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. 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.
By Jethro Odeyemi, W. J. Zhang
arXiv:2607. 22779v1 Announce Type: cross Abstract: Hand gesture recognition via surface electromyography (sEMG) is fundamental to prosthetic control.
By Federico Del Pup, Elisa Tentori, Manfredo Atzori
arXiv:2606. 27855v1 Announce Type: cross Abstract: Deep learning models for surface electromyography (sEMG) can benefit substantially from subject-specific (re-)calibration, since no sufficiently large and diverse datasets are available to train fully generic decoders.
By Stephan J. Lehmler, Tobias Glasmachers, Ioannis Iossifidis
arXiv:2607. 03783v1 Announce Type: new Abstract: Cross-subject generalization remains a fundamental challenge in surface electromyography (sEMG)-based gesture recognition.
By Hamed Rafiei, Ali Mousavi
arXiv:2606. 31349v1 Announce Type: cross Abstract: Surface electromyography (sEMG)-based gesture recognition has emerged as a promising technology for natural human-computer interaction.
By Yurui Liu, Xiao-Cong Zhong, Qisong Wang, Xuefu Wang, Dan Liu, Jinwei Sun