The Bayes Point Machine for computer-user frustration detection via pressuremouse
The Bayes Point Machine for computer-user frustration detection via pressuremouse
复制标题
贝叶斯点机通过压力鼠标检测计算机用户的挫败感
DOI：
10.1145/971478.971495
复制
发表时间：
2001
期刊：
影响因子：
--
通讯作者：
Rosalind W. Picard
中科院分区：
文献类型：
--
作者：
Yuan Qi;Carson Reynolds;Rosalind W. Picard
我们在计算机鼠标上安装了八个压力传感器，并从填写包含可用性错误的网络表格的受试者那里收集鼠标压力信号。这种方法基于这样的假设：受试者在遇到令人沮丧的事件后倾向于对小鼠施加过大的压力。然后，我们训练贝叶斯点机，尝试对每个用户行为的两个区域进行分类：表单填写过程顺利进行时的鼠标压力，以及可用性错误后的鼠标压力。与当前流行的支持向量机等分类器不同，贝叶斯点机是一种植根于贝叶斯理论的新型分类技术。使用新的高效贝叶斯近似算法“期望传播”进行训练，贝叶斯点机实现了 88% 的人相关分类准确率，在我们的实验中优于支持向量机。由此产生的系统可用于人机交互的许多应用，包括自适应界面设计。
We mount eight pressure sensors on a computer mouse and collect mouse pressure signals from subjects who fill out web forms containing usability bugs. This approach is based on a hypothesis that subjects tend to apply excess pressure to the mouse after encountering frustrating events. We then train a Bayes Point Machine in an attempt to classify two regions of each user's behavior: mouse pressure where the form- filling process is proceeding smoothly, and mouse pressure following a usability bug. Different from current popular classifiers such as the Support Vector Machine, the Bayes Point Machine is a new classification technique rooted in the Bayesian theory. Trained with a new efficient Bayesian approximation algorithm, Expectation Propagation, the Bayes Point Machine achieves a person-dependent classification accuracy rate of 88%, which outperforms the Support Vector Machine in our experiments. The resulting system can be used for many applications in human-computer interaction including adaptive interface design.