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A membership-function-based broad learning system for human-robot interaction force estimation under drawing task.

Biwei Tang1, Ruiqing Li1, Jing Luo2

  • 1School of Automation, Wuhan University of Technology, Luoshi Road, Wuhan, 430070, Hubei, China.

Medical & Biological Engineering & Computing
|June 3, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for estimating human-robot interaction force using broad learning systems (BLS) and surface electromyography (sEMG) signals. Combining time and frequency domain sEMG features significantly improves interaction force estimation accuracy and safety.

Keywords:
Broad learning systemForce estimationHuman-robot interactionsEMG

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Area of Science:

  • Robotics
  • Biomedical Engineering
  • Machine Learning

Background:

  • Accurate estimation of interaction force is crucial for ensuring safety in human-robot interaction (HRI).
  • Previous methods often neglect historical surface electromyography (sEMG) data, leading to incomplete force estimation and reduced accuracy.
  • Surface electromyography (sEMG) signals contain valuable information about human muscle force.

Purpose of the Study:

  • To propose a novel method for estimating interaction force in HRI.
  • To leverage broad learning system (BLS) and human sEMG signals for improved force estimation.
  • To enhance the accuracy and completeness of interaction force estimation by incorporating historical sEMG data.

Main Methods:

  • Developed a new linear membership function to calculate the contribution of sEMG signals at different time points.
  • Integrated sEMG features and their time-dependent contributions into the input layer of a broad learning system (BLS).
  • Explored five different sEMG features, including time domain (TD) and frequency domain (FD), and their combinations.

Main Results:

  • The proposed method effectively utilizes historical sEMG data through a novel membership function.
  • Combining time domain (TD) and frequency domain (FD) features of sEMG signals demonstrably enhances interaction force estimation quality.
  • Experimental results on a drawing task show the proposed BLS-based method outperforms existing techniques in estimation accuracy.

Conclusions:

  • The novel approach integrating BLS with comprehensive sEMG feature analysis offers a significant advancement in interaction force estimation.
  • The developed linear membership function successfully captures the temporal dynamics of sEMG signals for more accurate force prediction.
  • This method enhances HRI safety by providing more precise interaction force estimations, particularly when using combined TD and FD sEMG features.