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Classification of acceleration waveforms during walking by wavelet transform

T Tamura1, M Sekine, M Ogawa

  • 1Institute for Medical and Dental Engineering, Tokyo Medical and Dental University, Japan. tamura@inst.i-mde.tmd.ac.jp

Methods of Information in Medicine
|February 21, 1998
PubMed
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Wavelet analysis successfully classified walking patterns. Downstairs walking was distinctly identified using the Daubecies 3 wavelet transform on acceleration signals.

Area of Science:

  • Biomechanics
  • Signal Processing
  • Human Movement Analysis

Background:

  • Classifying human locomotion is crucial for applications like gait analysis and assistive device control.
  • Distinguishing between level walking, ascending, and descending stairs using sensor data presents a challenge.

Purpose of the Study:

  • To classify acceleration signals during level, upstairs, and downstairs walking using wavelet analysis.
  • To determine if wavelet transform can differentiate between various walking conditions based on body-center acceleration.

Main Methods:

  • Subjects' acceleration signals near the center of gravity were recorded during level, upstairs, and downstairs walking.
  • Daubecies 3 wavelet transform was applied to analyze sequential acceleration data.
  • Sum of squared values at specific wavelet levels (-4 and -5) were compared for discrimination.

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Main Results:

  • Downstairs walking was successfully discriminated from other gaits, showing the largest value at wavelet level -5.
  • Level walking could be distinguished from upstairs walking at wavelet level -4.
  • Wavelet transform effectively discriminated continuous dynamic responses of different walking types.

Conclusions:

  • Wavelet analysis, specifically the Daubecies 3 transform, is a viable method for classifying walking patterns.
  • Acceleration signal analysis using wavelets can differentiate between level, upstairs, and downstairs locomotion.
  • This technique offers potential for real-time gait monitoring and analysis.