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首页> 外文会议>Iranian Conference on Biomedical Engineering >Classification of ADLs Using Muscle Activation Waveform Versus Thirteen EMG Features
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Classification of ADLs Using Muscle Activation Waveform Versus Thirteen EMG Features

机译：使用肌肉激活波形对ADL的分类与十三个EMG功能

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摘要

Movement classification has been a challenging problem in neuroprosthesis control. Many studies have taken into account the classification of movement using time and frequency domain features extracted from the electromyogram signals while calculating these features are usually time consuming. In this paper, we compared the capability of muscle activation waveform in the classification of five arm movements during activities of daily living, also known as ADLs, versus 13 different prevalent electromyogram features. We tested our technique on the electromyogram signal recorded from six healthy male right handed subjects. We, also, selected the muscles that are supposed to be the intact muscles in a tetraplegic spinal cord injury patient. Our results indicated that there exists significant higher accuracy with recruiting muscle activation waveform in classification, while the complexity of calculating features is eliminated.
机译：运动分类是神经间隙控制中有挑战性的问题。许多研究已经考虑了使用从电灰度信号中提取的时间和频域特征的运动分类，同时计算这些特征通常是耗时的。在本文中，我们将肌肉激活波形在日常生活活动中的五个臂运动的分类中进行了比较，也称为ADL，而具有13种不同的普遍普遍的电灰度。我们测试了从六个健康男性右手主体记录的电灰度信号上的技术。此外，我们也选择了应该是四轮脊髓损伤患者的完整肌肉的肌肉。我们的结果表明，在分类中招募肌肉激活波形，消除了计算特征的复杂性，存在显着更高的准确性。

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