IR-UWB Sensor Based Fall Detection Method Using CNN Algorithmopen access
- Authors
- Han, Taekjin; Kang, Wonho; Choi, Gyunghyun
- Issue Date
- Oct-2020
- Publisher
- MDPI
- Keywords
- IR-UWB radar sensor; fall detection; fall; ADL classification; deep learning classifier; convolutional neural network
- Citation
- SENSORS, v.20, no.20, pp.1 - 23
- Indexed
- SCIE
SCOPUS
- Journal Title
- SENSORS
- Volume
- 20
- Number
- 20
- Start Page
- 1
- End Page
- 23
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145001
- DOI
- 10.3390/s20205948
- Abstract
- Falls are the leading cause of fatal injuries in the elderly such as fractures, and secondary damage from falls can lead to death. As such, fall detection is a crucial topic. However, due to the trade-off relationship between privacy preservation, user convenience, and fall detection performance, it is generally difficult to develop a fall detection system that simultaneously satisfies all conditions. The main goal of this study is to build a practical fall detection framework that can effectively classify the various behavior types into "Fall" and "Activities of daily living (ADL)" while securing privacy preservation and user convenience. For this purpose, signal data containing the motion information of objects was collected using a non-contact, unobtrusive, and non-restraint impulse-radio ultra wideband (IR-UWB) radar. These data were then applied to a convolutional neural network (CNN) algorithm to create an object behavior type classifier that can classify the behavior types of objects into "Fall" and "ADL." The data were collected by actually performing various activities of daily living, including falling. The performance of the classifier yielded satisfactory results. By combining an IR-UWB and CNN algorithm, this study demonstrates the feasibility of building a practical fall detection system that exceeds a certain level of detection accuracy while also ensuring privacy preservation and user convenience.
- Files in This Item
-
- Appears in
Collections - 서울 기술경영전문대학원 > 서울 기술경영학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145001)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.