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: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: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.
Surface electromyography (sEMG) provides a wearable, noninvasive interface to neuromuscular activity for movement decoding and human-computer interaction. Population-scale decoding remains difficult b...
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: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.
arXiv:2609.36687v1 Announce Type: cross Abstract: Neural codecs encode continuous signals into compact sequences of discrete tokens, providing an interface for efficient transmission, storage, and to...
LiteEMG-FM is an efficient hybrid CNN‑Transformer foundation model designed for electromyography (EMG) sensing. It is pretrained on 16 diverse upper‑ and lower‑limb EMG datasets, enabling representations that generalize across users and datasets. The model incorporates a lightweight, always‑on 1D‑CNN wake‑up module that filters rest and non‑target activity, activating LiteEMG‑FM only for valid gestures, and is evaluated in full inference offloading, split inference, and full on‑device processing scenarios, showing superior performance over state‑of‑the‑art time‑series foundation models and supervised baselines, especially in zero‑calibration cross‑participant and data‑scarce conditions.
Pattern-recognition control promises a myoelectric prosthesis that responds to many intended gestures rather than one or two, but the promise has stayed in the laboratory. A recogniser trained on one person rarely transfers to the next, and useful performance usually demands a fresh round of labelled calibration from the end user.
arXiv:2607. 27565v1 Announce Type: new Abstract: Pattern-recognition control promises a myoelectric prosthesis that responds to many intended gestures rather than one or two, but the promise has stayed in the laboratory.
arXiv:2607. 22779v1 Announce Type: cross Abstract: Hand gesture recognition via surface electromyography (sEMG) is fundamental to prosthetic control.
MyoSem is an EMG–action semantic alignment framework that maps hand electromyography signals and multi‑view natural‑language action descriptions into a shared feature space for bidirectional retrieval. It constructs semantic prototypes from text, encodes EMG with local activation‑aware representations, and aggregates evidence using semantic queries. Evaluated on multiple datasets and protocols, MyoSem outperforms existing methods in bidirectional EMG‑text retrieval, demonstrating its effectiveness in moving EMG hand action understanding toward language‑queryable semantic retrieval.