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Related Experiment Video

Updated: Nov 10, 2025

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
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Classification of Standing and Walking States Using Ground Reaction Forces.

Ji Su Park1,2, Sang-Mo Koo2, Choong Hyun Kim1

  • 1Center for Bionics, Korea Institute of Science and Technology, Seoul 02792, Korea.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel insole device for real-time classification of standing and walking states using ground reaction force (GRF) measurements. The developed waveform length technique enhances accuracy for wearable robot applications and daily activity monitoring.

Keywords:
activities of daily livingcenter of pressureforce sensing resistorsgait analysisground reaction forceinsolem-healthwaveform lengthwearable devices

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Area of Science:

  • Robotics
  • Biomechanics
  • Wearable Technology

Background:

  • Wearable robots, including gait rehabilitation robots, necessitate precise real-time wearer state classification (standing vs. walking).
  • Existing methods using ground reaction force (GRF) can be prone to errors, particularly with shuffling movements.

Purpose of the Study:

  • To develop and validate a novel technique for real-time classification of standing and walking states using an insole device.
  • To improve the accuracy of state classification for wearable robot control and activity monitoring.

Main Methods:

  • An insole device equipped with force sensing resistors was used to measure GRF.
  • A new technique utilizing waveform length of the center of pressure changes within a time window was developed.
  • The technique was applied to a conventional threshold method and an artificial neural network (ANN) model for classification.

Main Results:

  • The developed waveform length technique significantly reduced classification errors compared to conventional GRF methods.
  • Real-time classification of standing and walking states was successfully achieved using the ANN model.
  • The insole device demonstrated effectiveness in distinguishing between standing and walking states, even with shuffling movements.

Conclusions:

  • The novel insole-based GRF measurement and waveform length analysis technique enables accurate real-time classification of wearer states.
  • This technology is suitable for enhancing wearable robot operations, such as gait rehabilitation.
  • The insole device offers potential for remote monitoring of daily living activities.