Publications
OUR RESEARCH
Scientific Publications
Here you can find the comprehensive list of publications from the members of the Research Center on Computer Vision and eXtended Reality (xRAI).
Use the tag cloud to filter papers based on specific research topics, or use the menus to filter by year, type of publication, or authors.
For each paper, you have the option to view additional details such as the Abstract, Links, and BibTex record.
Research is formalized curiosity. It is poking and prying with a purpose
Zora Neale Hurston
2025
Bruschi, Valeria; Generosi, Andrea; Terenzi, Alessandro; Mengoni, Maura; Cecchi, Stefania
A Preliminary Study on the Effect of Spatial Sound Reproduction based on Physiological Responses and Facial Expressions of the Listener Proceedings Article
In: 2025 Immersive and 3D Audio: from Architecture to Automotive (I3DA), pp. 1–7, IEEE, Bologna, Italy, 2025, ISBN: 979-8-3315-5828-4.
@inproceedings{bruschi_preliminary_2025,
title = {A Preliminary Study on the Effect of Spatial Sound Reproduction based on Physiological Responses and Facial Expressions of the Listener},
author = {Valeria Bruschi and Andrea Generosi and Alessandro Terenzi and Maura Mengoni and Stefania Cecchi},
url = {https://ieeexplore.ieee.org/document/11202118/},
doi = {10.1109/I3DA65421.2025.11202118},
isbn = {979-8-3315-5828-4},
year = {2025},
date = {2025-09-01},
urldate = {2025-11-06},
booktitle = {2025 Immersive and 3D Audio: from Architecture to Automotive (I3DA)},
pages = {1–7},
publisher = {IEEE},
address = {Bologna, Italy},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Bruschi, Valeria; Generosi, Andrea; Dourou, Nefeli Aikaterini; Spinsante, Susanna; Mengoni, Maura; Cecchi, Stefania
Vehicle Sound Interaction: A Preliminary Study on Driver’s Experience Affected by Immersive Sound Reproduction Proceedings Article
In: 2025 Immersive and 3D Audio: from Architecture to Automotive (I3DA), pp. 1–9, IEEE, Bologna, Italy, 2025, ISBN: 979-8-3315-5828-4.
@inproceedings{bruschi_vehicle_2025,
title = {Vehicle Sound Interaction: A Preliminary Study on Driver’s Experience Affected by Immersive Sound Reproduction},
author = {Valeria Bruschi and Andrea Generosi and Nefeli Aikaterini Dourou and Susanna Spinsante and Maura Mengoni and Stefania Cecchi},
url = {https://ieeexplore.ieee.org/document/11202078/},
doi = {10.1109/I3DA65421.2025.11202078},
isbn = {979-8-3315-5828-4},
year = {2025},
date = {2025-09-01},
urldate = {2025-11-06},
booktitle = {2025 Immersive and 3D Audio: from Architecture to Automotive (I3DA)},
pages = {1–9},
publisher = {IEEE},
address = {Bologna, Italy},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Generosi, Andrea; Villafan, Josè Yuri; Ferretti, Maddalena; Mengoni, Maura
A recommender-based web platform to boost tourism in marginal territories Journal Article
In: Information Technology & Tourism, vol. 27, no. 3, pp. 797–831, 2025, ISSN: 1098-3058, 1943-4294.
@article{generosi_recommender-based_2025,
title = {A recommender-based web platform to boost tourism in marginal territories},
author = {Andrea Generosi and Josè Yuri Villafan and Maddalena Ferretti and Maura Mengoni},
url = {https://link.springer.com/10.1007/s40558-025-00327-1},
doi = {10.1007/s40558-025-00327-1},
issn = {1098-3058, 1943-4294},
year = {2025},
date = {2025-09-01},
urldate = {2025-09-30},
journal = {Information Technology & Tourism},
volume = {27},
number = {3},
pages = {797–831},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Generosi, Andrea; Villafan, Josè Yuri; Montanari, Roberto; Mengoni, Maura
Integration of Data Fusion and Deep Neural Networks for In-vehicle Symbiotic HMI Design Proceedings Article
In: Antona, Margherita; Stephanidis, Constantine (Ed.): Universal Access in Human-Computer Interaction, pp. 326–342, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-93847-4 978-3-031-93848-1.
@inproceedings{antona_integration_2025,
title = {Integration of Data Fusion and Deep Neural Networks for In-vehicle Symbiotic HMI Design},
author = {Andrea Generosi and Josè Yuri Villafan and Roberto Montanari and Maura Mengoni},
editor = {Margherita Antona and Constantine Stephanidis},
url = {https://link.springer.com/10.1007/978-3-031-93848-1_22},
doi = {10.1007/978-3-031-93848-1_22},
isbn = {978-3-031-93847-4 978-3-031-93848-1},
year = {2025},
date = {2025-01-01},
urldate = {2025-09-30},
booktitle = {Universal Access in Human-Computer Interaction},
volume = {15780},
pages = {326–342},
publisher = {Springer Nature Switzerland},
address = {Cham},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Angelis, Grazia De; Saviano, Emilio; Scuotto, Chiara; Iannello, Nicoló M.; Generosi, Andrea; Luca, Valerio De; Rega, Angelo; Triberti, Stefano; Affuso, Gaetana; Gallo, Luigi; Limone, Pierpaolo
2025, (Accepted: 2025-10-16T18:01:10Z).
Abstract | Links | BibTeX | Tags: Psychology, User study, Virtual Reality
@unpublished{de_angelis_utilitarian_2025,
title = {Utilitarian choice and self-serving cognitive distortions: using VR to investigate adolescents’ responses and enhance interventions},
author = {Grazia De Angelis and Emilio Saviano and Chiara Scuotto and Nicoló M. Iannello and Andrea Generosi and Valerio De Luca and Angelo Rega and Stefano Triberti and Gaetana Affuso and Luigi Gallo and Pierpaolo Limone},
url = {https://www.unipegaso.iris.cineca.it/handle/20.500.12607/62203},
year = {2025},
date = {2025-01-01},
urldate = {2026-02-06},
abstract = {11},
note = {Accepted: 2025-10-16T18:01:10Z},
keywords = {Psychology, User study, Virtual Reality},
pubstate = {published},
tppubtype = {unpublished}
}
11
Pirani, Massimiliano; Generosi, Andrea; Spalazzi, Luca
A perspective on generative artificial intelligence for the enhancement of methods in system dynamics Proceedings Article
In: 20th IRDO International Conference: Innovative, sustainable & socially responsible society 2025, I. Perko, M. Mulej, P. Glavic, A. Hrast, & T. Jere Jakulin (Eds.), 2025, ISBN: 978-961-7141-11-5.
Abstract | Links | BibTeX | Tags:
@inproceedings{pirani_perspective_2025,
title = {A perspective on generative artificial intelligence for the enhancement of methods in system dynamics},
author = {Massimiliano Pirani and Andrea Generosi and Luca Spalazzi},
url = {https://www.irdo.si/irdo2025/posters/45.pdf},
isbn = {978-961-7141-11-5},
year = {2025},
date = {2025-01-01},
urldate = {2025-11-05},
booktitle = {20th IRDO International Conference: Innovative, sustainable & socially responsible society 2025, I. Perko, M. Mulej, P. Glavic, A. Hrast, & T. Jere Jakulin (Eds.)},
abstract = {System Dynamics (SD) offers a set of indispensable tools for systems engineering, systems thinking, and cybernetics. To date, the development of such valuable models — crucial for ensuring causality and reproducibility in studies addressing socio-technical and social sustainability issues — has been predominantly manual. Furthermore, the criteria and processes involved in creating models such as Causal Loop Diagrams (CLD) and Stock and Flow Diagrams (SFD) are, in many cases, neither explainable nor reproducible. Recently, some studies have expressed some potential for generative artificial intelligence to be considered as a valuable tool for the automated construction of CLD models. Although promising, still many problems affect this technique but, most of all, there is a clear gap in the construction of SFD and in the transformation of a CLD into an equivalent SFD. SFDs are indeed essential when a dynamic has to be encoded into a mathematical system, in order to allow simulations, predictions and, most of all, synthesis of some control system. In this work, we would like to provide a perspective on the envisioned methodologies that have the potential to provide breakthroughs in this area, with a special focus on generative artificial intelligence, which is currently the best available technology for these kinds of transformations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
System Dynamics (SD) offers a set of indispensable tools for systems engineering, systems thinking, and cybernetics. To date, the development of such valuable models — crucial for ensuring causality and reproducibility in studies addressing socio-technical and social sustainability issues — has been predominantly manual. Furthermore, the criteria and processes involved in creating models such as Causal Loop Diagrams (CLD) and Stock and Flow Diagrams (SFD) are, in many cases, neither explainable nor reproducible. Recently, some studies have expressed some potential for generative artificial intelligence to be considered as a valuable tool for the automated construction of CLD models. Although promising, still many problems affect this technique but, most of all, there is a clear gap in the construction of SFD and in the transformation of a CLD into an equivalent SFD. SFDs are indeed essential when a dynamic has to be encoded into a mathematical system, in order to allow simulations, predictions and, most of all, synthesis of some control system. In this work, we would like to provide a perspective on the envisioned methodologies that have the potential to provide breakthroughs in this area, with a special focus on generative artificial intelligence, which is currently the best available technology for these kinds of transformations.
Generosi, Andrea; Villafan, Josè Yuri; Montanari, Roberto; Mengoni, Maura
Integration of Data Fusion and Deep Neural Networks for In-vehicle Symbiotic HMI Design Proceedings Article
In: Universal Access in Human-Computer Interaction, pp. 326–342, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-93847-4 978-3-031-93848-1.
@inproceedings{generosi_integration_2025,
title = {Integration of Data Fusion and Deep Neural Networks for In-vehicle Symbiotic HMI Design},
author = {Andrea Generosi and Josè Yuri Villafan and Roberto Montanari and Maura Mengoni},
url = {https://link.springer.com/10.1007/978-3-031-93848-1_22},
doi = {10.1007/978-3-031-93848-1_22},
isbn = {978-3-031-93847-4 978-3-031-93848-1},
year = {2025},
date = {2025-01-01},
urldate = {2025-09-30},
booktitle = {Universal Access in Human-Computer Interaction},
volume = {15780},
pages = {326–342},
publisher = {Springer Nature Switzerland},
address = {Cham},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Agostinelli, Thomas; Ceccacci, Silvia; Generosi, Andrea; Giancamilli, Giovanni; Mengoni, Maura
A Multi-Criteria Analysis Method in Algorithm-Driven Design Proceedings Article
In: Stefano, Paolo Di; Gherardini, Francesco; Nigrelli, Vincenzo; Rizzi, Caterina; Sequenzia, Gaetano; Tumino, Davide (Ed.): Design Tools and Methods in Industrial Engineering IV, pp. 67–74, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-76596-4 978-3-031-76597-1.
@inproceedings{di_stefano_multi-criteria_2025,
title = {A Multi-Criteria Analysis Method in Algorithm-Driven Design},
author = {Thomas Agostinelli and Silvia Ceccacci and Andrea Generosi and Giovanni Giancamilli and Maura Mengoni},
editor = {Paolo Di Stefano and Francesco Gherardini and Vincenzo Nigrelli and Caterina Rizzi and Gaetano Sequenzia and Davide Tumino},
url = {https://link.springer.com/10.1007/978-3-031-76597-1_8},
doi = {10.1007/978-3-031-76597-1_8},
isbn = {978-3-031-76596-4 978-3-031-76597-1},
year = {2025},
date = {2025-01-01},
urldate = {2025-09-30},
booktitle = {Design Tools and Methods in Industrial Engineering IV},
pages = {67–74},
publisher = {Springer Nature Switzerland},
address = {Cham},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Marfoglia, Alessandra; Santilli, Tommaso; Generosi, Andrea; Mengoni, Maura; Giaconi, Catia; Ceccacci, Silvia
Co-designing a Virtual Museum Application with a Haptic Interface Involving People with Vision Impairments and Blindness Journal Article
In: The International Journal of the Inclusive Museum, vol. 18, no. 2, pp. 201–230, 2025, ISSN: 1835-2014, 1835-2022.
@article{marfoglia_co-designing_2025,
title = {Co-designing a Virtual Museum Application with a Haptic Interface Involving People with Vision Impairments and Blindness},
author = {Alessandra Marfoglia and Tommaso Santilli and Andrea Generosi and Maura Mengoni and Catia Giaconi and Silvia Ceccacci},
url = {https://cgscholar.com/bookstore/works/codesigning-a-virtual-museum-application-with-a-haptic-interface-involving-people-with-vision-impairments-and-blindness},
doi = {10.18848/1835-2014/CGP/v18i02/201-230},
issn = {1835-2014, 1835-2022},
year = {2025},
date = {2025-01-01},
urldate = {2025-09-30},
journal = {The International Journal of the Inclusive Museum},
volume = {18},
number = {2},
pages = {201–230},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2024
Agostinelli, Thomas; Generosi, Andrea; Ceccacci, Silvia; Mengoni, Maura
Validation of computer vision-based ergonomic risk assessment tools for real manufacturing environments Journal Article
In: Scientific Reports, vol. 14, no. 1, pp. 27785, 2024, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Centered Design, Industry 4.0
@article{agostinelli_validation_2024,
title = {Validation of computer vision-based ergonomic risk assessment tools for real manufacturing environments},
author = {Thomas Agostinelli and Andrea Generosi and Silvia Ceccacci and Maura Mengoni},
url = {https://www.nature.com/articles/s41598-024-79373-4},
doi = {10.1038/s41598-024-79373-4},
issn = {2045-2322},
year = {2024},
date = {2024-11-01},
urldate = {2024-12-28},
journal = {Scientific Reports},
volume = {14},
number = {1},
pages = {27785},
abstract = {This study contributes to understanding semi-automated ergonomic risk assessments in industrial manufacturing environments, proposing a practical tool for enhancing worker safety and operational efficiency. In the Industry 5.0 era, the human-centric approach in manufacturing is crucial, especially considering the aging workforce and the dynamic nature of the entire modern industrial sector, today integrating digital technology, automation, and sustainable practices to enhance productivity and environmental responsibility. This approach aims to adapt work conditions to individual capabilities, addressing the high incidence of work-related musculoskeletal disorders (MSDs). The traditional, subjective methods of ergonomic assessment are inadequate for dynamic settings, highlighting the need for affordable, automatic tools for continuous monitoring of workers’ postures to evaluate ergonomic risks effectively during tasks. To enable this perspective, 2D RGB Motion Capture (MoCap) systems based on computer vision currently seem the technologies of choice, given their low intrusiveness, cost, and implementation effort. However, the reliability and applicability of these systems in the dynamic and varied manufacturing environment remain uncertain. This research benchmarks various literature proposed MoCap tools and examines the viability of MoCap systems for ergonomic risk assessments in Industry 5.0 by exploiting one of the benchmarked semi-automated, low-cost and non-intrusive 2D RGB MoCap system, capable of continuously monitoring and analysing workers’ postures. By conducting experiments across varied manufacturing environments, this research evaluates the system’s effectiveness in assessing ergonomic risks and its adaptability to different production lines. Results reveal that the accuracy of risk assessments varies by specific environmental conditions and workstation setups. Although these systems are not yet optimized for expert-level risk certification, they offer significant potential for enhancing workplace safety and efficiency by providing continuous posture monitoring. Future improvements could explore advanced computational techniques like machine learning to refine ergonomic assessments further.},
keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Centered Design, Industry 4.0},
pubstate = {published},
tppubtype = {article}
}
This study contributes to understanding semi-automated ergonomic risk assessments in industrial manufacturing environments, proposing a practical tool for enhancing worker safety and operational efficiency. In the Industry 5.0 era, the human-centric approach in manufacturing is crucial, especially considering the aging workforce and the dynamic nature of the entire modern industrial sector, today integrating digital technology, automation, and sustainable practices to enhance productivity and environmental responsibility. This approach aims to adapt work conditions to individual capabilities, addressing the high incidence of work-related musculoskeletal disorders (MSDs). The traditional, subjective methods of ergonomic assessment are inadequate for dynamic settings, highlighting the need for affordable, automatic tools for continuous monitoring of workers’ postures to evaluate ergonomic risks effectively during tasks. To enable this perspective, 2D RGB Motion Capture (MoCap) systems based on computer vision currently seem the technologies of choice, given their low intrusiveness, cost, and implementation effort. However, the reliability and applicability of these systems in the dynamic and varied manufacturing environment remain uncertain. This research benchmarks various literature proposed MoCap tools and examines the viability of MoCap systems for ergonomic risk assessments in Industry 5.0 by exploiting one of the benchmarked semi-automated, low-cost and non-intrusive 2D RGB MoCap system, capable of continuously monitoring and analysing workers’ postures. By conducting experiments across varied manufacturing environments, this research evaluates the system’s effectiveness in assessing ergonomic risks and its adaptability to different production lines. Results reveal that the accuracy of risk assessments varies by specific environmental conditions and workstation setups. Although these systems are not yet optimized for expert-level risk certification, they offer significant potential for enhancing workplace safety and efficiency by providing continuous posture monitoring. Future improvements could explore advanced computational techniques like machine learning to refine ergonomic assessments further.
Generosi, Andrea; Bruschi, Valeria; Cecchi, Stefania; Dourou, Nefeli Aikaterini; Montanari, Roberto; Mengoni, Maura
An Innovative System for Driver Monitoring and Vehicle Sound Interaction Proceedings Article
In: 2024 IEEE International Workshop on Metrology for Automotive (MetroAutomotive), pp. 159–164, IEEE, Bologna, Italy, 2024, ISBN: 979-8-3503-8498-7.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Deep Learning, Human Computer Interaction
@inproceedings{generosi_innovative_2024,
title = {An Innovative System for Driver Monitoring and Vehicle Sound Interaction},
author = {Andrea Generosi and Valeria Bruschi and Stefania Cecchi and Nefeli Aikaterini Dourou and Roberto Montanari and Maura Mengoni},
url = {https://ieeexplore.ieee.org/document/10615427/},
doi = {10.1109/MetroAutomotive61329.2024.10615427},
isbn = {979-8-3503-8498-7},
year = {2024},
date = {2024-06-01},
urldate = {2024-12-28},
booktitle = {2024 IEEE International Workshop on Metrology for Automotive (MetroAutomotive)},
pages = {159–164},
publisher = {IEEE},
address = {Bologna, Italy},
abstract = {An important aspect of Advanced Driver-Assistance Systems is the real-time monitoring of the driver and the interaction with him/her. In this scenario, the proposed work is focused on the development of an innovative system capable of analyzing the driver’s state and interact with him/her in a innovative way. In particular, the driver monitoring is obtained through the implementation of a multimodal approach that exploits deep learning and data fusion techniques while the interaction is achieved through sound signals elaborated with digital signal processing algorithm for the creation of an immersive scenario.},
keywords = {Artificial Intelligence, Deep Learning, Human Computer Interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
An important aspect of Advanced Driver-Assistance Systems is the real-time monitoring of the driver and the interaction with him/her. In this scenario, the proposed work is focused on the development of an innovative system capable of analyzing the driver’s state and interact with him/her in a innovative way. In particular, the driver monitoring is obtained through the implementation of a multimodal approach that exploits deep learning and data fusion techniques while the interaction is achieved through sound signals elaborated with digital signal processing algorithm for the creation of an immersive scenario.
Jamali, Reza; Generosi, Andrea; Villafan, Josè Yuri; Mengoni, Maura; Pelagalli, Leonardo; Battista, Gianmarco; Martarelli, Milena; Chiariotti, Paolo; Mansi, Silvia Angela; Arnesano, Marco; Castellini, Paolo
Facial Expression Recognition for Measuring Jurors’ Attention in Acoustic Jury Tests Journal Article
In: Sensors, vol. 24, no. 7, pp. 2298, 2024, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Emotion Recognition
@article{jamali_facial_2024,
title = {Facial Expression Recognition for Measuring Jurors’ Attention in Acoustic Jury Tests},
author = {Reza Jamali and Andrea Generosi and Josè Yuri Villafan and Maura Mengoni and Leonardo Pelagalli and Gianmarco Battista and Milena Martarelli and Paolo Chiariotti and Silvia Angela Mansi and Marco Arnesano and Paolo Castellini},
url = {https://www.mdpi.com/1424-8220/24/7/2298},
doi = {10.3390/s24072298},
issn = {1424-8220},
year = {2024},
date = {2024-04-01},
urldate = {2024-12-28},
journal = {Sensors},
volume = {24},
number = {7},
pages = {2298},
abstract = {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.},
keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Emotion Recognition},
pubstate = {published},
tppubtype = {article}
}
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.
Generosi, Andrea; Villafan, Josè Yuri; Montanari, Roberto; Mengoni, Maura
A Multimodal Approach to Understand Driver’s Distraction for DMS Proceedings Article
In: Antona, Margherita; Stephanidis, Constantine (Ed.): Universal Access in Human-Computer Interaction, pp. 250–270, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-60875-9.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Human Computer Interaction
@inproceedings{generosi_multimodal_2024,
title = {A Multimodal Approach to Understand Driver’s Distraction for DMS},
author = {Andrea Generosi and Josè Yuri Villafan and Roberto Montanari and Maura Mengoni},
editor = {Margherita Antona and Constantine Stephanidis},
doi = {10.1007/978-3-031-60875-9_17},
isbn = {978-3-031-60875-9},
year = {2024},
date = {2024-01-01},
booktitle = {Universal Access in Human-Computer Interaction},
pages = {250–270},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {This study introduces a multimodal approach for enhancing the accuracy of Driver Monitoring Systems (DMS) in detecting driver distraction. By integrating data from vehicle control units with vision-based information, the research aims to address the limitations of current DMS. The experimental setup involves a driving simulator and advanced computer vision, deep learning technologies for facial expression recognition, and head rotation analysis. The findings suggest that combining various data types—behavioral, physiological, and emotional—can significantly improve DMS’s predictive capability. This research contributes to the development of more sophisticated, adaptive, and real-time systems for improving driver safety and advancing autonomous driving technologies.},
keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Human Computer Interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
This study introduces a multimodal approach for enhancing the accuracy of Driver Monitoring Systems (DMS) in detecting driver distraction. By integrating data from vehicle control units with vision-based information, the research aims to address the limitations of current DMS. The experimental setup involves a driving simulator and advanced computer vision, deep learning technologies for facial expression recognition, and head rotation analysis. The findings suggest that combining various data types—behavioral, physiological, and emotional—can significantly improve DMS’s predictive capability. This research contributes to the development of more sophisticated, adaptive, and real-time systems for improving driver safety and advancing autonomous driving technologies.
Mengoni, Maura; Ceccacci, Silvia; Generosi, Andrea
Emotion Recognition and Affective Computing Book Section
In: Interaction Techniques and Technologies in Human-Computer Interaction, CRC Press, 2024, ISBN: 978-1-003-49067-8.
Abstract | BibTeX | Tags: Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Emotion Recognition
@incollection{mengoni_emotion_2024,
title = {Emotion Recognition and Affective Computing},
author = {Maura Mengoni and Silvia Ceccacci and Andrea Generosi},
isbn = {978-1-003-49067-8},
year = {2024},
date = {2024-01-01},
booktitle = {Interaction Techniques and Technologies in Human-Computer Interaction},
publisher = {CRC Press},
abstract = {This chapter explores the challenging topic of emotion recognition by affective computing. The importance of considering and understanding people’s emotions in interaction design is discussed, focusing on the role of human emotions in the entire life cycle of human–system interaction as a means to innovate products and services. The measurement of emotions is also analyzed, including the classification of human emotions and recognition methods, as well as current techniques for measuring emotional responses. An emotional-based approach and related technologies are considered in managing the entire life cycle of human–system interaction as an innovation driver. This chapter also presents how to use affective computing in cross-transversal applications, concentrating on potential applications and different case studies. This chapter concludes with a look towards a world of emotional intelligence, where affective computing plays a crucial role in collecting and analyzing emotional data to support innovative product and service experiences.},
keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Emotion Recognition},
pubstate = {published},
tppubtype = {incollection}
}
This chapter explores the challenging topic of emotion recognition by affective computing. The importance of considering and understanding people’s emotions in interaction design is discussed, focusing on the role of human emotions in the entire life cycle of human–system interaction as a means to innovate products and services. The measurement of emotions is also analyzed, including the classification of human emotions and recognition methods, as well as current techniques for measuring emotional responses. An emotional-based approach and related technologies are considered in managing the entire life cycle of human–system interaction as an innovation driver. This chapter also presents how to use affective computing in cross-transversal applications, concentrating on potential applications and different case studies. This chapter concludes with a look towards a world of emotional intelligence, where affective computing plays a crucial role in collecting and analyzing emotional data to support innovative product and service experiences.
Giancamilli, Giovanni; Generosi, Andrea; Agostinelli, Thomas; Mazzuto, Giovanni; Mengoni, Maura
Methodological Framework to Define Connected Machines’ Specifications for Smart Factories Proceedings Article
In: Carfagni, Monica; Furferi, Rocco; Stefano, Paolo Di; Governi, Lapo; Gherardini, Francesco (Ed.): Design Tools and Methods in Industrial Engineering III, pp. 399–406, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-58094-9.
Abstract | Links | BibTeX | Tags: Cyber-Physical Systems, Industry 4.0, Smart Manufacturing
@inproceedings{giancamilli_methodological_2024,
title = {Methodological Framework to Define Connected Machines’ Specifications for Smart Factories},
author = {Giovanni Giancamilli and Andrea Generosi and Thomas Agostinelli and Giovanni Mazzuto and Maura Mengoni},
editor = {Monica Carfagni and Rocco Furferi and Paolo Di Stefano and Lapo Governi and Francesco Gherardini},
doi = {10.1007/978-3-031-58094-9_44},
isbn = {978-3-031-58094-9},
year = {2024},
date = {2024-01-01},
booktitle = {Design Tools and Methods in Industrial Engineering III},
pages = {399–406},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {This paper discusses the integration of Industry 4.0 technologies in manufacturing facilities to implement the Smart Factory paradigm exploiting Cyber-Physical Systems, Internet of Things, Big Data and Cloud Computing as key enabling technologies (KET). The study aims to define a methodological framework to design and implement connected machines to realize the Smart Factory model by interconnecting the KETs through the pragmatically implementation of digital twins from design to production.},
keywords = {Cyber-Physical Systems, Industry 4.0, Smart Manufacturing},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper discusses the integration of Industry 4.0 technologies in manufacturing facilities to implement the Smart Factory paradigm exploiting Cyber-Physical Systems, Internet of Things, Big Data and Cloud Computing as key enabling technologies (KET). The study aims to define a methodological framework to design and implement connected machines to realize the Smart Factory model by interconnecting the KETs through the pragmatically implementation of digital twins from design to production.
2023
Dourou, Nefeli; Bruschi, Valeria; Generosi, Andrea; Mengoni, Maura; Cecchi, Stefania
The Effect of Immersive Audio Rendering on Listeners’ Emotional State Proceedings Article
In: 2023 Immersive and 3D Audio: from Architecture to Automotive (I3DA), pp. 1–7, IEEE, Bologna, Italy, 2023, ISBN: 979-8-3503-1104-4.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Emotion Recognition, Human Computer Interaction
@inproceedings{dourou_effect_2023,
title = {The Effect of Immersive Audio Rendering on Listeners’ Emotional State},
author = {Nefeli Dourou and Valeria Bruschi and Andrea Generosi and Maura Mengoni and Stefania Cecchi},
url = {https://ieeexplore.ieee.org/document/10289263/},
doi = {10.1109/I3DA57090.2023.10289263},
isbn = {979-8-3503-1104-4},
year = {2023},
date = {2023-09-01},
urldate = {2024-12-28},
booktitle = {2023 Immersive and 3D Audio: from Architecture to Automotive (I3DA)},
pages = {1–7},
publisher = {IEEE},
address = {Bologna, Italy},
abstract = {Immersive audio rendering techniques allow for generating a 3D scenario where the listener can perceive the sound from all directions. An important aspect of these approaches is the subjective perception of the listener and how these types of systems are perceived from the emotional point of view and how they can influence the listener's mood. In this context, a deep investigation of immersive sound perception considering subjective perception in terms of flowing emotion is performed. Starting from a 4-channels immersive audio system and an emotion-aware system based on the analysis of the user's facial expressions, several experiments have been performed to investigate a correlation between immersive perception and the listener's emotions.},
keywords = {Artificial Intelligence, Emotion Recognition, Human Computer Interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
Immersive audio rendering techniques allow for generating a 3D scenario where the listener can perceive the sound from all directions. An important aspect of these approaches is the subjective perception of the listener and how these types of systems are perceived from the emotional point of view and how they can influence the listener's mood. In this context, a deep investigation of immersive sound perception considering subjective perception in terms of flowing emotion is performed. Starting from a 4-channels immersive audio system and an emotion-aware system based on the analysis of the user's facial expressions, several experiments have been performed to investigate a correlation between immersive perception and the listener's emotions.
Ceccacci, Silvia; Generosi, Andrea; Giraldi, Luca; Mengoni, Maura
Emotional Valence from Facial Expression as an Experience Audit Tool: An Empirical Study in the Context of Opera Performance Journal Article
In: Sensors, vol. 23, no. 5, pp. 2688, 2023, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Emotion Recognition, Tourism, User experience
@article{ceccacci_emotional_2023,
title = {Emotional Valence from Facial Expression as an Experience Audit Tool: An Empirical Study in the Context of Opera Performance},
author = {Silvia Ceccacci and Andrea Generosi and Luca Giraldi and Maura Mengoni},
url = {https://www.mdpi.com/1424-8220/23/5/2688},
doi = {10.3390/s23052688},
issn = {1424-8220},
year = {2023},
date = {2023-03-01},
urldate = {2024-12-28},
journal = {Sensors},
volume = {23},
number = {5},
pages = {2688},
abstract = {This paper aims to explore the potential offered by emotion recognition systems to provide a feasible response to the growing need for audience understanding and development in the field of arts organizations. Through an empirical study, it was investigated whether the emotional valence measured on the audience through an emotion recognition system based on facial expression analysis can be used with an experience audit to: (1) support the understanding of the emotional responses of customers toward any clue that characterizes a staged performance; and (2) systematically investigate the customer’s overall experience in terms of their overall satisfaction. The study was carried out in the context of opera live shows in the open-air neoclassical theater Arena Sferisterio in Macerata, during 11 opera performances. A total of 132 spectators were involved. Both the emotional valence provided by the considered emotion recognition system and the quantitative data related to customers’ satisfaction, collected through a survey, were considered. Results suggest how collected data can be useful for the artistic director to estimate the audience’s overall level of satisfaction and make choices about the specific characteristics of the performance, and that emotional valence measured on the audience during the show can be useful to predict overall customer satisfaction, as measured using traditional self-report methods.},
keywords = {Artificial Intelligence, Emotion Recognition, Tourism, User experience},
pubstate = {published},
tppubtype = {article}
}
This paper aims to explore the potential offered by emotion recognition systems to provide a feasible response to the growing need for audience understanding and development in the field of arts organizations. Through an empirical study, it was investigated whether the emotional valence measured on the audience through an emotion recognition system based on facial expression analysis can be used with an experience audit to: (1) support the understanding of the emotional responses of customers toward any clue that characterizes a staged performance; and (2) systematically investigate the customer’s overall experience in terms of their overall satisfaction. The study was carried out in the context of opera live shows in the open-air neoclassical theater Arena Sferisterio in Macerata, during 11 opera performances. A total of 132 spectators were involved. Both the emotional valence provided by the considered emotion recognition system and the quantitative data related to customers’ satisfaction, collected through a survey, were considered. Results suggest how collected data can be useful for the artistic director to estimate the audience’s overall level of satisfaction and make choices about the specific characteristics of the performance, and that emotional valence measured on the audience during the show can be useful to predict overall customer satisfaction, as measured using traditional self-report methods.
Generosi, Andrea; Agostinelli, Thomas; Mengoni, Maura
Smart retrofitting for human factors: a face recognition-based system proposal Journal Article
In: International Journal on Interactive Design and Manufacturing (IJIDeM), vol. 17, no. 1, pp. 421–433, 2023, ISSN: 1955-2505.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Centered Design, Industry 4.0, Machine Learning
@article{generosi_smart_2023,
title = {Smart retrofitting for human factors: a face recognition-based system proposal},
author = {Andrea Generosi and Thomas Agostinelli and Maura Mengoni},
url = {https://doi.org/10.1007/s12008-022-01035-4},
doi = {10.1007/s12008-022-01035-4},
issn = {1955-2505},
year = {2023},
date = {2023-02-01},
urldate = {2024-12-28},
journal = {International Journal on Interactive Design and Manufacturing (IJIDeM)},
volume = {17},
number = {1},
pages = {421–433},
abstract = {Industry nowadays must deal with the so called “fourth industrial revolution”, i.e. Industry 4.0. This revolution is based on the introduction of new paradigms in the manufacturing industry such as flexibility, efficiency, safety, digitization, big data analysis and interconnection. However, human factors’ integration is usually not considered, although included as one of the paradigms. Some of these human factors’ most overlooked aspects are the customization of the worker’s user experience and on-board safety. Moreover, the issue of integrating state of the art technologies on legacy machines is also of utmost importance, as it can make a considerable difference on the economic and environmental aspects of their management, by extending the machine’s life cycle. In response to this issue, the Retrofitting paradigm, the addition of new technologies to legacy machines, has been considered. In this paper we propose a novel modular system architecture for secure authentication and worker’s log-in/log-out traceability based on face recognition and on state-of-the-art Deep Learning and Computer Vision techniques, as Convolutional Neural Networks. Starting from the proposed architecture, we developed and tested a device designed to retrofit legacy machines with such capabilities, keeping particular attention to the interface usability in the design phase, little considered in retrofitting applications along with other Human Factors, despite being one of the pillars of Industry 4.0. This research work’s results showed a dramatic improvement regarding machines on-board access safety.},
keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Centered Design, Industry 4.0, Machine Learning},
pubstate = {published},
tppubtype = {article}
}
Industry nowadays must deal with the so called “fourth industrial revolution”, i.e. Industry 4.0. This revolution is based on the introduction of new paradigms in the manufacturing industry such as flexibility, efficiency, safety, digitization, big data analysis and interconnection. However, human factors’ integration is usually not considered, although included as one of the paradigms. Some of these human factors’ most overlooked aspects are the customization of the worker’s user experience and on-board safety. Moreover, the issue of integrating state of the art technologies on legacy machines is also of utmost importance, as it can make a considerable difference on the economic and environmental aspects of their management, by extending the machine’s life cycle. In response to this issue, the Retrofitting paradigm, the addition of new technologies to legacy machines, has been considered. In this paper we propose a novel modular system architecture for secure authentication and worker’s log-in/log-out traceability based on face recognition and on state-of-the-art Deep Learning and Computer Vision techniques, as Convolutional Neural Networks. Starting from the proposed architecture, we developed and tested a device designed to retrofit legacy machines with such capabilities, keeping particular attention to the interface usability in the design phase, little considered in retrofitting applications along with other Human Factors, despite being one of the pillars of Industry 4.0. This research work’s results showed a dramatic improvement regarding machines on-board access safety.
Ferretti, Maddalena; Rigo, Caterina; Generosi, Andrea; Mengoni, Maura
In: 2023, ISBN: 9788899237431.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Machine Learning, Tourism
@incollection{ferretti_interconnected_2023,
title = {Interconnected Values. An incremental and collaborative digital platform as a branding tool to boost resilience in marginal territories},
author = {Maddalena Ferretti and Caterina Rigo and Andrea Generosi and Maura Mengoni},
url = {https://iris.univpm.it/handle/11566/335422},
isbn = {9788899237431},
year = {2023},
date = {2023-01-01},
urldate = {2024-12-28},
abstract = {This contribution suggests a reflection on new technologies and their impact on marginal areas in Italy, through an ongoing research and design project on branding as a driver of operative and transformative actions. The creation of an innovative platform for the enhancement of Inner Areas (SNAI 2014) is implemented in the framework of “Branding4Resilience” (B4R), a three-year project of national interest (PRIN 2017 - Young Line) funded by MUR and coordinated by UNIVPM, on four Italian inner areas in the regions of Piedmont, Trentino, Sicily and Marche; the B4R Platform design process is tested on the Appennino Basso Pesarese-Anconetano, involving nine municipalities in the inner Marche Region.
Currently, artificial intelligence is applied in tourism to elaborate data on potential guests, to propose highly customized experiences, with a limited assessment of the impacts on the territory. For fragile territories, branding could represent not only a marketing solution but mainly a reactivation strategy, to be co-created with communities, local actors and visitors. B4R investigates an innovative path in which an incremental and collaborative platform for territorial branding can trigger transformation processes to increase the resilience of communities living in marginal contexts; an interdisciplinary approach was the key to co-designing a platform that generates a long-lasting value for the territory, to foster urban transformations and achieve sustainable development objectives.},
keywords = {Artificial Intelligence, Machine Learning, Tourism},
pubstate = {published},
tppubtype = {incollection}
}
This contribution suggests a reflection on new technologies and their impact on marginal areas in Italy, through an ongoing research and design project on branding as a driver of operative and transformative actions. The creation of an innovative platform for the enhancement of Inner Areas (SNAI 2014) is implemented in the framework of “Branding4Resilience” (B4R), a three-year project of national interest (PRIN 2017 - Young Line) funded by MUR and coordinated by UNIVPM, on four Italian inner areas in the regions of Piedmont, Trentino, Sicily and Marche; the B4R Platform design process is tested on the Appennino Basso Pesarese-Anconetano, involving nine municipalities in the inner Marche Region.
Currently, artificial intelligence is applied in tourism to elaborate data on potential guests, to propose highly customized experiences, with a limited assessment of the impacts on the territory. For fragile territories, branding could represent not only a marketing solution but mainly a reactivation strategy, to be co-created with communities, local actors and visitors. B4R investigates an innovative path in which an incremental and collaborative platform for territorial branding can trigger transformation processes to increase the resilience of communities living in marginal contexts; an interdisciplinary approach was the key to co-designing a platform that generates a long-lasting value for the territory, to foster urban transformations and achieve sustainable development objectives.
Currently, artificial intelligence is applied in tourism to elaborate data on potential guests, to propose highly customized experiences, with a limited assessment of the impacts on the territory. For fragile territories, branding could represent not only a marketing solution but mainly a reactivation strategy, to be co-created with communities, local actors and visitors. B4R investigates an innovative path in which an incremental and collaborative platform for territorial branding can trigger transformation processes to increase the resilience of communities living in marginal contexts; an interdisciplinary approach was the key to co-designing a platform that generates a long-lasting value for the territory, to foster urban transformations and achieve sustainable development objectives.
Carulli, Marina; Generosi, Andrea; Bordegoni, Monica; Mengoni, Maura
Design of XR Applications for Museums, Including Technology Maximising Visitors’ Experience Proceedings Article
In: Gerbino, Salvatore; Lanzotti, Antonio; Martorelli, Massimo; Buil, Ramón Mirálbes; Rizzi, Caterina; Roucoules, Lionel (Ed.): Advances on Mechanics, Design Engineering and Manufacturing IV, pp. 1460–1470, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-15928-2.
Abstract | Links | BibTeX | Tags: Augmented Reality, Cultural Heritage, Extended reality, Virtual Reality
@inproceedings{carulli_design_2023,
title = {Design of XR Applications for Museums, Including Technology Maximising Visitors’ Experience},
author = {Marina Carulli and Andrea Generosi and Monica Bordegoni and Maura Mengoni},
editor = {Salvatore Gerbino and Antonio Lanzotti and Massimo Martorelli and Ramón Mirálbes Buil and Caterina Rizzi and Lionel Roucoules},
doi = {10.1007/978-3-031-15928-2_127},
isbn = {978-3-031-15928-2},
year = {2023},
date = {2023-01-01},
booktitle = {Advances on Mechanics, Design Engineering and Manufacturing IV},
pages = {1460–1470},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {eXtended Reality (XR) technology can enhance the visitors’ experience of museums. Due to the variety of XR technologies available that differ in performance, quality of the experience they provide, and cost, it is helpful to refer to the evaluation of the various technologies performed through user studies to select the most suitable ones. This paper presents a set of empirical studies on XR application for museums to select the appropriate technologies to meet visitors’ expectations and maximise the willingness of repeating and recommending the experience. They provide valuable insights for developing Virtual Museum applications increasing the level of presence and experience economy.},
keywords = {Augmented Reality, Cultural Heritage, Extended reality, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
eXtended Reality (XR) technology can enhance the visitors’ experience of museums. Due to the variety of XR technologies available that differ in performance, quality of the experience they provide, and cost, it is helpful to refer to the evaluation of the various technologies performed through user studies to select the most suitable ones. This paper presents a set of empirical studies on XR application for museums to select the appropriate technologies to meet visitors’ expectations and maximise the willingness of repeating and recommending the experience. They provide valuable insights for developing Virtual Museum applications increasing the level of presence and experience economy.
Generosi, Andrea; Caresana, Flavio; Dourou, Nefeli; Bruschi, Valeria; Cecchi, Stefania; Mengoni, Maura
An Experimentation to Measure the Influence of Music on Emotions Proceedings Article
In: Krömker, Heidi (Ed.): HCI in Mobility, Transport, and Automotive Systems, pp. 142–157, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-35908-8.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Deep Learning, Emotion Recognition
@inproceedings{generosi_experimentation_2023,
title = {An Experimentation to Measure the Influence of Music on Emotions},
author = {Andrea Generosi and Flavio Caresana and Nefeli Dourou and Valeria Bruschi and Stefania Cecchi and Maura Mengoni},
editor = {Heidi Krömker},
doi = {10.1007/978-3-031-35908-8_11},
isbn = {978-3-031-35908-8},
year = {2023},
date = {2023-01-01},
booktitle = {HCI in Mobility, Transport, and Automotive Systems},
pages = {142–157},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Several emotion-adaptive systems frameworks have been proposed to enable listeners’ emotional regulation through music reproduction. However, the majority of these frameworks has been implemented only under in-Lab or in-car conditions, in the second case focusing on improving driving performance. Therefore, to the authors’ best knowledge, no research has been conducted for mobility settings, such as trains, planes, yacht, etc. Focusing on this aspect, the proposed approach reports the results obtained from the study of relationship between listener’s induced emotion and music reproduction exploiting an advanced audio system and an innovative technology for face expressions’ recognition. Starting from an experiment in a university lab scenario, with 15 listeners, and a yacht cabin scenario, with 11 listeners, participants’ emotional variability has been deeply investigated reproducing 4 audio enhanced music tracks, to evaluate the listeners’ emotional “sensitivity” to music stimuli. The experimental results indicated that, during the reproduction in the university lab, listeners’ “happiness” and “anger” states were highly affected by the music stimuli and highlighted a possible relationship between music and listeners’ compound emotions. Furthermore, listeners’ emotional engagement was proven to be more affected by music stimuli in the yacht cabin, rather than the university lab.},
keywords = {Artificial Intelligence, Deep Learning, Emotion Recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
Several emotion-adaptive systems frameworks have been proposed to enable listeners’ emotional regulation through music reproduction. However, the majority of these frameworks has been implemented only under in-Lab or in-car conditions, in the second case focusing on improving driving performance. Therefore, to the authors’ best knowledge, no research has been conducted for mobility settings, such as trains, planes, yacht, etc. Focusing on this aspect, the proposed approach reports the results obtained from the study of relationship between listener’s induced emotion and music reproduction exploiting an advanced audio system and an innovative technology for face expressions’ recognition. Starting from an experiment in a university lab scenario, with 15 listeners, and a yacht cabin scenario, with 11 listeners, participants’ emotional variability has been deeply investigated reproducing 4 audio enhanced music tracks, to evaluate the listeners’ emotional “sensitivity” to music stimuli. The experimental results indicated that, during the reproduction in the university lab, listeners’ “happiness” and “anger” states were highly affected by the music stimuli and highlighted a possible relationship between music and listeners’ compound emotions. Furthermore, listeners’ emotional engagement was proven to be more affected by music stimuli in the yacht cabin, rather than the university lab.
Agostinelli, Thomas; Generosi, Andrea; Ceccacci, Silvia; Pretaroli, Rosita; Mengoni, Maura
A Method and Experimentation to Benchmark XR Technologies Enhancing Archeological Museum Experience Proceedings Article
In: Antona, Margherita; Stephanidis, Constantine (Ed.): Universal Access in Human-Computer Interaction, pp. 3–16, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-35897-5.
Abstract | Links | BibTeX | Tags: Augmented Reality, Cultural Heritage, Extended reality, Virtual Reality
@inproceedings{agostinelli_method_2023,
title = {A Method and Experimentation to Benchmark XR Technologies Enhancing Archeological Museum Experience},
author = {Thomas Agostinelli and Andrea Generosi and Silvia Ceccacci and Rosita Pretaroli and Maura Mengoni},
editor = {Margherita Antona and Constantine Stephanidis},
doi = {10.1007/978-3-031-35897-5_1},
isbn = {978-3-031-35897-5},
year = {2023},
date = {2023-01-01},
booktitle = {Universal Access in Human-Computer Interaction},
pages = {3–16},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {The use of eXtended Reality (XR) technologies, including augmented reality (AR), virtual reality (VR), and mixed reality (MR), has become increasingly popular in museums to enhance the visitor experience. However, the impact of XR technologies on Learning Performance in the context of archeological museums needs to be better understood. This study aims to investigate the relationships between Usability, Presence and Learning Performance by developing XR experiences showcasing archeological artefacts and conducting user testing to evaluate their effectiveness. A laboratory test is conducted to compare a VR application with a mobile AR one, presenting the digital models of five archeological findings. Descriptive statistics are used to compare the two case studies, providing valuable insights into the impact of XR technologies on the visitor experience from a learning perspective. The study confirms that Usability has a more significant effect on learning than Presence and can help designers and museum managers better understand the factors contributing to a successful XR experience. The findings suggest that while Presence is an important factor in improving visitors’ experience, Usability should be the priority when designing XR experiences for museums.},
keywords = {Augmented Reality, Cultural Heritage, Extended reality, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
The use of eXtended Reality (XR) technologies, including augmented reality (AR), virtual reality (VR), and mixed reality (MR), has become increasingly popular in museums to enhance the visitor experience. However, the impact of XR technologies on Learning Performance in the context of archeological museums needs to be better understood. This study aims to investigate the relationships between Usability, Presence and Learning Performance by developing XR experiences showcasing archeological artefacts and conducting user testing to evaluate their effectiveness. A laboratory test is conducted to compare a VR application with a mobile AR one, presenting the digital models of five archeological findings. Descriptive statistics are used to compare the two case studies, providing valuable insights into the impact of XR technologies on the visitor experience from a learning perspective. The study confirms that Usability has a more significant effect on learning than Presence and can help designers and museum managers better understand the factors contributing to a successful XR experience. The findings suggest that while Presence is an important factor in improving visitors’ experience, Usability should be the priority when designing XR experiences for museums.
2022
Generosi, Andrea; Agostinelli, Thomas; Ceccacci, Silvia; Mengoni, Maura
A novel platform to enable the future human-centered factory Journal Article
In: The International Journal of Advanced Manufacturing Technology, vol. 122, no. 11-12, pp. 4221–4233, 2022, ISSN: 0268-3768, 1433-3015.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Human-Centered Design
@article{generosi_novel_2022,
title = {A novel platform to enable the future human-centered factory},
author = {Andrea Generosi and Thomas Agostinelli and Silvia Ceccacci and Maura Mengoni},
url = {https://link.springer.com/10.1007/s00170-022-09880-z},
doi = {10.1007/s00170-022-09880-z},
issn = {0268-3768, 1433-3015},
year = {2022},
date = {2022-10-01},
urldate = {2024-12-28},
journal = {The International Journal of Advanced Manufacturing Technology},
volume = {122},
number = {11-12},
pages = {4221–4233},
abstract = {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.},
keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Human-Centered Design},
pubstate = {published},
tppubtype = {article}
}
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.
Generosi, Andrea; Villafan, José Yuri; Giraldi, Luca; Ceccacci, Silvia; Mengoni, Maura
A Test Management System to Support Remote Usability Assessment of Web Applications Journal Article
In: Information, vol. 13, no. 10, pp. 505, 2022, ISSN: 2078-2489.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Deep Learning, Usability, User experience, User interfaces
@article{generosi_test_2022,
title = {A Test Management System to Support Remote Usability Assessment of Web Applications},
author = {Andrea Generosi and José Yuri Villafan and Luca Giraldi and Silvia Ceccacci and Maura Mengoni},
url = {https://www.mdpi.com/2078-2489/13/10/505},
doi = {10.3390/info13100505},
issn = {2078-2489},
year = {2022},
date = {2022-10-01},
urldate = {2024-12-28},
journal = {Information},
volume = {13},
number = {10},
pages = {505},
abstract = {Nowadays, web designers are forced to have an even deeper perception of how users approach their products in terms of user experience and usability. Remote Usability Testing (RUT) is the most appropriate tool to assess the usability of web platforms by measuring the level of user attention, satisfaction, and productivity. RUT does not require the physical presence of users and evaluators, but for this very reason makes data collection more difficult. To simplify data collection and analysis and help RUT moderators collect and analyze user’s data in a non-intrusive manner, this research work proposes a low-cost comprehensive framework based on Deep Learning algorithms. The proposed framework, called Miora, employs facial expression recognition, gaze recognition, and analytics algorithms to capture data about other information of interest for in-depth usability analysis, such as interactions with the analyzed software. It uses a comprehensive evaluation methodology to elicit information about usability metrics and presents the results in a series of graphs and statistics so that the moderator can intuitively analyze the different trends related to the KPI used as usability indicators. To demonstrate how the proposed framework could facilitate the collection of large amounts of data and enable moderators to conduct both remote formative and summative tests in a more efficient way than traditional lab-based usability testing, two case studies have been presented: the analysis of an online shop and of a management platform. Obtained results suggest that this framework can be employed in remote usability testing to conduct both formative and summative tests.},
keywords = {Artificial Intelligence, Deep Learning, Usability, User experience, User interfaces},
pubstate = {published},
tppubtype = {article}
}
Nowadays, web designers are forced to have an even deeper perception of how users approach their products in terms of user experience and usability. Remote Usability Testing (RUT) is the most appropriate tool to assess the usability of web platforms by measuring the level of user attention, satisfaction, and productivity. RUT does not require the physical presence of users and evaluators, but for this very reason makes data collection more difficult. To simplify data collection and analysis and help RUT moderators collect and analyze user’s data in a non-intrusive manner, this research work proposes a low-cost comprehensive framework based on Deep Learning algorithms. The proposed framework, called Miora, employs facial expression recognition, gaze recognition, and analytics algorithms to capture data about other information of interest for in-depth usability analysis, such as interactions with the analyzed software. It uses a comprehensive evaluation methodology to elicit information about usability metrics and presents the results in a series of graphs and statistics so that the moderator can intuitively analyze the different trends related to the KPI used as usability indicators. To demonstrate how the proposed framework could facilitate the collection of large amounts of data and enable moderators to conduct both remote formative and summative tests in a more efficient way than traditional lab-based usability testing, two case studies have been presented: the analysis of an online shop and of a management platform. Obtained results suggest that this framework can be employed in remote usability testing to conduct both formative and summative tests.
Generosi, Andrea; Agostinelli, Thomas; Mengoni, Maura; Ceccacci, Silvia
Augmented Reality for assembly operation training: does immersion affect the recall performance? Proceedings Article
In: 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), pp. 58–63, IEEE, Rome, Italy, 2022, ISBN: 978-1-6654-8574-6.
Abstract | Links | BibTeX | Tags: Augmented Reality, Extended reality, Industry 4.0
@inproceedings{generosi_augmented_2022,
title = {Augmented Reality for assembly operation training: does immersion affect the recall performance?},
author = {Andrea Generosi and Thomas Agostinelli and Maura Mengoni and Silvia Ceccacci},
url = {https://ieeexplore.ieee.org/document/9967520/},
doi = {10.1109/MetroXRAINE54828.2022.9967520},
isbn = {978-1-6654-8574-6},
year = {2022},
date = {2022-10-01},
urldate = {2024-12-28},
booktitle = {2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)},
pages = {58–63},
publisher = {IEEE},
address = {Rome, Italy},
abstract = {This study aims at comparing three assembly training applications based on different XR technologies characterized by different degrees of immersion (i.e., an MR application based on Hololens 2, a desktop AR application and a digital handbook visualized on a monitor). A total of 54 subjects, recruited among students and personnel of Università Politecnica delle Marche, have been involved. They were assigned to 3 groups age and gender matching. Each group is asked to complete the training related to the assembly of a Lego commercial set (i.e., LEGO 10593), using one of the three considered applications. Results allows us to observe the effects of the immersion on the recall performances, assessed in terms of recall completion time, assembly mistakes, picking mistakes and sequence mistakes.},
keywords = {Augmented Reality, Extended reality, Industry 4.0},
pubstate = {published},
tppubtype = {inproceedings}
}
This study aims at comparing three assembly training applications based on different XR technologies characterized by different degrees of immersion (i.e., an MR application based on Hololens 2, a desktop AR application and a digital handbook visualized on a monitor). A total of 54 subjects, recruited among students and personnel of Università Politecnica delle Marche, have been involved. They were assigned to 3 groups age and gender matching. Each group is asked to complete the training related to the assembly of a Lego commercial set (i.e., LEGO 10593), using one of the three considered applications. Results allows us to observe the effects of the immersion on the recall performances, assessed in terms of recall completion time, assembly mistakes, picking mistakes and sequence mistakes.