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 language | English |
|---|---|
| Pages (from-to) | 725-737 |
| Number of pages | 13 |
| Journal | International Journal of Precision Engineering and Manufacturing |
| Volume | 21 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2020 Apr 1 |
Bibliographical note
Funding Information:This research was partly supported by the Technology Innovation Development Program for the Small and Medium-Sized Enterprise through the Ministry of SMEs and Startups (Grant Number: S2412356) and the Korea Institute of Science and Technology (KIST) Institutional Program (Project no. 2E27980). The authors are gratefully appreciate of the supports. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Publisher Copyright:
© 2019, Korean Society for Precision Engineering.
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