TL;DR
The results demonstrated that NewRep effectively enhanced the real-time decoding capability of myoelectric control, offering a promising approach for practical applications.
Electromyography is a bioelectric signal recorded during muscle contraction or relaxation. The EMG signal is subject‐specific, reflecting which muscle is contracting at what intensity. In recent years, measuring electromyography has become easier with measuring devices designed as bracelets. Previous studies focused on...
This study proposes an enhanced hand gesture classification approach for myoelectric prosthetic hands using wearable EMG sensors, Savitzky-Golay (SG) filtering, and a compact convolutional neural network (CNN).
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.
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.
An affordable, multi-channel wearable FMG armband that reliably captures forces at multiple forearm muscle locations during different gestures and such a simple, low-cost, and effective scheme based on FMG can help control prosthetic and robotic devices using human intentions.
DMD is established as an effective, interpretable feature-extraction method for sEMG-based gesture classification, with applications in rehabilitation engineering, prosthetic control, and human-computer interaction.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
