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Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
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A Context-Aware Smartphone-Based 3D Indoor Positioning Using Pedestrian Dead Reckoning.

Boshra Khalili1, Rahim Ali Abbaspour1, Alireza Chehreghan2

  • 1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran P.O. Box 14155-6619, Iran.

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Summary
This summary is machine-generated.

This study introduces an adaptive pedestrian dead reckoning (PDR) method for accurate indoor positioning. It improves robustness by recognizing various phone-carrying modes, activities, and speeds, achieving high accuracy for both men and women.

Keywords:
PDRcontext-awareindoor positioningmachine learningsmartphone sensors

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Area of Science:

  • Computer Science
  • Robotics
  • Geospatial Science

Background:

  • Location-based services (LBS) drive the need for precise indoor positioning.
  • Pedestrian dead reckoning (PDR) offers infrastructure-free indoor positioning but suffers from cumulative errors and sensitivity to user behavior.
  • Existing PDR methods struggle with varying walking speeds, activities (e.g., stairs), and phone-handling modes (e.g., texting, calling).

Purpose of the Study:

  • To develop an adaptive PDR method that enhances indoor positioning accuracy and robustness.
  • To address the limitations of traditional PDR by accounting for diverse pedestrian activities, speeds, and phone-carrying postures.
  • To improve PDR performance across different user demographics (gender) and motion states.

Main Methods:

  • Implemented an adaptive PDR approach integrating Support Vector Machine (SVM) and Decision Tree (DTree) algorithms for motion state and speed classification.
  • Utilized sensor fusion (accelerometer, gyroscope, magnetometer with GDA filter) for heading estimation and pressure sensors for floor detection.
  • Adjusted step detection and step length estimation parameters based on identified walking speed, gender, and motion state.

Main Results:

  • Achieved high classification accuracy for motion states and walking speeds: 97.03% for women and 97.67% for men.
  • Demonstrated significantly reduced relative distance estimation errors across various phone-carrying modes (texting, calling, swinging) for both genders.
  • Reported low mean absolute positioning errors (e.g., 1.28m for women texting, 1.26m for men texting) over a 159.2m trajectory in a multi-story building.

Conclusions:

  • The proposed adaptive PDR method effectively overcomes the limitations of traditional PDR, offering robust and accurate indoor positioning.
  • The system's ability to adapt to different user activities, speeds, and phone-handling modes significantly enhances positioning reliability.
  • This research provides a promising solution for improving indoor navigation systems in diverse real-world scenarios.