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: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.
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: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.
The study evaluates how well a surface‑EMG gesture recogniser trained on intact‑limb data transfers to transradial amputees. Zero‑shot transfer fails; the model needs a few labelled repetitions from the new user to outperform a per‑user classifier, achieving a macro‑F1 of 0.779 versus 0.589. Training on a larger pool of intact subjects, or combining intact and amputee data, yields the best cross‑population performance.
arXiv:2606. 31349v1 Announce Type: cross Abstract: Surface electromyography (sEMG)-based gesture recognition has emerged as a promising technology for natural human-computer interaction.
arXiv:2607. 22779v1 Announce Type: cross Abstract: Hand gesture recognition via surface electromyography (sEMG) is fundamental to prosthetic control.
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...
The paper investigates how to personalize a cross-user surface electromyography (sEMG) encoder when only a few calibration repetitions are available. Four methods—prototypical adaptation, linear probes, scaled fine‑tuning, and full fine‑tuning—were evaluated across 77 subjects on two databases. Full fine‑tuning consistently achieved the highest accuracy, but a gradient‑free prototypical rule captured 52–78 % of the benefit without per‑user weight copies, enabling quick donning‑time personalization.
MyoFlow introduces a discriminative flow-matching framework for high‑density surface electromyography (HD‑sEMG) gesture recognition that addresses distribution shifts caused by electrode re‑donning and physiological variability. By using a domain‑conditioned rectified flow to transport encoded windows toward gesture anchors, the method enables zero‑shot prediction without a separate classifier head. On the Hyser dataset, MyoFlow outperforms the strongest diffusion‑based baseline by 4.24 % in cross‑session accuracy and 6.37 % in cross‑subject accuracy, and achieves 91.71 % mean zero‑shot accuracy and 97.39 % mean few‑shot accuracy on the CEMHSEY dataset.
The study investigates how the source of normalization statistics affects the performance of wrist electrodermal activity (EDA) affect‑recognition models. Using the SAFE‑EDA convolutional network pretrained on expert artifact annotations, the authors compare models trained with normalization derived only from training subjects versus from the held‑out subject’s full recording. They find that pretraining improves macro‑F1 when using training‑only statistics, but the benefit diminishes when using the held‑out subject’s data, and that artifact supervision outperforms self‑supervised pretraining. Across multiple configurations, pretrained models generally perform better, though the interaction with per‑user normalization varies by dataset.
arXiv:2607. 03783v1 Announce Type: new Abstract: Cross-subject generalization remains a fundamental challenge in surface electromyography (sEMG)-based gesture recognition.
The paper evaluates six onboarding strategies for federated wearable models on five datasets using a leakage‑controlled protocol that fixes source checkpoints and separates calibration from evaluation. Results show that while average accuracy is high, person‑level performance can drop significantly, with some methods causing negative transfer for certain users. The study highlights that mean accuracy alone is insufficient and provides an auditable benchmark and failure map for future development.