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Subject-independent slow fall detection with wearable sensors via deep learning

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Title: Subject-independent slow fall detection with wearable sensors via deep learning
Authors: Chen, X
Jiang, S
Lo, B
Item Type: Conference Paper
Abstract: One of the major healthcare challenges is elderly fallers. A fall can lead to disabilities and even mortality. With the current Covid-19 pandemic, insufficient resources could be provided for the care of elderlies, and care workers often may not be able to visit them. Therefore, a fall may get undetected or delayed leading to serious harm or consequences. Automatic fall detection systems could provide the necessary detection and warnings for timely intervention. Although many sensor-based fall detection systems have been proposed, most systems focus on the sudden fall and have not considered the slow fall scenario, a typical fall instance for elderly fallers. In this paper, a robust activity (RA) and slow fall detection system is proposed. The system consists of a waist-worn wearable sensor embedded with an inertial measurement unit (IMU) and a barometer, and a reference ambient barometer. A deep neural network (DNN) is developed for fusing the sensor data and classifying fall events. The results have shown that the IMU-barometer design yield better detection of fall events and the DNN approach (90.33% accuracy) outperforms traditional machine learning algorithms.
Issue Date: 9-Dec-2020
Date of Acceptance: 25-Aug-2020
URI: http://hdl.handle.net/10044/1/88023
DOI: 10.1109/sensors47125.2020.9278625
Publisher: IEEE
Start Page: 1
End Page: 4
Journal / Book Title: 2020 IEEE SENSORS
Copyright Statement: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Engineering & Physical Science Research Council (E
British Council (UK)
British Council (UK)
Funder's Grant Number: EP/K503733/1
330760239
2017-RLWK9-11046
Conference Name: 2020 IEEE SENSORS
Publication Status: Published
Start Date: 2020-10-25
Finish Date: 2020-10-28
Conference Place: Rotterdam, Netherlands
Online Publication Date: 2020-12-09
Appears in Collections:Department of Surgery and Cancer
Institute of Global Health Innovation