Descrizione del progetto
Il programma di ricerca e sviluppo RESTART – finanziato dall’Unione Europea – NextGenerationEU nell’ambito del PNRR – M4C2, Investimento 1.3, Avviso n. 341 del 15.03.2022 del Ministero dell’Università e della Ricerca (MUR) – prevede l’attuazione di bandi a cascata per un importo superiore a 32,4 milioni di euro, per sostenere progetti di Ricerca Fondamentale, Ricerca Industriale, Sviluppo Sperimentale e Studi di Fattibilità attraverso l’erogazione di opportuni finanziamenti.
L’obiettivo è raggiungere soggetti pubblici e privati, esterni al Partenariato RESTART, fortemente interessati a introdurre innovazioni significative in relazione a prodotti, processi o servizi.
L’ampiezza di queste azioni di finanziamento a cascata consentirà di coinvolgere un vasto gruppo di soggetti esterni al Partenariato:
- Organismi di Ricerca (OdR) pubblici o privati
- Start-up e Spin-off
- Micro, Piccole e Medie Imprese (MPMI)
- Grandi Imprese (GI)
2025
Rocchi, Erica; Zini, Daniela; Lamonaca, Melissa; Carli, Marco
Proxemic Behavior in a Multi-User Virtual Reality Experience for Multilingual Cultural Heritage Education Proceedings Article
In: Proceedings of 2025 14th International Symposium on Image and Signal Processing and Analysis (ISPA), IEEE, 2025.
@inproceedings{Rocchi_ISPA_2025,
title = {Proxemic Behavior in a Multi-User Virtual Reality Experience for Multilingual Cultural Heritage Education},
author = {Erica Rocchi and Daniela Zini and Melissa Lamonaca and Marco Carli},
year = {2025},
date = {2025-10-29},
urldate = {2025-10-29},
booktitle = {Proceedings of 2025 14th International Symposium on Image and Signal Processing and Analysis (ISPA)},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rocchi, Erica; Ferrarotti, Anna; Carli, Marco
A comparison of the Meta Quest Pro and HTC Vive Focus 3 eye-tracking systems: analysis of data accuracy and spatial precision Journal Article
In: IEEE Access, 2025, ISSN: 2169-3536.
@article{Rocchi_A_2025,
title = {A comparison of the Meta Quest Pro and HTC Vive Focus 3 eye-tracking systems: analysis of data accuracy and spatial precision},
author = {Erica Rocchi and Anna Ferrarotti and Marco Carli},
doi = {10.1109/ACCESS.2025.3562672},
issn = {2169-3536},
year = {2025},
date = {2025-04-22},
urldate = {2025-04-22},
journal = {IEEE Access},
abstract = {Virtual Reality’s rise has highlighted eye gaze as a key interaction method. Data reliability becomes critical in this context, with gaze accuracy and precision serving as leading indicators of data quality. This study compared the spatial accuracy and precision of Meta Quest Pro and HTC Vive Focus 3 headsets using eye movement data collected from 30 users under head-free and head-constrained conditions. The targets were placed at different depths from the users. The analysis revealed inconsistencies between manufacturer-provided data, obtained under ideal conditions, and data collected in different settings. Moreover, the results showed greater spatial accuracy for Meta Quest Pro, and higher spatial precision for HTC Vive Focus 3. The aim of this study is to offer an extensive examination of the performance of these systems, thus assisting researchers in choosing suitable eye-tracking technology for diverse applications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Virtual Reality’s rise has highlighted eye gaze as a key interaction method. Data reliability becomes critical in this context, with gaze accuracy and precision serving as leading indicators of data quality. This study compared the spatial accuracy and precision of Meta Quest Pro and HTC Vive Focus 3 headsets using eye movement data collected from 30 users under head-free and head-constrained conditions. The targets were placed at different depths from the users. The analysis revealed inconsistencies between manufacturer-provided data, obtained under ideal conditions, and data collected in different settings. Moreover, the results showed greater spatial accuracy for Meta Quest Pro, and higher spatial precision for HTC Vive Focus 3. The aim of this study is to offer an extensive examination of the performance of these systems, thus assisting researchers in choosing suitable eye-tracking technology for diverse applications.
2024
Ferrarotti, Anna; Baldoni, Sara; Carli, Marco; Battisti, Federica
On the identification of the leading sensory cue in mulsemedia VR applications Proceedings Article
In: 2024 16th International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2024.
@inproceedings{Ferrarotti_QOMEX_2024,
title = {On the identification of the leading sensory cue in mulsemedia VR applications},
author = {Anna Ferrarotti and Sara Baldoni and Marco Carli and Federica Battisti},
doi = {10.1109/QoMEX61742.2024.10598293},
year = {2024},
date = {2024-06-18},
urldate = {2024-06-18},
booktitle = {2024 16th International Conference on Quality of Multimedia Experience (QoMEX)},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ferrarotti, Anna; Baldoni, Sara; Carli, Marco; Battisti, Federica
Interaction goes virtual: towards collaborative XR Proceedings Article
In: Proceedings of the 2024 ACM International Conference on Interactive Media Experiences, ACM, 2024.
@inproceedings{Ferrarotti_IMX_2024,
title = {Interaction goes virtual: towards collaborative XR},
author = {Anna Ferrarotti and Sara Baldoni and Marco Carli and Federica Battisti},
doi = {10.1145/3639701.3661089},
year = {2024},
date = {2024-06-07},
urldate = {2024-06-07},
booktitle = {Proceedings of the 2024 ACM International Conference on Interactive Media Experiences},
publisher = {ACM},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ferrarotti, Anna; Baldoni, Sara; Carli, Marco; Battisti, Federica
Stress Assessment for Augmented Reality Applications Based on Head Movement Features Journal Article
In: IEEE Transactions on Visualization and Computer Graphics, vol. 30, no. 10, pp. 6970-6983, 2024.
@article{Ferrarotti_TVCG_2024,
title = {Stress Assessment for Augmented Reality Applications Based on Head Movement Features},
author = {Anna Ferrarotti and Sara Baldoni and Marco Carli and Federica Battisti},
doi = {10.1109/TVCG.2024.3385637},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Visualization and Computer Graphics},
volume = {30},
number = {10},
pages = {6970-6983},
abstract = {Augmented reality is one of the enabling technologies of the upcoming future. Its usage in working and learning scenarios may lead to a better quality of work and training by helping the operators during the most crucial stages of processes. Therefore, the automatic detection of stress during augmented reality experiences can be a valuable support to prevent consequences on people's health and foster the spreading of this technology. In this work, we present the design of a non-invasive stress assessment approach. The proposed system is based on the analysis of the head movements of people wearing a Head Mounted Display while performing stress-inducing tasks. First, we designed a subjective experiment consisting of two stress-related tests for data acquisition. Then, a statistical analysis of head movements has been performed to determine which features are representative of the presence of stress. Finally, a stress classifier based on a combination of Support Vector Machines has been designed and trained. The proposed approach achieved promising performances thus paving the way for further studies in this research direction.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Augmented reality is one of the enabling technologies of the upcoming future. Its usage in working and learning scenarios may lead to a better quality of work and training by helping the operators during the most crucial stages of processes. Therefore, the automatic detection of stress during augmented reality experiences can be a valuable support to prevent consequences on people's health and foster the spreading of this technology. In this work, we present the design of a non-invasive stress assessment approach. The proposed system is based on the analysis of the head movements of people wearing a Head Mounted Display while performing stress-inducing tasks. First, we designed a subjective experiment consisting of two stress-related tests for data acquisition. Then, a statistical analysis of head movements has been performed to determine which features are representative of the presence of stress. Finally, a stress classifier based on a combination of Support Vector Machines has been designed and trained. The proposed approach achieved promising performances thus paving the way for further studies in this research direction.
