## 6 Discussion  
### High-Resolution Gait Dataset vs. COCO in BPNET Training  
This study compares the performance of two BPNET models trained on distinct datasets: the proposed high-resolution gait dataset and the widely used COCO 2017 dataset. The evaluation focused on the models' accuracy in identifying key body joint positions from 2D images. The findings revealed that the model trained on the proposed dataset consistently achieved superior performance compared to the COCO-trained model. This was evident in two key metrics, namely, average precision (AP) and average recall (AR), across various intersection over union (IoU) thresholds. Notably, the proposed dataset achieved higher AP and AR for most IoU thresholds, signifying its effectiveness in pinpointing key joint locations. Moreover, the model trained on the proposed dataset exhibited lower mean per joint position error (MPJPE) values for the majority of test subjects, further substantiating its accuracy. Upon closer examination of the MPJPE results, however, it became evident that there were slightly higher error values for specific joints (left/right hips and left knee) at particular camera angles (45 and 135 degrees). These elevated error rates can be attributed to occlusions caused by the camera positioning at those angles. This observation highlights the necessity for further improvement in the model's and dataset's ability to handle occlusions effectively. Figure 7: **(a)** Comparison results of MPJPE for different views between the proposed test images at 0 degrees vs. COCO test images (both approximately, 830 images). **(b)** Comparison results of MPJPE for different views between the proposed test images at 45 degrees vs. COCO test images (both approximately, 830 images). **(c)** Comparison results of MPJPE for different views between the proposed test images at 135 degrees vs. COCO test images (both approximately, 830 images). The abbreviations are as follows: L/A: left ankle, L/K: left knee, L/H: left hip, R/A: right ankle, R/K: right knee, R/H: right hip, L/W: left wrist, L/E: left elbow, L/S: left shoulder, R/W: right wrist, R/E: right elbow, R/S: right shoulder. The proposed dataset demonstrably outperformed the COCO dataset despite having only a quarter of the image count. This highlights the significance of our research, as a gait-focused dataset leads to substantial reductions in computational cost and training time. The superiority of the proposed dataset stems from several factors:  
* **High-resolution VICON data:** The proposed dataset leverages accurate 2D key joint positions captured using the high-resolution VICON motion capture system. This stands in stark contrast to the COCO dataset, where key point locations are manually annotated, potentially leading to lower accuracy. * **Specificity for gait analysis:** The proposed dataset focuses on controlled normal gait patterns, minimizing external factors like occlusions, complex backgrounds, and lighting variations prevalent in COCO. The controlled environment allows for a more precise evaluation of BPNET, resulting in superior accuracy. * **Alignment with study objectives:** The proposed dataset is specifically tailored to capture human motion and gait, aligning directly with the study's objectives of human key point identification. In contrast, the diverse object categories and poses in COCO make it less suitable for our specific research focus. However, it is important to recognize the constraints inherent to the proposed dataset. The dataset was primarily designed for normal gait motions and may not perform optimally for other random and rapid human movements. Furthermore, the dataset's recordings are confined to controlled laboratory environments, which may result in the observed results being sensitive to occlusions encountered in real-world settings. It is therefore recommended that these limitations be taken into account when applying the dataset to scenarios involving non-standard gait movements or settings with significant occlusions.