# Augmented Reality: Mapping Methods and Tools for Enhancing the Human Role in Healthcare HMI
## 4 Discussion
### Registration and Tracking  
Most of the reviewed studies necessitate complicated registration and tracking processes between the patient and the AR system. Registration enables the superimposition and orientation of computer-generated objects, such as X-ray images or navigation modes, in the correct position in situ. Tracking is the process that follows registration and allows the displayed virtual object to maintain the correct position, adapting over time according to the movements of the user or the 3D space. In order to allow a correct integration of virtual models with real objects, it is therefore essential to know the spatio-temporal relationships between the real and virtual worlds; in fact, it is of paramount importance for AR applications to have a quick and accurate estimation of the visualization position with respect to real objects in order to obtain a correct alignment between the real world and the virtual content. The most widely adopted tracking and registration methods can be grouped into the following categories: marker-based optical tracking systems, using elements that are easily detectable using computer vision and image segmentation techniques; markerless tracking systems; sensor-based tracking systems, e.g., electromagnetic, inertial, or acoustic; hybrid tracking systems, combining one or more of the above tracking systems. Depending on their applications, some research groups did not include any automatic virtual content registration functionality, leaving the orthopedic surgeon to perform a manual alignment [28, 72, 74, 75, 29]. However, the authors reported that manual alignment can often be a tedious process [75]. Furthermore, the 3D alignment quality of virtual models with regard to the real world is highly subjective, prone to human error [38], and it relies on both the accuracy of the registration and the OST HMD calibration [28]. Although these may become obsolete shortly, most studies refer to marker-based tracking systems. Leading marker-based tracking technologies, such as Vitoria Image Targets, primarily use printable planar markers [57, 77, 83, 84], black and white square or cubic patterns [24, 27, 29, 33, 34, 37, 45, 48, 51, 56, 67, 73, 80, 81], QR code [39, 40, 71] and colored reference arrays [50, 60, 61]. Other systems employ non-planar markers such as retroreflective markers [55, 69] and colored markers [85]. Even though marker-based or sensor-based registration and tracking methods have been mainly used for AR displays during orthopedic interventions, such technologies are highly dependent on complicated calibration processes, and often require further preparation time and equipment [31]. Indeed, introducing markers into the surgical field of view could be a difficult and impractical process in real operating scenarios [29], as they often risk overcrowding the surgeon's FOV or require additional incisions. In addition, the accuracy of the registration process can be highly dependent on the placement of the markers, so the system may need to be recalibrated due to patient movement [63, 80], or movement of soft tissue anatomical parts [71], as might be expected in overweight subjects, where losses of the initial coupling between the anatomical structure and the markers could more frequently occur [48, 58]. In other cases, it was observed that differences in lighting affect the computation of the position of the markers [85] and that the selected tracking approach can fail due to marker occlusions, making the navigation impossible till the occlusion removal [27, 55]. Due to the limitation of these techniques, markerless registration relying on anatomical landmarks is an interesting option to minimize the registration procedure's influence on the surgical framework. Examples are methods based on the stereoscopic vision that use different camera angles to visualize intraoperative anatomy [47].