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:2512. 07997v2 Announce Type: replace-cross Abstract: Gestures are an integral part of our daily interactions with the environment.
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...
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.
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:2609.22128v1 Announce Type: cross Abstract: The wrist plays a critical role in upper-limb function by enabling precise hand positioning, force regulation, and object manipulation. Continuous br...
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. 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:2506. 08795v2 Announce Type: replace-cross Abstract: Limb loss affects millions globally, impairing physical function and reducing quality of life.
The paper compares two leading motion‑imitation reinforcement learning pipelines—HyFyDy, which uses detailed musculotendon modeling, and MuJoCo, which focuses on computational speed. Using the same human motion‑capture and EMG data, both pipelines reproduce kinematics similarly, but HyFyDy’s muscle activation predictions align more closely with experimental EMG (RMSE 0.164, r = 0.4) than MuJoCo’s (RMSE 0.344, r = 0.11). The authors conclude that HyFyDy’s higher physiological realism makes it currently more suitable for musculoskeletal modeling, though both systems need further development for GPU‑parallelizable environments and robotic assistive‑device design.
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.
arXiv:2606. 24960v1 Announce Type: new Abstract: Tailoring stroke rehabilitation requires assessing how movements are organized, not merely if they succeed.
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.