## 3 Case of Study: The RESIMA System
### The Localization System and the UEI Tool  
The localization feature exploits Ultrasound Sensors (US) to measure the distance between the user and anchors in the environment. Each node in the environment is equipped with an ultrasound receiver (400 ER080 by Tecnosens), while the ultrasound transmitter (400 ET080 by Tecnosens) is embedded in the user module. Inertial sensors are also used to implement complementary user tracking tasks. More details concerning the system realization are available in [51].  
The user localization is performed through a trilateration algorithm exploiting measured distances between the user and the sensor nodes. The principle of standard trilateration algorithms is well-known. To summarize, to calculate the position of an entity within a region, its distance from at least three fixed (and not aligned) nodes must be measured. In real scenarios, increasing the number of nodes conveys a certain degree of redundancy to the method, which allows for increasing its accuracy, robustness and fault tolerance. Moreover, the availability of a redundant number of nodes allows for implementing advanced trilateration techniques, such as the Multi-Trilateration Algorithm (MTA) used in the RES-IMA system and shown in Figure 1. The MTA algorithm estimates the user coordinates by considering and computing all possible combinations of three measured user-node (not aligned) distances [50]. The approach adopted sorts all the estimated user coordinates in order to filter out outlier estimations. The latter, which is the main advantage of MTA with respect to the standard trilateration algorithm, allows for enhancing the robustness of the localization method against fake or not accurate distance measurements. The user position is hence estimated by computing the mean value of the positions depurated from outliers. The results discussed in [50; 51; 55] demonstrate the suitability of the proposed approach. It should be observed that in the trilateration approach, and so also in the MTA algorithm, increasing the number of nodes could improve, to a certain extent, the accuracy of the localization system. However, this assessment must be performed case by case by taking into account the characteristic of the considered environment.  
In particular, performances of the localization system have been investigated in different real environments. As an example, an experimental set-up made up of six fixed nodes installed in a 43 m\({}^{2}\) environment and a reference grid. A statistical analysis of residuals between expected and measured user coordinates allows estimating a system uncertainty less than 5.0 cm for both the x and the y axis. The estimated accuracy value is compliant with application in the field of Assistive Technology, which usually requires accuracy in the range of ten centimeters.  
Figure 1: Schematization of the MTA algorithm. \(M\) potential coordinates of the user in the plane, (\(x_{j}\), \(y_{j}\)), are estimated by considering all allowed triads of sensor nodes.  
The UEI tool aims to support the end-user while performing indoor navigation or other tasks requiring interaction with the environment. In particular, environment-related information (e.g., location of obstacles or services) is appropriately combined with users' position in order to properly set out candidate messages valuable for improving their performance, especially in terms of efficiency in exploring the environment and safety. The adopted paradigms continuously compare user-objects distance to safety or working ranges, as addressed in [51]. The way to estimate the above-referred distances is discussed in the next section. Finally, the DSS is mainly in charge of sorting candidate messages generated by the UEI tool in order to provide the user with an optimized degree of information.