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Movement Intention Detection of Human Body Based on Electromyographic Signal Analysis Using Fuzzy C-Means Clustering Algorithm
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 Title & Authors
Movement Intention Detection of Human Body Based on Electromyographic Signal Analysis Using Fuzzy C-Means Clustering Algorithm
Park, Kiwon; Hwang, Gun-Young;
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Electromyographic (EMG) signals have been widely used as motion commands of prosthetic arms. Although EMG signals contain meaningful information including the movement intentions of human body, it is difficult to predict the subject`s motion by analyzing EMG signals in real-time due to the difficulties in extracting motion information from the signals including a lot of noises inherently. In this paper, four Ag/AgCl electrodes are placed on the surface of the subject`s major muscles which are in charge of four upper arm movements (wrist flexion, wrist extension, ulnar deviation, finger flexion) to measure EMG signals corresponding to the movements. The measured signals are sampled using DAQ module and clustered sequentially. The Fuzzy C-Means (FCMs) method calculates the center values of the clustered data group. The fuzzy system designed to detect the upper arm movement intention utilizing the center values as input signals shows about 90% success in classifying the movement intentions.
Biomedical Signal Analysis;Electromyography;Fuzzy C-Means Data Clustering;Intention of Body Movement;
 Cited by
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