## 4 Discussion  
The developed platform was tested on six individuals, with distinct height, weight, and skin color. Through these tests, it was possible to conclude that the model trained through the dataset was robust enough to predict the key points in different individuals. The model also did not prove to be sensitive to variations in light, since during the tests there was no control of the lighting conditions. Thus, it is possible to say that the results obtained  
Figure 19: Robotic arm at the auscultation points of individuals 1 (left images) and 3 (right images).  
Figure 20: Points auscultated (red and green) in patients 1, 3, 5, 6 by the developed platform.  
in terms of localization were quite good, being sufficient to perform the auscultation task, which does not require precise positioning.  
The robotic arm was able to reach the desired auscultation points (6 of 8, due to the limited reach of the arm) successfully, depending on the individual's physical structure, namely his back length. The reason for this is that in patients 2 and 4 the robotic arm was unable to go to points \(L4\) and \(R4\), because when descending from point \(L3\) to point \(L4\), the elbow joint hit the robotic arm's support table, as can be seen in Figure 21.  
This was due to these patients being shorter, because by the calculation of the 3D spatial coordinates, as shown in Figure 18, these patients have the lowest \(y\) coordinate value at points \(L4\) and \(R4\) among all. From the depth image (Figure 18) it can be assumed that the robotic arm can successfully reach all desired auscultation points in all patients that have a \(y\) coordinate, at points \(L4\) and \(R4\), above \(-114.0\) mm. The remaining auscultation points in patients 2 and 4 were successfully reached and are shown in Figure 22. The results are very similar to the previous four patients, in the first four auscultation points.  
On the other hand, there is the possibility of the person moving; the system is not designed to reorient itself whenever the person moves, but this can be minimized if the person becomes seated and as stable as possible, as is also required in other medical  
Figure 21: Collision of the robotic arm elbow joint with the bench where it is installed, when trying to reach the point \(L4\) of patients 2 and 4.  
Figure 22: Points auscultated (red) in patients 2 and 4.  
examinations. The results so far are preliminary, and all the individuals tested were pleased with the robot's operation and did not feel any discomfort.  
When compared with the work by [PERSON] et al. [24], the system presented on this paper uses a RGB-D camera to capture an image of the patients back, while the system from [PERSON] et al. uses a RGB-D camera to capture a 3D point cloud scan of the patient, and processes this point cloud to determine the auscultation points. Regarding practical experiments, [PERSON] et al. only performed experiments with four subjects, and no information is presented regarding the percentage of detected auscultation points [24]. Regarding the work by [PERSON] et al. [23], its goal was to develop a robotic auscultation platform for estimation of the landing positions to hear the sounds of four cardiac valves, but there is no information of tests having been conducted on live subjects (it has only been tested on a mannequin). Furthermore, this work uses a LiDAR camera to acquire the 3D contour of the body surface as point cloud data.