Taha, Ahmad; Taha, Mohammad M.A.; Barakat, Basel; Taylor, William; Abbasi, Qammer H. and Ali Imran, Muhammad. 2021. 'AI-Based Fall Detection Using Contactless Sensing'. In: 2021 IEEE Sensors. Sydney, Australia 31 October - 3 November 2021. [Conference or Workshop Item]

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Abstract or Description

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 or Workshop Item (Paper)
Identification Number (DOI):	https://doi.org/10.1109/SENSORS47087.2021.9639715
Additional Information:	“© 2021 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.” Funding: This work is supported in parts by EPSRC grants no. EP T0210201 and EP T0210631.
Keywords:	Falls detection, Channel State information, Machine learning, Random Forest
Departments, Centres and Research Units:	Computing
Dates:	Date Event November 2021 Accepted 17 December 2021 Published
Event Location:	Sydney, Australia
Date range:	31 October - 3 November 2021
Item ID:	38183
Date Deposited:	31 Jan 2025 12:12
Last Modified:	31 Jan 2025 16:07
URI:	https://research.gold.ac.uk/id/eprint/38183

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