AI-Based Fall Detection Using Contactless Sensing

Taha, A. , Taha, M. M.A., Barakat, B., Taylor, W. , Abbasi, Q. H. and Imran, M. A. (2021) AI-Based Fall Detection Using Contactless Sensing. In: IEEE Sensors 2021, Sydney, Australia, 31 Oct - 04 Nov 2021, ISBN 9781728195018 (doi: 10.1109/SENSORS47087.2021.9639715)

Abstract

Falls are a major health concern for the elderly as it threatens their livelihood and independence. Nearly 50% of the older adults, aged over 65 years old, fall in a span of 5 years, with 62% sustaining injuries and 28% extensive protracting injuries. This paper presents a high accuracy contactless falls detection framework based on channel state information extracted from software-defined radios. The aim is to develop a system capable of detecting whether an individual subject is present within the sensing area, or if the subject is falling, and, finally, if the subject is performing one of three other activities, including sitting, standing, and walking. The results showed a promising detection accuracy of 95.6% and 98%, using the 10-fold cross-validation and train-test split methods, based on the Random Forest classifier, respectively. Furthermore, we present a real-time analysis of the system to highlight its capability to detect, analyze, and report falls in real-time.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Taha, Dr Ahmad and Abbasi, Dr Qammer and Imran, Professor Muhammad and Taylor, William
• Authors: Taha, A., Taha, M. M.A., Barakat, B., Taylor, W., Abbasi, Q. H., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISSN: 2168-9229
• ISBN: 9781728195018
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in 2021 IEEE Sensors
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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• Organisation

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
307829		Quantum-Inspired Imaging for Remote Monitoring of Health & Disease in Community Healthcare	Jonathan Cooper	Engineering and Physical Sciences Research Council (EPSRC)	EP/T021020/1	ENG - Biomedical Engineering