University of Oulu

Ayman Al-Kababji, Abbes Amira, Faycal Bensaali, Abdulah Jarouf, Lisan Shidqi, Hamza Djelouat, An IoT-based framework for remote fall monitoring, Biomedical Signal Processing and Control, Volume 67, 2021, 102532, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2021.102532

An IoT-based framework for remote fall monitoring

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Author: Al-Kababji, Ayman1; Amira, Abbes2; Bensaali, Faycal1;
Organizations: 1Department of Electrical Engineering, Qatar University, Doha, Qatar
2Institute of Artificial Intelligence, De Montfort University, Leicester, United Kingdom
3Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe2021050428641
Language: English
Published: Elsevier, 2021
Publish Date: 2023-03-09
Description:

Abstract

Fall 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.

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Series: Biomedical signal processing and control
ISSN: 1746-8094
ISSN-E: 1746-8108
ISSN-L: 1746-8094
Volume: 67
Article number: 102532
DOI: 10.1016/j.bspc.2021.102532
OADOI: https://oadoi.org/10.1016/j.bspc.2021.102532
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
CWT
Funding: This 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.
Copyright information: © 2021 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
  https://creativecommons.org/licenses/by-nc-nd/4.0/