arXiv:2512. 07997v2 Announce Type: replace-cross Abstract: Gestures are an integral part of our daily interactions with the environment.
arXiv:2607. 22779v1 Announce Type: cross Abstract: Hand gesture recognition via surface electromyography (sEMG) is fundamental to prosthetic control.
Muscle activity is fundamental to human movement, and understanding its patterns is critical for injury prevention and rehabilitation. Conventional muscle activity monitoring relies on specialized sen...
Pose2Muscle is a pose-driven framework that estimates discrete muscle activity states without requiring surface electromyography (sEMG) during inference. It reformulates muscle estimation as a structured prediction problem, using multi-scale spatio-temporal attention and a directed acyclic graph-based decoder to capture motion patterns and maintain multiple candidate hypotheses. The authors introduce the PoseEMG-43 dataset, comprising 2,992 movement instances from 43 daily-life actions performed by 14 participants, and demonstrate that Pose2Muscle outperforms baseline methods with high accuracy and correlation metrics.
arXiv:2607. 15972v1 Announce Type: new Abstract: Accurate hand gesture recognition using surface electromyography (sEMG) typically relies on multichannel sensor arrays and computationally intensive models.
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.
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:2606. 23771v1 Announce Type: cross Abstract: Extended Reality (XR) presents a challenging use case for 5G and 6G networks, requiring high data-rates and lowlatency communication to deliver a truly immersive experience.
arXiv:2409. 17340v2 Announce Type: replace-cross Abstract: Loss of hand function due to conditions like stroke or multiple sclerosis significantly impacts daily activities.
arXiv:2607. 07850v1 Announce Type: new Abstract: For seemless control of advanced hand prostheses and augmented reality, accurate and immediate hand gestures recognition is essential.
arXiv:2606. 29423v1 Announce Type: new Abstract: The importance of gesture recognition has been acknowledged in many domains requiring real-time recognition systems.
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.
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.