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Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands
Open accessAbstract
Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too.
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“Pattern recognition control of myoelectric prostheses is also getting popularity due to its better classification of EMG signals (user intentions) using various methods, i.e., linear discriminant analysis classifier, tacit learning system, deep learning with convolutional neural network (Adewuyi et al., 2016; Atzori et al., 2016; Fani et al., 2016; Oyama et al., 2016), etc.”
From Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review. (2017) · cites this paper as Atzori et al., 2016
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MethodImageNet classification with deep convolutional neural networks
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“However, in the field of computer vision in 2012, deep learning approaches won one of the largest object recognition challenges (the ILSVRC) decreasing the previous top-5 error rate by more than 10% (Krizhevsky and Hinton, 2010; Goodfellow et al., 2016).”
BackgroundHuman-level control through deep reinforcement learning
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Items 501–600 of 681
Huang, Y., Zhang, X., Yan, D., Ye, H., Chan, C., Jiang, N., & Zhong, R. (2026) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ae42ea · Cited by 2
“…studies utilizing deep learning on sEMG have focused on classification-based tasks, such as identifying static postures or movement intentions (Atzori et al 2016, Vijayvargiya et al 2021, Ren et al 2022, Latifah et al 2024), rather than reconstructing continuous physiological variables like…”
Few-shot prototype adaptation for generalizable electromyography gesture recognition
Lee, H., Lim, B. Z. H., Jiang, M., Yang, Z., & Zhao, Q. (2026) · Scientific Reports · doi:10.1038/s41598-026-40352-6 · Cited by 2
“Our work specifically addresses these limitations by introducing novel architectural and learning mechanisms that explicitly target model generalization and few-shot adaptation, distinguishing our approach from previous solutions that primarily focused on improving single-user performance 11–14”
InfluentialBiomimetic bidirectional hand neuroprostheses for restoring somatosensory and motor functions
Iberite, F., Mendez, V., Mazzoni, A., Shokur, S., & Micera, S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822828-9.00011-3 · Cited by 2
Kirchhofer, S., Château, T., Bouzgarrou, C., & Lemaire, J. (2022) · Cybernetics & Systems · doi:10.1080/01969722.2021.1985227 · Cited by 2
“First, common architectures were tried as a convolutional neural network of Ding et al. (2018) or Atzori, Cognolato, and Muller (2016).”
MethodProportional Myoelectric Control in a Virtual Reality Environment
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“For EMG-based gesture recognition, we used the CNN proposed in [23], a re-implementation of the model proposed in [22].”
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Dynamic Label Smoothing Strategy for Biosignal Classification
Chen, P., Li, D., Tang, Y., Togo, S., Yokoi, H., & Jiang, Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446938 · Cited by 2
“For a fair comparison, we employ the same data splitting and experimental setting methods as previous researches [11], [12], [13], [14], [15].”
MethodUnsupervised Domain Adaptation for Inter-Session Re-Calibration of Ultrasound-Based HMIs
Lykourinas, A., Rottenberg, X., Catthoor, F., & Skodras, A. N. (2024) · Sensors · doi:10.3390/s24155043 · Cited by 2
“In medical HMIs, bio-potentials such as electroencephalogram (EEG) [3], electromyogram (EMG) [4,5], and electroocullogram (EOG) [6]—generated by electrical sources within the human body and thus reflecting the function of organs by means of electrical activity—have been extensively utilized.”
BackgroundClassification of SEMG Signal Based Hand Movements by Using Support Vector Machine
Zahra, S. R., Ismail, S., Ali, M. D., Khan, M. A., Darus, M. Y., & Mazhar, T. (2024) · IEEE Symposium on Computers & Informatics (ISCI) · doi:10.1109/isci62787.2024.10668378 · Cited by 2
“Atzori et al. [6] comparing the results obtained with the mentioned conventional classification methods, it is evident that average classical classification techniques yield lower results than those obtained with the best traditional classification methods.”
ResultEnhancing fidelity of mechanistic cutting force model using hybrid data mining approach
Vaishnav, S., Sharma, B., & Desai, K. A. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-14822-y · Cited by 2
“These models have limitations, such as the need for extensive experimental data for model training and a black-box model architecture obscuring the underlying physical phenomena [13].”
A Novel Tactile Sensing Skin for Surface Perception
Das, S., Thiyagarajan, K., Kodagoda, S., Krishnan, A., & Bhattacharjee, M. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3565586 · Cited by 2
Semi-Supervised Adaptive Learning for Decoding Movement Intent from Electromyograms
Dantas, H., Mathews, V. J., & Warren, D. J. (2019) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2019.8902698 · Cited by 2
Asai, K., & Takase, N. (2020) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1145/3408127.3408182 · Cited by 2
“CNNs have been applied to the sEMG-based hand-gesture recognition, achieving high performance in classification accuracy [16, 17, 18].”
MethodNimbekar, A., Dinesh, Y. V. S., Gautam, A., Hunsigida, V., Nali, A. R., & Acharyya, A. (2022) · IEEE Latin American Symposium on Circuits and Systems (LASCAS) · doi:10.1109/lascas53948.2022.9789091 · Cited by 2
Deep Learning Approach in Hand Motion Recognition Using Electromyography Signal: A Review
Triwiyanto, T., Rahmawati, T., Pudji, A., Mak’ruf, M. R., & Syaifudin, S. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-1804-9_11 · Cited by 2
An Exploration of the Optimal Feature-Classifier Combinations for Transradial Prosthesis Control
Douglas, F., Gover, H., Docherty, C., Shields, G., Leventi, K., & Caterina, G. D. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871951 · Cited by 2
“The inclusion of these within the feature vector have been shown to improve performance beyond that of more traditional feature sets, albeit only marginally [2], [9].”
BackgroundInfluentialChannel-distribution Hybrid Deep Learning for sEMG-based Gesture Recognition
Lu, K., Guo, H., Qi, F., Gong, P., Gu, Z., Sun, L., & Huang, H. (2022) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio55434.2022.10011951 · Cited by 2
“Atzori et al. [14] took the raw surface EMG signal as image input and combined it with LeNet to obtain higher classification accuracy than conventional machine learning on Nina Pro DB1 [15].”
MethodConvolutional Neural Network Based Human Movement Recognition Using Surface Electromyography
Ruan, T., Yin, K., & Zhou, S. (2018) · IEEE International Conference on Micro/Nano Sensors for AI, Healthcare, and Robotics (NSENS) · doi:10.1109/nsens.2018.8713564 · Cited by 2
Methods of the hand electromyogram analysis
Budko, R., Чернов, Н. Н., & Budko, N. (2019) · Известия высших учебных заведений. Приборостроение · doi:10.17586/0021-3454-2019-62-12-1098-1104 · Cited by 2
Gesture Recognition Based on Nano-gold Flexible Sensor using Different Training Modes
Sun, S., Liu, S., Huang, P., Jiang, Y., Wang, Y., Fu, M., Yuan, S., Xue, J., Deng, H., Liu, Z., et al. (2019) · IEEE International Conference on Cyborg and Bionic Systems (CBS) · doi:10.1109/cbs46900.2019.9114495 · Cited by 2
“Atzori et al demonstrated that convolutional neural networks and simple architecture could produce accurate results [12].”
BackgroundWhite-Box Attacks on the CNN-Based Myoelectric Control System
Xue, B., Wu, L., Liu, A., Zhang, X., & Chen, X. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-93046-2_13 · Cited by 2
A Fusion Framework to Enhance sEMG-Based Gesture Recognition Using TD and FD Features
Luo, Y., Luo, T., Xia, Q., Yan, H., Xie, L., Yan, Y., & Yin, E. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-92310-5_20 · Cited by 2
Smart Solar Power‐Assisted Wheelchairs For the Handicapped
Bhatnagar, A., Pancholi, S., & Janyani, V. (2022) · doi:10.1002/9781119785651.ch8 · Cited by 2
Rani, P., Pancholi, S., Shaw, V., Atzori, M., & Kumar, S. (2023) · doi:10.36227/techrxiv.22093685.v1 · Cited by 2
Slope recognition based on human body surface EMG signal Using CNN
Ren, W., Liu, Y., Song, Q., & Deng, H. (2022) · International Conference on Electronic Communication and Artificial Intelligence (ICECAI) · doi:10.1109/iwecai55315.2022.00019 · Cited by 2
Li, M., Liu, Z., Tang, S., Ge, J., & Zhang, F. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.975131 · Cited by 1
“, 2018), k-nearest neighbor (KNN), support vector machine (SVM), random forest, and convolutional neural network (CNN) (Atzori et al., 2016).”
Backgroundde Godoy, R. V., Lahr, G. J. G., Dwivedi, A., Reis, T. J. S., Polegato, P. H., Becker, M., Caurin, G. A. P., & Liarokapis, M. V. (2022) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob52689.2022.9925307 · Cited by 1
“For these reasons, sEMG-based hand gesture recognition literature [20]–[23] has heavily relied on DL techniques already consolidated in temporal data classification tasks, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).”
MethodHybrid Deep Learning Models for Hand Gesture Recognition with EMG Signals
Neenu, R., & Titus, G. (2024) · International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE) · doi:10.1109/amathe61652.2024.10582166 · Cited by 2
“All three models use the same CNN design, which is based on the one that Atzori et. al [7] proposed.”
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Feature weighting concatenated multi-head self-attention for amputee EMG classification
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“More recently, deep neural networks have become popular, as they can automatically learn intricate features difficult for humans to discern (Atzori et al., 2016; Khushaba et al., 2022; Xu et al., 2022).”
Enhanced EMG‐Based Hand Gesture Recognition by Using Generalized Deep Infomax Networks
Soosaimariyan, M. V., Allasi, H. L., Chitra, K., & Gnanadurai, J. B. (2025) · Journal of Sensors · doi:10.1155/js/9496890 · Cited by 1
“This characteristic aligns well with the core purpose of HCI design: gathering user intentions to control human behavior and creating devices that support movements based on these intentions [8, 9].”
Liang, J.-W., Feng, H., Zhang, B., Liang, J.-M., Liu, Y., & Li, Z. (2025) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc65474.2025.11090507 · Cited by 1
1D Convolutional Neural Architecture Search for sEMG Hand Gesture Recognition
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“Initially, in the research on gesture identification using deep learning models, Atzori et al. [7] introduced a method based on Convolutional Neural Networks (CNNs) to classify sEMG signals.”
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Decoding Hand Gestures Under Arm Movements Based on Electromyography and Acceleration Signals
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“mporal features we modify the 3x3 convolution in the Fire Module to be a 3x1 and rename it a “Temporal Fire Module” for clarity. This temporal first approach is diametrically opposite to Atzori et al [28] and Geng et al [31], [38] who both explicitly perform spatial convolution first in their networks. The final convo…”
MethodResultInfluentialsEMG-Based Hand Gesture Classification with Transient Signal
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Amputee Electromyography Signal Classification Using Convolutional Neural Network
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“Tüm bu çalışmalara rağmen, önerilen yöntemler günlük hayatta hassas ve gürbüz bir kontrolün gerçekleşmesini sağlayarak çeşitli el hareketlerine duyarlı akıllı bir protezin ortaya konulması için yeterli değildir [7].”
Understanding Forearm Muscle Coordination in Children
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REVIEW ON DEEP LEARNING APPROACHES TO THE CONTROL OF PROSTHETIC HANDS WITH ELECTROMYOGRAPHY SIGNALS
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Generalized Force Estimation using Machine Learning and Deep Learning with EMG and Motion Data
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Advances in High-Resolution, Miniaturized Bioelectrical Neural Interface Design
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Perspectives on EMG-Controlled Prosthetic Robotic Hands: Trends and Challenges
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Research on Incremental Learning of Upper Limb Action Pattern Recognition Based on MechanomyographyPreprint
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“[60] Over 18 years Transradial Arm EMG DL PR and Classification 3”
BackgroundJia, B., Jing, C., Zhao, C., & Yang, G. (2022) · International Conference on Information Economy, Data Modeling and Cloud Computing (ICIDC) · doi:10.4108/eai.17-6-2022.2322815
“As a new help mode, the diversified sports help means and the diversified combination promotion, will better serve the poor student financial aid work and will gradually improve and standardize [16-18].”
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“Compared with the methos based on hand-crafted feature extractor [5, 24, 26-28], the FCRN has little performance disadvantage, and their average accuracies all are lower than 80%.”
MethodClassification Model for Hand and Wrist Multiple-Mode Pattern Based on Deep Learning
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Movement Recognition via Channel-Activation-Wise sEMG AttentionPreprint
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“, 2016 [45] Random forest - SVM 150 Time domain 0.”
BackgroundAnalysis of Hand Movement from Surface EMG Signals Using Artificial Neural Network
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Forearm Movements Classification Research to Increase Subjects Independence
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Deep Learning based sEMG decoding for TelePresence device control
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“have been proposed [16] [17], shifting the paradigm from feature engineering to feature learning.”
BackgroundImage set preparation: A platform to prepare a myoelectric signal to train a CNN
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Metadata
- Authors
- Atzori, M., Cognolato, M., & Müller, H.
- Affiliations
- HES-SO University of Applied Sciences and Arts Western Switzerland (CH)
- Corresponding author
- Atzori, Manfredo
- Venue
- Frontiers in Neurorobotics
- Publisher
- Frontiers Media SA
- Published
- 2016-09-07
- Volume
- 10
- Pages
- 9-9
- ISSN
- 1662-5218 (ISSN-L)
- PMC
- PMC5013051
- OpenAlex
- W2516710120
- Cited by
- 684
- References
- 71
- Citations by year
- 2026: 53 · 2025: 70 · 2024: 72 · 2023: 92 · 2022: 93 · 2021: 96 · 2020: 77 · 2019: 74 · 2018: 42 · 2017: 15
- FWCI
- 20.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Neurorobotics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- ArODES (HES-SO (https://www.hes-so.ch/)) (submittedVersion, cc-by)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (#132700, #160837)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- surface electromyography, hand movement classification, prosthetic hand control, convolutional neural networks, transradial amputees, data augmentation, neural network architecture, classification accuracy
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