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...
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.
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.
The results demonstrated that NewRep effectively enhanced the real-time decoding capability of myoelectric control, offering a promising approach for practical 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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