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LiteEMG-FM: An Efficient and Deployable Foundation Model for Robust EMG Sensing
Electromyography (EMG) signals vary substantially across individuals, body regions, recording sessions, and sensing hardware, limiting the generalization of models for assistive devices and human-computer interaction. Existing time-series foundation models are also computationally expensive for real
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