Saccade deviation indicators for automated eye tracking analysis

Eye tracking has been around for more than 100 years and the technology has improved at an incredible rate. With the advancement of technology, eye tracking can even be done from a mobile phone, which allows for large scale eye tracking studies to be performed. Unfortunately, eye tracking analysis is still a time consuming activity especially when done on a large scale, due to the high dependence on human expertise. This paper introduces saccade deviation indices (SDI) and saccade length indices (SLI), metrics to assist in faster analysis of eye tracking data. In addition, bench-mark deviation vectors (BDV) are introduced to highlight repetitive path deviation in eye tracking data. In order to obtain these metrics, a benchmark user is used to determine where and by how much the participants deviated from the expected scan path. A study was performed, recording the eye movements of participants while using a mobile procurement application. The results were compared to the results of an expert usability study to establish the feasibility of this approach. Preliminary results indicate that the SDI and SLI can reduce the time that an experts spend analysing eye tracking data. Additional time is saved by highlighting possible usability issues, by mapping BDV back onto the user interfaces, indicating where the user deviated from the expected scan path.

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