Comparison of Filtering Methods in Measuring Human ZMP Using Kinect Sensor

Toshiyuki Nagasawa, Yuta Tawaki, Toshiyuki Murakami

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper aims to verify the detection accuracy of Zero Moment Point (ZMP) measurements of the human body using the Kinect sensor. In this paper, ZMP is measured based on a multi-link model of the human body. ZMP calculation algorithms using a Low Pass Filter (LPF) and Savitzky-Golay (SG) filter were constructed to investigate filtering methods that can measure ZMP with high accuracy and small lag. The accuracy and responsiveness of these filters were evaluated through experiments measuring ZMP during human walking. Experimental results show that the detection error in ZMP is dominated by the differential noise in the acceleration calculation, and applying the filter only to measured acceleration values increases responsiveness with the same accuracy as when applied to ZMP. We also found that the SG filter is superior to LPF in noise suppression and responsiveness of ZMP measurements, though it occurs overshoot in measurements.

Original languageEnglish
Title of host publicationIECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
ISBN (Electronic)9781665480253
DOIs
Publication statusPublished - 2022
Event48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 - Brussels, Belgium
Duration: 2022 Oct 172022 Oct 20

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
Volume2022-October

Conference

Conference48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022
Country/TerritoryBelgium
CityBrussels
Period22/10/1722/10/20

Keywords

  • Digital Filtering
  • Human motion analysis
  • Human multi-link model
  • Kinect Sensor
  • Zero Moment Point

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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