Passively recognising human activities through lifelogging

  • Authors:

  • Aiden R. Doherty;Niamh Caprani;Ciarán í Conaire;Vaiva Kalnikaite;Cathal Gurrin;Alan F. Smeaton;Noel E. O'Connor

  • Affiliations:

  • CLARITY: Centre for Sensor Web Technologies, Dublin City University, Glasnevin, Dublin 9, Ireland and School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland;CLARITY: Centre for Sensor Web Technologies, Dublin City University, Glasnevin, Dublin 9, Ireland and School of Electronic Engineering, Dublin City University, Glasnevin, Dublin 9, Ireland;CLARITY: Centre for Sensor Web Technologies, Dublin City University, Glasnevin, Dublin 9, Ireland and School of Electronic Engineering, Dublin City University, Glasnevin, Dublin 9, Ireland;GE Digital Energy (Smallworld), Elizabeth House, 1 High Street, Cambridge CB4 1WR, UK;CLARITY: Centre for Sensor Web Technologies, Dublin City University, Glasnevin, Dublin 9, Ireland and School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland;CLARITY: Centre for Sensor Web Technologies, Dublin City University, Glasnevin, Dublin 9, Ireland and School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland;CLARITY: Centre for Sensor Web Technologies, Dublin City University, Glasnevin, Dublin 9, Ireland and School of Electronic Engineering, Dublin City University, Glasnevin, Dublin 9, Ireland

  • Venue:
  • Computers in Human Behavior
  • Year:
  • 2011

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Abstract

Lifelogging is the process of automatically recording aspects of one's life in digital form. This includes visual lifelogging using wearable cameras such as the SenseCam and in recent years many interesting applications for this have emerged and are being actively researched. One of the most interesting of these, and possibly the most far-reaching, is using visual lifelogs as a memory prosthesis but there are also applications in job-specific activity recording, general lifestyle analysis and market analysis. In this work we describe a technique which allowed us to develop automatic classifiers for visual lifelogs to infer different lifestyle traits or characteristics. Their accuracy was validated on a set of 95k manually annotated images and through one-on-one interviews with those who gathered the images. These automatic classifiers were then applied to a collection of over 3million lifelog images collected by 33 individuals sporadically over a period of 3.5years. From this collection we present a number of anecdotal observations to demonstrate the future potential of lifelogging to capture human behaviour. These anecdotes include: the eating habits of office workers; to the amount of time researchers spend outdoors through the year; to the observation that retired people in our study appear to spend quite a bit of time indoors eating with friends. We believe this work demonstrates the potential of lifelogging techniques to assist behavioural scientists in future.