CN103941859A - 一种利用信号功率区分不同手势的算法 - Google Patents
一种利用信号功率区分不同手势的算法 Download PDFInfo
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Abstract
本发明涉及一种通过计算手臂肌肉电流信号平均功率的方法来进行手势区分的算法。算法说明如下:设有L个通道的肌肉电流信号分别为,其中是通道数目,是时间上的采样点,设有效信号起始点为,信号持续时间为N,则首先计算每路信号的平均功率,然后根据计算出的每路信号的平均功率与预先设定好的阈值进行比较,即可进行手势的判断。以,识别握拳(G1),伸掌(G2),握拳前勾(G3)和握拳后摆(G4)四种手势为例,首先检测信号的起始点和终止点,得出有效信号的长度,然后计算每路信号的平均功率,分别为
Description
技术领域
本发明采用计算手臂肌肉电流信号(EMG)平均功率的方法来进行手势识别,它相较于其它手势识别算法,优点在于:
计算复杂度低,只需计算每路手臂肌肉电流信号(EMG)的平均功率,与相应的阈值进行比较即可进行手势识别;计算延迟小,基本能够实时给出信号对应的手势。
背景技术
信号分析中,频谱和功率分析是一种常用方法。我们采用功率分析方法对手臂肌肉电流信号(EMG)做特征参数分析,以此来区别各个手势。
发明内容
设有L个通道的手臂肌肉电流信号(EMG)分别为 ,其中是通道数目,是时间上的采样点,设有效信号起始点为,信号持续时间为。则首先计算每路信号的平均功率为
(公式1)
根据计算出的每路信号的平均功率与预先设定好的阈值进行比较,并做一定的相关性限制,即可进行手势的判断。
以,识别握拳(G1),伸掌(G2),握拳前勾(G3)和握拳后摆(G4)四种手势为例。首先检测信号的起始点和终止点,计算出有效信号的长度,根据公式1计算出每路信号的功率,分别为,根据每种手势对每路信号设置的相应的最大和最小阈值,分别为
根据设置的阈值,即可进行手势判断,算法伪代码描述如下:
G1_hit=G2_hit=G3_hit=G4_hit=0;
If P1>=G1_CH1_MIN_TH && P1<=G1_CH1_MAX_TH
G1_hit++;
End
If P2>=G1_CH2_MIN_TH && P2<=G1_CH2_MAX_TH
G1_hit++;
End
If P3>=G1_CH3_MIN_TH && P1<=G1_CH3_MAX_TH
G1_hit++;
End
If P4>=G1_CH4_MIN_TH && P1<=G1_CH4_MAX_TH
G1_hit++;
End
If P1>=G2_CH1_MIN_TH && P1<=G2_CH1_MAX_TH
G2_hit++;
End
If P2>=G2_CH2_MIN_TH && P2<=G2_CH2_MAX_TH
G2_hit++;
End
If P3>=G2_CH3_MIN_TH && P1<=G2_CH3_MAX_TH
G2_hit++;
End
If P4>=G2_CH4_MIN_TH && P1<=G2_CH4_MAX_TH
G2_hit++;
End
If P1>=G3_CH1_MIN_TH && P1<=G3_CH1_MAX_TH
G3_hit++;
End
If P2>=G3_CH2_MIN_TH && P2<=G3_CH2_MAX_TH
G3_hit++;
End
If P3>=G3_CH3_MIN_TH && P1<=G3_CH3_MAX_TH
G3_hit++;
End
If P4>=G3_CH4_MIN_TH && P1<=G3_CH4_MAX_TH
G3_hit++;
End
If P1>=G4_CH1_MIN_TH && P1<=G4_CH1_MAX_TH
G4_hit++;
End
If P2>=G4_CH2_MIN_TH && P2<=G4_CH2_MAX_TH
G4_hit++;
End
If P3>=G4_CH3_MIN_TH && P1<=G4_CH3_MAX_TH
G4_hit++;
End
If P4>=G4_CH4_MIN_TH && P1<=G4_CH4_MAX_TH
G4_hit++;
End
If G4_hit==4
Return gesture is G4;
Else if G3_hit==4
Return gesture is G3;
Else if G2_hit>G1_hit && G2_hit>=3
Return gesture is G2;
Else if G1_hit>=3
Return gesture is G1;
Else
Return no gesture is detected;
End。
Claims (3)
1.一种利用信号功率进行不同手势区分的算法,其特征为,计算每路有效肌肉电流信号(EMG)的平均功率,根据预先设定的阈值进行手势判断。
2.如公式 给出的计算每路信号的平均功率的算法。
3.当每路信号的阈值设定好后,如说明书伪代码给出的判断手势的算法。
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Cited By (1)
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CN106980367A (zh) * | 2017-02-27 | 2017-07-25 | 浙江工业大学 | 一种基于肌电地形图的手势识别方法 |
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CN101987048A (zh) * | 2009-08-03 | 2011-03-23 | 深圳先进技术研究院 | 假肢控制方法和系统 |
WO2011055326A1 (en) * | 2009-11-04 | 2011-05-12 | Igal Firsov | Universal input/output human user interface |
CN102349037A (zh) * | 2009-03-13 | 2012-02-08 | 微软公司 | 用于人机接口的基于肌电图的可佩戴控制器 |
CN102729254A (zh) * | 2012-07-04 | 2012-10-17 | 杭州电子科技大学 | 基于触觉临场感的遥操作机器人肌电控制方法 |
CN103558918A (zh) * | 2013-11-15 | 2014-02-05 | 上海威璞电子科技有限公司 | 智能手表基于手臂肌肉电流信号做手势识别的方案 |
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- 2014-03-21 CN CN201410106117.8A patent/CN103941859A/zh active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
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CN102349037A (zh) * | 2009-03-13 | 2012-02-08 | 微软公司 | 用于人机接口的基于肌电图的可佩戴控制器 |
CN101987048A (zh) * | 2009-08-03 | 2011-03-23 | 深圳先进技术研究院 | 假肢控制方法和系统 |
WO2011055326A1 (en) * | 2009-11-04 | 2011-05-12 | Igal Firsov | Universal input/output human user interface |
CN102729254A (zh) * | 2012-07-04 | 2012-10-17 | 杭州电子科技大学 | 基于触觉临场感的遥操作机器人肌电控制方法 |
CN103558918A (zh) * | 2013-11-15 | 2014-02-05 | 上海威璞电子科技有限公司 | 智能手表基于手臂肌肉电流信号做手势识别的方案 |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106980367A (zh) * | 2017-02-27 | 2017-07-25 | 浙江工业大学 | 一种基于肌电地形图的手势识别方法 |
CN106980367B (zh) * | 2017-02-27 | 2020-08-18 | 浙江工业大学 | 一种基于肌电地形图的手势识别方法 |
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