Background: Basic life support (BLS) is crucial in the emergency response system, as sudden cardiac arrest is still a major cause of death worldwide. Unfortunately, only a minority of victims receive cardiopulmonary resuscitation (CPR) from bystanders. In this context, training could be helpful to save more lives, and technology-enhanced BLS simulation is one possible solution. Objective: The aim of this study is to assess the feasibility and acceptability of our augmented reality (AR) prototype as a tool for BLS training. Methods: Holo-BLSD is an AR self-instruction training system, in which a standard CPR manikin is “augmented” with an interactive virtual environment that reproduces realistic scenarios. Learners can use natural gestures, body movements, and spoken commands to perform their tasks, with virtual 3D objects anchored to the manikin and the environment. During the experience, users were trained to use the device while being guided through an emergency simulation and, at the end, were asked to complete a survey to assess the feasibility and acceptability of the proposed tool (5-point Likert scale; 1=Strongly Disagree, 5=Strongly Agree). Results: The system was rated easy to use (mean 4.00, SD 0.94), and the trainees stated that most people would learn to use it very quickly (mean 4.00, SD 0.89). Voice (mean 4.48, SD 0.87), gaze (mean 4.12, SD 0.97), and gesture interaction (mean 3.84, SD 1.14) were judged positively, although some hand gesture recognition errors reduced the feeling of having the right level of control over the system (mean 3.40, SD 1.04). Conclusions: We found the Holo-BLSD system to be a feasible and acceptable tool for AR BLS training.

Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of “events”. The innovative way they acquire data presents several advantages over standard devices, especially in poor lighting and high-speed motion conditions. However, the novelty of these sensors results in the lack of a large amount of training data capable of fully unlocking their potential. The most common approach implemented by researchers to address this issue is to leverage simulated event data. Yet, this approach comes with an open research question: how well simulated data generalize to real data? To answer this, we propose to exploit, in the event-based context, recent Domain Adaptation (DA) advances in traditional computer vision, showing that DA techniques applied to event data help reduce the sim-to-real gap. To this purpose, we propose a novel architecture, which we call Multi-View DA4E (MV-DA4E), that better exploits the peculiarities of frame-based event representations while also promoting domain invariant characteristics in features. Through extensive experiments, we prove the effectiveness of DA methods and MV-DA4E on N-Caltech101. Moreover, we validate their soundness in a real-world scenario through a cross-domain analysis on the popular RGB-D Object Dataset (ROD), which we extended to the event modality (RGB-E).

Finding proper ways to address learning about sustainability is a relevant issue. Sustainability learning has to face some inherent complexities, due to two main factors: a) the interdisciplinary domains related to sustainability issues, such as ecology, economics, politics and culture, and b) the involvement of several social structures, such as individuals, families and communities. One recent research proposal is to exploit serious games to foster learning in this area. This resulted in a significant increase in the number of approaches discussed in the literature over the last few years. Notwithstanding this growing scenario, sustainability serious games still lack a reasoned evaluation, in order to clarify their possible applications and to define effective design strategies to approach them. To this end, in this paper we investigate the current state of the art of serious games for sustainability, identifying and discussing the most common applications. The research process included both scientific publications and unpublished materials. References were searched according to guiding questions, which helped focus the extraction of information, and through recursive browsing of their citations. Based on the research results, we propose a taxonomy for sustainability serious games and a classification of reviewed works according to this taxonomy. We also analyze design strategies, drawn from the literature, expressly conceived for the development of effective sustainability serious games. Finally, we discuss the current challenges and present possible areas of research in this field.

In case of cardiac arrest, prompt intervention of bystanders can be vital in saving lives. Basic Life Support and Defibrillation (BLSD) is a procedure designed to deliver a proficient emergency first response. Developing skills in BLSD in a large part of the population is a primary educational goal of resuscitation medicine. In this context, novel computer science technologies like Augmented Reality (AR) and Virtual Reality (VR) can alleviate some of the drawbacks of traditional instructor-led courses, especially concerning time and cost constraints. This paper presents Holo-BLSD, an AR system that allows users to learn and train the different operations involved in BLSD and receive an automatic assessment. The system uses a standard manikin which is \quotes{augmented} by an interactive virtual environment that reproduces realistic emergency scenarios. The proposed approach has been validated through a user study. Subjective results confirmed the usability of the devised tool and its capability to stimulate learners' attention. Objective results indicated no statistical significance in the differences between the examiners' evaluation of users who underwent traditional and AR training; they also showed a close agreement between expert and automatic assessments, suggesting that Holo-BLSD can be regarded as an effective self-learning method and a reliable self-evaluation tool.