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Robust Step Counting for Inertial Navigation with Mobile Phones.

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This study presents a robust method for indoor inertial navigation using mobile phone sensors. The system accurately detects physical displacement and counts steps by filtering out non-walking movements.

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Area of Science:

  • Mobile sensing
  • Inertial navigation
  • Signal processing

Background:

  • Mobile phones are increasingly used for applications beyond calls and data transfer, including indoor inertial navigation.
  • Developing standalone indoor navigation applications using common mobile phone inertial sensors (accelerometer, gyroscope, magnetometer) is challenging due to hardware variations, data noise, and non-displacement movements.

Purpose of the Study:

  • To develop a robust standalone application for detecting user displacement and counting steps using only mobile phone inertial sensor data.
  • To overcome the complexities of hardware disparity, sensor noise, and extraneous phone movements in indoor navigation.

Main Methods:

  • Utilized quaternions and a Kalman filter to project sensor readings into an Earth-Centered Inertial (ECI) reference system.
  • Combined a Peak-valley detector with an ensemble of Support Vector Machines (SVMs) and a standard deviation-based classifier.
  • Developed algorithms to identify and filter out signal segments not corresponding to walking behavior.

Main Results:

  • Achieved robust detection of physical displacement and accurate step counting.
  • Successfully filtered out non-walking related movements, enhancing navigation accuracy.
  • Validated the proposal through an extensive experimental dataset comprising 140 records from 75 individuals.

Conclusions:

  • The proposed method effectively filters non-walking movements, enabling reliable indoor inertial navigation and step counting using standard mobile phone sensors.
  • This approach offers a viable solution for enhancing the functionality of mobile devices in location-aware applications.