Efficient deep neural network model for classification of grasp types using sEMG signals
Article
Coskun, Musab, Yildirim, Ozal, Demir, Yakup and Acharya, U. Rajendra. 2022. "Efficient deep neural network model for classification of grasp types using sEMG signals." Journal of Ambient Intelligence and Humanized Computing. 13 (9), pp. 4437-4450. https://doi.org/10.1007/s12652-021-03284-9
| Article Title | Efficient deep neural network model for classification of grasp types using sEMG signals |
|---|---|
| ERA Journal ID | 42085 |
| Article Category | Article |
| Authors | Coskun, Musab, Yildirim, Ozal, Demir, Yakup and Acharya, U. Rajendra |
| Journal Title | Journal of Ambient Intelligence and Humanized Computing |
| Journal Citation | 13 (9), pp. 4437-4450 |
| Number of Pages | 14 |
| Year | 2022 |
| Publisher | Springer |
| Place of Publication | Germany |
| ISSN | 1868-5137 |
| 1868-5145 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s12652-021-03284-9 |
| Web Address (URL) | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105969172&doi=10.1007%2fs12652-021-03284-9&partnerID=40&md5=0f8f1c3de1280c6012c479a5cdb89dd8 |
| Abstract | Grasping is a challenging problem in robotics and prosthetic applications due to its control requirements. The visual perception and analyzing electromyography (EMG) signals are the two ways to give the inputs to robots and prosthetic amputees for grasping abilities. The EMG is a diagnostic manner that evaluates the fitness condition of skeletal muscles. Examination or evaluation of the EMG signals is time-consuming and arduous for experts. Hence, the state-of-the-art methods in artificial intelligence (AI) is employed to improve the accuracy rate for the detection and classification of EMG signals for grasping. Recently, deep learning architectures have been used in many engineering applications such as diagnosis of health conditions, computer vision, and human machine interaction (HMI). In this study, a new deep one-dimensional convolutional neural network model (1D-CNN) is proposed to classify six types of hand movements. Our proposed 1D-CNN model implemented using surface EMG (sEMG) has obtained the highest accuracy of 94.94% in classifying six hand movements. The strength of our model is that, it can perform the automated classification of various hard grasps using only one channel data. Our developed prototype model is ready to be tested with more data and can be used to assist in musculoskeletal disorders. |
| Keywords | CNN; Deep learning; Robotic grasping; EMG classifcation |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Bingol University, Turkey |
| Firat University, Turkey | |
| Kumamoto University, Japan | |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan |
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