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Volume: 31 | Article ID: art00003
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Discovery of activities via statistical clustering of fixation patterns
  DOI :  10.2352/ISSN.2470-1173.2019.12.HVEI-206  Published OnlineJanuary 2019
Abstract

Human behavior often consists of a series of distinct activities, each characterized by a unique pattern of interaction with the visual environment. This is true even in a restricted domain, such as a pilot flying an airplane; in this case, activities with distinct visual signatures might be things like communicating, navigating, monitoring, etc. We propose a novel analysis method for gaze-tracking data, to perform blind discovery of these hypothetical activities. The method is in some respects analogous to recurrence analysis, which has previously been applied to eye movement data. In the present case, however, we compare not individual fixations, but groups of fixations aggregated over a fixed time interval (t). We assume that the environment has been divided into a finite set of discrete areas-of-interest (AOIs). For a given time interval, we compute the proportion of time spent fixating each AOI, resulting in an N-dimensional vector, where N is the number of AOIs. These proportions can be converted to integer counts by multiplying by t divided by the average fixation duration, a parameter that we fix at 283 milliseconds. We compare different intervals by computing the chi-squared statistic. The p-value associated with the statistic is the likelihood of observing the data under the hypothesis that the data in the two intervals were generated by a single process with a single set of probabilities governing the fixation of each AOI. We cluster the intervals, first by merging adjacent intervals that are sufficiently similar, optionally shifting the boundary between non-merged intervals to maximize the difference. Then we compare and cluster non-adjacent intervals. The method is evaluated using synthetic data generated by a hand-crafted set of activities. While the method generally finds more activities than put into the simulation, we have obtained agreement as high as 80% between the inferred activity labels and ground truth.

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  Cite this article 

Jeffrey B Mulligan, "Discovery of activities via statistical clustering of fixation patternsin Proc. IS&T Int’l. Symp. on Electronic Imaging: Human Vision and Electronic Imaging,  2019,  pp 206-1 - 206-8,  https://doi.org/10.2352/ISSN.2470-1173.2019.12.HVEI-206

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