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  • ISSN 1006-3080
  • CN 31-1691/TQ

基于肌音信号的头部动作模式识别

顾晓琳 吴清 夏春明 章悦 钟豪

顾晓琳, 吴清, 夏春明, 章悦, 钟豪. 基于肌音信号的头部动作模式识别[J]. 华东理工大学学报(自然科学版), 2017, (5): 704-711. doi: 10.14135/j.cnki.1006-3080.2017.05.016
引用本文: 顾晓琳, 吴清, 夏春明, 章悦, 钟豪. 基于肌音信号的头部动作模式识别[J]. 华东理工大学学报(自然科学版), 2017, (5): 704-711. doi: 10.14135/j.cnki.1006-3080.2017.05.016
GU Xiao-lin, WU Qing, XIA Chun-ming, ZHANG Yue, ZHONG Hao. Pattern Recognition of Head Movement Based on Mechanomyographic Signal[J]. Journal of East China University of Science and Technology, 2017, (5): 704-711. doi: 10.14135/j.cnki.1006-3080.2017.05.016
Citation: GU Xiao-lin, WU Qing, XIA Chun-ming, ZHANG Yue, ZHONG Hao. Pattern Recognition of Head Movement Based on Mechanomyographic Signal[J]. Journal of East China University of Science and Technology, 2017, (5): 704-711. doi: 10.14135/j.cnki.1006-3080.2017.05.016

基于肌音信号的头部动作模式识别

doi: 10.14135/j.cnki.1006-3080.2017.05.016

Pattern Recognition of Head Movement Based on Mechanomyographic Signal

  • 摘要: 肌音信号(MMG)是一种肌肉收缩时发出的低频信号,通过测量分析颈部前后两侧的胸锁乳突肌和头夹肌的肌音信号,成功识别点头、抬头、左摆、右摆、左转、右转6个头部动作模式。实验中采集了4个通道的数据,经滤波、归一化的预处理后,用不等长分割法分割出动作帧。提取了动作帧的小波包系数能量及双谱对角切片特征,经主元分析法(PCA)和Fisher线性判别分析(FLDA)降维,用支持向量机(SVM)分类。最后对小波包系数能量和双谱对角切片特征进行FLDA降维,识别率达95.92%。

     

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出版历程
  • 收稿日期:  2017-01-10
  • 刊出日期:  2017-10-28

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