Keystroke Dynamics as Part of Lifelogging

TL;DR

This paper advocates for including keystroke dynamics in lifelogging, presenting a longitudinal dataset, analyzing typing patterns, and exploring potential correlations with daily factors like sleep and mood.

Contribution

It introduces a new longitudinal keystroke dataset and analyzes its potential for lifelogging applications and health monitoring.

Findings

Keystroke timing varies significantly among individuals.

Little day-to-day variation in top bigram timings for some participants.

No significant correlation between keystroke dynamics and sleep scores.

Abstract

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.