SEMA5B
PulseAugur coverage of SEMA5B — every cluster mentioning SEMA5B across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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Koopman operator theory predicts grip force from EMG signals
Researchers have developed a novel method using Koopman operator theory to predict grip force from surface electromyography (sEMG) signals. This approach aims to improve robotic rehabilitation by accurately estimating a…
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Spiking Neural Networks Offer Energy-Efficient Muscle Fatigue Detection
Researchers have developed an energy-efficient framework for detecting muscle fatigue using Spiking Neural Networks (SNNs). This approach leverages sparse, event-driven computation and temporal modeling, making it suita…
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SonoRank uses ultrasound for calibration-free prosthetic finger control
Researchers have developed SonoRank, a novel method for detecting finger flexion using forearm ultrasound sequences, aiming to overcome the limitations of current prosthetic hand technology. Unlike existing ultrasound-b…
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New PGUDA framework boosts sEMG gesture recognition accuracy
Researchers have developed a novel framework called PGUDA (Pressure-Guided Unsupervised Domain Adaptation) to improve the accuracy of gesture recognition using surface electromyography (sEMG) signals. This method addres…
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Memorization indicators may help detect overfitting in sEMG deep learning
Researchers have explored the use of memorization indicators to detect overfitting in deep learning models used for surface electromyography (sEMG) decoders, particularly when limited sample sizes are available for subj…
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Peking University team secures funding for new embodied AI data acquisition tech
SnowOrigin, a team with roots at Peking University, has secured investment from notable figures like Gong Hongjia and Lu Qi, along with overseas institutions. The company is developing a new generation of data acquisiti…
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Silent speech synthesis uses sEMG and lipreading with masking
Researchers have developed a new framework for silent speech synthesis that combines surface electromyography (sEMG) and lipreading data. This approach uses modality masking during training to improve robustness against…
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New wave modeling framework offers continuous temporal representations for event-based signals
Researchers have developed a novel framework for modeling continuous temporal representations of event-based signals, such as those from biological processes like sEMG. This approach maps input signals into a complex-va…
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New study compares sEMG encoding accuracy across speech modes
A new research paper explores the effectiveness of Speech Articulatory Coding (SPARC) features for predicting surface electromyography (sEMG) envelopes across different speech modes. The study found that SPARC features …