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Inferring Human Activity in Mobile Devices by Computing Multiple Contexts.

Ruizhi Chen1, Tianxing Chu2, Keqiang Liu3,4

  • 1Conrad Blucher Institute for Surveying & Science, Texas A&M University Corpus Christi, Corpus Christi, TX 78412-5868, USA. ruizhi.chen@tamucc.edu.

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Summary
This summary is machine-generated.

This study presents a framework for inferring human activities using mobile device sensors. The multi-context approach significantly improves activity recognition accuracy compared to using only spatial context.

Keywords:
human activity recognitionlocation awarenessmobile context computationsmartphone positioning

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Ubiquitous Computing

Background:

  • Human activity recognition (HAR) is crucial for intelligent mobile systems.
  • Previous HAR methods often rely on limited contextual information, impacting accuracy.
  • Integrating diverse contextual data offers potential for improved HAR.

Purpose of the Study:

  • To introduce a novel framework for inferring human activities using mobile devices.
  • To explore the impact of incorporating spatial, temporal, spatiotemporal, and user contexts.
  • To enhance the accuracy of real-time activity recognition.

Main Methods:

  • Developed a framework to compute spatial, temporal, spatiotemporal, and user contexts from mobile sensor data.
  • Captured contextual tuples every second, integrating various context types.
  • Employed a Naïve Bayes classifier to evaluate activity probabilities in real-time.
  • Collected a large dataset (710,436 tuples) over one week with three participants.

Main Results:

  • The multi-context solution achieved a classification accuracy of 88.8%.
  • The spatial-context-only solution achieved a classification accuracy of 61.7%.
  • The proposed multi-context framework significantly outperformed the spatial-context-only approach.

Conclusions:

  • Integrating multiple contexts (spatial, temporal, spatiotemporal, user) substantially improves human activity recognition accuracy on mobile devices.
  • The developed framework provides an effective method for real-time activity inference.
  • This research highlights the importance of comprehensive contextual information for advanced mobile sensing applications.