Abstract:The paper develops a prediction model based on the surface electromyography (sEMG) signals to recognize the five different arm-related motions. We collect 100 groups of the three-channels signals on Biceps, Triceps, Brachioradialis as the raw data set. Then we extract four features from the data to describe the characteristics of different motions in the data set. In the pre-processing stage, we compare three types of EMD-based filtration methods to denoise signals and choose the most efficient one, then use p… Show more
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