[1]杜育乘,张婷婷,王晓云. 基于表面肌电相位同步分析的跌倒辨识研究[J].中国医学物理学杂志,2018,35(3):313-322.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.013]
　DU Yucheng,ZHANG Tingting,WANG Xiaoyun. Fall recognition based on surface electromyography phase synchronization analysis[J].Chinese Journal of Medical Physics,2018,35(3):313-322.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.013]
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 基于表面肌电相位同步分析的跌倒辨识研究()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
35卷
期数:
2018年第3期
页码:
313-322
栏目:
医学信号处理与医学仪器
出版日期:
2018-03-20

文章信息/Info

Title:
 Fall recognition based on surface electromyography phase synchronization analysis
文章编号:
1005-202X（2018）03-0313-10
作者:
 杜育乘1; 张婷婷2; 王晓云2
 １.杭州电子科技大学智能控制与机器人研究所， 浙江 杭州 310018； 2.广东省工伤康复中心， 广东 广州 510000
Author(s):
 DU Yucheng1;  ZHANG Tingting2;  WANG Xiaoyun2
 1. Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou 310018, China; 2. Guangdong Provincial Work Injury Rehabilitation Center, Guangzhou 510000, China
关键词:
 ; 希尔伯特相位同步; 相位同步指数; 肌电信号; 肌肉; 跌倒
Keywords:
 ; Hilbert phase synchronization;  phase synchronization index;  electromyography signals;  muscles;  fall
分类号:
TP29
DOI:
DOI:10.3969/j.issn.1005-202X.2018.03.013
文献标志码:
A
摘要:
 人体有意识的活动是由大脑皮层和运动神经肌肉组织两大体系内部及相互之间信息的同步化震荡实现的，本文通过分析各通道肌电信号相位同步性来区别有意识的日常活动和无意识的跌倒。实验肌电数据从5名健康受试者身上采集，在5名受试者完成4个不同动作（走路、跌倒、坐下、坐下站起）时，采集其胫骨前肌、腓肠肌、股直肌、半腱肌4路肌电信号。运用希尔伯特相位同步分析方法，计算相位同步指数。先用原始肌电信号对不同动作下各肌肉组间的同步性情况进行比较分析；再将肌电信号进行小波包分解，研究不同频段下肌肉间肌电信号同步性情况。实验表明，摔倒动作中胫骨前肌和股直肌以及股直肌和半腱肌肌电信号间的同步性情况与其他有意识动作中的情况有着明显差异。利用这一特征，用普通的fisher线性分类器对日常活动动作与跌倒进行判别，用全频段信号和所选频段信号对跌倒的识别率分别达到85.5%和91.0%，表明肌肉间相位同步性情况可以反映肌肉间的协同工作情况，可用于跌倒辩识。
Abstract:
 The conscious activities of the human body are achieved by the internal and mutual synchronized oscillations of cerebral cortex and muscular tissue. Herein we aim to recognize unconscious fall from conscious daily activities by analyzing the phase synchronization of collected electromyography (EMG) signals. The EMG signals from tibialis anterior muscle, gastrocnemius muscle, rectus femoris and semitendinosus are all collected from 5 healthy participants when they do four different motions (walk, fall, sit down, sit down and stand up). Hilbert phase synchronization analysis is applied to calculate phase synchronization indexes, and the original surface EMG signals are used for comparing and analyzing the phase synchronization between muscles when participants do different motions. Subsequently, we apply wavelet packet decomposition to electromyography for studying the surface EMG signal phase synchronization in different frequency bands. The test results reveal that when participants fall, the phase synchronization between tibialis anterior muscle and rectus femoris and that between rectus femoris and semitendinosus are significantly different from the phase synchronization of other conscious motions, which indicates that we can use fisher linear classifier to recognize fall from other conscious motions by the phase synchronization of muscles. The fall recognition rates reach 85.5% and 91.0% when using full-band signals and the signals from chosen frequency band. In conclusion, the phase synchronization index reflects the situation of cooperative work between muscles, which can be used in fall recognition.

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备注/Memo

备注/Memo:
 【收稿日期】2017-11-18
【基金项目】国家自然科学基金（61671197）；浙江省自然科学基金（LY17F030021）
【作者简介】杜育乘，硕士研究生，研究方向：脑肌电耦合分析，E-mail: 327019804@qq.com
更新日期/Last Update: 2018-03-21