Facial Expression Recognition for Measuring Jurors' Attention in Acoustic Jury Tests

The perception of sound greatly impacts users' emotional states, expectations, affective relationships with products, and purchase decisions. Consequently, assessing the perceived quality of sounds through jury testing is crucial in product design. However, the subjective nature of jurors' responses may limit the accuracy and reliability of jury test outcomes. This research explores the utility of facial expression analysis in jury testing to enhance response reliability and mitigate subjectivity. Some quantitative indicators allow the research hypothesis to be validated, such as the correlation between jurors' emotional responses and valence values, the accuracy of jury tests, and the disparities between jurors' questionnaire responses and the emotions measured by FER (facial expression recognition). Specifically, analysis of attention levels during different statuses reveals a discernible decrease in attention levels, with 70 percent of jurors exhibiting reduced attention levels in the 'distracted' state and 62 percent in the 'heavy-eyed' state. On the other hand, regression analysis shows that the correlation between jurors' valence and their choices in the jury test increases when considering the data where the jurors are attentive. The correlation highlights the potential of facial expression analysis as a reliable tool for assessing juror engagement. The findings suggest that integrating facial expression recognition can enhance the accuracy of jury testing in product design by providing a more dependable assessment of user responses and deeper insights into participants' reactions to auditory stimuli.

Automated Measurement of Geometric Features in Curvilinear Structures Exploiting Steger's Algorithm

Accurately assessing the geometric features of curvilinear structures on images is of paramount importance in many vision-based measurement systems targeting technological fields such as quality control, defect analysis, biomedical, aerial, and satellite imaging. This paper aims at laying the basis for the development of fully automated vision-based measurement systems targeting the measurement of elements that can be treated as curvilinear structures in the resulting image, such as cracks in concrete elements. In particular, the goal is to overcome the limitation of exploiting the well-known Steger's ridge detection algorithm in these applications because of the manual identification of the input parameters characterizing the algorithm, which are preventing its extensive use in the measurement field. This paper proposes an approach to make the selection phase of these input parameters fully automated. The metrological performance of the proposed approach is discussed. The method is demonstrated on both synthesized and experimental data.

Algorithms for Vision-Based Quality Control of Circularly Symmetric Components

Quality inspection in the industrial production field is experiencing a strong technological development that benefits from the combination of vision-based techniques with artificial intelligence algorithms. This paper initially addresses the problem of defect identification for circularly symmetric mechanical components, characterized by the presence of periodic elements. In the specific case of knurled washers, we compare the performances of a standard algorithm for the analysis of grey-scale image with a Deep Learning (DL) approach. The standard algorithm is based on the extraction of pseudo-signals derived from the conversion of the grey scale image of concentric annuli. In the DL approach, the component inspection is shifted from the entire sample to specific areas repeated along the object profile where the defect may occur. The standard algorithm provides better results in terms of accuracy and computational time with respect to the DL approach. Nevertheless, DL reaches accuracy higher than 99% when performance is evaluated targeting the identification of damaged teeth. The possibility of extending the methods and the results to other circularly symmetrical components is analyzed and discussed.

SwimmerNET: Underwater 2D Swimmer Pose Estimation Exploiting Fully Convolutional Neural Networks

Professional swimming coaches make use of videos to evaluate their athletes' performances. Specifically, the videos are manually analyzed in order to observe the movements of all parts of the swimmer's body during the exercise and to give indications for improving swimming technique. This operation is time-consuming, laborious and error prone. In recent years, alternative technologies have been introduced in the literature, but they still have severe limitations that make their correct and effective use impossible. In fact, the currently available techniques based on image analysis only apply to certain swimming styles; moreover, they are strongly influenced by disturbing elements (i.e., the presence of bubbles, splashes and reflections), resulting in poor measurement accuracy. The use of wearable sensors (accelerometers or photoplethysmographic sensors) or optical markers, although they can guarantee high reliability and accuracy, disturb the performance of the athletes, who tend to dislike these solutions. In this work we introduce swimmerNET, a new marker-less 2D swimmer pose estimation approach based on the combined use of computer vision algorithms and fully convolutional neural networks. By using a single 8 Mpixel wide-angle camera, the proposed system is able to estimate the pose of a swimmer during exercise while guaranteeing adequate measurement accuracy. The method has been successfully tested on several athletes (i.e., different physical characteristics and different swimming technique), obtaining an average error and a standard deviation (worst case scenario for the dataset analyzed) of approximately 1 mm and 10 mm, respectively.

Low-Cost and High-Performance Solution for Positioning and Monitoring of Large Structures

Systems for accurate attitude and position monitoring of large structures, such as bridges, tunnels, and offshore platforms are changing in recent years thanks to the exploitation of sensors based on Micro-ElectroMechanical Systems (MEMS) as an Inertial Measurement Unit (IMU). Currently adopted solutions are, in fact, mainly based on fiber optic sensors (characterized by high performance in attitude estimation to the detriment of relevant costs large volumes and heavy weights) and integrated with a Global Position System (GPS) capable of providing low-frequency or single-update information about the position. To provide a cost-effective alternative and overcome the limitations in terms of dimensions and position update frequency, a suitable solution and a corresponding prototype, exhibiting performance very close to those of the traditional solutions, are presented and described hereinafter. The solution leverages a real-time Kalman filter that, along with the proper features of the MEMS inertial sensor and Real-Time Kinematic (RTK) GPS, allows achieving performance in terms of attitude and position estimates suitable for this kind of application. The results obtained in a number of tests underline the promising reliability and effectiveness of the solution in estimating the attitude and position of large structures. In particular, several tests carried out in the laboratory highlighted high system stability; standard deviations of attitude estimates as low as 0.04° were, in fact, experienced in tests conducted in static conditions. Moreover, the prototype performance was also compared with a fiber optic sensor in tests emulating actual operating conditions; differences in the order of a few hundredths of a degree were found in the attitude measurements.

Testing Surgical Face Masks in an Emergency Context: The Experience of Italian Laboratories during the COVID-19 Pandemic Crisis

The first wave of the COVID-19 pandemic brought about a broader use of masks by both professionals and the general population. This resulted in a severe worldwide shortage of devices and the need to increase import and activate production of safe and effective surgical masks at the national level. In order to support the demand for testing surgical masks in the Italian context, Universities provided their contribution by setting up laboratories for testing mask performance before releasing products into the national market. This paper reports the effort of seven Italian university laboratories who set up facilities for testing face masks during the emergency period of the COVID-19 pandemic. Measurement set-ups were built, adapting the methods specified in the EN 14683:2019+AC. Data on differential pressure (DP) and bacterial filtration efficiency (BFE) of 120 masks, including different materials and designs, were collected over three months. More than 60% of the masks satisfied requirements for DP and BFE set by the standard. Masks made of nonwoven polypropylene with at least three layers (spunbonded-meltblown-spunbonded) showed the best results, ensuring both good breathability and high filtration efficiency. The majority of the masks created with alternative materials and designs did not comply with both standard requirements, resulting in suitability only as community masks. The effective partnering between universities and industries to meet a public need in an emergency context represented a fruitful example of the so-called university "third-mission".

A Smartphone Integrated Hand-Held Gap and Flush Measurement System for in Line Quality Control of Car Body Assembly

This paper presents the design and the characterization of a portable laser triangulation measurement system for measuring gap and flush in the car body assembly process. Targeting Human in the Loop (HILT) operations in the manufacturing sector, and in line with the vision of human empowerment with Industry 4.0 technologies, the instrument embeds features to ease operators’ activity and compensate possible misuse that could affect the robustness and the quality of data acquired. The device is based on a smartphone integrated with a miniaturized laser triangulation system installed in a cover. The device embodies additional sensors and control systems in order to guarantee operators’ safety (switching on and off the laser line based on specific conditions), support operators during the measurement execution task, and optimize the image acquisition process for minimizing the uncertainty associated to the measurement. The smartphone performs on-board processing and allows Wi-Fi communication with the plant IT infrastructure. Compliance to Industry 4.0 requirements is guaranteed using OPC-UA (Open Platform Communications—Unified Architecture) communication protocol enabling the exchange of live data with the plant middleware. The smartphone provides also an advanced high-resolution color display and well proven and ergonomic human–machine interfaces, which have been fully exploited in the design. The paper introduces the system optical layout and then presents the algorithms implemented to realize the gap and flush measurement. The paper finally presents the calibration of the instrument and estimates its calibration uncertainty in laboratory conditions. Then it discusses how performance decays when the operator handles the instrument on a reference car body. Finally, it shows the analysis of uncertainty when the device is used on real car bodies of different colors in a production line. It is observed that the measurement uncertainty of the whole measurement chain (measurand + instrument + operator + uncontrolled environmental conditions) is larger than the instrument calibration uncertainty because the measurement process is affected by the operator and the variable conditions of the production line.