A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal
Recently, deep models have been very popular because they achieve excellent performance with many classification problems. Deep networks have high computational complexities and require specific hardware. To overcome this problem (without decreasing classification ability), a hand-modeled feature se...
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/5/2007 |
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| author | Mehmet Baygin Prabal Datta Barua Sengul Dogan Turker Tuncer Sefa Key U. Rajendra Acharya Kang Hao Cheong |
| author_facet | Mehmet Baygin Prabal Datta Barua Sengul Dogan Turker Tuncer Sefa Key U. Rajendra Acharya Kang Hao Cheong |
| author_sort | Mehmet Baygin |
| collection | DOAJ |
| description | Recently, deep models have been very popular because they achieve excellent performance with many classification problems. Deep networks have high computational complexities and require specific hardware. To overcome this problem (without decreasing classification ability), a hand-modeled feature selection method is proposed in this paper. A new shape-based local feature extractor is presented which uses the geometric shape of the frustum. By using a frustum pattern, textural features are generated. Moreover, statistical features have been extracted in this model. Textures and statistics features are fused, and a hybrid feature extraction phase is obtained; these features are low-level. To generate high level features, tunable Q factor wavelet transform (TQWT) is used. The presented hybrid feature generator creates 154 feature vectors; hence, it is named Frustum154. In the multilevel feature creation phase, this model can select the appropriate feature vectors automatically and create the final feature vector by merging the appropriate feature vectors. Iterative neighborhood component analysis (INCA) chooses the best feature vector, and shallow classifiers are then used. Frustum154 has been tested on three basic hand-movement sEMG datasets. Hand-movement sEMG datasets are commonly used in biomedical engineering, but there are some problems in this area. The presented models generally required one dataset to achieve high classification ability. In this work, three sEMG datasets have been used to test the performance of Frustum154. The presented model is self-organized and selects the most informative subbands and features automatically. It achieved 98.89%, 94.94%, and 95.30% classification accuracies using shallow classifiers, indicating that Frustum154 can improve classification accuracy. |
| first_indexed | 2024-03-09T20:20:40Z |
| format | Article |
| id | doaj.art-b0f78ee32d0f407f923e2b83603cf1d8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T20:20:40Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b0f78ee32d0f407f923e2b83603cf1d82023-11-23T23:49:41ZengMDPI AGSensors1424-82202022-03-01225200710.3390/s22052007A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG SignalMehmet Baygin0Prabal Datta Barua1Sengul Dogan2Turker Tuncer3Sefa Key4U. Rajendra Acharya5Kang Hao Cheong6Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan 75000, TurkeySchool of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350, AustraliaDepartment of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, TurkeyDepartment of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, TurkeyDepartment of Orthopedics and Traumatology, Bingöl State Hospital, Ministry of Health, Bingöl 12000, TurkeyDepartment of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, SingaporeScience, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore 487372, SingaporeRecently, deep models have been very popular because they achieve excellent performance with many classification problems. Deep networks have high computational complexities and require specific hardware. To overcome this problem (without decreasing classification ability), a hand-modeled feature selection method is proposed in this paper. A new shape-based local feature extractor is presented which uses the geometric shape of the frustum. By using a frustum pattern, textural features are generated. Moreover, statistical features have been extracted in this model. Textures and statistics features are fused, and a hybrid feature extraction phase is obtained; these features are low-level. To generate high level features, tunable Q factor wavelet transform (TQWT) is used. The presented hybrid feature generator creates 154 feature vectors; hence, it is named Frustum154. In the multilevel feature creation phase, this model can select the appropriate feature vectors automatically and create the final feature vector by merging the appropriate feature vectors. Iterative neighborhood component analysis (INCA) chooses the best feature vector, and shallow classifiers are then used. Frustum154 has been tested on three basic hand-movement sEMG datasets. Hand-movement sEMG datasets are commonly used in biomedical engineering, but there are some problems in this area. The presented models generally required one dataset to achieve high classification ability. In this work, three sEMG datasets have been used to test the performance of Frustum154. The presented model is self-organized and selects the most informative subbands and features automatically. It achieved 98.89%, 94.94%, and 95.30% classification accuracies using shallow classifiers, indicating that Frustum154 can improve classification accuracy.https://www.mdpi.com/1424-8220/22/5/2007frustum patternFrustum154sEMG signal classificationgrasp detection |
| spellingShingle | Mehmet Baygin Prabal Datta Barua Sengul Dogan Turker Tuncer Sefa Key U. Rajendra Acharya Kang Hao Cheong A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal Sensors frustum pattern Frustum154 sEMG signal classification grasp detection |
| title | A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal |
| title_full | A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal |
| title_fullStr | A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal |
| title_full_unstemmed | A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal |
| title_short | A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal |
| title_sort | hand modeled feature extraction based learning network to detect grasps using semg signal |
| topic | frustum pattern Frustum154 sEMG signal classification grasp detection |
| url | https://www.mdpi.com/1424-8220/22/5/2007 |
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