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- W4328007169 abstract "Prediction of actively exerted torque from the ankle joint complex (AJC) is critical for human-robot interaction because this is the real torque that the patient exerts. In this work, we develop a prediction framework to predict the actively exerted torque from the surface electromyography (sEMG) signal. The modified non-negative matrix factorization (mNMF) algorithm is used to extract muscle activation information (MAI) and muscle synergy information (MSI); the pseudo-siamese network (PSN) is used to fit the MAI and MSI to actively exerted torque. The prediction accuracy is compared under different ankle attitudes, synergistic feature extraction algorithms, and prediction algorithms, and the results show that the prediction accuracy of the proposed framework is 94.15%, and the variance of prediction accuracy is just 3.56% under different ankle attitudes. The proposed framework can provide the real actively exerted torque for the active rehabilitation training of ankle joint, so as to improve the enthusiasm and initiative of patients during rehabilitation training." @default.
- W4328007169 created "2023-03-22" @default.
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- W4328007169 date "2024-02-01" @default.
- W4328007169 modified "2023-10-14" @default.
- W4328007169 title "Prediction of Actively Exerted Torque From Ankle Joint Complex Based on Muscle Synergy" @default.
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- W4328007169 doi "https://doi.org/10.1109/tie.2023.3257380" @default.
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