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Updated: Jun 6, 2026

Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics
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Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics

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Active physical human-exoskeleton interaction based on motion intention adaptive recognition and synchronous

Weiguo Shi1,2, Weiqun Wang1,2, Jiaxing Wang1,2

  • 1School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.

Frontiers in Neurorobotics
|June 5, 2026
PubMed
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This study introduces a new method for recognizing human movement intentions using biophysical signals and adaptive learning. This improves exoskeleton control and synchronous tracking accuracy for better human-robot interaction.

Area of Science:

  • Robotics
  • Biomedical Engineering
  • Machine Learning

Background:

  • Human-exoskeleton interaction is crucial but faces challenges in motion intention recognition and synchronous tracking.
  • Existing methods often struggle with accuracy and long-term performance degradation.

Purpose of the Study:

  • To propose a novel motion intention recognition method using biophysical information fusion and adaptive learning.
  • To enhance the accuracy and robustness of human-exoskeleton synchronous tracking.

Main Methods:

  • Developed a lower-limb joint angle prediction model integrating surface electromyography (sEMG), historical joint angles, and centers of gravity.
  • Utilized convolutional neural networks, Mamba networks, and multilayer perceptron for feature extraction, fusion, and prediction.
Keywords:
active rehabilitationgait traininglower limb exoskeletonsEMG based motion intention recognitionsynchronous tracking

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  • Implemented an online adaptive method using style transfer mapping to maintain model performance over time.

    • Main Results:

    • The proposed model accurately predicts lower-limb joint angles by fusing multiple biophysical and kinematic signals.
    • The online adaptive method effectively addressed recognition accuracy decline, ensuring sustained predictive performance.
    • Real-time exoskeleton synchronous tracking was achieved based on the predicted joint angles.

    Conclusions:

    • The integrated biophysical information fusion and adaptive learning approach significantly improves human motion intention recognition for exoskeletons.
    • The developed methods offer a feasible and effective solution for real-time, synchronous human-exoskeleton control.
    • This work advances the field of human-robot interaction by enhancing control accuracy and user experience.