Maura Mengoni’s research while affiliated with Marche Polytechnic University and other places

The digital games industry has grown exponentially due to the diversification of games and the increasing multiplicity of the user target base. The market explosion and the great variety make digital game cataloguing and classification challenging issues whose effectiveness can advance scientific research and address design, development and distribution. Firstly, the present study reviews previous cataloguing for video and serious games through systematic literature review and, joining together the findings from the literature review, develops a unified cataloguing model based on five definitions. This model can aid designers in tailoring their applications and contribute to disseminating game design knowledge in academic research. Then, a matrix that correlates design principles and the cataloguing model’s metadata is applied to the cataloguing model, obtaining a unified classification system. Together, they offer a comprehensive framework for understanding the multifaceted landscape of digital games, addressing the limitations of existing domain-specific approaches and providing a versatile tool for game designers. Research validation exploits a two-stage cluster analysis using agglomerative hierarchical and k-means clustering on the data extracted from a sample of digital games. The results show the framework's effectiveness in categorizing digital games without a clear-cut distinction between video and serious games. The system's application in real-world scenarios suggests its potential to guide game development. Future work will refine the proposal based on feedback from digital game designers, expanding the research scope to include a broader range of games.

This paper introduces a web-platform system that performs semi-automatic compute of several risk indexes, based on the considered evaluation method (e.g., RULA—Rapid Upper Limb Assessment, REBA—Rapid Entire Body Assessment, OCRA—OCcupational Repetitive Action) to support ergonomics risk estimation, and provides augmented analytics to proactively improve ergonomic risk monitoring based on the characteristics of workers (e.g., age, gender), working tasks, and environment. It implements a body detection system, marker-less and low cost, based on the use of RGB cameras, which exploits the open-source deep learning model CMU (Carnegie Mellon University), from the tf-pose-estimation project, assuring worker privacy and data protection, which has been already successfully assessed in standard laboratory conditions. The paper provides a full description of the proposed platform and reports the results of validation in a real industrial case study regarding a washing machine assembly line composed by 5 workstations. A total of 15 workers have been involved. Results suggest how the proposed system is able to significantly speed up the ergonomic assessment and to predict angles and perform a RULA and OCRA analysis, with an accuracy comparable to that obtainable from a manual analysis, even under the unpredictable conditions that can be found in a real working environment.