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4 CONCLUSION AND
DISCUSSION
We first compared eye and mouse data with the sa-
liency metric PCC. We did not find significant con-
sistency between participants’ eyes and mouse positi-
ons (inter) and between participants’ eyes (intra). Ho-
wever, results showed that participants behaved in a
more similar way when they had the same task with
the same location (reading task).
Then, we got deeper with dynamic analyses. We
showed that using distance and correlation, we were
able to highlight more interesting coordinations bet-
ween eyes and mouse. We had better results on Y axis
than X axis and succeed to demonstrate behaviour dif-
ferences between tasks. In addition, scroll analyses,
clearly showed a relation between eyes position and
scroll speed while browsing and amplitude before the
scroll.
Finally, we made a model for each task able to
predict the area around the mouse’s cursor in which
the eyes had 70% chances to be located in. Howe-
ver, eyes location uncertainty compared to mouse po-
sition remained high, even if we succeed to enhance
the model during target finding task by observing bru-
tal changes on X axis.
In this paper, we presented results of a prelimi-
nary study, used as a validation to conduct a bigger
experiment, including more participants. This will al-
low us to analyse the impact of participants’ age on
their mouse movements. Moreover, we did not use
scroll events analyses to enhance our models. In fu-
ture work, we think that doing so, could boost the
precision of the model by reducing the area around
the mouse’s cursor. We could also investigate new re-
lations between the scroll and the eyes by analysing
scroll in 2D. Then, we could use machine learning
models to integrate new features and more user beha-
viours such as mouse patterns. Finally, our main ob-
jective is to propose the most accurate model in order
to use it in real time to predict web user behaviours.
ACKNOWLEDGEMENT
We thank French Research and Technology Associa-
tion (ANRT) and Sublime Skinz for supporting this
work.
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