Modeling emotion unobtrusively in interactive digital systems

虽然办公环境是计算机使用的起源，但交互式系统最近已经融入我们的家庭，我们的休闲活动和我们的关系。在这些活动中，用户体验在很大程度上由用户的情感体验支配;然而，当前的计算机系统仍然不理解或适应用户的情感状态。虽然在情感建模方面已经取得了进展，但当前的方法（例如，用生理传感器装备用户）对于在实验室之外使用来说通常太突兀和昂贵，并且因此对于许多情感计算应用缺乏实用性。而不是仪器的用户，本研究提出的主要目标是发明新的方法来检测和建模的情感是不引人注目的用户，从而提高情感检测在自然环境中。我们将通过挖掘用户与其设备和环境交互时留下的现有痕迹来实现这一目标。首先，我们将使用来自计算机交互的痕迹（视觉动态和光标运动学），其次，我们将使用生物特征痕迹（瞳孔扩张，姿势感知），第三，我们将使用来自社交媒体更新和短信的语言痕迹。我们还将通过综合多个项目来提高单个模型的性能。这三个主题代表了基础研究，并将推进人机交互，情感计算和情感建模领域。

Although office environments are the provenance of computer usage, interactive systems have recently woven themselves into our homes, our leisure activities, and our relationships. In these activities, user experience is governed in large part by the user's affective experience; however, current computer systems still do not understand or adapt to the emotional state of the user. Although progress has been made on modeling emotion, current approaches (e.g., instrumenting a user with physiological sensors) are often too obtrusive and expensive for use outside of laboratories, and thus lack utility for many affective computing applications. Rather than instrument a user, the primary objective proposed by this research is to invent novel methods for detecting and modeling emotion that are unobtrusive to the user, thus improving emotion detection in natural settings. We will accomplish this by mining existing traces left behind as users interact with their devices and their environment. First, we will use traces from computer interaction (keystroke dynamics and cursor kinematics), second we will use biometric traces (pupil dilation, posture sensing), and third, we will use linguistic traces from social media updates and text messages. We will also improve individual model performance through synthesis of the multiple projects. These three themes represent foundational research and will advance the fields of human-computer interaction, affective computing, and emotion modeling.