  In this paper we present the case for including keystroke dynamics in
lifelogging. We describe how we have used a simple keystroke logging
application called Loggerman, to create a dataset of longitudinal keystroke
timing data spanning a period of more than 6 months for 4 participants. We
perform a detailed analysis of this data by examining the timing information
associated with bigrams or pairs of adjacently-typed alphabetic characters. We
show how there is very little day-on-day variation of the keystroke timing
among the top-200 bigrams for some participants and for others there is a lot
and this correlates with the amount of typing each would do on a daily basis.
We explore how daily variations could correlate with sleep score from the
previous night but find no significant relation-ship between the two. Finally
we describe the public release of this data as well including as a series of
pointers for future work including correlating keystroke dynamics with mood and
fatigue during the day.
