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: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: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.
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:2409. 17340v2 Announce Type: replace-cross Abstract: Loss of hand function due to conditions like stroke or multiple sclerosis significantly impacts daily activities.
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 presents a soft, active electromyography (EMG) interface that enables word-level silent speech recognition (SSR) using machine learning. The device, worn on the hand, employs a fingertip electrode positioned near the lips to acquire EMG signals only when needed, and incorporates liquid metal interconnects, transparent flexible printed circuit electrodes, and elastomer encapsulation for mechanical stability during finger motion. A deep neural network trained on these stable signals achieved a mean accuracy of 97.2 ± 1.3 % across three subjects for a 30‑word vocabulary, and real‑time drone control demonstrated its practicality in noisy, privacy‑sensitive environments.
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. 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:2506. 08795v2 Announce Type: replace-cross Abstract: Limb loss affects millions globally, impairing physical function and reducing quality of life.
arXiv:2606. 24900v1 Announce Type: new Abstract: This paper proposes a new approach to near-sensor computing, in which a lightweight Neural Architecture Search (NAS) is performed directly on the deployment device to find the best tiny neural architecture for analyzing the real-time data acquired through sensors.