Research about Motor Unit Visualization with Surface EMG Signals

表面肌电信号运动单位可视化研究

基本信息

批准号：
15360219
负责人：
MAEKAWA Satoshi
金额：
$ 8万
依托单位：
National Institute of Information and Communications Technology (2004)Kobe University (2003)
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-15360219/
关键词：
Surface EMG Motor Unit Visualization Independent Component Analysis Blind Deconvolution Overcomplete Current Source Estimation boosting KDA 筋電図電流源

项目摘要

This research was executed for the motor unit decomposition and making the activity visible from the surface electromyogram as follows.A)Improvement of motor unit decomposition techniqueWe proposed a new motor unit decomposition technique with overcomplete bases to introduce the statistical model. It was confirmed that this technique was applied to the real measurement surface EMG signals, and it had the decomposition performance equal with blind deconvolution. Moreover, it was confirmed that each motor unit activity might be separable even if the number of observation channels were less than the number of active motor units.B)Examination of effectiveness of recognition technique using statistical modelWe proposed the recognition system using both KDA and boosting mechanism and confirmed that it's recognition performance was just like an existing technique such as SVM. In general, to maximize generalization performance, parameter tuning process such as cross validation, whose computati … More on cost is very expensive, is needed. To solve this problem, we proposed new index value for parameter selection, and with this index, appropriate parameters can be selected without so expensive computation cost.C)The three dimensional position estimation of each decomposed motor unitThe 3D position of the depolarization of individual motor unit was estimated by using the 3D finite element method from the potential distribution on the skin surface which was estimated with motor unit decomposition technique from surface EMG signals. As a result, it was confirmed that 3D position and dynamics of dopolarization of individual motor unit might be estimated. Moreover, the size of innervation zone and temporal dynamics of current intensity of depolarization could be estimated.In addition, this study had been started in 2003 fiscal year under Dr.Kotani head researcher, Assistant Professor of Kobe University. However he died in May, 2004, so the head was changed suddenly. I write down that contribution of Dr.Kotani covers the whole of the above results. Less

这项研究是针对运动单元分解执行的，并使表面肌电图可见的活动如下。已经证实，该技术应用于实际测量表面EMG信号，并且其分解性能与盲卷积相等。此外，已经证实，即使观察通道的数量小于主动电动机单元的数量，每个电机单位活动也可能是分开的。B）使用统计模型We使用KDA和增强机制提出了识别系统的识别技术的有效性，并确认它的识别性能与SVM这样的现有技术就像现有技术一样。通常，为了最大程度地提高概括性能，需要参数调整过程，例如交叉验证，其计算……更多的成本非常昂贵。为了解决这个问题，我们提出了用于选择参数的新索引价值，并且使用此索引，可以在没有如此昂贵的计算成本的情况下选择适当的参数。结果，可以估计单个电机单元的三op毛的3D位置和动力学。此外，可以估计神经区的大小和当前去极化强度的临时动力学。此外，这项研究还于2003财政年度开始了科比大学助理教授。然而，他于2004年5月去世，所以头突然改变了。我写下了Kotani博士的贡献，涵盖了上述整个结果。较少的