Fall-Detection Algorithm Using Plantar Pressure and Acceleration Data

Chang Min Lee, Jisu Park, Shinsuk Park, Choong Hyun Kim

Research output: Contribution to journalArticle

Abstract

In this study, experiments are conducted for four types of falls and eight types of activities of daily living with an integrated sensor system that uses both an inertial measurement unit and a plantar-pressure measurement unit and the fall-detection performance is evaluated by analyzing the acquired data with the threshold method and the decision-tree method. In general, the decision-tree method shows better performance than the threshold method, and the fall-detection accuracy increases when the acceleration and center-of-pressure (COP) data are used together, rather than when each data point is used separately. The results show that the fall-detection algorithm that applies both acceleration and COP data to the decision-tree method has a fall-detection accuracy of 95% or higher and a sufficient lead time of 317 ms on average.

Original languageEnglish
JournalInternational Journal of Precision Engineering and Manufacturing
DOIs
Publication statusAccepted/In press - 2019 Jan 1

Keywords

  • Activities of daily living
  • Center of pressure
  • Decision tree
  • Fall detection
  • Force sensing resistor
  • Inertial measurement unit

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Cite this

Fall-Detection Algorithm Using Plantar Pressure and Acceleration Data. / Lee, Chang Min; Park, Jisu; Park, Shinsuk; Kim, Choong Hyun.

In: International Journal of Precision Engineering and Manufacturing, 01.01.2019.

Research output: Contribution to journalArticle

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