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A Novel Instruction Gesture Set Determination Scheme for Robust Myoelectric Control Applications
IEEE Transactions on Bio-Medical Engineering
|October 11, 2024
Summary
This study introduces a new method to determine electromyography (EMG) gesture sets, significantly reducing user dependence for more robust myoelectric control in prosthetics and HCI applications.
Area of Science:
- Biomedical Engineering
- Human-Computer Interaction
- Rehabilitation Technology
Background:
- Myoelectric control relies on electromyography (EMG) pattern recognition, but user dependence limits its practical application.
- Robust myoelectric control is crucial for advancements in prosthetics, rehabilitation medicine, and human-computer interaction (HCI).
Purpose of the Study:
- To address the user dependence issue in EMG pattern recognition.
- To propose a novel scheme for determining instruction gesture sets in a user-independent mode.
Main Methods:
- Utilized T-distributed stochastic neighbor embedding (T-SNE) for dimensionality reduction of high-dimensional surface EMG data.
- Analyzed data from multiple users and gestures to identify gesture combinations with minimal individual differences and high separability.
Main Results:
- Validated the scheme on two large-scale EMG gesture databases with diverse acquisition devices and subjects.
- Optimal gesture sets achieved significantly higher recognition accuracies (12.57%–36.92%) compared to inferior sets in user-independent and electrode-offset modes.
Conclusions:
- The proposed scheme effectively reduces user dependence in EMG pattern recognition by selecting gesture sets with superior separability.
- Demonstrated significant improvements in recognition accuracy, confirming the scheme's effectiveness for robust myoelectric control applications.
