Deep Learning-Based Identification Algorithm for Transitions Between Walking Environments Using Electromyography
Summary
This study developed a deep learning algorithm using only electromyography (EMG) signals to accurately classify transitions between different walking terrains. The model achieved 95.4% accuracy, enabling better control for walking assistive devices.
Area of Science:
- Biomedical Engineering
- Robotics
- Human-Computer Interaction
Background:
- Existing terrain identification for walking assistive devices often relies on sensor fusion.
- Studies specifically classifying gait transitions using only electromyography (EMG) signals are limited.
Purpose of the Study:
- To propose an identification algorithm for transitions between various walking environments using only EMG signals.
- To leverage deep learning for classifying gait environment changes based on lower extremity muscle activity.
Main Methods:
- Measured EMG signals from 27 subjects across multiple lower extremity muscles during walking on diverse terrains (flat, stairs, slopes) and transitions.
- Utilized an artificial neural network (ANN) model, inputting the entire EMG profile during the stance phase.
- Evaluated classification accuracy using all muscle activations and a subset of key muscle groups.
Main Results:
- The ANN model achieved a high classification accuracy of 95.4% for identifying transitions between walking environments using all measured muscle activations.
- A reduced set of muscle activations (knee extensor, ankle extensor, metatarsophalangeal flexor) yielded a classification accuracy of 90.9%.
Conclusions:
- Transitions between different gait environments can be accurately identified using only EMG signals during the stance phase.
- The developed ANN model demonstrates the potential for precise gait transition classification, paving the way for advanced walking assistive devices.
