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Related Experiment Video

Updated: Nov 9, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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A factorization-based algorithm to predict EMG data using only kinematics information.

Marta Manzano1, Gil Serrancolí1

  • 1Department of Mechanical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain.

International Journal for Numerical Methods in Biomedical Engineering
|April 9, 2021
PubMed
Summary

This study introduces a fast algorithm to predict electromyography (EMG) signals using only kinematic data during running. This method accurately forecasts muscle activity in real-time, simplifying biomechanical analysis.

Keywords:
EMG predictionkinematic and muscle synergiesmuscle excitationsignal factorization

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Area of Science:

  • Biomechanics
  • Biomedical Engineering
  • Signal Processing

Background:

  • Electromyography (EMG) analysis is crucial for understanding muscle function in rehabilitation, training, and device control.
  • Traditional EMG measurements can be time-consuming and challenging to acquire.
  • There is a need for efficient methods to obtain EMG data, especially in real-time applications like running.

Purpose of the Study:

  • To develop and validate a simple, real-time algorithm for predicting EMG signals.
  • To assess the feasibility of predicting EMG signals using only kinematic data.
  • To determine the accuracy of the proposed prediction method across different muscles and subjects.

Main Methods:

  • A novel algorithm was developed to predict EMG signals based on the factorization of kinematic data.
  • The method utilizes calibration subject data to predict EMG signals for new subjects using only kinematic information.
  • The algorithm was tested in real-time during running, with predictions made in under a second.

Main Results:

  • The algorithm accurately predicted lower-limb muscle EMG signals in real-time.
  • Correlation coefficients between predicted and experimental EMG signals exceeded 0.7 for 10 out of 11 muscles.
  • The overall median correlation coefficient was higher than 0.8, indicating high prediction accuracy.

Conclusions:

  • The proposed method enables accurate, real-time prediction of EMG signals using solely kinematic measurements.
  • This approach offers a significant advancement for EMG analysis, reducing experimental burden.
  • The findings support the potential of this algorithm for various applications in sports science, rehabilitation, and human-computer interaction.