Deep Neural Network Approach in EMG-Based Force Estimation for Human–Robot Interaction Article Swipe
Hang Su
,
Wen Qi
,
Zhijun Li
,
Ziyang Chen
,
Giancarlo Ferrigno
,
Elena De Momi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tai.2021.3066565
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tai.2021.3066565
In the human-robot interaction, especially when hand contact appears directly on the robot arm, the dynamics of the human arm presents an essential component in human-robot interaction and object manipulation. Modeling and estimation of the human arm dynamics show great potential for achieving more natural and safer interaction. To enrich the dexterity and guarantee the accuracy of the manipulation, mapping the motor functionality of muscle using biosignals becomes a popular topic. In this article, a novel algorithm was constructed using deep learning to explore the potential model between surface electromyography (sEMG) signals of the human arm and interaction force for human-robot interaction. Its features were extracted by adopting the convolutional neural network from the sEMG signals automatically without using prior knowledge of the biomechanical model. The experiments prove the lower error (< 0.4 N) of the designed regression by comparing it with other approaches, such as artificial neural network and long short-term memory. It should be also mentioned that the antinoise ability is an important index to apply this technique in practical applications. Hence, we also add different Gaussian noises into the dataset to demonstrate the robustness against measurement noises by using the proposed model. Finally, it demonstrates the performance of the proposed algorithm using the Myo controller and KUKA LWR4+ robot.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tai.2021.3066565
- OA Status
- green
- Cited By
- 89
- References
- 42
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W3136016134Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tai.2021.3066565Digital Object Identifier
- Title
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Deep Neural Network Approach in EMG-Based Force Estimation for Human–Robot InteractionWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-06-30Full publication date if available
- Authors
-
Hang Su, Wen Qi, Zhijun Li, Ziyang Chen, Giancarlo Ferrigno, Elena De MomiList of authors in order
- Landing page
-
https://doi.org/10.1109/tai.2021.3066565Publisher landing page
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://hdl.handle.net/11311/1166006Direct OA link when available
- Concepts
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Computer science, Artificial neural network, Estimation, Artificial intelligence, Robot, Human–robot interaction, Engineering, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
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89Total citation count in OpenAlex
- Citations by year (recent)
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2025: 28, 2024: 18, 2023: 20, 2022: 20, 2021: 3Per-year citation counts (last 5 years)
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42Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
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| primary_location.raw_source_name | IEEE Transactions on Artificial Intelligence |
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| publication_date | 2021-06-30 |
| publication_year | 2021 |
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