{"title":"Towards the Creation of Tools for Automatic Quality of Experience Evaluation with Focus on Interactive Virtual Environments","authors":"Juan Antonio De Rus Arance, M. Montagud, M. Cobos","doi":"10.1145/3573381.3596508","DOIUrl":null,"url":null,"abstract":"This paper contains the research proposal of Juan Antonio De Rus presented at the IMX 23 Doctoral Symposium. Virtual Reality (VR) applications are already used to support diverse tasks such as online meetings, education, or training, and the usages grow every year. To enrich the experience VR scenarios, include multimodal content (video, audio, text, synthetic content) and multi-sensory stimuli are typically included. Tools to evaluate the Quality of Experience (QoE) of such scenarios are needed. Traditional tools used to evaluate the QoE of users performing any kind of task typically involves surveys, user testing or analytics. However, these methods provide limited insights for our tasks with VR and have shortcomings and a limited scalability. In this doctoral study we have formulated a set of open research questions and objectives on which we plan to generate contributions and knowledge in the field of Affective Computing (AC) and Multimodal Interactive Virtual Environments. Hence, in this paper we present a set of tools we are developing to automatically evaluate QoE in different use cases. They include dashboards to monitor in real time reactions to different events in the form of emotions and affections predicted by different models based on physiological data, as well as the creation of a dataset for AC and its associated methodology.","PeriodicalId":120872,"journal":{"name":"Proceedings of the 2023 ACM International Conference on Interactive Media Experiences","volume":"168 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2023 ACM International Conference on Interactive Media Experiences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3573381.3596508","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper contains the research proposal of Juan Antonio De Rus presented at the IMX 23 Doctoral Symposium. Virtual Reality (VR) applications are already used to support diverse tasks such as online meetings, education, or training, and the usages grow every year. To enrich the experience VR scenarios, include multimodal content (video, audio, text, synthetic content) and multi-sensory stimuli are typically included. Tools to evaluate the Quality of Experience (QoE) of such scenarios are needed. Traditional tools used to evaluate the QoE of users performing any kind of task typically involves surveys, user testing or analytics. However, these methods provide limited insights for our tasks with VR and have shortcomings and a limited scalability. In this doctoral study we have formulated a set of open research questions and objectives on which we plan to generate contributions and knowledge in the field of Affective Computing (AC) and Multimodal Interactive Virtual Environments. Hence, in this paper we present a set of tools we are developing to automatically evaluate QoE in different use cases. They include dashboards to monitor in real time reactions to different events in the form of emotions and affections predicted by different models based on physiological data, as well as the creation of a dataset for AC and its associated methodology.
本文包含Juan Antonio De Rus在imx23博士研讨会上提出的研究计划。虚拟现实(VR)应用程序已经用于支持各种任务,如在线会议、教育或培训,并且其使用每年都在增长。为了丰富VR场景的体验,通常包括多模态内容(视频、音频、文本、合成内容)和多感官刺激。需要评估这些场景的体验质量(QoE)的工具。用于评估用户执行任何类型任务的QoE的传统工具通常包括调查、用户测试或分析。然而,这些方法对我们的VR任务提供的见解有限,并且存在缺点和有限的可扩展性。在这项博士研究中,我们制定了一套开放的研究问题和目标,我们计划在情感计算(AC)和多模态交互虚拟环境领域产生贡献和知识。因此,在本文中,我们展示了一组我们正在开发的工具,用于在不同用例中自动评估QoE。它们包括仪表板,用于实时监控人们对不同事件的反应,这些反应以基于生理数据的不同模型预测的情绪和情感的形式出现,以及创建AC数据集及其相关方法。