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Related Experiment Video

Updated: Jun 25, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

A strategy for identifying locomotion modes using surface electromyography.

He Huang¹, Todd A Kuiken, Robert D Lipschutz

¹Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA. huang@ele.uri.edu

IEEE Transactions on Bio-Medical Engineering

|February 20, 2009

Summary

This study introduces a new phase-dependent surface electromyography (EMG) pattern recognition (PR) strategy to accurately identify user locomotion modes. The method shows reliable classification for various walking styles, paving the way for advanced prosthetic leg control.

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Area of Science:

Biomedical Engineering
Rehabilitation Engineering
Signal Processing

Background:

Locomotion mode identification is crucial for advanced prosthetic limb control.
Surface electromyography (EMG) signals are nonstationary during human locomotion, posing challenges for accurate pattern recognition (PR).
Existing PR methods struggle with the dynamic nature of EMG signals during gait.

Purpose of the Study:

To develop and validate a novel phase-dependent EMG PR strategy for classifying user locomotion modes.
To improve the accuracy and timeliness of prosthetic limb control systems.
To assess the system's performance in able-bodied individuals and transfemoral (TF) amputees.

Main Methods:

A phase-dependent EMG PR strategy was developed, analyzing signal characteristics across different gait phases.
EMG data were collected from eight able-bodied subjects and two TF amputees walking on various terrains.
Ten electrodes were placed over muscles above the knee to simulate residual limb EMG in TF amputees.

Main Results:

The developed PR system achieved reliable classification of seven distinct locomotion modes.
Average classification errors in able-bodied subjects across four phases were 12.4%, 6.0%, 7.5%, and 5.2%.
Comparable classification performance was observed in a pilot study with TF amputees, indicating system robustness.

Conclusions:

The proposed phase-dependent EMG PR strategy effectively classifies locomotion modes, even with nonstationary leg EMG signals.
This approach holds significant potential for enhancing the control of neural-controlled artificial legs.
The findings support the development of more intuitive and responsive prosthetic limb technologies.