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Lookup NU author(s): Dr Ravi Suppiah, Dr Khalid Abidi, Dr Anurag Sharma
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Physiological Signals like Electromography (EMG) and Electroencephalography (EEG) can be analysed and decoded to provide vital information that can be used in a range of applications like rehabilitative robotics and remote device control. The process of acquiring and using these signals requires many compute-intensive tasks like signal acquisition, signal processing, feature extraction, and machine learning. Performing these activities on a PC-based system with well-established software tools like Python and Matlab is the first step in designing solutions based upon these signals. In the application domain of rehabilitative robotics, one of the main goals is to develop solutions that can be deployed for the use of individuals who need them in improving their Acitivities-for-Daily Living (ADL). To achieve this objective, the final solution must be deployed onto an embedded solution that allows high portability and ease-of-use. Porting a solution from a PC-based environment onto a resource-constraint one such as a microcontroller poses many challenges. In this research paper, we propose the use of an ARM-based Corex M-4 processor. We explore the various stages of the design from the initial testing and validation, to the deployment of the proposed algorithm on the controller, and further investigate the use of Cepstrum features to obtain a high classification accuracy with minimal input features. The proposed solution is able to achieve an average classification accuracy of 95.34% for all five classes in the EMG domain and 96.16% in the EEG domain on the embedded board.
Author(s): Suppiah R, Noori K, Abidi K, Sharma A
Publication type: Article
Publication status: Published
Journal: Biomedical Physics & Engineering Express
Year: 2024
Volume: 10
Issue: 4
Online publication date: 04/06/2024
Acceptance date: 23/05/2024
Date deposited: 17/06/2024
ISSN (electronic): 2057-1976
Publisher: IOP Publishing
URL: https://doi.org/10.1088/2057-1976/ad4f8d
DOI: 10.1088/2057-1976/ad4f8d
Data Access Statement: The data that support the findings of this study are openly available at the following URL/DOI: https:// doi.org/10.1038/sdata.2014.47
PubMed id: 38781938
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