**5.5 Experimental results**

After pilot testing, the system was placed in an experimental workplace inside the sleep laboratory of Clinic of Children and Adolescents in University Hospital Martin, Slovakia.

Application is designed in a simple layout and also assists medical staff (as we can see as an example in **Figure 10**) to obtain the best results without any sophisticated manipulation.

**Figure 9.**

*Graphical user interface online EU questionnaire (excerpt).*

#### **Figure 10.**

*Head positioning assistance: (a) incorrect head position; the command is tilt head right. (b) Correct position of head.*

*Usage of RGB-D Multi-Sensor Imaging System for Medical Applications DOI: http://dx.doi.org/10.5772/intechopen.106567*

**Figure 11.** *The resulting 3D model of patient: (a) frontal view. (b) Rotated view.*

After capturing the stack of images from single sensors, the 3D reconstruction of data can be provided. Using intrinsic camera parameters, we can compute the colored point cloud from depth and color frame. The point clouds of individual views must be also denoised and transformed into a common coordinate system. As a denoising method, the statistical outlier filter was used. The resulting 3D model with the possibility of rotation is shown in **Figure 11**.

The partial point clouds from single sensors are registered by the global registration RANSAC method and local refinement is done by ICP (Iterative Closest Point) algorithm. For further scientific research, it is very interesting to implement and compare different filtration algorithms for depth maps or point clouds, registration methods, and calibration algorithms that can improve the accuracy of models. Nowadays, it exists a lot of machine learning methods that can obtain the relevant features from the head (from depth maps, RGB images, or directly from 3D models) and will finalize the feature vector for automated diagnostics.
