Abstract
Muscle onset detection using Electromyography (EMG) plays an important role in movement pattern recognition, motor and posture control, and assessment of neuromuscular and psychomotor diseases. When signal-to-noise ratio (SNR) of EMG signals is low owing to Gaussian noise and Electrocardiography (ECG), conventional detection methods are not effective. In this paper, we propose an automatic and user-independent detection method based on fuzzy entropy (FuzzyEn), which has advantages of high accuracy, light computational load and simple parameter selection. Besides, the processing time of the proposed method was short enough for rehabilitation robotic control.
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Lyu, M., Xiong, C., Zhang, Q., He, L. (2014). Fuzzy Entropy-Based Muscle Onset Detection Using Electromyography (EMG). In: Zhang, X., Liu, H., Chen, Z., Wang, N. (eds) Intelligent Robotics and Applications. ICIRA 2014. Lecture Notes in Computer Science(), vol 8917. Springer, Cham. https://doi.org/10.1007/978-3-319-13966-1_9
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DOI: https://doi.org/10.1007/978-3-319-13966-1_9
Publisher Name: Springer, Cham
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