Eye Movement Analysis for Activity Recognition Using Electrooculography
Eye Movement Analysis for Activity Recognition Using Electrooculography
复制标题
DOI：
10.1109/tpami.2010.86
复制
发表时间：
2011-04-01
影响因子：
23.6
通讯作者：
Troester, Gerhard
中科院分区：
文献类型：
--
作者：
Bulling, Andreas;Ward, Jamie A.;Troester, Gerhard
在这项工作中，我们研究眼动分析作为一种新的感知方式的活动识别。使用眼电图（EOG）系统记录眼动数据。我们首先描述和评估算法用于检测眼电图信号的三个眼动特征扫视，固定，眨眼，并提出了一种方法用于评估重复的眼动模式。然后，我们设计90个不同的功能，这些特性的基础上，并选择其中的一个子集，使用最小冗余最大相关性（mRMR）功能选择。我们验证的方法，在办公室环境中使用的一个例子集的五个活动类：复制文本，阅读打印纸，手写笔记，观看视频，浏览网页使用八个参与者的研究。我们还包括没有特定活动的时期（NULL类）。使用支持向量机（SVM）分类器和个人独立（leave-one-person-out）训练，我们在所有类别和参与者中获得了76.1%的平均准确率和70.5%的召回率。这项工作展示了基于眼睛的活动识别（ESTA）的前景，并开启了ESTA对其他活动的更广泛适用性的讨论，这些活动很难，甚至是不可能的，使用常见的传感模式来检测。
In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals-saccades, fixations, and blinks-and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance (mRMR) feature selection. We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and person-independent (leave-one-person-out) training, we obtain an average precision of 76.1 percent and recall of 70.5 percent over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.