The development of autonomous driving cars is a complex activity, which poses challenges about ethics, safety, cybersecurity, and social acceptance. The latter, in particular, poses new problems since passengers are used to manually driven vehicles; hence, they need to move their trust from a person to a computer. To smooth the transition towards autonomous vehicles, a delicate calibration of the driving functions should be performed, making the automation decision closest to the passengers’ expectations. The complexity of this calibration lies in the presence of a person in the loop: different settings of a given algorithm should be evaluated by assessing the human reaction to the vehicle decisions. With this work, we for an objective method to classify the people’s reaction to vehicle decisions. By adopting machine learning techniques, it is possible to analyze the passengers’ emotions while driving with alternative vehicle calibrations. Through the analysis of these emotions, it is possible to obtain an objective metric about the comfort feeling of the passengers. As a result, we developed a proof-of-concept implementation of a simple, yet effective, emotions recognition system. It can be deployed either into real vehicles or simulators, during the driving functions calibration.

During the last decades, innovative aircraft health management systems have been receiving increasing interest from Original Equipment Manufacturers (OEMs) and aircraft operators. Their implementation could lead to substantial benefits: drastic cuts in turnaround time, operation costs, and Life Cycle Costs (LCCs) as well as sharp increases in system availability, safety, and reliability. An interconnectivity step-up is hence needed to guarantee a seamless data transfer. In this paper, an integrated open-source solution for reliable data transmission and near real-time graphical visualization is proposed. After a comprehensive calibration and verification campaign performed on a test stand, the overall system has been successfully validated on structural data measured using a network of Fiber Bragg Gratings (FBGs) mounted on a radio-controlled model aircraft. The result is an effective and robust system able to monitor near real-time critical parameters and health status of structures. With this system, the temperature and displacements of the structure can be displayed on a heat map arranged on a 3D model and visualized through a computer application on the ground. The proposed methodology can be applied to heterogeneous scenarios, ranging from maintenance planning activities to performance checks, providing an all-in-one solution for flight data management as well as other applications in the structural monitoring domain.

Teaching is an activity that requires understanding the class’s reaction to evaluate the teaching methodology effectiveness. This operation can be easy to achieve in small classrooms, while it may be challenging to do in classes of 50 or more students. This paper proposes a novel Internet of Things (IoT) system to aid teachers in their work based on the redundant use of non-invasive techniques such as facial expression recognition and physiological data analysis. Facial expression recognition is performed using a Convolutional Neural Network (CNN), while physiological data are obtained via Photoplethysmography (PPG). By recurring to Russel’s model, we grouped the most important Ekman’s facial expressions recognized by CNN into active and passive. Then, operations such as thresholding and windowing were performed to make it possible to compare and analyze the results from both sources. Using a window size of 100 samples, both sources have detected a level of attention of about 55.5% for the in-presence lectures tests. By comparing results coming from in-presence and pre-recorded remote lectures, it is possible to note that, thanks to validation with physiological data, facial expressions alone seem useful in determining students’ level of attention for in-presence lectures.

Testing is essential to validate the effort put into building a new system. In engineering fields like aerospace, marine, mechanical, and others, these tests are often performed in environments similar to the one in which the system will be immersed in order to verify its suitability to operate. They often require tools that allow the data collected to be viewed in real-time or near real-time and in a way that can be easily interpreted by an operator. The purpose of this paper is therefore to present a new software that simplifies the visualization of data coming from fiber Bragg grating sensors. In this regard, it offers multiple functions and display modes useful for carrying out tests with greater awareness of what is happening at a certain moment. The software has been successfully tested and verified through an experimental campaign in the laboratory and the field.

For some years now, the idea of a fully autonomous driving car has been monopolizing the attention of the entire automotive sector. The main motivation behind all this interest is that autonomous driving cars could potentially improve road safety simply by subtracting human error, which causes thousands of road fatalities worldwide every year. One of the biggest challenges, for which researchers are working hard to give answers, concerns the search of solutions to improve the people’s response to this vehicles once they are put on the road: an incorrect answer can lead to their unsuccess, with a great monetary loss for all the companies that have invested in these technologies. Recently, we worked on a project that went in this direction and which regarded the possibility to change the driving style of the autonomous driving cars based on the passengers’ facial expressions. This is particularly useful in the event that they experience fear: the car may react to these feelings by adopting a careful driving style and stopping if these feelings do not improve within a certain period. In this paper, we want to discuss about the improvements we have made to this project.