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

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