Japanese Janken recognition by support vector machine based on electromyogram of wrist
Japanese Janken recognition by support vector machine based on electromyogram of wrist
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DOI：
10.37936/ecti-cit.2017112.105092
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发表时间：
2016-02
期刊：
影响因子：
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通讯作者：
Daiki Hiraoka;S. Ito;Momoyo Ito;M. Fukumi
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文献类型：
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作者：
Daiki Hiraoka;S. Ito;Momoyo Ito;M. Fukumi
近年来，生物信号作为一种人机交互工具受到越来越多的关注。最重要的是，肌电图（EMG）已经应用于许多研究中。在本研究中，我们提出了一种识别手部动作的方法。用8个干式传感器测量腕部肌电图。我们专注于四个动作，如“石头-剪刀-布”和“中性”。“中立”是指什么都不做的状态。该方法采用快速傅里叶变换（FFT）对测量的肌电数据进行处理，去除噪声。接下来，我们基于高斯函数组合传感器的值。在这个高斯函数中，方差和均值分别为0.2和0。然后，我们通过线性变换对这些值进行归一化。随后，我们将值的大小调整为-1到1的范围。最后，支持向量机（SVM）进行学习和判别。我们对三个科目进行了实验。该方法对3个被试的识别准确率分别为96.9%、95.3%、92.2%。因此，我们认为高斯函数对传感器位置差具有鲁棒性，因为该函数结合了两个相邻通道。在之前的方法中，识别正确率为77.1%。因此，本文提出的方法在精度上优于以往的方法。在以后的工作中，我们将对一个没有学过的被试进行日语Janken的判别实验。
Recent years, biosignal is receiving attention as a tool of human interface. Above all, electromyogram (EMG) has already applied to many researches. In this study, we propose a method which can discriminate hand motions. We measured an electromyogram of wrist by using 8 dry type sensors. We focused on four motions, such as "Rock-Scissors-Paper" and "Neutral". "Neutral" is a state that does not do anything. In the proposed method, we apply fast Fourier transformation (FFT) to measured EMG data, and then remove hum noise. Next, we combine values of sensors based on a Gaussian function. In this Gaussian function, variance and mean are 0.2 and 0, respectively. After that, we apply normalization by linear transformation to the values. Subsequently, we resize the values into range from -1 to 1. Finally, support vector machine (SVM) conducts learning and discrimination. We conducted experiments in three subjects. Discrimination accuracy of the proposed method for three subjects was 96.9%, 95.3%, 92.2%, respectively. Therefore, we think that the Gaussian function is robust to difference of sensor position because this function combines both adjacent channels. In the previous method, the discrimination accuracy rate was 77.1%. Therefore, the proposed method is better in accuracy than the previous method. In future work, we will conduct an experiment which discriminates Japanese Janken of a subject who is not learned.