Research Papers

Augmented Affective-Cognition for Usability Study of In-Vehicle System User Interface

[+] Author and Article Information
Feng Zhou

The G.W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
813 Ferst Drive,
Atlanta, GA 30332-0405
e-mail: fzhou35@gatech.edu

Yangjian Ji

Industrial Engineering Centre,
Department of Mechanical Engineering,
Zhejiang University,
38 Zheda Rd, Hangzhou,
Zhejiang 310027, China
e-mail: mejyj@zju.edu.cn

Roger J. Jiao

The G.W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
813 Ferst Drive,
Atlanta, GA 30332-0405
e-mail: rjiao@gatech.edu

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTERS AND INFORMATION DIVISION IN ENGINEERING. Manuscript received July 19, 2013; final manuscript received December 1, 2013; published online February 12, 2014. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 14(2), 021001 (Feb 12, 2014) (11 pages) Paper No: JCISE-13-1128; doi: 10.1115/1.4026222 History: Received July 19, 2013; Revised December 01, 2013

Usability of in-vehicle systems has become increasingly important for ease of operations and safety of driving. The user interface (UI) of in-vehicle systems is a critical focus of usability study. This paper studies how to use advanced computational, physiology- and behavior-based tools and methodologies to determine affective/emotional states and behavior of an individual in real time and in turn how to adapt the human-vehicle interaction to meet users' cognitive needs based on the real-time assessment. Specifically, we set up a set of physiological sensors that are capable of collecting EEG, facial EMG, skin conductance response, and respiration data and a set of motion sensing and tracking equipment that is capable of capturing eye ball movement and objects which the user is interacting with. All hardware components and software are integrated into an augmented sensor platform that can perform as “one coherent system” to enable multimodal data processing and information inference for context-aware analysis of emotional states and cognitive behavior based on the rough set inference engine. Meanwhile subjective data are also recorded for comparison. A usability study of in-vehicle system UI is shown to demonstrate the potential of the proposed methodology.

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Fig. 1

System architecture of augmented affective cognition for usability studies of in-vehicle system UIs

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Fig. 2

Sensor platform for usability studies of in-vehicle system UIs

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Fig. 3

Illustration of SCR features

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Fig. 4

Boxplot of statistics of tasks

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Fig. 5

Usability issues in the navigation system UI

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Fig. 6

Usability issues in the radio and music player UI

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Fig. 7

Usability issues in the air conditioner UI

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Fig. 8

Usability issues in the dashboard UI



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