Discrete Wavelet Transform based sEMG Data Alignment for Gesture-Free Hand Intention Recognition

• Published in: IEEE sensors journal p. 1
• Main Authors: Yan, Lingfeng, Li, Hongxin, Tang, Jingsheng, Li, Wenqi, Lu, Huimin, Zhou, Zongtan
• Format: Journal Article
• Language: English
• Published: IEEE 28.08.2025
• Subjects: data alignment; discrete wavelet transform; Discrete wavelet transforms; Electrodes; Feature extraction; Gesture-free hand intention recognition; Hands; human-computer interaction; Muscles; Signal resolution; surface electromyographic (sEMG); Time-domain analysis; Time-frequency analysis; Training; Transforms
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2025.3602235

In scenarios where collaborators and non-collaborators coexist, it is important to enable covert information exchange among collaborators while evading detection. Based on isometric muscle contraction, a surface electromyography (sEMG) based gesture-free hand intention recognition system has been developed to transmit messages securely. However, non-stationary sEMG signals, electrode displacement, and individual differences restrict the performance of single-day, cross-day, and cross-subject experiments. To overcome the difficulty, a discrete wavelet transform-based sEMG data alignment (DWT-DA) method is proposed to reduce the distribution difference between the source and target domains. The DWT-DA consists of three stages: time-frequency domain signal decomposition (TFD-SD), subject sub-signal recombination (SR), and time-frequency domain signal reconstruction (TFD-SR). The TFD-SD decomposes the original signal into several sub-signals focused on a specific frequency band or time-frequency region. The SR recombines the decomposed sub-signals to promote complementary enhancement between the features of each sub-signal. The TFD-SR reconstructs the processed sub-signal into a time-domain signal to achieve data augmentation and alignment. Experiments are conducted on a self-collected gesture-free hand intention recognition dataset, demonstrating excellent performance of the method on single-day, cross-day, and cross-subject evaluations. The code is available at https://github.com/ylfzero/EMG_DWT_DA.