Human activity recognition from environmental background sounds for Wireless Sensor Networks

  • Zhan Y
  • Miura S
  • Nishimura J
 et al. 
  • 16

    Readers

    Mendeley users who have this article in their library.
  • 8

    Citations

    Citations of this article.

Abstract

Sound feature extraction Mel frequency cepstral coefficients (MFCC) and classification dynamic time warping (DTW) algorithms are applied to recognizing the background sounds in the human daily activities. Applying these algorithms to fourteen typical daily activity sounds, average recognition accuracy of 92.5% can be achieved. In these algorithms, how two parameters (i.e., Mel filters number and frame-to-frame overlap) affect system's calculation burden and accuracy is also investigated. By adjusting these two parameters to a suitable combination, the calculation burden can be reduced by 61.6% while maintaining the system's average accuracy rate at approximate 90%. This is promising for future integrating with other sensor(s) to fulfill daily activity recognition work by using power aware wireless sensor networks (WSN) system.

Author-supplied keywords

  • Calculation burden
  • DTW
  • MFCC
  • Sound recognition
  • WSN

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Yi Zhan

  • Shun Miura

  • Jun Nishimura

  • Tadahiro Kuroda

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free