Modeling emotion unobtrusively in interactive digital systems

虽然办公环境是计算机使用的源头，但交互系统最近已经融入到我们的家庭、休闲活动和人际关系中。在这些活动中，用户体验在很大程度上取决于用户的情感体验；然而，当前的计算机系统仍然不能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，用生理传感器测量用户)对于在实验室之外的使用来说往往过于引人注目和昂贵，因此对于许多情感计算应用来说缺乏实用性。这项研究提出的主要目标不是用仪器检测用户，而是发明新的方法来检测和建模对用户不显眼的情绪，从而改进自然环境中的情绪检测。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点。首先，我们将使用计算机交互的痕迹(击键动力学和光标运动学)，其次，我们将使用生物识别痕迹(瞳孔放大，姿势感知)，第三，我们将使用社交媒体更新和短信中的语言痕迹。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，并将推动人机交互、情感计算和情感建模领域的发展。 我们将通过将模型应用于三个及时和相关的领域来评估模型的效果：计算机游戏、计算机中介沟通和福祉。我们的结果将在广泛的领域为加拿大人提供优势，从评估玩家在玩电脑游戏时的体验到远程诊断新妈妈的产后抑郁症。我们的工作可以改善加拿大人的生活。例如，特定的情感状态可以促进创造力，提高工作满意度，甚至增强我们的免疫反应。使用模型来了解情绪如何在不同的触发因素下发生变化，可以为改善人们情绪幸福感的政策和选择提供信息。

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. We will evaluate the efficacy of the models through application to three timely and relevant domains: computer games, computer-mediated communication, and well-being. Our results will provide advantages to Canadians in areas as broad as evaluating player experience during computer game play to remotely diagnosing post-partum depression in new mothers. Our work can improve the lives of Canadians. For example, specific affective states can promote creativity, improve job satisfaction, and even strengthen our immune response. Using models to understand how emotion changes with different triggers can inform policies and choices that will improve the emotional well-being of people.