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Related Experiment Video

Updated: Jul 17, 2026

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
06:52

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

Lower limb motion intention recognition using multi-source able-bodied gait signals.

Baoyu Li1, Guanghua Xu2, Jinju Pei1

  • 1Xi'an Jiaotong University, No 28, Xianning West Road, Xi'an, Shaanxi, 710049, China.

Journal of Neural Engineering
|July 15, 2026
PubMed
Summary

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This study enhances lower limb prosthesis control by integrating musculoskeletal modeling and advanced signal processing for better motion intention recognition. Optimized muscle selection and feature extraction significantly improve prediction accuracy for prosthetic limb movements.

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Robotics
  • Biomechanics

Background:

  • Accurate motion intention recognition is crucial for lower limb prostheses but is limited by sparse muscle signal usage.
  • Existing methods often struggle with the complexity and variability of biological signals.

Purpose of the Study:

  • To develop and validate a framework for enhanced motion intention recognition in lower limb prostheses.
  • To improve the accuracy and reliability of predicting joint movements for prosthetic control.

Main Methods:

  • A novel framework combining musculoskeletal modeling, advanced feature extraction (Variational Mode Decomposition for nonlinear sEMG features), and intelligent feature selection (Random Forest with Bagging).
  • A muscle screening method was employed to identify key muscles for accurate joint motion representation.
Keywords:
LSTMMotion intentionfeature selectionmulti-source signals fusionmuscle screening

More Related Videos

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Related Experiment Videos

Last Updated: Jul 17, 2026

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
06:52

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

  • An LSTM regression model was utilized for continuous joint motion prediction.

    • Main Results:

    • The proposed framework significantly improved prediction accuracy for knee and ankle joint angles and moments compared to traditional methods.
    • Knee joint prediction achieved R² values of 0.93±0.05 for angle and 0.95±0.036 for moment.
    • Ankle joint prediction demonstrated high accuracy with R² values of 0.93±0.03 for angle and 0.98±0.021 for moment.

    Conclusions:

    • Integrating optimal muscle selection, nonlinear feature enhancement, and intelligent feature selection is key to advancing motion intention recognition.
    • This approach offers a promising pathway for developing more intuitive and responsive lower limb prosthetic devices.