## 3 Experimental Results
### Experimental Results of the Second Part of the Dataset  
Figure 14 illustrates the 3D skeleton for an arm flexion exercise, showing the front view (red) with significant depth errors--the upper body appears tilted forward and the lower body backward. The side view (blue) is incomplete, missing some left-side joints. The final combined model (pink) corrects arm positions but retains depth errors in the left hand, highlighting challenges in integrating views for complete accuracy. Figure 14: A 3D human skeleton of upper arm flexion in the second dataset. Figure 13: Depth coordinate changes during upper arm adduction (Test 3): **(a)**–right elbow joint; **(b)**–right wrist joint. Figure 15 displays depth coordinate changes during arm flexion in light clothing. Depth errors are most pronounced when the right arm is raised. Although trajectories for the right elbow and wrist are consistent, they differ in distance. The final depth trajectories are smoother and better centered, but some errors remain, indicating areas for further refinement. In Figure 16, the same exercise in dark clothing shows similar depth error patterns but with increased magnitude, suggesting that darker clothing affects depth accuracy. The final depth trajectories are smoother but still show noticeable discrepancies. Figure 17 presents the 3D results for arm adduction, showing depth errors in the front view (red) and missing left-side joints in the side view (blue). The final model (pink) improves alignment but retains depth errors in the left hand, underscoring the difficulty of accurate multi-view integration. Figure 16: Depth coordinate changes during upper arm flexion in dark clothing: (**a**)—right elbow joint; (**b**)—right wrist joint  
Figure 15: Depth coordinate changes during upper arm flexion in light clothing: (**a**)—right elbow joint; (**b**)—right wrist joint. Figure 18 shows depth coordinate variations during arm retraction in light clothing. The greatest depth error occurs when the arm is extended sideways. Despite generally smooth trajectories and improved centering in the final result, depth inaccuracies remain, particularly in the extended position. In Figure 19, the depth coordinate changes for dark clothing show similar trends to light clothing but with more pronounced errors. This reinforces the influence of clothing color on depth accuracy. The final results show smoother trajectories, but the left side still exhibits uneven depth due to visibility issues. Figure 17: A 3D human skeleton of upper arm retraction. Figure 18: Depth coordinate changes during upper arm retraction in light clothing: (**a**)—right elbow joint; (**b**)—right wrist joint. For standing arm flexion, light clothing yields a mean error of 0.1104 m with a standard deviation of 0.0292 m at the right elbow, as well as a mean of 0.1384 m with a standard deviation of 0.0475 m at the right wrist. In dark clothing, the errors are slightly higher, with means of 0.1349 m (SD = 0.0518 m) for the right elbow and 0.1454 m (SD = 0.0621 m) for the right wrist. Figure 20 shows 3D results for upper arm adduction. Errors are evident in the front and side views, with the final model (pink) improving alignment but still displaying depth inaccuracies in the left hand. This highlights the ongoing challenges in achieving accurate 3D reconstructions from multiple views. Figure 21 illustrates depth coordinate changes for upper arm adduction in light clothing. Errors are most significant when the arm is extended forward. Despite improvements in smoothness and centering, depth inaccuracies persist, particularly during arm extension. Figure 19: Depth coordinate changes during upper arm retraction in dark clothing: (**a**)—right elbow joint; (**b**)—right wrist joint. Figure 20: A 3D human skeleton of upper arm adduction. In Figure 22, the depth coordinate changes for dark clothing show similar trends to light clothing but with greater depth errors.