Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks
Hand gesture recognition with surface electromyography (sEMG) is indispensable for Muscle-Gesture-Computer Interface. The usual focus of it is upon performance evaluation involving the accuracy and robustness of hand gesture recognition. However, addressing the reliability of such classifiers has be...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9674873/ |
| _version_ | 1797805078428516352 |
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| author | Yuzhou Lin Ramaswamy Palaniappan Philippe De Wilde Ling Li |
| author_facet | Yuzhou Lin Ramaswamy Palaniappan Philippe De Wilde Ling Li |
| author_sort | Yuzhou Lin |
| collection | DOAJ |
| description | Hand gesture recognition with surface electromyography (sEMG) is indispensable for Muscle-Gesture-Computer Interface. The usual focus of it is upon performance evaluation involving the accuracy and robustness of hand gesture recognition. However, addressing the reliability of such classifiers has been absent, to our best knowledge. This may be due to the lack of consensus on the definition of model reliability in this field. An uncertainty-aware model has the potential to self-evaluate the quality of its inference, thereby making it more reliable. Moreover, uncertainty-based rejection has been shown to improve the performance of sEMG-based hand gesture recognition. Therefore, we first define model reliability here as the quality of its uncertainty estimation and propose an offline framework to quantify it. To promote reliability analysis, we propose a novel end-to-end uncertainty-aware finger movement classifier, i.e., evidential convolutional neural network (ECNN), and illustrate the advantages of its multidimensional uncertainties such as vacuity and dissonance. Extensive comparisons of accuracy and reliability are conducted on NinaPro Database 5, exercise A, across CNN and three variants of ECNN based on different training strategies. The results of classifying 12 finger movements over 10 subjects show that the best mean accuracy achieved by ECNN is 76.34%, which is slightly higher than the state-of-the-art performance. Furthermore, ECNN variants are more reliable than CNN in general, where the highest improvement of reliability of 19.33% is observed. This work demonstrates the potential of ECNN and recommends using the proposed reliability analysis as a supplementary measure for studying sEMG-based hand gesture recognition. |
| first_indexed | 2024-03-13T05:46:48Z |
| format | Article |
| id | doaj.art-3ad225c914514b8e82ecb1dba2a13c1f |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:46:48Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-3ad225c914514b8e82ecb1dba2a13c1f2023-06-13T20:08:17ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102022-01-01309610710.1109/TNSRE.2022.31415939674873Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional NetworksYuzhou Lin0https://orcid.org/0000-0003-3184-0523Ramaswamy Palaniappan1https://orcid.org/0000-0001-5296-8396Philippe De Wilde2https://orcid.org/0000-0002-4332-1715Ling Li3https://orcid.org/0000-0002-4026-0216School of Computing, University of Kent, Canterbury, Kent, U.KSchool of Computing, University of Kent, Canterbury, Kent, U.KDivision of Natural Sciences, University of Kent, Canterbury, Kent, U.KSchool of Computing, University of Kent, Canterbury, Kent, U.KHand gesture recognition with surface electromyography (sEMG) is indispensable for Muscle-Gesture-Computer Interface. The usual focus of it is upon performance evaluation involving the accuracy and robustness of hand gesture recognition. However, addressing the reliability of such classifiers has been absent, to our best knowledge. This may be due to the lack of consensus on the definition of model reliability in this field. An uncertainty-aware model has the potential to self-evaluate the quality of its inference, thereby making it more reliable. Moreover, uncertainty-based rejection has been shown to improve the performance of sEMG-based hand gesture recognition. Therefore, we first define model reliability here as the quality of its uncertainty estimation and propose an offline framework to quantify it. To promote reliability analysis, we propose a novel end-to-end uncertainty-aware finger movement classifier, i.e., evidential convolutional neural network (ECNN), and illustrate the advantages of its multidimensional uncertainties such as vacuity and dissonance. Extensive comparisons of accuracy and reliability are conducted on NinaPro Database 5, exercise A, across CNN and three variants of ECNN based on different training strategies. The results of classifying 12 finger movements over 10 subjects show that the best mean accuracy achieved by ECNN is 76.34%, which is slightly higher than the state-of-the-art performance. Furthermore, ECNN variants are more reliable than CNN in general, where the highest improvement of reliability of 19.33% is observed. This work demonstrates the potential of ECNN and recommends using the proposed reliability analysis as a supplementary measure for studying sEMG-based hand gesture recognition.https://ieeexplore.ieee.org/document/9674873/Convolutional neural networkevidential deep learninghand gesture recognitionmodel reliabilitysurface electromyography (sEMG)uncertainty-awareness |
| spellingShingle | Yuzhou Lin Ramaswamy Palaniappan Philippe De Wilde Ling Li Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks IEEE Transactions on Neural Systems and Rehabilitation Engineering Convolutional neural network evidential deep learning hand gesture recognition model reliability surface electromyography (sEMG) uncertainty-awareness |
| title | Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks |
| title_full | Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks |
| title_fullStr | Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks |
| title_full_unstemmed | Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks |
| title_short | Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks |
| title_sort | reliability analysis for finger movement recognition with raw electromyographic signal by evidential convolutional networks |
| topic | Convolutional neural network evidential deep learning hand gesture recognition model reliability surface electromyography (sEMG) uncertainty-awareness |
| url | https://ieeexplore.ieee.org/document/9674873/ |
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