## 3 Case of Study: The RESIMA System
### The System Management Tool and User-Objects Distance Estimation  
The dedicated LabVIEW tool, shown in Figure 2, has been developed to properly manage the whole system. The front panel is mainly composed by:  
* An Environment map (right-hand side), where obstacles are in red and services are in blue;
* The coordinates of the obstacles, services and nodes (black squares in the map);
* The coordinates of the user (green indicator in the map);
* The distances from the user to the nodes;
* A Nearby/Found alert led for services, and a Warning/Danger alert led for obstacles;
* Functional commands which will be explained in the following notes;
* Warning and Danger indicators, which starts lighting when the user exceeds safety thresholds thus approaching or being very close to an obstacle, respectively;
* Nearby and Found indicators, which in the same way as the previous case notify the user of being closer or reached a service. As mentioned in Section 3.1, to properly implement the UEI functionality, user-obstacle/service distances must be conveniently estimated. To such aim, obstacle/services contours have been discretized by a discretization step, which allows defining nodes to be used to estimate distances from the user. An example of user-objects distance representation, allowed by the SMT, is shown in Figure 3. This information is valuable for the implementation of the UEI functionality, with particular regard to the assessment of the obstacle collision risk and the service proximity condition [51]. The same information can be also valuably used to implement a navigation aiding system. Actually, many of such functionalities exploit the information related to the minimum user-object distance, as shown in Figure 4.  
Figure 2: The System Management Tool. Figure 5 shows results of the system performances analysis in terms of the delay time introduced by the UEI tool in estimating the overall user-object-nodes distance, as a function of the discretization step, used to define obstacle/services nodes. The minimum discretization step (0.05 m) corresponds to a number of 1160 nodes, requiring the same number of distances to be computed. In order to explore different scenarios, for each discretization step, 50 different user positions have been considered. Figure 5 shows, for each discretization step, the maximum value of the delay time estimated for the whole set of user positions. In particular, Figure 5a shows the results obtained in case the UEItool continuously updates the user-objects distance maps (e.g., graphical outputs shown in Figures 3 and 4), while Figure 5b shows results in case the tool only computes user-object distances without providing any graphical output. As it can be observed, computational times, in the second case, which is the typical working condition during the real operation of the system, are negligible considering the time scale of the addressed application. Moreover, as expected, the standard deviation of the delay time computed across the 50 different user positions demonstrated a negligible dependence on the user position. The standard deviation of the delay time computed across the 50 different user positions demonstrated  
Figure 4: The minimum user-objects distance estimation functionality implemented in the Management Tool. Figure 3: The overall user-object-nodes distance estimation functionality implemented in the Management Tool. a negligible dependence on the user position. It is important to note that the choice to show the maximum value (instead of the average value) has been further motivated by the need to investigate the system performance in the worst case, which could compromise the system efficiency in real applications. For the sake of completeness, Figure 6 shows the delay time in computing user-object  
Figure 5: The delay time of the UEI tool in computing user-objects distances in the case of (**a**) continuously updating distance maps and (**b**) just computing user-object distances.