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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

AI-Based Fall Detection Using Contactless Sensing

Taha, Ahmad, Taha, Mohammad M.A. and Barakat, Basel (2021) AI-Based Fall Detection Using Contactless Sensing. 2021 IEEE Sensors. ISSN 2168-9229

Item Type: Article


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.

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Depositing User: Basel Barakat


Item ID: 14289
Identification Number:
ISSN: 2168-9229
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Users with ORCIDS

ORCID for Basel Barakat: ORCID iD

Catalogue record

Date Deposited: 17 Jan 2022 09:55
Last Modified: 25 Jan 2022 08:45


Author: Basel Barakat ORCID iD
Author: Ahmad Taha
Author: Mohammad M.A. Taha

University Divisions

Faculty of Technology > School of Computer Science



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