Muscle activation and electromyography studies

Últimos artículos

• MyoCodec: A Streaming Neural Codec for Electromyography

  Jihwan Lee, Kleanthis Avramidis, Junhyeok Lee, Tiantian Feng, Najim Dehak, Shrikanth Narayanan · 30 de septiembre de 2026

  Neural codecs encode continuous signals into compact sequences of discrete tokens, providing an interface for efficient transmission, storage, and token-based sequence modeling. This paradigm has been widely adopted in modern speech and audio frameworks; however, the biosignal domain still lacks a n…

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

  Yuwei Jia, Cheng Zhong, Jinyang Yu, Zhe Cui · 23 de septiembre de 2026

  Public surface electromyography (EMG) datasets vary widely in electrode layout, channel count, frequency support, and size. Simply mixing them for pretraining can misalign channel semantics, introduce spectral targets that some devices cannot observe, and let large or high-channel-count datasets dom…

• ST-Topo GAN: A Motor EEG-to-EMG Decoding Model Matched to Wrist Movement Complexity

  Ye Sun, Mingxuan Qu, Jing Wang, Dezhong Yao, Gang Liu · 22 de septiembre de 2026

  The wrist plays a critical role in upper-limb function by enabling precise hand positioning, force regulation, and object manipulation. Continuous brain--muscle interfaces (BMIs) offer a promising approach for motor restoration by decoding neural activity into muscle activation signals. However, exi…

• Multi-Subject Pretraining Enables Short-Calibration Personalization for Closed-Corpus Surface EMG Speech Decoding

  Chenqian Le, Beatrice Fumagalli, Yasamin Esmaeili, Xupeng Chen, Tianyu He, Nikasadat Emami, Adeen Flinker, Yao Wang · 21 de septiembre de 2026

  Surface electromyography (sEMG)-based silent speech interfaces are limited by cross-user variability and calibration burden. We study a limited-data setting in which each of 27 speech-typical participants contributed less than 0.5 h of data (21.3 min on average) across Aloud and Mimed speech. Within…

• Intact-to-Amputee Transfer in Surface-EMG Gesture Decoding: Training Source and Calibration Budget

  Jethro Odeyemi, W. J. Zhang · 18 de septiembre de 2026

  A recogniser trained on one person rarely transfers to the next, and useful performance usually demands a fresh round of labelled calibration from the end user. A systematic review of 1077 studies quantifies where the evidence is thin: amputees appear in about one in six. Here a montage-agnostic cro…

• Personalising a Cross-User Surface Electromyography Encoder Under a Small Calibration Budget

  Jethro Odeyemi, W. J. Zhang · 18 de septiembre de 2026

  A myoelectric interface needs calibration from the user before it will function. Earlier work has treated calibration as a quantity, but has not asked the question of what a device should do with the calibration repetitions once they have been collected. This paper views personalizing the cross-user…

• MyoFlow: Anchor-Tied Rectified Flow for HD-sEMG Gesture Recognition Across Sessions and Subjects

  Chenhao Wu, Dingjie Peng, Satoshi Funabashi, Satoshi Konishi, Wuqiang Yang, Hiroshi Onoda, Hironori Washizaki, Jiang Liu · 16 de septiembre de 2026

  High-density surface electromyography (HD-sEMG) gesture recognition supports prosthetic control, assistive robotics, and rehabilitation, but electrode re-donning and physiological variability cause distribution shifts that degrade accuracy across sessions and subjects. Generative HD-sEMG models prim…

• Prism-SQA: An Interpretable and Adaptable Neural Framework for Surface Electromyography Quality Assessment

  Kuan-Chen Wang, Kai-Chun Liu, Ping-Cheng Yeh, Sheng-Yu Peng, Yu Tsao · 14 de septiembre de 2026

  sEMG is vulnerable to various contaminants that distort signal morphology and spectral content. Accurate signal quality assessment (SQA) is essential for identifying such degradation and ensuring reliable clinical analyses and decisions. Recent neural network-based SQA methods achieve accurate quali…

• Deep Neural Networks for Learning Intent from sEMG Signals to Support Hardware Devices for Post-Stroke Neurorehabilitation

  Zakariyya Brewster, Divy Wadhwani, Emily Yan, Aidan Wang, Karma Namgyal, Shuting Xie, Markiyan Konyk, Tala Abdelmaguid · 10 de septiembre de 2026

  Finger-specific motor intent is a clinically meaningful control signal for post-stroke neurorehabilitation, where residual muscle activity may remain measurable despite weak or incomplete movement. We study five-finger multilabel intent decoding from impaired-arm high-density surface electromyograph…

• One Residual with Three Reuses: A Wristband Front End for Gesture Sensing

  Sam Rifaki · 18 de agosto de 2026

  Continuous wrist-worn hand sensing for gesture interfaces and motor symptom monitoring needs an always-on front end that fits inside a coin-cell power budget while pairing a micro-electro-mechanical-systems (MEMS) inertial measurement unit (IMU) with a 60 GHz frequency-modulated continuous-wave (FMC…

• Learning Varying Physical Therapist-Patient Interactions for Robot-mediated Upper Limb Task-Specific Training

  Jia Quan Loh, Vincent Crocher, Marlena Klaic, Denny Oetomo, Ying Tan · 18 de agosto de 2026

  Upper extremity motor function recovery is positively linked to Task-Specific Training (TST) and sufficient therapy dosage. Rehabilitation robots can increase TST dosage via controlled, repetitive treatment and free therapists to simultaneously manage other patients, but it has yet to demonstrate si…

• Topology-Unified 2D Pose Estimation across Intact, Residual and Prosthetic Limbs

  Tianye Qi, Tengyue Zhang, Jiaying Ying, Tianqing Zhu, Xin Yu · 14 de agosto de 2026

  Driven by the availability of large-scale datasets, Human Pose Estimation (HPE) plays a critical role in numerous downstream tasks. However, mainstream benchmarks exhibit severe representation bias, predominantly featuring able-bodied individuals. While a few pioneering datasets have attempted to ad…

• Prototype Adaptation for Zero-Shot sEMG Movement Classification

  Rui Liu, Benjamin Paassen · 29 de julio de 2026

  Surface electromyography (sEMG) enables the control of prostheses, allowing upper-limb amputees to re-gain some hand function. Most current research focuses on recognizing basic movements for prosthesis control. However, in most daily activities, such as opening a door, combined movements are essent…

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

  Federico Del Pup, Elisa Tentori, Manfredo Atzori · 28 de julio de 2026

  Hand gesture recognition via surface electromyography (sEMG) is fundamental to prosthetic control. In this field, deep learning approaches have become the gold standard. However, current architectures struggle to scale; model performance typically decreases as the number of hand movements increases.…

• An Exploratory Study of Single Channel Surface Electromyography for Hand Gesture Classification

  Daanish Hindustani · 20 de julio de 2026

  Accurate hand gesture recognition using surface electromyography (sEMG) typically relies on multichannel sensor arrays and computationally intensive models. This limits practical deployment in low-power and embedded systems. This study investigates the feasibility of classifying ten hand gestures us…

• Koopman-driven grip force prediction through EMG sensing

  Tomislav Bazina, Ervin Kamenar, Maria Fonoberova, Igor Mezi\'c · 16 de julio de 2026

  Loss of hand function due to conditions like stroke or multiple sclerosis significantly impacts daily activities. Robotic rehabilitation provides tools to restore hand function, while novel methods based on surface electromyography (sEMG) enable the adaptation of the device's force output according …

• SonoRank: Towards Calibration-Free Real-Time Finger Flexion Detection from Forearm Ultrasound Sequences

  Dean Zadok, Alon Wolf, Alex M. Bronstein, Oren Salzman · 9 de julio de 2026

  Powered prosthetic hands are frequently abandoned, largely due to the limited functionality of current devices that rely on surface electromyography (sEMG). Sonomyography (ultrasound) has emerged as a promising alternative, owing to its ability to observe muscle activity in real time and control a g…

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

  Sofia Gilardini, Chenfei Ma, Kianoush Nazarpour · 7 de julio de 2026

  Decoding hand kinematics from surface electromyography (EMG) is a core challenge in wearable biosignal processing with clinical relevance for prosthetic control and motor rehabilitation. Most representation learning approaches for EMG focus on discrete gesture classification, and few focus on contin…

• Conservative Subject Invariant EMG-based Gesture Recognition

  Hamed Rafiei, Ali Mousavi · 7 de julio de 2026

  Cross-subject generalization remains a fundamental challenge in surface electromyography (sEMG)-based gesture recognition. Although deep learning methods have improved within-subject performance, they often rely on subject-specific data and struggle to balance invariance and discriminability. In thi…

• PGUDA: Pressure-Guided Unsupervised Domain Adaptation with Cross-Modal Knowledge Distillation for sEMG-Based Gesture Recognition

  Yurui Liu, Xiao-Cong Zhong, Qisong Wang, Xuefu Wang, Dan Liu, Jinwei Sun · 1 de julio de 2026

  Surface electromyography (sEMG)-based gesture recognition has emerged as a promising technology for natural human-computer interaction. However, its practical deployment remains challenging due to severe performance degradation caused by feature distribution discrepancies across different subjects a…

• Applicability of memorization indicators for early spotting of overfitting while recalibrating sEMG-decoders on low sample sizes

  Stephan J. Lehmler, Tobias Glasmachers, Ioannis Iossifidis · 29 de junio de 2026

  Deep learning models for surface electromyography (sEMG) can benefit substantially from subject-specific (re-)calibration, since no sufficiently large and diverse datasets are available to train fully generic decoders. However, for user acceptance, the number of repetitions that can realistically be…

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

  Chiyue Wang, Dong She, Yang Gao, Zhanpeng Jin · 2 de junio de 2026

  Electromyography (EMG) directly reflects muscle activation and is a key sensing modality for gesture recognition, prosthetic control, and wearable interaction. Existing EMG methods, however, commonly formulate hand action understanding as classification over fixed labels, making it difficult to supp…

• NeuroEdge: Real-Time Hand Gesture Recognition with High-Density EMG Using Deep Learning at the Edge

  Peter Chudinov, Zhenyu Lin, Jay Motamarry, Srihita Panati, Xiaorong Zhang, Zhuwei Qin · 29 de mayo de 2026

  High-density electromyography (HD-EMG) has emerged as a powerful modality for decoding fine-grained neuromuscular activity, enabling real-time neural-machine interfaces (NMIs) for applications such as prosthetic control, rehabilitation, and augmented interaction. While deep learning approaches such …

• Translating Signals to Languages for sEMG-Based Activity Recognition

  Ming Wang, Haoxuan Qu, Qiuhong Ke, Wei Zhou, Hossein Rahmani, Jun Liu · 22 de mayo de 2026

  Surface electromyography (sEMG) signal-based activity recognition has attracted increasing research attention in recent years. To develop accurate sEMG signal-based activity recognizers, numerous approaches have been proposed. Some studies focus on designing larger and more expressive model architec…

• Unsupervised clustering and classification of upper limb EMG signals during functional movements: a data-driven

  L. F. Salazar \'Alvarez, D. Escobar-Saltar\'en, M. B. Salazar S\'anchez, S. C. Henao-Aguirre · 21 de mayo de 2026

  This study presents a comprehensive approach for the clustering and classification of upper-limb surface electromyography (sEMG) signals during functional reach and grasp movements. The methodology was applied to the NINAPRO DB4 dataset, which provides multichannel EMG recordings of 52 gestures. A f…