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• W3201371723 abstract "In this paper, we propose the long–short-term memory (LSTM)-based voluntary and non-voluntary (VNV) muscle contraction classification algorithm in an electrical stimulation (ES) environment. In order to measure the muscle quality (MQ), we employ the non-voluntary muscle contraction signal, which occurs by the ES. However, if patient movement, such as voluntary muscle contractionm, occurs during the ES, the electromyography (EMG) sensor captures the VNV muscle contraction signals. In addition, the voluntary muscle contraction signal is a noise component in the MQ measurement technique, which uses only non-voluntary muscle contraction signals. For this reason, we need the VNV muscle contraction classification algorithm to classify the mixed EMG signal. In addition, when recording EMG while using the ES, the EMG signal is significantly contaminated due to the ES signal. Therefore, after we suppress the artifact noise, which is contained in the EMG signal, we perform VNV muscle contraction classification. For this, we first eliminate the artifact noise signal using the ES suppression algorithm. Then, we extract the feature vector, and then the feature vector is reconstructed through the feature selection process. Finally, we design the LSTM-based classification model and compare the proposed algorithm with the conventional method using the EMG data. In addition, to verify the performance of the proposed algorithm, we quantitatively compared results in terms of the confusion matrix and total accuracy. As a result, the performance of the proposed algorithm was higher than that of the conventional methods, including the support vector machine (SVM), artificial neural network (ANN), and deep neural network (DNN)." @default.
• W3201371723 created "2021-09-27" @default.
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• W3201371723 date "2021-09-17" @default.
• W3201371723 modified "2023-09-27" @default.
• W3201371723 title "Voluntary Muscle Contraction Detection Algorithm Based on LSTM for Muscle Quality Measurement Algorithm" @default.
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• W3201371723 doi "https://doi.org/10.3390/app11188676" @default.
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