A new framework for classification of multi-category hand grasps using EMG signals

Miften, Firas Sabar and Diykh, Mohammed and Abdulla, Shahab ORCID: https://orcid.org/0000-0002-1193-6969 and Siuly, Siuly and Green, Jonathan H. ORCID: https://orcid.org/0000-0003-1468-1970 and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 (2021) A new framework for classification of multi-category hand grasps using EMG signals. Artificial Intelligence in Medicine, 112:102005. pp. 1-14. ISSN 0933-3657


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.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version, in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021)
Faculty/School / Institute/Centre: Current - USQ College (8 Jun 2020 -)
Date Deposited: 12 Jan 2021 00:01
Last Modified: 22 Jun 2021 09:53
Uncontrolled Keywords: EMGLSGSAB-k-means; hand grasps; feature extraction
Fields of Research (2008): 11 Medical and Health Sciences > 1199 Other Medical and Health Sciences > 119999 Medical and Health Sciences not elsewhere classified
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation
42 HEALTH SCIENCES > 4201 Allied health and rehabilitation science > 420106 Physiotherapy
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970111 Expanding Knowledge in the Medical and Health Sciences
Socio-Economic Objectives (2020): 20 HEALTH > 2099 Other health > 209999 Other health not elsewhere classified
Identification Number or DOI: https://doi.org/10.1016/j.artmed.2020.102005
URI: http://eprints.usq.edu.au/id/eprint/40510

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