Combining Low and Mid-Level Gaze Features for Desktop Activity Recognition
Combining Low and Mid-Level Gaze Features for Desktop Activity Recognition
复制标题
结合低级和中级注视功能进行桌面活动识别
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Eduardo Velloso
中科院分区：
文献类型：
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作者：
Namrata Srivastava;Joshua Newn;Eduardo Velloso
人类活动识别（HAR）是一个重要的研究领域，由于其潜在的上下文感知的交互系统的建设。虽然基于运动的活动识别是一个既定的研究领域，但识别久坐不动的活动仍然是一个开放的研究问题。以前的作品已经探索了基于眼睛的活动识别作为一个潜在的方法，这一挑战，专注于统计措施来自眼动特性-低层次的凝视功能-或一些知识的区域感兴趣（AOI）的刺激-高层次的凝视功能。在本文中，我们扩展了这一机构的工作，采用增加了中级凝视功能，功能，增加了一个层次的抽象的低层次的功能与一些知识的活动，但不是刺激。我们评估了我们的方法从24个参与者进行8个桌面计算活动收集的数据集。我们训练了一个分类器，该分类器扩展了从现有文献中获得的26个低级特征，并增加了24个新的候选中级凝视特征。我们的研究结果显示，整体分类性能为0.72（F1分数），当添加我们的中级凝视特征时，准确率提高了4%。最后，我们讨论了结合低和中级凝视功能的影响，以及基于眼睛的活动识别的未来方向。
Human activity recognition (HAR) is an important research area due to its potential for building context-aware interactive systems. Though movement-based activity recognition is an established area of research, recognising sedentary activities remains an open research question. Previous works have explored eye-based activity recognition as a potential approach for this challenge, focusing on statistical measures derived from eye movement properties---low-level gaze features---or some knowledge of the Areas-of-Interest (AOI) of the stimulus---high-level gaze features. In this paper, we extend this body of work by employing the addition of mid-level gaze features; features that add a level of abstraction over low-level features with some knowledge of the activity, but not of the stimulus. We evaluated our approach on a dataset collected from 24 participants performing eight desktop computing activities. We trained a classifier extending 26 low-level features derived from existing literature with the addition of 24 novel candidate mid-level gaze features. Our results show an overall classification performance of 0.72 (F1-Score), with up to 4% increase in accuracy when adding our mid-level gaze features. Finally, we discuss the implications of combining low- and mid-level gaze features, as well as the future directions for eye-based activity recognition.
影响因子：
4.1
作者：
Mannini A;Intille SS;Rosenberger M;Sabatini AM;Haskell W
通讯作者：
Haskell W
影响因子：
16.6
作者：
Bulling, Andreas;Blanke, Ulf;Schiele, Bernt
通讯作者：
Schiele, Bernt