On-line learning EMG driven Interface and high speed learning and rule generation

最近，像手机这样的信息终端已经被广泛使用。据此，建立了蓝牙等无线电通信行业标准。因此，可以组合并执行各种接口。然而，时至今日，能够对便携式机具进行各种操作并能控制网络的设备（简称“总操作设备”）还没有出现。此外，从操作角度来看，手表类型更可取。因此，我们研究了肌电图（EMG），这是一种由活体运动产生的信号。首先，通过输入部分的电极测量肌电信号的时间序列数据。其次，在信号处理部分对该数据进行放大和A/D变换。接下来，将放大后的数据转换为傅立叶功率谱。最后，我们在学习评价部分对各种数据进行评价。我们的目标是利用DSP训练板构建一个高速、高准确度、可在线学习的肌电信号识别系统。为了达到较高的准确率，我们使用快速傅里叶变换（FFT）进行特征提取，简单主成分分析（SPCA）进行特征压缩，神经网络（NN）进行识别。本文提出了一种基于多元主成分分析的肌电图识别方法。计算机仿真结果表明，该方法在提高识别精度和速度方面是有效的。此外，我们使用遗传算法来对抗规则生成系统，以提高肌电图的识别精度。该方法使用遗传算法选择的输入属性生成数学函数。与传统方法相比，这些功能可以实现更高的精度。最后测试了小波变换的消噪性能。该方法先去除小波变换后的小分量，然后对信号进行反变换。这些信号被用于肌电识别并评估其准确性。少

Recently, information terminals such as a cellular phone have been widely used. According to this, industrial standard of radio communication such as Bluetooth has been established. As a result, it would be possible to combine and to perform various interfaces. However, now a day, the device that has various operations of portable machines and tools and can control networks (call "total operation device" for short) has not been provided yet. Moreover, the wristwatch type is preferable in the viewpoint of operationality. Therefore, we investigate ElectroMyoGram (EMG) which is a signal generated from a living body with movement of a subject.First, time series data for EMG is measured by electrodes in the input part. Second, this data is amplified and A/D transform is performed in the signal processing part. Next, this amplified data is converted to Fourier power spectra. Finally, we evaluate various data in the learning-evaluation part.We aim for construction of a high-speed and high-acc … More urate EMG recognition system which can do on-line learning using DSP training board. In order to achieve high accuracy, we used Fast Fourier Transform (FFT) for feature extraction, Simple-PCA (SPCA) for feature compression, and a neural network (NN) for recognition. In particular, we presented a novel method based on Multiple PCA to improve recognition accuracy for EMG. From results of computer simulation, it is shown that our approach is effective for improvement in recognition accuracy and speed.Furthermore, we used a genetic algorithm for condteracting a rule generation system which can improve recognition accuracy for EMG. This method yielded mathematical functions using input attributes selected by the genetic algorithms. These functions can achieve a high accuracy compared to conventional approach.Finally we tested noize elimination performance using wavelet transform. In this method, small components after the wavelet transform are eliminated and then signals are inversely transformed. These signals were used for EMG recognition and evaluated its accuracy. Less