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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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Sadık, E. Ş. (2026) · IEEE Access · doi:10.1109/access.2026.3737231
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Li, Y., Kim, J., & Choi, S.-I. (2026) · Research Square · doi:10.21203/rs.3.rs-10837198/v1
Cross-Subject Aligned Contrastive Learning for sEMG Gesture Recognition
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Zhang, A., Zheng, H., & Wu, Y. (2026) · Sensors · doi:10.3390/s26206394
“Convolutional models have been used for prosthetic hand-movement classification [7], supported by the NinaPro datasets [8,9].”
AI-Powered VR Rehabilitation Using EMG Signals: A Systematic Review
Alotaibi, N., Al-Nafjan, A., & Alhakbani, N. (2026) · Applied Sciences · doi:10.3390/app162110576
Elveren, Z., Kaymakçı, Y. K., Bahat, Z., & Dikmen, I. C. (2026) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap71869.2026.11727936
Aydoğan, Y., & Cantürk, İ. (2026) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-026-03704-y
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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