arXiv:2607. 04820v1 Announce Type: new Abstract: Decoding hand kinematics from surface electromyography (EMG) is a core challenge in wearable biosignal processing with clinical relevance for prosthetic control and motor rehabilitation.
By Sofia Gilardini, Chenfei Ma, Kianoush Nazarpour
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: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...
By Yuwei Jia, Cheng Zhong, Jinyang Yu, Zhe Cui
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.
By Nia Touko, Matthew O A Ellis, Cristiano Capone, Alessio Burrello, Elisa Donati, Luca Manneschi
SignGPT is a unified, pose‑based framework that performs gloss‑free sign language translation (SLT) and generation (SLG) by integrating part‑aware hierarchical representations of body, hand, and facial motion into a shared language model. It uses asymmetric multi‑token prediction and progressive training for bidirectional modeling, and is evaluated on How2Sign (ASL) and Phoenix‑2014T (DGS) with benchmark comparisons, qualitative analyses, and component ablations. An exploratory study with 12 Deaf ASL signers demonstrates a sign‑to‑sign response pipeline, suggesting that unified modeling can support sign language conversation (SLC).
By Ronghui Li, Jun Dong, Zhongyuan Hu, Zunnan Xu, Jun Zhou, Liyuan Chen, Shuoling Liu, Jiangpeng Yan, Jie Guo, Xiu Li, Linchao Bao
DuoGesture is a co‑speech gesture generation model that separates gesture synthesis into a semantic stream and a beat stream, coordinated by a Semantic Variational Information Bottleneck that decides when semantic gestures override rhythmic motion. The semantic stream uses Motion‑Grounded Semantic Conditioning, replacing word embeddings with motion‑language representations to provide motion‑aligned semantic priors for rare gesture triggers. The beat stream is regularised by an Inertial Beat Prior, an anthropometry‑weighted arm‑chain module that reduces jitter and improves rhythmic consistency. Experiments show DuoGesture outperforms strong baselines and ablations confirm the complementary roles of semantic grounding, stochastic stream selection, and biomechanical regularisation.
By Ferdinand Paar, Lanmiao Liu, Asl{\i} \"Ozy\"urek, Serge Thill, Esam Ghaleb