MyoSem is a multimodal alignment framework that maps EMG signals to text-based action semantics for bidirectional retrieval and improved generalization in hand action understanding.
InAdvances in Neural Informa- tion Processing Systems
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AEMG learns reusable neuromuscular representations from eight standardized EMG datasets by modeling contraction events as tokens and using self-supervised pre-training to improve robustness to new users and reduce calibration data needs.
citing papers explorer
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MyoSem: Aligning Electromyography to Natural-Language Action Semantics for Hand Action Understanding
MyoSem is a multimodal alignment framework that maps EMG signals to text-based action semantics for bidirectional retrieval and improved generalization in hand action understanding.
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From Muscle Bursts to Motor Intent: Self-Supervised Token Modeling for Heterogeneous EMG
AEMG learns reusable neuromuscular representations from eight standardized EMG datasets by modeling contraction events as tokens and using self-supervised pre-training to improve robustness to new users and reduce calibration data needs.