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Article: High-density surface emg-based gesture recognition using a 3d convolutional neural network

TitleHigh-density surface emg-based gesture recognition using a 3d convolutional neural network
Authors
KeywordsConvolutional neural network (CNN)
Deep learning
Finger gesture recognition
High-density surface EMG (HD-sEMG)
Issue Date2020
Citation
Sensors (Switzerland), 2020, v. 20, n. 4, article no. 1201 How to Cite?
AbstractHigh-density surface electromyography (HD-sEMG) and deep learning technology are becoming increasingly used in gesture recognition. Based on electrode grid data, information can be extracted in the form of images that are generated with instant values of multi-channel sEMG signals. In previous studies, image-based, two-dimensional convolutional neural networks (2D CNNs) have been applied in order to recognize patterns in the electrical activity of muscles from an instantaneous image. However, 2D CNNs with 2D kernels are unable to handle a sequence of images that carry information concerning how the instantaneous image evolves with time. This paper presents a 3D CNN with 3D kernels to capture both spatial and temporal structures from sequential sEMG images and investigates its performance on HD-sEMG-based gesture recognition in comparison to the 2D CNN. Extensive experiments were carried out on two benchmark datasets (i.e., CapgMyo DB-a and CSL-HDEMG). The results show that, where the same network architecture is used, 3D CNN can achieve a better performance than 2D CNN, especially for CSL-HDEMG, which contains the dynamic part of finger movement. For CapgMyo DB-a, the accuracy of 3D CNN was 1% higher than 2D CNN when the recognition window length was equal to 40 ms, and was 1.5% higher when equal to 150 ms. For CSL-HDEMG, the accuracies of 3D CNN were 15.3% and 18.6% higher than 2D CNN when the window length was equal to 40 ms and 150 ms, respectively. Furthermore, 3D CNN achieves a competitive performance in comparison to the baseline methods.
Persistent Identifierhttp://hdl.handle.net/10722/327266
ISSN
2023 Impact Factor: 3.4
2023 SCImago Journal Rankings: 0.786
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChen, Jiangcheng-
dc.contributor.authorBi, Sheng-
dc.contributor.authorZhang, George-
dc.contributor.authorCao, Guangzhong-
dc.date.accessioned2023-03-31T05:30:07Z-
dc.date.available2023-03-31T05:30:07Z-
dc.date.issued2020-
dc.identifier.citationSensors (Switzerland), 2020, v. 20, n. 4, article no. 1201-
dc.identifier.issn1424-8220-
dc.identifier.urihttp://hdl.handle.net/10722/327266-
dc.description.abstractHigh-density surface electromyography (HD-sEMG) and deep learning technology are becoming increasingly used in gesture recognition. Based on electrode grid data, information can be extracted in the form of images that are generated with instant values of multi-channel sEMG signals. In previous studies, image-based, two-dimensional convolutional neural networks (2D CNNs) have been applied in order to recognize patterns in the electrical activity of muscles from an instantaneous image. However, 2D CNNs with 2D kernels are unable to handle a sequence of images that carry information concerning how the instantaneous image evolves with time. This paper presents a 3D CNN with 3D kernels to capture both spatial and temporal structures from sequential sEMG images and investigates its performance on HD-sEMG-based gesture recognition in comparison to the 2D CNN. Extensive experiments were carried out on two benchmark datasets (i.e., CapgMyo DB-a and CSL-HDEMG). The results show that, where the same network architecture is used, 3D CNN can achieve a better performance than 2D CNN, especially for CSL-HDEMG, which contains the dynamic part of finger movement. For CapgMyo DB-a, the accuracy of 3D CNN was 1% higher than 2D CNN when the recognition window length was equal to 40 ms, and was 1.5% higher when equal to 150 ms. For CSL-HDEMG, the accuracies of 3D CNN were 15.3% and 18.6% higher than 2D CNN when the window length was equal to 40 ms and 150 ms, respectively. Furthermore, 3D CNN achieves a competitive performance in comparison to the baseline methods.-
dc.languageeng-
dc.relation.ispartofSensors (Switzerland)-
dc.subjectConvolutional neural network (CNN)-
dc.subjectDeep learning-
dc.subjectFinger gesture recognition-
dc.subjectHigh-density surface EMG (HD-sEMG)-
dc.titleHigh-density surface emg-based gesture recognition using a 3d convolutional neural network-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.3390/s20041201-
dc.identifier.pmid32098264-
dc.identifier.scopuseid_2-s2.0-85079839354-
dc.identifier.volume20-
dc.identifier.issue4-
dc.identifier.spagearticle no. 1201-
dc.identifier.epagearticle no. 1201-
dc.identifier.isiWOS:000522448600252-

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