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基于表面肌电图的基本手势分类

Basic Hand Gestures Classification Based on Surface Electromyography.

作者信息

Palkowski Aleksander, Redlarski Grzegorz

机构信息

Department of Mechatronics and High Voltage Engineering, Gdańsk University of Technology, Ulica G. Narutowicza 11/12, 80-233 Gdańsk, Poland.

出版信息

Comput Math Methods Med. 2016;2016:6481282. doi: 10.1155/2016/6481282. Epub 2016 May 19.

DOI:10.1155/2016/6481282
PMID:27298630
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4889824/
Abstract

This paper presents an innovative classification system for hand gestures using 2-channel surface electromyography analysis. The system developed uses the Support Vector Machine classifier, for which the kernel function and parameter optimisation are conducted additionally by the Cuckoo Search swarm algorithm. The system developed is compared with standard Support Vector Machine classifiers with various kernel functions. The average classification rate of 98.12% has been achieved for the proposed method.

摘要

本文提出了一种利用双通道表面肌电图分析对手势进行创新分类的系统。所开发的系统使用支持向量机分类器，其核函数和参数优化通过布谷鸟搜索群算法额外进行。将所开发的系统与具有各种核函数的标准支持向量机分类器进行比较。所提出的方法实现了98.12%的平均分类率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a81/4889824/3155de90edcd/CMMM2016-6481282.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a81/4889824/3155de90edcd/CMMM2016-6481282.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a81/4889824/3155de90edcd/CMMM2016-6481282.002.jpg

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本文引用的文献

1
A system for heart sounds classification.
PLoS One. 2014 Nov 13;9(11):e112673. doi: 10.1371/journal.pone.0112673. eCollection 2014.
2
Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.基于 PSO 优化 SVM 的肌电信号分类在神经肌肉疾病诊断中的应用。
Comput Biol Med. 2013 Jun;43(5):576-86. doi: 10.1016/j.compbiomed.2013.01.020. Epub 2013 Feb 27.
3
Support vector machine-based classification scheme for myoelectric control applied to upper limb.基于支持向量机的肌电控制分类方案在上肢中的应用
使用Myo运动控制器将肌电图（EMG）技术应用于内侧和外侧肘部附着点病的治疗。
Australas Phys Eng Sci Med. 2019 Sep;42(3):701-710. doi: 10.1007/s13246-019-00770-5. Epub 2019 Jun 14.
IEEE Trans Biomed Eng. 2008 Aug;55(8):1956-65. doi: 10.1109/TBME.2008.919734.
4
Control of multifunctional prosthetic hands by processing the electromyographic signal.通过处理肌电信号来控制多功能假手。
Crit Rev Biomed Eng. 2002;30(4-6):459-85. doi: 10.1615/critrevbiomedeng.v30.i456.80.