吉林大学学报(信息科学版)

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基于小波变换的按摩手法肌电信号分析

王晓1, 王丽荣2a, 宋雅娟2b, 任丽晔2a, 冯萍2b   

  1. 1. 长春理工大学 电子信息工程学院, 长春 130022; 2. 长春大学 a. 电子信息工程学院; b. 计算机科学技术学院, 长春 130022
  • 收稿日期:2012-09-15 出版日期:2013-05-27 发布日期:2013-06-07
  • 作者简介:王晓(1987—), 女, 河南永城人, 长春理工大学硕士研究生,主要从事检测技术与自动化装置研究, (Tel)86-13756175435(E-mail)wangxiao1018@yahoo.cn; 通讯作者:王丽荣(1966—), 女, 长春人, 长春大学教授, 主要从事模式识别与智能控制研究, (Tel)86-18643149558(E-mail)wlr10012003@yahoo.com.cn。
  • 基金资助:

    吉林省科技厅自然基金资助项目(201215112); 长春市科技局基金资助项目(10GH14); 吉林省科技厅自然基金资助项目(201215111)

Analysis of Electromyography Signal in Massage Technique Based on Wavelet Transform

WANG Xiao1, WANG Li-rong2a, SONG Ya-juan2b, REN Li-ye2a, FENG Ping2b   

  1. 1. College of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China; 2a. College ofElectronic and Information Engineering; 2b. College of Computer Science and Technology, Changchun University, Changchun 130022, China
  • Received:2012-09-15 Online:2013-05-27 Published:2013-06-07

摘要:

为通过滚法的量化结果分析为按摩手法的评定及训练提供一定的参考依据, 利用表面肌电仪采集按摩师及初学者滚法相关肌群的表面肌电信号, 采用小波变换算法对信号进行多分辨率分析, 进而研究其能量分布。实验结果表明, 在滚法操作中, 按摩师75%的能量集中在低频段, 其中肱桡肌的能量占总能量的40%, 而初学者能量分布与之有显著差异。在掌握正确的操作要领前提下, 初学者应注重肱桡肌能量的训练。

关键词: 肌电信号, 小波变换, 特性参数, 信号分析

Abstract:

Surface electromyography signal system was used to collect the related muscle groups electromyography signals of the expert and beginners during rolling method, then the signals were analyzed by multi-resolution based on wavelet transform to get the characteristic parameters of the rolling method, and the aim of the method is to provide some valuable references for massage evaluation and training. The analysis of experimental results show that 75% of the experts energy in rolling method is concentrated in the low-frequency band, while 40% of the total energy is from brachioradialis. And Energy distribution of beginners is different from the experts. So in the later training, beginners should pay attention to the training of brachioradialis under the precondition of the right operational position.

Key words: electromyography signal, wavelet transform, characteristic parameter, signal analysis

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