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Related Experiment Video

Updated: Jul 28, 2025

Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy

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Published on: August 5, 2022

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

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 system Force estimation Human-robot interaction sEMG

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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.