This paper presents one of the first studies on estimating group emotional states in immersive communication environments. We designed a two-phase experiment to assess group emotion recognition in both controlled and natural settings. In the forced-emotion phase, 32 participants mimicked predefined emotions in 2D and 360° video. In the natural-emotion phase, the OpenLAV dataset elicited stronger and more diverse affective responses than LIRIS-ACCEDE, indicating better suitability for triggering emotions. We also found that neutral states are mostly detected as slightly negative valence, suggesting a systematic bias in affect estimation. The study also found greater variability in valence than in arousal, potentially due to the professional setting of the experiment. Overall, the findings demonstrate the feasibility of coarse group emotion detection in 360° video showing a high correlation with participants’ self-perceptions. We believe this tool could be useful for making immersive communication more effective for audience monitoring, enhancing presentation skills, but also more inclusive for people with emotion understanding difficulties.

Extended reality has generated great interest in recent years, among other things, due to the potential it has to transmit non-verbal communication and offer solutions to current distancing problems. This work proposes a gamified experience of three use cases that meet real-life scenarios: teleassistance, guided route, and remote technical support. The objective is to solve each of the scenario tasks that involve collaboration between local and remote users through 360-degree video communication. Specifically, 2D and 3D objects hide symbols that local users must decipher with the help of remote users, who have the necessary information to do that. In this way, the technology (360-degree video communication) and additional tools included in the system are tested: a magnifying glass to offer remote users certain areas of the scene with higher quality, visualization of the hands with the headset to make the experience more realistic, and the cartoon avatars to increase perceived social presence during the interaction. This experience considers the needs of real hybrid meeting applications that have been gamified to test 360-degree video communication but apply to other XR technologies.

This paper evaluates a virtual teleportation system based on 360-degree video communication in a field study at a secondary school and a guided debate training session. Socioemotional aspects, including social presence and avatar representation, were assessed by eight teens. Key findings include a higher "wow" effect for those joining the immersive environment, and challenges with avatar interaction such as raising their hand. The system shows promise as an educational tool for debate training and student engagement.

In recent years, advances in immersive multimedia technologies, such as extended reality (XR) technologies, have led to more realistic and user-friendly devices. However, these devices are often bulky and uncomfortable, still requiring tether connectivity for demanding applications. The deployment of the fifth generation of telecommunications technologies (5G) has set the basis for XR offloading solutions with the goal of enabling lighter and fully wearable XR devices. In this paper, we present a traffic dataset for two demanding XR offloading scenarios that substantially extend those available in the current state of the art, captured using a fully developed end-to-end XR offloading solution. We also propose a set of accurate traffic models for the proposed scenarios based on the captured data, accompanied by a simple and consistent method to generate synthetic data from the fitted models. Finally, using an open-source 5G radio access network (RAN) emulator, we validate the models both at the application and resource allocation layers. Overall, this work aims to provide a valuable contribution to the field with data and tools for designing, testing, improving, and extending XR offloading solutions in academia and industry.

Task offloading has become a key term in the field of immersive media technologies: it can enable lighter and cheaper devices while providing them higher remote computational capabilities. In this paper we present our TCP-based offloading architecture. The architecture, has been specifically designed for immersive media offloading tasks with a particular care in reducing any processing overhead which can degrade the network performance. We tested the architecture for different offloading scenarios and conditions on two different wireless networks: WiFi and 5G millimeter wave technologies. Besides, to test the network on alternative millimeter wave configurations, currently not available on the actual 5G millimeter rollouts, we used a 5G Radio Access Network (RAN) real-time emulator. This emulator was also used to test the offloading architecture for an simulated immersive user sharing network resources with other users. We provide insights of the importance of user prioritization techniques for successful immersive media offloading. The results show a great performance for the tested immersive media scenarios, highlighting the relevance of millimeter wave technology for the future of immersive media applications.

In this paper we evaluate the suitability of handwriting patterns as potential biomarkers to model Parkinson's disease (PD). Although the study of PD is attracting the interest of many researchers around the world, databases to evaluate handwriting patterns are scarce and knowledge about patterns associated to PD is limited and biased to the existing datasets. This paper introduces a database with a total of 935 handwriting tasks collected from 55 PD patients and 94 healthy controls (45 young and 49 old). Three feature sets are extracted from the signals: neuromotor, kinematic, and nonlinear dynamic. Different classifiers are used to discriminate between PD and healthy subjects: support vector machines, k-nearest neighbors, and a multilayer perceptron. The proposed features and classifiers enable to detect PD with accuracies between 81% and 97%. Additionally, new insights are presented on the utility of the studied features for monitoring and detecting PD.

Adaptive Media Playout (AMP), the adaptive modification of media playback speed, has been previously proposed as a technique to modify the playout delay of a user with respect to other users or with real time, thus having a crucial role in social TV and live video streaming. In this paper, we study the subjective impact of the playout rate change, which had received very limited attention in the past. In particular, we analyze the effect of the subject and the source content in the subjective opinion.We have found that most of the previous works underestimated the impact of AMP in perceived quality, and that the speed change rate should not be modified beyond 10% from the original, as a general rule. We have also developed a user scoring model which takes into account the variability between users and sources. Our results can help developing cost models for playout control systems based on AMP, as well as provide some insights to the analysis of other types of subjective assessment tests.

Simulator sickness and visual discomfort are known undesired effects of immersive media systems. In this paper, we propose a methodology to systematically evaluate the effect of camera motion in cybersickness for omnidirectional video contents. By simulating camera motion, we have isolated its effect from other content factors, showing that sickness depends on the motion pattern amplitude and frequency. We have also found that discomfort can be mitigated by introducing fixed-position visual anchors. This could help in the prevention of cybersickness on sensitive users.

The first prototype of a low cost dictation machine for Spanish is described (DIVO). The main characteristics of our recognition approach are: bottomup, hypothesis-verification strategy; large vocabulary, speaker dependent, isolated word recognition. Its modular structure is the cue for quick development and testing of different implementation alternatives. Two of them are presented: one is based in Static phoneme Modeling (SM) and the other uses Discrete Hidden Markov Modeling (DHMM). The system runs on a standard PC compatible (286 or higher) equipped with a DSP board and is fully voice controlled. This first version of the system can address multiple vocabulary sets of up to 2000 words each, with immediate response and reasonable performance. Modules for increasing vocabulary and performance are being developed. I.