A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal
Article
Article Title | A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal |
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ERA Journal ID | 34304 |
Article Category | Article |
Authors | Baygin, Mehmet, Barua, Prabal Datta, Dogan, Sengul, Tuncer, Turker, Key, Sefa, Acharya, U. Rajendra and Cheong, Kang Hao |
Journal Title | Sensors |
Journal Citation | 22 (5) |
Article Number | 2007 |
Number of Pages | 18 |
Year | 2022 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 1424-8220 |
1424-8239 | |
Digital Object Identifier (DOI) | https://doi.org/10.3390/s22052007 |
Web Address (URL) | https://www.mdpi.com/1424-8220/22/5/2007 |
Abstract | 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. |
Keywords | grasp detection; frustum pattern; Frustum154; sEMG signal classification |
ANZSRC Field of Research 2020 | 400399. Biomedical engineering not elsewhere classified |
400399. Biomedical engineering not elsewhere classified | |
Byline Affiliations | Ardahan University, Turkiye |
School of Business | |
University of Technology Sydney | |
Cogninet Australia, Australia | |
Firat University, Turkey | |
Ministry of Health, Turkey | |
Ngee Ann Polytechnic, Singapore | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan | |
Singapore University of Technology and Design |
https://research.usq.edu.au/item/z01q7/a-hand-modeled-feature-extraction-based-learning-network-to-detect-grasps-using-semg-signal
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