Comparative analysis of real-time fall detection using fuzzy logic web services and machine learning

Pandya, B, Pourabdollah, A ORCID: https://orcid.org/0000-0001-7737-1393 and Lotfi, A ORCID: https://orcid.org/0000-0002-5139-6565, 2020. Comparative analysis of real-time fall detection using fuzzy logic web services and machine learning. Technologies, 8 (4): 74. ISSN 2227-7080

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Abstract

Falls are the main cause of susceptibility to severe injuries in many humans, especially for older adults aged 65 and over. Typically, falls are being unnoticed and interpreted as a mere inevitable accident. Various wearable fall warning devices have been created recently for older people. However, most of these devices are dependent on local data processing. Various algorithms are used in wearable sensors to track a real-time fall effectively, which focuses on fall detection via fuzzy-as-a-service based on IEEE 1855–2016, Java Fuzzy Markup Language (FML) and service-oriented architecture. Moreover, several approaches are used to detect a fall using machine learning techniques via human movement positional data to avert any accidents. For fuzzy logic web services, analysis is performed using wearable accelerometer and gyroscope sensors, whereas in machine learning techniques, k-NN, decision tree, random forest and extreme gradient boost are used to differentiate between a fall and non-fall. This study aims to carry out a comparative analysis of real-time fall detection using fuzzy logic web services and machine learning techniques and aims to determine which one is better for real-time fall detection. Research findings exhibit that the proposed fuzzy-as-a-service could easily differentiate between fall and non-fall occurrences in a real-time environment with an accuracy, sensitivity and specificity of 90%, 88.89% and 91.67%, respectively, while the random forest algorithm of machine learning achieved 99.19%, 98.53% and 99.63%, respectively.

Item Type:	Journal article
Description:	This article belongs to the Collection Selected Papers from the PETRA Conference Series.
Publication Title:	Technologies
Creators:	Pandya, B., Pourabdollah, A. and Lotfi, A.
Publisher:	MDPI AG
Date:	December 2020
Volume:	8
Number:	4
ISSN:	2227-7080
Identifiers:	Number Type 10.3390/technologies8040074 DOI 1392934 Other
Rights:	© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Jill Tomkinson
Date Added:	07 Dec 2020 15:38
Last Modified:	31 May 2021 15:11
URI:	https://irep.ntu.ac.uk/id/eprint/41802

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