| Autor: |
Chuanjiang Li, Huiyin Deng, Shiyi Yin, Chenming Wang, Yanfei Zhu |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Results in Engineering, Vol 18, Iss , Pp 101150- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2590-1230 |
| DOI: |
10.1016/j.rineng.2023.101150 |
| Popis: |
Most gesture recognition studies based on surface electromyography (sEMG) signals focus on filtering, in which the lack of diversity for considered noises can still be the problem. In this work, a denoising method based on Synchrosqueezing Wavelet Transform with Differential Evolution optimized threshold (DEOT-SWT) is proposed. The sEMG signals of ten gestures with three mixed noises, including power line interference (PLI), baseline drift (BW), and white Gaussian noise (WGN), are firstly investigated and filtered by DEOT-SWT, which are collected from seven subjects by utilizing two wearable sEMG signal sensors. Then, the most commonly used Hudgins time-domain feature set is extracted for recognizing ten gestures. Three metrics are adopted to evaluate filtering performance: signal-to-noise ratio (SNR), root mean square error and R-squared value. The gesture recognition accuracy is utilized to verify the practical effect of DEOT-SWT in sEMG-based gesture recognition applications. The results of the experiments demonstrate that the DEOT-SWT algorithm accomplishes desirable denoising performance with an average recognition accuracy of 95.95% (±3.88) in comparison to the classic Infinite Impulse Response (IIR) algorithm and the empirical mode decomposition (EMD) algorithm. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
