On the Place of Text Data in Lifelogs, and Text Analysis via Semantic Facets

Current research in lifelog data has not paid enough attention to analysis of cognitive activities in comparison to physical activities. We argue that as we look into the future, wearable devices are going to be cheaper and more prevalent and textual data will play a more significant role. Data captured by lifelogging devices will increasingly include speech and text, potentially useful in analysis of intellectual activities. Analyzing what a person hears, reads, and sees, we should be able to measure the extent of cognitive activity devoted to a certain topic or subject by a learner. Test-based lifelog records can benefit from semantic analysis tools developed for natural language processing. We show how semantic analysis of such text data can be achieved through the use of taxonomic subject facets and how these facets might be useful in quantifying cognitive activity devoted to various topics in a person's day. We are currently developing a method to automatically create taxonomic topic vocabularies that can be applied to this detection of intellectual activity

LoggerMan, a comprehensive logging and visualisation tool to capture computer usage

As we become increasingly dependent on our computers and spending a major part of our day interacting with these machines, it is becoming important for lifeloggers and human-computer interaction (HCI) researchers to capture this aspect of our life. In this paper, we present LoggerMan, a comprehensive logging tool to capture many aspects of our computer usage. It also comes with reporting capabilities to give insights to the data owner about his/her computer usage. By this work, we aim to fill the current lack of logging software in this domain, which would help us and other researchers as well to build data sets for HCI experiments and also to better understand computer usage patterns. Our tool is published online (loggerman.org) to be used freely by the community

Keystroke Dynamics as Part of Lifelogging

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.Comment: Accepted to 27th International Conference on Multimedia Modeling, Prague, Czech Republic, June 202