LRF-ELM对肌电信号的分类

2017 International Artificial Intelligence and Data Processing Symposium (IDAP) Pub Date : 2017-09-01 DOI:10.1109/IDAP.2017.8090239

Furkan Ayaz, A. Ari, D. Hanbay

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引用次数: 1

摘要

肌电图(EMG)信号可以定义为肌肉细胞的电活动。它通常用于运动识别，神经肌肉疾病的治疗和假手控制。在本研究中，提出了6种不同握持对象手型的肌电信号分类方法。首先对肌电信号进行短时傅里叶变换，得到肌电信号的时间frekan表示。然后对这些T-F图像进行分割，并对其均值进行求值，以降低图像的维数。基于局部接受域的极限学习机(ELM-LRF)用于手的形状分类。评价准确率为94.12%。

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Classification of EMG signals by LRF-ELM

Electromyogram (EMG) signal can be defined as the electrical activity of muscles cells. It is commonly used in motion recognition, treatment of neuromuscular disorders and prosthetic hand control. In this study, classification of EMG signals obtained from 6 different hand shapes of holding object was proposed. At first Short Time Fourier Transform of the EMG signal were evaluated to obtain their Time-Frekans representation. After than these T-F images were segmented and their mean values were evaluated to reduce the dimension of the images. Local Receptive Fields based Extreme Learning Machines (ELM-LRF) used to classification of these hand shapes of holding object. Evaluated accuracy is 94.12 %.

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2017 International Artificial Intelligence and Data Processing Symposium (IDAP)

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