AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The proposed model captures more comprehensive information about temporal energy, shape, and frequency characteristics of the EMG signal through feature fusion, enabling it to effectively learn short EMG bursts.
Surface electromyography (sEMG) signals enable intuitive human-machine interaction by capturing muscle activation patterns associated with hand gestures. However, the accurate recognition of hand gestures using sEMG signals still remains challenging due to the complex nature of the signal variations. This work proposes a wavelet-based deep learning framework for EMG gesture recognition using time-frequency representations. The segmented sEMG signal is converted into the Continuous Wavelet Transform (CWT) spectrogram to obtain the multi-scale muscle activation. Two lightweight deep learning frameworks are developed to effectively learn gesture representations from these wavelet features. The Wavelet Attention Convolutional Neural Network (WA-CNN) integrates scale and channel attention to emphasize informative frequency bands and EMG electrodes. The Wavelet State-Space Model (WSSM) incorporates efficient temporal modeling of gesture dynamics. Experimental results on the Ninapro DB2 dataset demonstrate that the proposed models outperform conventional machine learning and deep learning baselines. In particular, WSSM achieves 95% accuracy while maintaining low model complexity suitable for real-time wearable EMG-based interaction systems.
This study investigates the feasibility of classifying ten hand gestures using a single sEMG channel combined with lightweight machine learning architectures and demonstrates that combining time and frequency features with Pearson filtering and a compact NN can achieve up to 90 percent accuracy.
Electromyography (EMG)-based pattern recognition is a key enabling technology for intuitive upper-limb prosthetic control; however, amputee EMG signals show substantial intersubject variability and nonstationary characteristics, which can limit the effectiveness of conventional fixed feature representations. This study proposes an Adaptive Multi-Representation Fusion Framework that combines an enhanced Adaptive Linear Series Decomposition Learner (Adaptive LSDL) with Time-Domain Power Spectral Descriptors (TDPSD) for amputee movement classification. The proposed Adaptive LSDL extends conventional decomposition by using adaptive threshold-region optimization and multitransform signal analysis, enabling subject-specific identification of the most discriminative decomposition characteristics while preserving low computational complexity. The complementary properties of TDPSD and Adaptive LSDL are then exploited through feature-level fusion, producing a unified representation that captures both local temporal-spectral complexity and adaptive structural signal information. Experimental evaluation was conducted using the Ninapro DB3 dataset, comprising 11 transradial amputees, and a MYO armband amputee dataset, comprising 6 transradial amputees. Subject-specific five-fold cross-validation was performed using a Linear Discriminant Analysis (LDA) classifier. Adaptive LSDL consistently improved performance relative to the original fixed-threshold LSDL representation, while fusion improved classification accuracy for 7 of 11 DB3 amputees and 5 of 6 MYO amputees. The largest fusion gains reached 4.39 and 5.60 percentage points for the DB3 and MYO cohorts, respectively. Furthermore, the proposed framework demonstrated competitive performance relative to a lightweight real-time convolutional neural network benchmark while requiring substantially lower computational complexity. These findings indicate that adaptive decomposition and representation-level information fusion provide an effective approach for exploiting subject-specific neuromuscular information while remaining suitable for computationally efficient prosthetic control applications.
This research enhances the feasibility of embedding deep learning models into wearable systems (e.g. wristband or wristwatch), facilitating more responsive and efficient gesture recognition in daily-life applications.
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