Feb. 9, 2024, 5:43 a.m. | Cho-Yuan Lee Kuan-Chen Wang Kai-Chun Liu Xugang Lu Ping-Chen Yeh Yu Tsao

cs.LG updates on arXiv.org arxiv.org

In practical scenarios involving the measurement of surface electromyography (sEMG) in muscles, particularly those areas near the heart, one of the primary sources of contamination is the presence of electrocardiogram (ECG) signals. To assess the quality of real-world sEMG data more effectively, this study proposes QASE-net, a new non-intrusive model that predicts the SNR of sEMG signals. QASE-net combines CNN-BLSTM with attention mechanisms and follows an end-to-end training strategy. Our experimental framework utilizes real-world sEMG and ECG data from two …

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