Abstract
The principle of functional grouping is one of the important principles that should be followed in interface design, but so far it lacks the support of the quantitative data for the importance of interface design. In order to validate the function grouping principle in interface design with quantitative data, this study designed three different levels of interface. Each interface had 17 functional icons which are divided into three groups on the interface. Thirty-five subjects performed the icon search task on interfaces while their eye movement data was recorded. The results showed that the interface laid out according to the principle of functional grouping has the shortest searching time, the least number of fixation points and the number of saccade, the shortest scanpath and the smallest fixation divergence, while the interface of icon completely random layout show the longest search time, the maximum number of fixation points and the number of saccade, the longest scanpath, the maximum fixation dispersion, and while the data results of the interface that is partially laid out in accordance with the principle of functional partitioning was in between. Therefore, it can be concluded that, when the layout of the interface is different according to the principle of functional grouping, human-computer interaction efficiency and eye movement behavior will be obviously affected. The layout of the interface may be objectively evaluated by utilizing the change of eye movement data.
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Acknowledgement
This research was funded by National Key R&D Program of China (2016YFC0802807) and Electronic information equipment system research of Key laboratory of basic research projects of national defense technology (DXZT-JC-ZZ-2015-016).
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Zhou, Q., Cheng, Y., Liu, Z., Chen, Y., Li, C. (2019). The Layout Evaluation of Man-Machine Interface Based on Eye Movement Data. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds) Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018). IEA 2018. Advances in Intelligent Systems and Computing, vol 824. Springer, Cham. https://doi.org/10.1007/978-3-319-96071-5_7
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