A new framework for classification of multi-category hand grasps using EMG signals
Article
Article Title | A new framework for classification of multi-category hand grasps using EMG signals |
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ERA Journal ID | 5031 |
Article Category | Article |
Authors | Miften, Firas Sabar (Author), Diykh, Mohammed (Author), Abdulla, Shahab (Author), Siuly, Siuly (Author), Green, Jonathan H. (Author) and Deo, Ravinesh C. (Author) |
Journal Title | Artificial Intelligence in Medicine |
Journal Citation | 112, pp. 1-14 |
Article Number | 102005 |
Number of Pages | 14 |
Year | 2021 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0933-3657 |
1873-2860 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.artmed.2020.102005 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0933365720312707 |
Abstract | Electromyogram (EMG) signals have had a great impact on many applications, including prosthetic or rehabilitation devices, human-machine interactions, clinical and biomedical areas. In recent years, EMG signals have been used as a popular tool to generate device control commands for rehabilitation equipment, such as robotic prostheses. This intention of this study was to design an EMG signal-based expert model for hand-grasp classification that could enhance prosthetic hand movements for people with disabilities. The study, thus, aimed to introduce an innovative framework for recognising hand movements using EMG signals. The proposed framework consists of logarithmic spectrogram-based graph signal (LSGS), AdaBoost k-means (AB-k-means) and an ensemble of feature selection (FS) techniques. First, the LSGS model is applied to analyse and extract the desirable features from EMG signals. Then, to assist in selecting the most influential features, an ensemble FS is added to the design. Finally, in the classification phase, a novel classification model, named AB-k-means, is developed to classify the selected EMG features into different hand grasps. The proposed hybrid model, LSGS-based scheme is evaluated with a publicly available EMG hand movement dataset from the UCI repository. Using the same dataset, the LSGS-AB-k-means design model is also benchmarked with several classifications including the state-of-the-art algorithms. The results demonstrate that the proposed model achieves a high classification rate and demonstrates superior results compared to several previous research works. This study, therefore, establishes that the proposed model can accurately classify EMG hand grasps and can be implemented as a control unit with low cost and a high classification rate. |
Keywords | EMGLSGSAB-k-means; hand grasps; feature extraction |
ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
420106. Physiotherapy | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Thi-Qar, Iraq |
School of Sciences | |
USQ College | |
Victoria University | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q60y1/a-new-framework-for-classification-of-multi-category-hand-grasps-using-emg-signals
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