ICA-CNN: Gesture Recognition Using CNN With Improved Channel Attention Mechanism and Multimodal Signals

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., & Hu, Q. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01155 · Cited by 9,641 · cited 2× · see citing works

A new strategy for multifunction myoelectric control

Hudgins, B., Parker, P., & Scott, R. N. (1993) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.204774 · Cited by 2,149 · see citing works

A robust, real-time control scheme for multifunction myoelectric control

Englehart, K., & Hudgins, B. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.813539 · Cited by 1,743 · see citing works

Electromyography data for non-invasive naturally-controlled robotic hand prostheses

Atzori, M., Gijsberts, A., Castellini, C., Barbara Caputo, B., Hager, A.-G. M., Elsig, S., Giatsidis, G., Bassetto, F., & Müller, H. (2014) · Scientific Data · doi:10.1038/sdata.2014.53 · Cited by 990 · see citing works

A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition

Hu, Y., Wong, Y., Wei, W., Du, Y., Kankanhalli, M., & Geng, W. (2018) · PLoS ONE · doi:10.1371/journal.pone.0206049 · Cited by 403 · see citing works

Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network

Zhai, X., Jelfs, B., Chan, R. H. M., & Tin, C. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00379 · Cited by 361 · see citing works

Transfer learning for sEMG hand gestures recognition using convolutional neural networks

Côté‐Allard, U., Fall, C. L., Campeau‐Lecours, A., Gosselin, C., Laviolette, F., & Gosselin, B. J. (2017) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2017.8122854 · Cited by 177 · see citing works

Persian sign language recognition using IMU and surface EMG sensors

Khomami, S. A., & Shamekhi, S. (2020) · Measurement · doi:10.1016/j.measurement.2020.108471 · Cited by 73 · see citing works

Assessing fractal dimension methods as feature extractors for EMG signal classification

Coelho, A. L. V., & Lima, C. A. M. (2014) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2014.07.009 · Cited by 51 · see citing works

Hand Gesture Recognition Based on Deep Learning Method

Xing, K., Ding, Z., Jiang, S., Ma, X., Yang, K., Yang, C., Li, X., & Jiang, F. (2018) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc.2018.00087 · Cited by 49 · see citing works

Classification of forearm movements from sEMG time domain features using machine learning algorithms

Jose, N., Raj, R., Adithya, P. K., & Sivanadan, K. S. (2017) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2017.8228118 · Cited by 34 · see citing works

Hand Gesture Recognition by Using sEMG Signals for Human Machine Interaction Applications

Sayın, F. S., Ozen, S., & Başpınar, U. (2018) · Signal Processing: Algorithms, Architectures, Arrangements, and Applications · doi:10.23919/spa.2018.8563394 · Cited by 31 · see citing works

Surface-Electromyography-Based Gesture Recognition Using a Multistream Fusion Strategy

Chen, Z., Yang, J., & Xie, H. (2021) · IEEE Access · doi:10.1109/access.2021.3059499 · Cited by 30 · see citing works

sEMG-based hand motion recognition by means of multi-class adaboost algorithm

Zhou, S., Yin, K., Liu, Z., Fei, F., & Guo, J. (2017) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2017.8324557 · Cited by 15 · see citing works

Improved Gesture Recognition Using Deep Neural Networks on sEMG

Alam, R.-U., Rhivu, S. R., & Haque, M. A. (2018) · International Conference on Engineering, Applied Sciences, and Technology (ICEAST) · doi:10.1109/iceast.2018.8434493 · Cited by 14 · see citing works

Using Antonyan Vardan Transform and Extreme Learning Machines for Accurate sEMG Signal Classification

Cene, V. H., dos Santos, R. R., & Balbinot, A. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513468 · Cited by 9 · see citing works

Multimodal deep learning network based hand ADLs tasks classification for prosthetics control

Li, Z., Zhou, H., Yang, D., & Shuiqing, X. (2017) · IEEE International Conference on Progress in Informatics and Computing · doi:10.1109/pic.2017.8359521 · Cited by 8 · see citing works

sEMG Signal Classification Using Ensemble Learning Classification Approach and DWT

Thakur, N., & Mathew, L. (2018) · International Conference on Current Trends towards Converging Technologies (ICCTCT) · doi:10.1109/icctct.2018.8551098 · Cited by 5 · see citing works

A comprehensive review of sEMG-IMU sensor fusion for upper limb movements pattern recognition

Zhang, H., Moctar, S. M. S., Boudaoud, S., & Rida, I. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103422 · Cited by 26 · see citing works

Gesture Recognition Using MLP-Mixer With CNN and Stacking Ensemble for sEMG Signals

Shen, S., Li, M., Mao, F., Chen, X., & Ran, R. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3347529 · Cited by 21 · see citing works

A Novel Multi-Feature Fusion Network With Spatial Partitioning Strategy and Cross-Attention for Armband-Based Gesture Recognition

Hu, F., Qian, M., He, K., Zhang, W., & Yang, X. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3487216 · Cited by 21 · see citing works

Improving Hand Gesture Recognition Robustness to Dynamic Posture Variations by Multimodal Deep Feature Fusion

Li, J., Zhang, B., Chen, W., Bu, C., Zhao, Y., & Zhao, X. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3447669 · Cited by 19 · see citing works

Facial Expression Recognition Based on Zero-Addition Pretext Training and Feature Conjunction-Selection Network in Human–Robot Interaction

Jiang, C.-S., Liu, Z., & She, J. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3303389 · Cited by 17 · see citing works

DeepTPA-Net: A Deep Triple Attention Network for sEMG-Based Hand Gesture Recognition

Li, Q., Luo, Z., Qi, R., & Zheng, J. J. (2023) · IEEE Access · doi:10.1109/access.2023.3312219 · Cited by 13 · see citing works

Improving Unimodal sEMG-Based Pattern Recognition Through Multimodal Generative Adversarial Learning

Wei, W., Ren, L., Zhou, M., Ma, Z., Zhao, K., & Xu, X. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3556214 · Cited by 9 · see citing works

Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction

Chen, S., Peng, C., Yang, B., Lin, J., Zhou, L., Jiang, Z., Liu, Z., Liu, Y., & Tang, L. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00083-5 · Cited by 9 · see citing works

Self-Attention Progressive Network for Infrared and Visible Image Fusion

Li, S., Han, M., Qin, Y., & Li, Q. (2024) · Remote Sensing · doi:10.3390/rs16183370 · Cited by 8 · see citing works

Wearable Sensor‐Based Multi‐modal Fusion Network for Automated Gait Dysfunction Assessment in Children with Cerebral Palsy

Tang, L., Xiang-rui, W., Lian, P., Lu, Z., Zheng, Q., Yang, X., Hu, Q., & Zheng, H. (2024) · Advanced Intelligent Systems · doi:10.1002/aisy.202300845 · Cited by 8 · see citing works

Parallel multi-scale convolutional neural network with incremental learning for bearing compound fault diagnosis under variable working conditions

Han, S., Sun, S., Pang, K., Li, J., Tian, F., Zhen, D., Feng, G., & Niu, P. (2025) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-025-05822-4 · Cited by 6 · see citing works

STMS-Net: Spatial Temporal Multi-Spectral Network for sEMG Gesture Recognition

Sijin, W., Jian-jun, T., Bingtao, Z., Tao, H., Zhu, L., & Xiang, M. (2025) · IEEE Access · doi:10.1109/access.2025.3560665 · Cited by 5 · see citing works

A wearable sensor-based dynamic gesture recognition model via broad attention learning

Liu, Y., Li, X., & Yang, L. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03567-6 · Cited by 6 · see citing works

Sensing Technologies for Hand Gesture Recognition in Human–Robot Interaction: A Review

Liang, B., Li, N., Li, G., Huang, J., Yu, Z., & Zhao, X. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3635622 · Cited by 4 · see citing works

AGMMLN: An Attention-Guided Multiscale Multitask Learning Network for Simultaneous Gesture and Force Level Recognition

Chen, Z., Yu, Y., Wang, L., Zhou, S., Wang, K., Li, H., & Li, X. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3416198 · Cited by 3 · see citing works

SCMA: Exploring Dual-Module Attention With Multi-Scale Kernels for Effective Feature Extraction

Samad, S. A., & Gitanjali, J. (2023) · IEEE Access · doi:10.1109/access.2023.3329581 · Cited by 3 · see citing works

Enhancing ergonomics in E-waste disassembly: the impact of collaborative robotics on muscle activation and coordination

Wang, X., Grimaldi, N. S., Zheng, M., Behdad, S., Yang, C., & Hu, B. (2024) · Ergonomics · doi:10.1080/00140139.2024.2436534 · Cited by 3 · see citing works

Robust Gesture Recognition and Classification for Visually Impaired Persons Using Growth Optimizer with Deep Stacked Autoencoder

Maashi, M. S., Al-Hagery, M. A., Rizwanullah, M., & Osman, A. E. (2023) · Journal of Disability Research · doi:10.57197/jdr-2023-0029 · Cited by 2 · see citing works

Research on industry label recognition method based on RoBERTa driven by data

Wen, Y., Fan, X., & Mo, P. (2025) · International Journal of Low-Carbon Technologies · doi:10.1093/ijlct/ctaf026 · Cited by 1 · see citing works

Muscle Strength Estimation of Key Muscle–Tendon Units During Human Motion Using ICA-Enhanced sEMG Signals and BP Neural Network Modeling

Liu, H., Park, J., Lee, J., & Wang, D. (2025) · Sensors · doi:10.3390/s25206273 · Cited by 1 · see citing works

Impact of Retail Collaborative Robots on Surrounding Human Neuromuscular Responses: An EMG Analysis

Wang, X., Luo, Y., Gu, Y., Gross, J. N., Yang, C., & Hu, B. (2026) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2026.3668974 · Cited by 1 · see citing works

Multi-Scale Attention Fusion Gesture-Recognition Algorithm Based on Strain Sensors

Zhang, Z., Cai, J., Dai, X., & Xiao, H. (2025) · Sensors · doi:10.3390/s25134200 · Cited by 1 · see citing works

MyoLite: Compressible Neural Networks Based on Biokinematics and Self-attention for Fingers Motion Tracking

Chen, W., Feng, L., & Lu, J. (2024) · IEEE International Conference on Advanced Information, Mechanical Engineering, Robotics and Automation (AIMERA) · doi:10.1109/aimera59657.2024.10735452

A Multi Modal Feature Fusion Approach to Hand Sign Recognition

Rahman, T. U., Rahaman, M. A., Hossain, M. M., Mim, U. H., & Eisa, N. J. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-7292-2_7

Fine-Grained Gesture Recognition via Narrow-Kernel CNN and Attention-based SEMG-ACC Fusion

Zhang, H., Boudaoud, S., & Rida, I. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11460574

Emotion-Aware Sign Language Recognition via Cross-Modal Fusion of ECG Signals and Skeletal Data

Duanmu, Y., Zhao, H., Cao, M., Sun, Z., Bao, H., & Zhang, W. J. (2026) · International Conference on Electronics Technology and Artificial Intelligence (ETAI) · doi:10.1109/etai68332.2026.11485249

A comprehensive survey of multi-modal manipulative hand gesture recognition: Sensors, datasets, and models

Wang, S., Laforet, J., Rida, I., Druart, B., Becheler, A., Istrate, D., & Boudaoud, S. (2026) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2026.104840 · Cited by 1 · see citing works

MSCN-LSTM: A Multi-Scale CNN and BiLSTM-Based Model for Multi-Modal Gesture Recognition Using EMG and IMU Signals

He, X., Xie, M., Yao, T., Zhou, Q., & Bi, Y. (2026) · International Conference on Control and Automation · doi:10.1109/icca69928.2026.11617970

Multimodal sEMG and Accelerometer Data Fusion for Fine-Grained Hand Gesture Recognition

Zhang, H., Boudaoud, S., & Rida, I. (2026) · IRBM · doi:10.1016/j.irbm.2026.100956

Hand gesture recognition based improved multi-channels CNN architecture using EMG sensors

Li, J., Wei, L., Wen, Y., Liu, X., & Wang, H. (2022) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-212390 · Cited by 7 · see citing works

Enhanced Lightweight CNN Using Joint Classification With Averaging Probability for sEMG-Based Subject-Independent Hand Gesture Recognition

Leelakittisin, B., Trakulruangroj, M., Sangnark, S., Wilaiprasitporn, T., & Sudhawiyangkul, T. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3296649 · Cited by 9 · see citing works

ECNN-CS: Efficient Convolutional Neural Network by Channel and Spatial Fusion for Hand Gesture Recognition Using sEMG Sensors

Ni, S., & Al‐qaness, M. A. A. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3648587 · Cited by 2 · see citing works

Research on IMU/EMG Gesture Recognition Based on Improved CNN

Xu, D., Wang, H., Zhang, J., Zhang, Y., & Feng, D. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-6613-2_202 · Cited by 1 · see citing works

Real-Time Multiple Gesture Recognition: Application of a Lightweight Individualized 1D CNN Model to an Edge Computing System

Yu, Z., Lu, Y., An, Q., Chen, C., Li, Y., & Wang, Y. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3165858 · Cited by 37 · see citing works