Data Reduction Methods for Life-Logged Datasets

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Life-logging utilises sensor technology to automate the capture of a person’s interaction with their environment. This produces useful information to assess wellbeing, but this information is often buried within the volume of data. In this chapter, we analyse a life-log comprising image data and contextual information and apply algorithms to collate, mine and categorise the data. Four approaches were investigated: (i) Self-reporting of important events by the person who collected the data; (ii) Clustering of images into location-based events using GPS metadata, (iii) Face detection within the images and (iv) Physiological monitoring using Galvanic Skin Response (GSR); as a way to identify more meaningful
images. Using a bespoke wearable system, comprising a smartphone and smartwatch, six healthy participants recorded a life-log in the form of images of their surroundings coupled with metadata in the form of timestamps, GPS locations, accelerometer data and known social interactions. Following
approximately 2.5 h of recording, the data reduction methodologies outlined above were applied to each participant’s dataset, yielding an 80–86% reduction in size which facilitates more realistic self quantification. However, each approach has some shortcoming and the data reduction method used will need personalisation and depend on the intended application.
Original languageEnglish
Title of host publicationSmart Assisted Living
EditorsFeng Chen
Number of pages15
ISBN (Electronic)978-3-030-25590-9
ISBN (Print)478169_1
Publication statusAccepted/In press - 14 Jul 2019


  • Life-log
  • Geo-data mining
  • Physiological
  • Face detection
  • GSR
  • Self-report
  • Data reduction


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