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Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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Related Experiment Video

Updated: May 20, 2026

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study
07:51

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

Published on: March 14, 2017

Using LS-SVM based motion recognition for smartphone indoor wireless positioning.

Ling Pei1, Jingbin Liu, Robert Guinness

  • 1Department of Navigation and Positioning, Finnish Geodetic Institute, FIN-02431 Masala, Finland. ruizhi.chen@fgi.fi

Sensors (Basel, Switzerland)
|July 11, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel indoor navigation system using smartphone motion recognition and wireless positioning. The system accurately identifies user movements, achieving high precision for enhanced indoor location services.

Keywords:
LS-SVMindoor navigationmotion recognitionpositioningsmartphonewireless

More Related Videos

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Related Experiment Videos

Last Updated: May 20, 2026

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study
07:51

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

Published on: March 14, 2017

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Robotics

Background:

  • Indoor navigation remains a challenge due to the unavailability of GPS signals.
  • Existing wireless positioning systems often suffer from accuracy limitations.
  • Integrating motion recognition can potentially improve indoor positioning performance.

Purpose of the Study:

  • To develop and evaluate an indoor navigation solution combining physical motion recognition with wireless positioning.
  • To enhance the accuracy and reliability of mobile user positioning in indoor environments.

Main Methods:

  • Extraction of 27 features from smartphone accelerometers and magnetometers.
  • Classification of eight common indoor motion states using a Least Square-Support Vector Machines (LS-SVM) algorithm.
  • Development of a motion recognition-assisted wireless positioning approach.

Main Results:

  • Motion states were recognized with up to 95.53% accuracy in test cases.
  • The motion recognition-assisted wireless positioning achieved a mean error of 1.22 m in static tests.
  • A mean error of 3.53 m was recorded in stop-go tests.

Conclusions:

  • The proposed system effectively combines motion recognition and wireless positioning for indoor navigation.
  • The integration of motion state recognition significantly aids in determining mobile user positions indoors.
  • The system demonstrates practical potential for improving indoor location-based services.