Classifying sEMG-based Hand Movements by Means of Principal Component Analysis

被引：0

作者：

Isakovic, Milica S. ^{[1
]}

Miljkovic, Nadica ^{[1
,2
]}

Popovic, Mirjana B. ^{[1
]}

机构：

[1] Univ Belgrade, Sch Elect Engn, Bul Kralja Aleksandra 73, Belgrade 11120, Serbia

[2] Tecnalia Serbia, Belgrade 11120, Serbia

来源：

2014 22ND TELECOMMUNICATIONS FORUM TELFOR (TELFOR) | 2014年

关键词：

feature extraction; healthy subjects; grasp; principal component analysis; surface electromyography; CLASSIFICATION;

D O I：

暂无

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

in order to improve surface electromyography (sEMG) based control of hand prosthesis, we applied Principal Component Analysis (PCA) for feature extraction. The sEMG data (downloaded from free NINAPRO database) were recorded during three grasping and 11 finger movements. We tested the accuracy of a simple piecewise quadratic classifier for two sets of features derived from PCA. Preliminary results from a group of healthy subjects suggest that the first two principal components aren't always sufficient for successful hand movement classification. The grasping movement classification error when using three features (22.7 +/- 10.7%) was smaller than the classification error for two features (33.4 +/- 12.5%) in all subjects.

引用

页码：545 / 548

页数：4

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