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Mobile Based Prompted Labeling of Large Scale Activity Data

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 8277)

Abstract

This paper describes the use of a prompted labeling solution to obtain class labels for user activity and context information on a mobile device. Based on the output from an activity recognition module, the prompt labeling module polls for class transitions from any of the activities (e.g. walking, running) to the standing still activity. Once a transition has been detected the system prompts the user, through the provision of a message on the mobile phone, to provide a label for the last activity that was carried out. This label, along with the raw sensor data is then stored locally prior to being uploaded to cloud storage. The paper provides technical details of how and when the system prompts the user for an activity label and discusses the information that can be gleaned from sensor data. This system allows for activity and context information to be collected on a large scale. Data can then be used within new opportunities in data mining and modeling of user context for a variety of applications.

Keywords

  • Mobile Device
  • Cloud Service
  • Activity Recognition
  • Context Aware Application
  • Context Aware Service

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-319-03092-0_2
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Cleland, I. et al. (2013). Mobile Based Prompted Labeling of Large Scale Activity Data. In: Nugent, C., Coronato, A., Bravo, J. (eds) Ambient Assisted Living and Active Aging. IWAAL 2013. Lecture Notes in Computer Science, vol 8277. Springer, Cham. https://doi.org/10.1007/978-3-319-03092-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-03092-0_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03091-3

  • Online ISBN: 978-3-319-03092-0

  • eBook Packages: Computer ScienceComputer Science (R0)