Automatic Cognitive Load Classification Using High-Frequency Interaction Events

作者：Tao Lin; Xiao Li; Zhiming Wu; Ningjiu Tang

来源：International Journal of Technology and Human Interaction, 2013, 9(3): 73-88.

DOI：10.4018/jthi.2013070106

<jats:p>There is still a challenge of creating an evaluation method which can not only unobtrusively collect data without supplement equipment but also objectively, quantitatively and in real-time evaluate cognitive load of user based the data. The study explores the possibility of using the features extracted from high-frequency interaction events to evaluate cognitive load to respond to the challenge. Specifically, back-propagation neural networks, along with two feature selection methods (nBset and SFS), were used as the classifier and it was able to use a set of features to differentiate three cognitive load levels with an accuracy of 74.27%. The main contributions of the research are: (1) demonstrating the use of combining machine learning techniques and the HFI features in automatically evaluating cognitive load; (2) showing the potential of using the HFI features in discriminating different cognitive load when suitable classifier and features are adopted.</jats:p>

• 出版日期2013-7
• 单位四川大学