estimated joint position and the ground truth joint position was computed for twelve major joints, namely, R/L shoulder, R/L elbow, R/L wrist, R/L hip, R/L knee, and R/L ankle. A joint level MPJPE was computed by taking the means of the distances across all frames. Any undetected or falsely detected key joint position due to image quality and occlusions from the camera view were discarded from the results before computing MPJPE. Consequently, approximately 2510 images were selected from both datasets for the purpose of computing the MPJPE as the testing set. The overall MPJPE values recorded for the COCO trained model were 3.759 mm (\(\pm\)1.795), while the overall MPJPE values for the proposed dataset were 3.211 mm (\(\pm\)1.730), as depicted in Figure 6. \begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**COCO Dataset**} & \multicolumn{2}{c}{**Proposed Dataset**} \\ \hline
**IoU (Intersection Over Union) Threshold** & **Average Precision (AP)** & **Average Recall (AR)** & **Average Precision (AP)** & **Average Recall (AR)** \\ \hline
0.5 & 0.668 & 0.458 & 0.873 & 0.915 \\ \hline
0.75 & 0.413 & 0.692 & 0.527 & 0.582 \\ \hline
0.5:0.95 & 0.547 & 0.471 & 0.565 & 0.617 \\ \hline \end{tabular}
\end{table} 
Table 1: Evaluation results between COCO (5012 images) and the proposed dataset (2224 images). Figure 6: Comparison results for individual joints as MPJPE. Two different models trained with the proposed dataset (23,741 images) vs. COCO 2017 (118,287 images) were tested with approximately 2510 images for calculating MPJPE.