A Novel Instruction Gesture Set Determination Scheme for Robust Myoelectric Control Applications

• Published in: IEEE transactions on biomedical engineering Vol. 72; no. 3; pp. 909 - 920
• Main Authors: Ruan, Yuwen, Chen, Xiang, Zhang, Xu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adult; Algorithms; Classification algorithms; Dimensional analysis; Electrodes; Electromyography; Electromyography - methods; Embedding; EMG pattern recognition; Feature extraction; Female; Gesture recognition; gesture set; Gestures; Human-computer interface; Humans; Male; Muscles; Myoelectric control; Myoelectricity; Nusselt number; Pattern recognition; Pattern Recognition, Automated - methods; Prostheses; Robust control; Signal Processing, Computer-Assisted; T-SNE; Thumb; user dependence; Wrist
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2024.3479232

Objective : Myoelectric control technology has important application value in rehabilitation medicine, prosthesis control, human-computer interaction (HCI) and other fields. However, the user dependence of electromyography (EMG) pattern recognition is one of the key problems hindering the implementation of robust myoelectric control applications. Aimed at solving the user dependence problem, this paper proposed a novel instruction gesture set determination scheme for EMG pattern recognition in user-independent mode. Methods: The scheme uses T-distributed stochastic neighbor embedding (T-SNE) dimensionality reduction to analyze high-dimensional surface EMG data from multiple users and gestures. This process can identify gesture combinations with minimal individual differences and high separability. Results: The proposed scheme was validated using two large-scale EMG gesture databases with different acquisition devices, subjects, and gestures. Optimal and inferior gesture sets of varying sizes were identified. In recognition experiments conducted in both user-independent and electrode-offset modes, the optimal gesture sets demonstrated significantly higher recognition accuracies compared to the inferior sets, with improvements ranging from 12.57% to 36.92%. Conclusion: The results demonstrated that the separability of the obtained optimal gesture sets was significantly superior to that of the inferior sets, confirming the effectiveness of the proposed scheme in reducing user dependence in EMG pattern recognition. Significance: The study has certain application value to promote the development of myoelectric control technology. Specifically, the scheme proposed can be used to determine instruction gesture sets with low user dependence and high separability for myoelectric control applications.