Anna Ferrarotti

@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},

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}

}

@article{Ferrarotti_ST_2024,

title = {QoE Assessment for cultural heritage fruition through immersive media},

author = {Anna Ferrarotti},

doi = {10.1016/j.sctalk.2024.100343},

year  = {2024},

date = {2024-03-31},

journal = {Science Talks},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}