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Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

A linear-attention based network for estimating continuous upper limb movement from surface electromyography.

Chuang Lin¹, Chunxiao Zhao², Na Li³

¹The School of Information Science and Technology, Dalian Maritime University, Lingshui Street, Dalian, 116000, China. linchuang_78@126.com.

Scientific Reports

|December 3, 2025

Summary

This summary is machine-generated.

We developed a Linear-Attention-based model (LABD) for estimating upper-limb movements using sEMG signals. LABD significantly outperformed other deep learning models in accurately predicting joint angles.

Keywords:

Continuous movement estimation Upper limb movements sEMG

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Published on: July 26, 2013

Area of Science:

Biomedical Engineering
Human-Machine Interaction
Machine Learning

Background:

Continuous kinematics estimation offers intuitive human-machine interaction compared to pattern recognition.
Accurate upper-limb movement estimation, particularly for elbow and shoulder joints, is crucial.
Muscle deformation can shift angle sensors, complicating accurate measurement.

Purpose of the Study:

To propose a novel Linear-Attention-based model (LABD) for estimating upper-limb joint angles.
To compare the performance of LABD against other deep learning models using sEMG data.
To address challenges posed by muscle artifacts in sensor-based joint angle estimation.

Main Methods:

Collected surface electromyography (sEMG) signals from eight sensors.
Utilized Vicon motion capture system for precise joint angle measurement.
Developed and evaluated the Linear-Attention-based model (LABD) against MLP, TCN, LSTM, and DABD.

Main Results:

LABD demonstrated superior performance in estimating upper-limb joint angles.
Pearson correlation coefficients (PCC) indicated high accuracy for LABD.
Wilcoxon signed-rank tests confirmed the statistical significance of LABD's outperformance.

Conclusions:

The proposed LABD is a highly effective deep learning model for continuous upper-limb kinematics estimation.
LABD offers a robust solution for accurate joint angle prediction from sEMG signals.
This approach enhances the potential for natural and intuitive human-machine interaction systems.