| Autor: |
Afroza Sultana, Md Tawhid Islam Opu, Farruk Ahmed, Md Shafiul Alam |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Healthcare Analytics, Vol 5, Iss , Pp 100296- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2772-4425 |
| DOI: |
10.1016/j.health.2023.100296 |
| Popis: |
Electromyogram (EMG) signal monitoring is an effective method for controlling the movements of a prosthetic limb. The classification of the EMG pattern of various finger motions in upper-arm amputees has drawn much attention in recent years to develop algorithms that provide adequate accuracy. However, due to the complexity of EMG data, movement detection is a challenging task. Therefore, an effective model is needed that can accurately process, analyze, and classify various hand and finger movements. This paper proposes a novel algorithm for processing and classifying 15 finger movements from surface EMG signals based on Welch power estimation from frequency analysis. Five time-domain features are extracted and trained with a machine learning classifier to classify 15 single fingers and combined finger gestures from eight healthy subjects. The experimental results show 92.30 % classification accuracy considering data from eight channels which was improved to 94.15 % after selecting two channels as dominating. For performance evaluation, 10-fold cross-validation is used during classification. We demonstrate an average accuracy of 92.35 % with 25 % test data. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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