Search

Article

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Extracting human gait series based on the wavelet transform

Fu Mao-Jing Zhuang Jian-Jun Ning Xin-Bao Zhan Qing-Bo Shao Yi Hou Feng-Zhen

Extracting human gait series based on the wavelet transform

Fu Mao-Jing, Zhuang Jian-Jun, Ning Xin-Bao, Zhan Qing-Bo, Shao Yi, Hou Feng-Zhen
PDF
Get Citation

(PLEASE TRANSLATE TO ENGLISH

BY GOOGLE TRANSLATE IF NEEDED.)

Metrics
  • Abstract views:  4128
  • PDF Downloads:  1751
  • Cited By: 0
Publishing process
  • Received Date:  22 July 2009
  • Accepted Date:  23 November 2009
  • Published Online:  05 March 2010

Extracting human gait series based on the wavelet transform

  • 1. (1)南京大学电子科学与工程系,生物医学电子工程研究所,近代声学教育部重点实验室,南京 210093; (2)南京大学电子科学与工程系,生物医学电子工程研究所,近代声学教育部重点实验室,南京 210093;中国药科大学基础科学部,南京 210009

Abstract: The wavelet transform was applied to process the accelerometer signals derived from human walking. The accelerometer signals were first decomposed at different levels utilizing the multi-scale and multi-resolution characteristics of the discrete wavelet transform. After the determination of both the mother wavelet and the optimal decomposition level, human gait series can thus be extracted from the eigen scale of the accelerometer signal. Compared with the method that detects peak values directly from accelerometer signals by thresholding, the wavelet transform gives higher detection rate of peak values on the eigen scale of the accelerometer signals. Even when the accelerometer signals are exposed to serious noise, experimental results still demonstrate that the wavelet approach can guarantee the precision of the extracted gait series, which is of vital importance for the subsequent analyses. It can be concluded that wavelet transform is an effective tool for the extraction of gait rhythmicity. The wavelet transform will be helpful in identifying the characteristics of other physiological signals.

Reference (18)

Catalog

    /

    返回文章
    返回