在沉浸式和非沉浸式虚拟现实中使用机器学习进行多模态情感分类

IF 4.4 3区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Rodrigo Lima, Alice Chirico, Rui Varandas, Hugo Gamboa, Andrea Gaggioli, Sergi Bermúdez i Badia
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引用次数: 0

摘要

情感计算已被广泛用于检测和识别情感状态。本研究的主要目标是利用机器学习算法自动检测情绪状态。实验过程包括在沉浸式和非沉浸式虚拟现实设置中使用电影片段激发情绪状态。实验记录并分析了参与者的生理信号,以训练机器学习模型识别用户的情绪状态。此外,还提供了两个主观评分情绪量表,对每个情绪电影片段进行评分。结果显示,在两种沉浸度下呈现的刺激没有明显差异。在情绪分类方面,对于生理信号和主观评分,依赖用户的模型比独立于用户的模型具有更好的性能。主观评分和生理信号的平均准确率分别为 69.29 ± 11.41% 和 71.00 ± 7.95%。另一方面,使用独立于用户的模型,我们获得的准确率分别为 54.0 ± 17.2% 和 24.9 ± 4.0%。我们将这些数据解释为受试者之间的高变异性造成的,这表明有必要建立与用户相关的分类模型。在今后的工作中,我们打算开发新的分类算法,并将其转化为实时实施。这将使根据用户的情绪状态实时适应虚拟现实环境成为可能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multimodal emotion classification using machine learning in immersive and non-immersive virtual reality

Multimodal emotion classification using machine learning in immersive and non-immersive virtual reality

Affective computing has been widely used to detect and recognize emotional states. The main goal of this study was to detect emotional states using machine learning algorithms automatically. The experimental procedure involved eliciting emotional states using film clips in an immersive and non-immersive virtual reality setup. The participants’ physiological signals were recorded and analyzed to train machine learning models to recognize users’ emotional states. Furthermore, two subjective ratings emotional scales were provided to rate each emotional film clip. Results showed no significant differences between presenting the stimuli in the two degrees of immersion. Regarding emotion classification, it emerged that for both physiological signals and subjective ratings, user-dependent models have a better performance when compared to user-independent models. We obtained an average accuracy of 69.29 ± 11.41% and 71.00 ± 7.95% for the subjective ratings and physiological signals, respectively. On the other hand, using user-independent models, the accuracy we obtained was 54.0 ± 17.2% and 24.9 ± 4.0%, respectively. We interpreted these data as the result of high inter-subject variability among participants, suggesting the need for user-dependent classification models. In future works, we intend to develop new classification algorithms and transfer them to real-time implementation. This will make it possible to adapt to a virtual reality environment in real-time, according to the user’s emotional state.

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来源期刊
Virtual Reality
Virtual Reality COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
8.30
自引率
14.30%
发文量
95
审稿时长
>12 weeks
期刊介绍: The journal, established in 1995, publishes original research in Virtual Reality, Augmented and Mixed Reality that shapes and informs the community. The multidisciplinary nature of the field means that submissions are welcomed on a wide range of topics including, but not limited to: Original research studies of Virtual Reality, Augmented Reality, Mixed Reality and real-time visualization applications Development and evaluation of systems, tools, techniques and software that advance the field, including: Display technologies, including Head Mounted Displays, simulators and immersive displays Haptic technologies, including novel devices, interaction and rendering Interaction management, including gesture control, eye gaze, biosensors and wearables Tracking technologies VR/AR/MR in medicine, including training, surgical simulation, rehabilitation, and tissue/organ modelling. Impactful and original applications and studies of VR/AR/MR’s utility in areas such as manufacturing, business, telecommunications, arts, education, design, entertainment and defence Research demonstrating new techniques and approaches to designing, building and evaluating virtual and augmented reality systems Original research studies assessing the social, ethical, data or legal aspects of VR/AR/MR.
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