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AuthorAl-Kababji, Ayman
AuthorAmira, Abbes
AuthorBensaali, Faycal
AuthorJarouf, Abdulah
AuthorShidqi, Lisan
AuthorDjelouat, Hamza
Available date2022-12-29T07:34:43Z
Publication Date2021
Publication NameBiomedical Signal Processing and Control
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.bspc.2021.102532
URIhttp://hdl.handle.net/10576/37818
AbstractFall detection is a serious healthcare issue that needs to be solved. Falling without quick medical intervention would lower elderly's chances of survival, especially if living alone. Hence, the need is there for developing fall detection algorithms with high accuracy. This paper presents a novel IoT-based system for fall detection that includes a sensing device transmitting data to a mobile application through a cloud-connected gateway device. Then, the focus is shifted to the algorithmic aspect where multiple features are extracted from 3-axis accelerometer data taken from existing datasets. The results emphasize on the significance of Continuous Wavelet Transform (CWT) as an influential feature for determining falls. CWT, Signal Energy (SE), Signal Magnitude Area (SMA), and Signal Vector Magnitude (SVM) features have shown promising classification results using K-Nearest Neighbors (KNN) and E-Nearest Neighbors (ENN). For all performance metrics (accuracy, recall, precision, specificity, and F1 score), the achieved results are higher than 95% for a dataset of small size, while more than 98.47% score is achieved in the aforementioned criteria over the UniMiB-SHAR dataset by the same algorithms, where the classification time for a single test record is extremely efficient and is real-time. 2021 Elsevier Ltd
SponsorThis paper was made possible by the National Priorities Research Program (NPRP) Grant No. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). In addition, the work of Al-Kababji is supported by the Qatar National Research Fund Graduate Sponsorship Research Award (GSRA6-2-0521-19034). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherElsevier
Subject3-axis accelerometer
CWT
Feature extraction algorithm selection
Mobile application
Wearable sensing device
TitleAn IoT-based framework for remote fall monitoring
TypeArticle
Volume Number67
dc.accessType Abstract Only


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