Fitted avatars: automatic skeleton adjustment for self-avatars in virtual reality

In the era of the metaverse, self-avatars are gaining popularity, as they can enhance presence and provide embodiment when a user is immersed in Virtual Reality. They are also very important in collaborative Virtual Reality to improve communication through gestures. Whether we are using a complex motion capture solution or a few trackers with inverse kinematics (IK), it is essential to have a good match in size between the avatar and the user, as otherwise mismatches in self-avatar posture could be noticeable for the user. To achieve such a correct match in dimensions, a manual process is often required, with the need for a second person to take measurements of body limbs and introduce them into the system. This process can be time-consuming, and prone to errors. In this paper, we propose an automatic measuring method that simply requires the user to do a small set of exercises while wearing a Head-Mounted Display (HMD), two hand controllers, and three trackers. Our work provides an affordable and quick method to automatically extract user measurements and adjust the virtual humanoid skeleton to the exact dimensions. Our results show that our method can reduce the misalignment produced by the IK system when compared to other solutions that simply apply a uniform scaling to an avatar based on the height of the HMD, and make assumptions about the locations of joints with respect to the trackers.This work was funded by the Spanish Ministry of Science and Innovation (PID2021-122136OB-C21). Jose Luis Ponton was also funded by the Spanish Ministry of Universities (FPU21/01927).Peer ReviewedPostprint (published version

Semi-automatic colonic content analysis for diagnostic

The analysis of the morphology and content of the gut is necessary in order to understand metabolic and functional gut activity and for diagnostic purposes. Magnetic resonance imaging (MRI) has become an important modality technique since it is able to visualize soft tissues using no ionizing radiation, and hence removes the need for any contrast agents. In the last few years, MRI of gastrointestinal function has advanced substantially, although scarcely any publication has been devoted to the analysis of the colon content. This paper presents a semi-automatic segmentation tool for the quantitative assessment of the unprepared colon from MRI images. This application has allowed for the analysis of the colon content in various clinical experiments. The results of the assessment have contributed to a better understanding of the functionality of the colon under different diet conditions. The last experiment carried out by medical doctors showed a marked influence of diet on colonic content, accounting for about 30% of the volume variations.Peer ReviewedPostprint (published version

Semi-automatic colonic content analysis for diagnostic

The analysis of the morphology and content of the gut is necessary in order to understand metabolic and functional gut activity and for diagnostic purposes. Magnetic resonance imaging (MRI) has become an important modality technique since it is able to visualize soft tissues using no ionizing radiation, and hence removes the need for any contrast agents. In the last few years, MRI of gastrointestinal function has advanced substantially, although scarcely any publication has been devoted to the analysis of the colon content. This paper presents a semi-automatic segmentation tool for the quantitative assessment of the unprepared colon from MRI images. This application has allowed for the analysis of the colon content in various clinical experiments. The results of the assessment have contributed to a better understanding of the functionality of the colon under different diet conditions. The last experiment carried out by medical doctors showed a marked influence of diet on colonic content, accounting for about 30% of the volume variations.Peer Reviewe

Colonic content assessment from MRI imaging using a semi-automatic approach

The analysis of the morphology and content of the gut is necessary in order to achieve a better understanding of its metabolic and functional activity. Magnetic resonance imaging (MRI) has become an important imaging technique since it is able to visualize soft tissues in an undisturbed bowel using no ionizing radiation. In the last few years, MRI of gastrointestinal function has advanced substantially. However, few studies have focused on the colon, because the analysis of colonic content is time consuming and cumbersome. This paper presents a semi-automatic segmentation tool for the quantitative assessment of the unprepared colon from MRI images. The techniques developed here have been crucial for a number of clinical experiments.Peer ReviewedPostprint (published version