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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Window size impact in human activity recognition.

Oresti Banos1, Juan-Manuel Galvez2, Miguel Damas3

  • 1Department of Computer Architecture and Computer Technology, Research Center for Information and Communications Technologies-University of Granada (CITIC-UGR), C/Calle Periodista Rafael Gomez Montero 2, Granada E18071, Spain. oresti@ugr.es.

Sensors (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study investigates windowing for activity recognition. A 1-2 second window size offers the best balance between fast detection and accurate results for on-body systems.

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Area of Science:

  • Computer Science
  • Signal Processing
  • Human-Computer Interaction

Background:

  • Signal segmentation is vital for activity recognition but lacks clear characterization.
  • Windowing is common for segmentation, yet optimal window size remains debated.
  • Current designs often rely on unverified assumptions from prior work.

Purpose of the Study:

  • To extensively study the windowing procedure in activity recognition.
  • To determine the impact of window size on recognition performance.
  • To provide guidelines for designing on-body activity recognition systems.

Main Methods:

  • Evaluated widely used activity recognition procedures.
  • Tested a broad range of window sizes and activities.
  • Focused on on-body activity recognition systems.

Main Results:

  • The 1-2 second window interval demonstrated the optimal trade-off.
  • This interval balances recognition speed and accuracy effectively.
  • Established guidelines for system design based on application needs.

Conclusions:

  • Window size significantly impacts activity recognition performance.
  • A 1-2 second window is recommended for on-body activity recognition.
  • The study clarifies assumptions and aids in system configuration.