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Related Experiment Video

Updated: May 14, 2026

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

Motion mode recognition and step detection algorithms for mobile phone users.

Melania Susi1, Valérie Renaudin, Gérard Lachapelle

  • 1PLAN Group, Schulich School of Engineering, The University of Calgary, Calgary, AB, Canada. melania.susi@ieee.org

Sensors (Basel, Switzerland)
|January 26, 2013
PubMed
Summary

New algorithms accurately detect pedestrian steps using handheld Microelectromechanical Systems (MEMS) sensors. These systems improve smartphone location accuracy by adapting to different user motion modes, achieving over 97% success.

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Area of Science:

  • Engineering
  • Computer Science
  • Robotics

Background:

  • Microelectromechanical Systems (MEMS) are crucial for modern smartphone functionalities.
  • MEMS sensors enable advanced features but require external updates (e.g., GPS) for accurate autonomous location due to signal degradation.
  • Traditional Zero Velocity UPdaTes (ZUPTs) for location accuracy are effective when sensors are foot-mounted but complex for handheld devices.

Purpose of the Study:

  • To develop algorithms for characterizing pedestrian gait cycles using handheld MEMS sensors.
  • To create a motion mode classifier for typical smartphone user movements.
  • To implement adaptive step detection algorithms based on detected motion modes.

Main Methods:

  • Development of algorithms to characterize gait cycles from handheld MEMS sensor data.

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

Published on: March 14, 2017

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Last Updated: May 14, 2026

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

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

  • Implementation of a classifier to identify distinct user motion modes (e.g., walking, running, stationary).
  • Application of adaptive step detection algorithms tailored to each identified motion mode.

    • Main Results:

    • A classifier successfully detected various motion modes characteristic of mobile phone users.
    • Adaptive algorithms demonstrated high success rates in step detection across all identified motion modes.
    • The developed system achieved over 97% accuracy in step detection for handheld MEMS sensor applications.

    Conclusions:

    • Handheld MEMS sensors can be effectively used for accurate pedestrian step detection.
    • Adaptive algorithms and motion mode classification significantly improve location accuracy for mobile devices.
    • This technology enhances the capabilities of smartphones for autonomous navigation and activity tracking.