Identifying emotional states using keystroke dynamics
Identifying emotional states using keystroke dynamics
复制标题
使用击键动力学识别情绪状态
DOI：
10.1145/1978942.1979046
复制
发表时间：
2011
期刊：
影响因子：
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通讯作者：
R. Mandryk
中科院分区：
文献类型：
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作者：
Clayton Epp;Michael Lippold;R. Mandryk
识别情绪的能力是建造智能计算机的重要组成部分。情感感知系统将有一个丰富的背景，从中可以做出关于如何与用户交互或调整他们的系统响应的适当决定。目前用于识别情绪的系统方法存在两个主要问题，限制了它们的适用性：它们可能是侵入性的，可能需要昂贵的设备。我们的解决方案是通过分析用户在标准键盘上打字模式的节奏来确定用户的情绪。我们进行了一项实地研究，通过自我报告收集参与者的击键和他们的情绪状态。从这些数据中，我们提取了击键特征，并为15种情绪状态创建了分类器。我们的顶级结果包括两级分类器，分别代表自信、犹豫、紧张、放松、悲伤和疲倦，准确率从77%到88%不等。此外，我们对愤怒和兴奋表现出了承诺，准确率为84%。
The ability to recognize emotions is an important part of building intelligent computers. Emotionally-aware systems would have a rich context from which to make appropriate decisions about how to interact with the user or adapt their system response. There are two main problems with current system approaches for identifying emotions that limit their applicability: they can be invasive and can require costly equipment. Our solution is to determine user emotion by analyzing the rhythm of their typing patterns on a standard keyboard. We conducted a field study where we collected participants' keystrokes and their emotional states via self-reports. From this data, we extracted keystroke features, and created classifiers for 15 emotional states. Our top results include 2-level classifiers for confidence, hesitance, nervousness, relaxation, sadness, and tiredness with accuracies ranging from 77 to 88%. In addition, we show promise for anger and excitement, with accuracies of 84%.
DOI：
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发表时间：
2006-08
期刊：
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影响因子：
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作者：
J. Hektner;Jennifer A. Schmidt;M. Csíkszentmihályi
通讯作者：
J. Hektner;Jennifer A. Schmidt;M. Csíkszentmihályi