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

在线学习肌电图驱动界面和高速学习和规则生成

基本信息

批准号：
15300073
负责人：
FUKUMI Minoru
金额：
$ 10.62万
依托单位：
The University of Tokushima
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2004
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15300073/
关键词：
EMG Neural network DSP Rule generation EMG sensor Noise elimination

项目摘要

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

近来，诸如蜂窝电话的信息终端已经被广泛使用。基于此，已经建立了诸如蓝牙的无线电通信的工业标准。结果，可以联合收割机并执行各种接口。然而，时至今日，还没有提供具有便携式机具的各种操作并能控制网络的装置（简称“总操作装置”）。此外，从操作性的观点来看，手表类型是优选的。因此，我们研究肌电图（EMG），这是一个从生物体产生的信号与对象的运动。首先，时间序列数据的肌电图是由电极在输入部分。第二，在信号处理部分中放大该数据并执行A/D变换。接下来，将该放大的数据转换为傅立叶功率谱。最后，在学习评估部分，我们对各种数据进行评估，旨在构建一个高速、高精度的学习评估系统。 ...更多信息利用DSP训练板设计了一个具有在线学习功能的肌电信号识别系统。为了达到高精度，我们使用快速傅立叶变换（FFT）的特征提取，简单PCA（SPCA）的特征压缩，和神经网络（NN）的识别。特别是，我们提出了一种新的方法，基于多重主成分分析，以提高识别准确率的肌电信号。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，我们还利用遗传算法构造了一个规则生成系统，提高了肌电信号的识别精度。该方法使用遗传算法选择的输入属性产生数学函数。与传统的去噪方法相比，这些函数具有较高的去噪精度。该方法首先消除小波变换后的小分量，然后对信号进行逆变换。这些信号被用于肌电信号识别，并评估其准确性。少

项目成果

期刊论文数量（37）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Analysis and Recognition of Wrist Motions by Using Multidimensional Directed Information and EMG signal

利用多维定向信息和肌电信号分析和识别手腕运动

DOI：
发表时间：
2004
期刊：
Proc.of North American Fuzzy Information Processing Society'2004
影响因子：
0
作者：
矢間;他;Y.Yazama 他
通讯作者：
Y.Yazama 他

Recognition system for EMG signals by using nonnegative matrix factorization

非负矩阵分解的肌电信号识别系统

DOI：
发表时间：
2003
期刊：
Proc.IJCNN'2003, Portland, USA
影响因子：
0
作者：
Y.Yazama;et al.
通讯作者：
et al.

EMG signal recognition system using feature vectors by genetic function identification

通过遗传功能识别使用特征向量的肌电信号识别系统

DOI：
发表时间：
期刊：
Journal of Signal Processing Vol.9,No.3(Accepted)
影响因子：
0
作者：
Y.Yazama;et al.
通讯作者：
et al.

Y.Matsumura 他: "Recognition of EMG Signal Patterns by Neural Networks"Proc.of Knowledge-Based Intelligent Information & Engineering Systems' 2003. Vol.1. 623-630 (2003)

Y.Matsumura 等人：“通过神经网络识别 EMG 信号模式”Proc.of Knowledge-Based Intelligence Information & Engineering Systems 2003。Vol.1 (2003)。

DOI：
发表时间：
期刊：
影响因子：
0
作者：
通讯作者：

Recognition of EMG signal patterns by neural networks

DOI：
10.1109/iconip.2002.1198158
发表时间：
2002-11
期刊：
Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.
影响因子：
0
作者：
Y. Matsumura;Y. Mitsukura;M. Fukumi;N. Akamatsu;F. Takeda
通讯作者：
Y. Matsumura;Y. Mitsukura;M. Fukumi;N. Akamatsu;F. Takeda