| Autor: |
V. M. Akhil, Rahul Satheesh, M. Ashmi, Charbel D. Tawk |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 11, Pp 129566-129575 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3332664 |
| Popis: |
Electromyography (EMG)-based clinical signal processing and feature extraction are crucial for diagnosing various diseases. In this work, using lower limb EMG data, scattering transform (ST)-based features and time-domain-based features were compared using a support vector machine (SVM) classifier. Several subjects were instructed to walk on a platform designed to transform its surface between even and uneven terrain. The EMG signals from the gastrocnemius (GM) and medial hamstring muscles (HM) of twenty-one healthy male subjects were acquired under different walking conditions (i.e., even forward, even backward, uneven forward, and uneven backward walking directions). The features were extracted using a time-domain-based method and ST method which were then fed into an SVM classifier. Test results showed that time-domain features can provide accuracy ranges between 97.31 and 99.57% and ST features can provide accuracy ranges between 99.42 and 100%. The high number of extracted features used showed that ST features require a longer processing time compared to time-domain features. In addition, the analysis of generated scatterplots showed how time-domain-based features contribute to the classification of walking conditions. The findings of this study have potential applications in both clinical and bioengineering fields. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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