arXiv Machine Learning July 7, 2026 By Sofia Gilardini, Chenfei Ma, Kianoush Nazarpour

KinEMbed: Decoding Kinematics from Electromyography via Cross-Modal Contrastive Learning

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 28

Multimodal Surface EMG Hand Gesture Recognition Using Query-Based Transformers for Prosthetic Control

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

llms multimodal benchmarks

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...

By Yuwei Jia, Cheng Zhong, Jinyang Yu, Zhe Cui

arXiv AI

Jun 2

MyoSem: Aligning Electromyography to Natural-Language Action Semantics for Hand Action Understanding

arXiv:2606. 00174v1 Announce Type: cross Abstract: Electromyography (EMG) directly reflects muscle activation and is a key sensing modality for gesture recognition, prosthetic control, and wearable interaction.

By Chiyue Wang, Dong She, Yang Gao, Zhanpeng Jin

safety

arXiv AI

Aug 25

TransHands: Repurposing Human Pose Encoders as Hand Pose Encoders

arXiv:2608.22341v1 Announce Type: cross Abstract: Lifting 3D hand poses from 2D monocular representations remains challenging due to the limited availability of large-scale, diverse 3D-annotated hand...

By Milo Piccioli, Gianluca Amprimo, Claudia Ferraris, Gabriella Olmo

llms computer-vision fine-tuning benchmarks

arXiv Computer Vision

Sep 22

BiView-Touch: Learning Bimanual Tactile Representations by Cross-Hand Completion

arXiv:2609.23352v1 Announce Type: new Abstract: Bimanual interaction produces complementary tactile views of the same physical process, yet existing tactile representation learning largely models the...

By Chenxin Liang, Youchen Lai, Chuqiao Lyu, Tianxing Chen, Shoujie Li, Wenbo Ding

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.

By Nia Touko, Matthew O A Ellis, Cristiano Capone, Alessio Burrello, Elisa Donati, Luca Manneschi

safety

One email a morning, machine-written

Related stories

Multimodal Surface EMG Hand Gesture Recognition Using Query-Based Transformers for Prosthetic Control

EMGBlend: Heterogeneity-Aware Self-Supervised Pretraining for Gesture and Force Decoding

MyoSem: Aligning Electromyography to Natural-Language Action Semantics for Hand Action Understanding

TransHands: Repurposing Human Pose Encoders as Hand Pose Encoders

BiView-Touch: Learning Bimanual Tactile Representations by Cross-Hand Completion

Lightweight Test-Time Adaptation for EMG-Based Gesture Recognition