Stress can be defined as a state of anxiety (or mental tension) caused by a particular situation. Everybody experiences stress to some level, but how we respond to stress significantly affects our well-being. Various events generate anxiety that leads to stress. For example, not having enough time to complete a task or being late are situations where anxiety (and stress) depends on a temporal factor: the scarcity of time. But people also slide into anxiety as they live in a condition that causes them to be tense, independently of time. The studies eliciting anxiety in laboratory settings have less widely considered this variant. This paper presents a proof of concept (PoC) that investigated the possibility of stimulating anxiety without time pressure through a purposely edited horror movie trailer, giving new insights into the emotional experiences evoked by controlled audiovisual stimuli. The PoC comprised an AI-based classifier to detect a person’s emotion among anxiety , relaxation , and none of the two based on the galvanic skin response (GSR), photoplethysmogram (PPG), and heart rate (HR), achieving an accuracy higher than 96%. Key application areas include media and marketing, and psychology. Media producers could improve their content to capture the audience better; psychologists could create tailored exposure experiences to promote gradual desensitization to stress triggers.

With the explosion of online video consumption, assessing and anticipating how users will evaluate the content they watch has become increasingly important. Traditional methods based on explicit user feedback are often limited in their ability to do this, as they can be time-consuming and expensive to collect. This study explores techniques to predict users’ ratings about a video’s ability to evoke emotions through emotional signals. In particular, it is proposed a method of emotional analysis that uses valence and arousal data as key signals for predicting user ratings through systems that use machine-learning techniques. Hence, an experiment in the wild involved 112 participants who completed questionnaires to create a dataset of emotional data and video quality ratings to train different intelligent systems. The best system comprised a Medium Gaussian Support Vector Machine (SVM) classifier that detected users’ ratings between Ineffective and Effective based on valence and arousal features as input, achieving an accuracy higher than 87%. The result demonstrated that it is possible to predict users’ ratings on the ability of the movie to elicit emotion, using users’ emotional states in terms of valence and arousal. The system has several advantages, such as eliminating the need for user reports, predicting user ratings in real-time more quickly and dynamically, and utilizing only the initial emotional state to predict users’ ratings. In addition, it has potential applications in advertising, education, and entertainment fields. Advertisers could better understand how consumers perceive their products and create more effective advertising campaigns; educational institutions could develop more engaging and effective learning materials; entertainment providers could create more popular and successful content.

During the production of pharmaceutical glass tubes, a machine-vision based inspection system can be utilized to perform the high-quality check required by the process. The necessity to improve detection accuracy, and increase production speed determines the need for fast solutions for defects detection. Solutions proposed in literature cannot be efficiently exploited due to specific factors that characterize the production process. In this work, we have derived an algorithm that does not change the detection quality compared to state-of-the-art proposals, but does determine a drastic reduction in the processing time. The algorithm utilizes an adaptive threshold based on the Sigma Rule to detect blobs, and applies a threshold to the variation of luminous intensity along a row to detect air lines. These solutions limit the detection effects due to the tube’s curvature, and rotation and vibration of the tube, which characterize glass tube production. The algorithm has been compared with state-of-the-art solutions. The results demonstrate that, with the algorithm proposed, the processing time of the detection phase is reduced by 86%, with an increase in throughput of 268%, achieving greater accuracy in detection. Performance is further improved by adopting Region of Interest reduction techniques. Moreover, we have developed a tuning procedure to determine the algorithm’s parameters in the production batch change. We assessed the performance of the algorithm in a real environment using the “certification” functionality of the machine. Furthermore, we observed that out of 1000 discarded tubes, nine should not have been discarded and a further seven should have been discarded.

This research study investigates the impact of a virtual dashboard on the quality of task execution in robotic teleoperation. More specifically, this study investigates how a virtual dashboard improves user awareness and grasp precision in a teleoperated pick-and-place task by providing users with critical information in real-time. An experiment was conducted with 30 participants in a robotic teleoperated task to measure their task performance in two different experimental conditions: a control group used conventional interfaces, and an experimental group utilized the virtual dashboard with additional information. Research findings indicate that integrating a virtual dashboard improves grasping accuracy, reduces user fatigue, and speeds up task completion, thereby improving task effectiveness and the quality of the experience.

A known issue in teleoperation concerns dexterity in grasping objects, which can be severely limited if not properly coupled with perfect stereoscopic vision and force/tactile feedback. This work presents the development and testing of a subsystem used for a virtual dashboard within the Sully teleoperation system, with hardware and software components. It is used to capture images, gather object information, and display a virtual dashboard that provides the operator with information about the distance and reachability of objects, to support stereoscopic vision visualized through a virtual reality headset. The hardware consists of two front cameras, used as eyes for the teleoperated robot, and two Time-of-Flight (ToF) sensors to estimate the distance between the teleoperated robot and the Object of Interest (OoI). The software processes images captured by cameras to identify the objects, detects the OoI, and estimates its distance from the robot using the measurements of ToF sensors. Performance results showed an optimal trade-off between image quality, frame rate, and use experience. Indeed, a mean accuracy of 88% was achieved in identifying and detecting OoIs, and an average mean absolute percentage error (MAPE) of 1.40% to estimate the distance from the robot to the OoI at 120 cm.