LAW-GAME Scientific Publications

Welcome to the Scientific Publications section of the LAW-GAME project. Here, you will find a comprehensive list of peer-reviewed articles, conference papers, and other scholarly works produced by our team. These publications highlight the innovative research, methodologies, and findings that have emerged from the LAW-GAME project. Our goal is to advance knowledge and foster collaboration within the scientific community by sharing our insights and breakthroughs. Explore the publications below:

Panopoulou, E., Aversa, D., & Vassos, S. (2023). AI-assisted Serious Games: Dialogue Management with Generative AI. In Proceedings of the ETCEI23, EDGE Intelligence 2023, Emerging Tech Conference (19-20 October 2023).

Available soon!

A. Pantazidis, A. Gazis, J. Soldatos, M. Touloupou, E. Kapassa and S. Karagiorgou, "Trusted Virtual Reality Environment for Training Security Officers," 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), Pafos, Cyprus, 2023, pp. 518-524

Virtual Reality (VR) applications are increasingly used to support ergonomic and safe training activities, including serious games for training security officers and other security related professionals. Nevertheless, they do not exploit opportunities for trusted and secure management of digital assets, which are typically offered by the blockchain infrastructures of emerging metaverse environments. This paper introduces a novel interactive and realistic VR-based serious game for training law enforcement officers in the analysis and understanding of terroristic activities. The game is driven by pragmatic models of terroristic actions and teaches its users how to predict and anticipate indicators of terroristic attacks. It also provides the means for generating datasets to train Artificial Intelligence (AI) modules that could help analyzing and predict potentially terroristic activities. Moreover, the paper provides an outlook for the evolution of the game in metaverse environments, where blockchain infrastructures can be used to both boost the cyber-resilience of the game and to safeguard the trustworthiness of the data generation process. Additional information can be found here!

Margariti, K., Velanas, P., Malliarakis, C., & Roussakis, V. (2023). LAW-GAME: Elevating Experiential Training Through Gamification Technologies. In Proceedings of the Research and Innovation Symposium for European Security and Defense (RISE-SD), 1st edition, 2023 (pp. 92-94). Satways Ltd., Athens. ISSN: 2945-1183.

 The aim of our project is to train police officers on the procedure, enhancing the transition between the theory and real-life practice through gamification technologies in a safe and controlled virtual environment. Essential tasks during the creation of LAW-GAME serious game are to virtualize and accurately recreate the real world, by realistically simulating and analyzing aspects of a real-world situations. Additional information can be found here!

Ospina-Bohórquez, A., Del Pozo, S., Courtenay, L. A., & González-Aguilera, D. (2023). Handheld stereo photogrammetry applied to crime scene analysis. Measurement, 216, 112861. ISSN 0263-2241.

This paper presents a promising handheld and non-invasive approach based on stereo-photogrammetry for crime scene documentation, reconstruction, and analysis. Specifically, the performance of a handheld stereo image device has been assessed to conduct three-dimensional analysis of crime scenes. To demonstrate the potential of this approach in this field, a crime scene was simulated and virtually reconstructed using the handheld stereo image device and a terrestrial laser scanner (TLS). After processing the required data, a metric analysis from both data sources (based on distances, areas, and angles extracted) was performed, compared, and evaluated using robust statistical analyses. From this research, it can be concluded that handheld stereo-photogrammetry allows the metric analysis of crime scenes to be carried out with sufficient precision. A maximum linear error of 8 mm and angular error of 3.6° was obtained, which positions this technology as promising for reconstructing of this type of complex scenes. Additional information can be found here!

Bastida, L., Sillaurren, S., Loizaga, E., Tomé, E., & Moya, A. (2024). Exploring human emotions: A virtual reality-based experimental approach integrating physiological and facial analysis. Multimodal Technologies and Interaction, 8(6), 47.

This paper, led by Tecnalia- WP6 leader, researches the classification of human emotions in a virtual reality (VR) context by analysing psychophysiological signals and facial expressions. Key objectives include exploring emotion categorisation models, identifying critical human signals for assessing emotions, and evaluating the accuracy of these signals in VR environments. A systematic literature review was performed through peer-reviewed articles, forming the basis for our methodologies. The integration of various emotion classifiers employs a ‘late fusion’ technique due to varying accuracies among classifiers. Notably, facial expression analysis faces challenges from VR equipment occluding crucial facial regions like the eyes, which significantly impacts emotion recognition accuracy. A weighted averaging system prioritises the psychophysiological classifier over the facial recognition classifiers due to its higher accuracy. Findings suggest that while combined techniques are promising, they struggle with mixed emotional states as well as with fear and trust emotions. The research underscores the potential and limitations of current technologies, recommending enhanced algorithms for effective interpretation of complex emotional expressions in VR. The study provides a groundwork for future advancements, aiming to refine emotion recognition systems through systematic data collection and algorithm optimisation. Additional information can be found here!