Surface Electromyography
PulseAugur coverage of Surface Electromyography — every cluster mentioning Surface Electromyography across labs, papers, and developer communities, ranked by signal.
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Continual learning boosts sEMG gesture recognition accuracy
Researchers have explored the application of continual learning strategies to recurrent neural networks for surface electromyography (sEMG) gesture recognition. Their evaluation on the NinaPro DB1 dataset revealed that …
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New encoder improves cross-user gesture recognition from sEMG data
Researchers have developed a novel montage-agnostic encoder designed to improve cross-user gesture recognition from surface electromyography (sEMG) data. This encoder utilizes shared weights for each electrode and locat…
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New encoder improves cross-user gesture recognition for myoelectric prosthetics
Researchers have developed a new montage-agnostic encoder designed to improve cross-user gesture recognition from surface electromyography (sEMG) signals. This encoder uses shared weights for each electrode, locating th…
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Single-channel sEMG shows promise for efficient hand gesture recognition
Researchers have explored the use of a single surface electromyography (sEMG) channel for hand gesture classification, aiming for more efficient and low-power systems. By extracting various time-domain and frequency-dom…
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LLMs translate sEMG signals into language for activity recognition
Researchers have developed a novel framework called LLM-sEMG that utilizes large language models (LLMs) for surface electromyography (sEMG) signal-based activity recognition. This approach converts continuous sEMG seque…