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Classification of User Behavior Patterns for Indoor Navigation Problem.

Aleksandra Borsuk1, Andrzej Chybicki1, Michał Zieliński1

  • 1Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, ul Narutowicza 11/12, 80-233 Gdańsk, Poland.

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Summary

This study introduces behavior-based indoor navigation, estimating location by matching user actions to a route. It uses smartphone sensors and LSTM models for accurate activity recognition, offering a cost-effective solution.

Keywords:
LSTMactivity classificationindoor navigationlinear accelerationmobile applicationsensor datasensor fusion data

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Area of Science:

  • Computer Science
  • Robotics
  • Human-Computer Interaction

Background:

  • Indoor navigation is challenging due to limitations of traditional Global Positioning System (GPS).
  • Existing indoor positioning systems often require significant infrastructure.
  • There is a need for cost-effective and scalable indoor navigation solutions.

Purpose of the Study:

  • To propose a novel behavior-based localization approach for indoor navigation.
  • To develop a system that estimates user position by matching observed behavior to a predefined route.
  • To validate the effectiveness of real-time activity recognition for navigation.

Main Methods:

  • Utilized smartphone sensors to collect velocity data.
  • Developed a Long Short-Term Memory (LSTM) based model for user behavior classification (standing, walking, stairs, elevators).
  • Classified activities within one-second time windows and full sequences for navigation estimation.

Main Results:

  • Achieved 75% accuracy in classifying individual user activities within one-second intervals.
  • Reached 98.6% accuracy for full-sequence activity classification using majority voting.
  • Demonstrated the viability of behavior-based localization for indoor navigation.

Conclusions:

  • Real-time activity recognition is a feasible foundation for indoor navigation systems.
  • Behavior-based localization offers a cost-effective alternative to infrastructure-dependent systems.
  • The proposed method is suitable for specific scenarios like guiding personnel in complex buildings.