arXiv Machine Learning
EMG Tokenization Preserves Movement Information at a Fraction of the Bitrate
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arXiv Machine Learning
Sep 23
EMGBlend: Heterogeneity-Aware Self-Supervised Pretraining for Gesture and Force Decoding
arXiv:2609.25582v1 Announce Type: new Abstract: Public surface electromyography (EMG) datasets vary widely in electrode layout, channel count, frequency support, and size. Simply mixing them for pret...
arXiv Machine Learning
Oct 7
Neuromotor Hierarchy Network: Physiological Inductive Biases for Robust Generalization in sEMG Decoding
arXiv:2610.07713v1 Announce Type: new Abstract: Surface electromyography (sEMG) provides a wearable, noninvasive interface to neuromuscular activity for movement decoding and human-computer interacti...
arXiv Machine Learning
Jun 24
Lightweight Test-Time Adaptation for EMG-Based Gesture Recognition
arXiv:2601. 04181v2 Announce Type: replace Abstract: Reliable long-term decoding of gestures from surface electromyography (EMG) is hindered by signal drift caused by electrode displacement, muscle fatigue, and/or posture changes.
Hugging Face Trending Papers
Oct 6
Neuromotor Hierarchy Network: Physiological Inductive Biases for Robust Generalization in sEMG Decoding
Surface electromyography (sEMG) provides a wearable, noninvasive interface to neuromuscular activity for movement decoding and human-computer interaction. Population-scale decoding remains difficult b...
arXiv Machine Learning
Oct 15
EMG-FM-Bench: A Comprehensive Benchmark for Foundation Model Transfer and Adaptation on Electromyography
EMG-FM-Bench is a benchmark that unifies 20 public electromyography datasets, totaling over one million EMG segments, to evaluate how foundation models transfer and adapt to EMG tasks. It tests nine pretrained models across four scenarios: frozen versus fully fine‑tuned performance, the benefit of pretraining versus training from scratch, generalization to new users with limited data, and performance across different EMG tasks. Results show that linear probing reveals useful pretrained representations, full fine‑tuning can significantly alter performance, pretraining benefits vary by model, performance drops on new users, and few‑shot adaptation improves macro‑F1 in most cases but only partially recovers lost performance.
arXiv Machine Learning
Jul 31
Recognition and Label-Free Adaptation Across Recording Sessions in Surface-EMG Gesture Decoding
arXiv:2607. 27568v1 Announce Type: new Abstract: Recognition accuracy obtained during a recording session does not persist when a user puts on the electrodes again after the electrodes had previously been removed.