Thelxinoë for Emotion Recognition and Touch Processing

Darlene Barker, H. Levkowitz
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Abstract

This paper presents the framework for the creation of touch in virtual reality (VR) with the processing of emotions from multiple sensors, and the processing of touch and sensation generation. We use Gradient Boosting (GB) and Long Short-Term Memory (LSTM) models to process the aggregated emotion data from brain waves, physiological changes, pupillometry, facial recognition, speech, and background effects to recreate the emotional state of a person in VR. In our real life day-to-day existence we connect with our environment, and be it real life or VR, we connect. Emotion creates that connection that enhances in person communication and by adding these two to the virtual world, we are pulling more of the real world into the experience which can only enhance it. Touch is the first sense we experience at birth. The contribution of this paper is the introduction of a multi-sensory collection framework called Thelxinoë that collects emotion data from multiple sensors including brain waves, facial expression, pupillometry, speech, and body movement. This is then aggregated within a black box that generates an output matching the emotions at the time of collection. Those emotions are used to facilitate the action of touch generated by the parties by creating sensations based on the initiated touch.
Thelxinoë为情感识别和触摸处理
本文提出了虚拟现实(VR)中触觉的创建框架,包括对来自多个传感器的情感的处理,以及触觉和感觉产生的处理。我们使用梯度增强(GB)和长短期记忆(LSTM)模型来处理来自脑电波、生理变化、瞳孔测量、面部识别、语音和背景效果的汇总情绪数据,以重现虚拟现实中人的情绪状态。在我们的现实生活中,我们每天都与我们的环境联系在一起,无论是现实生活还是虚拟现实,我们都在联系。情感创造了人与人之间的联系,加强了人与人之间的交流,通过将这两者添加到虚拟世界中,我们将更多的现实世界拉入体验中,这只会增强它。触觉是我们出生时的第一感觉。本文的贡献在于引入了一个名为Thelxinoë的多感官收集框架,该框架从多个传感器收集情感数据,包括脑电波、面部表情、瞳孔测量、语言和身体运动。然后将其聚合在一个黑盒中,该黑盒生成与收集时的情绪相匹配的输出。这些情绪被用来促进由双方产生的触摸行为,通过基于初始触摸创造感觉。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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