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Extraction of Motion Parameters using Acceleration Sensors

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2019, 24(10), pp.33-39
  • DOI : 10.9708/jksci.2019.24.10.033
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 16, 2019
  • Accepted : October 23, 2019
  • Published : October 31, 2019

Yonghee Lee 1 Kang-Woo Lee 1

1한라대학교

Accredited

ABSTRACT

In this paper, we propose a parametric model for analyzing the motion information obtained from the acceleration sensors to measure the activity of the human body. The motion of the upper body and the lower body does not occur at the same time, and the motion analysis method using a single motion sensor involves a lot of errors. In this study, the 3-axis accelerometer is attached to the arms and legs, the body's activity data are measured, the momentum of the arms and legs are calculated for each channel, and the linear predictive coefficient is obtained for each channel. The periodicity of the upper body and the lower body is determined by analyzing the correlation between the channels. The linear predictive coefficient and the periodic value are used as data to measure the type of exercise and the amount of exercise. In the proposed method, we measured four types of movements such as walking, stair climbing, slow hill climbing, and fast hill descending. In order to verify the usefulness of the parameters, the recognition results are presented using the linear predictive coefficient and the periodic value for each motion as the neural network input.

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