عرض بسيط للتسجيلة

المؤلفAl-Kababji, Ayman
المؤلفAmira, Abbes
المؤلفBensaali, Faycal
المؤلفJarouf, Abdulah
المؤلفShidqi, Lisan
المؤلفDjelouat, Hamza
تاريخ الإتاحة2022-12-29T07:34:43Z
تاريخ النشر2021
اسم المنشورBiomedical Signal Processing and Control
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1016/j.bspc.2021.102532
معرّف المصادر الموحدhttp://hdl.handle.net/10576/37818
الملخصFall 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
راعي المشروعThis 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.
اللغةen
الناشرElsevier
الموضوع3-axis accelerometer
CWT
Feature extraction algorithm selection
Mobile application
Wearable sensing device
العنوانAn IoT-based framework for remote fall monitoring
النوعArticle
رقم المجلد67
dc.accessType Abstract Only


الملفات في هذه التسجيلة

الملفاتالحجمالصيغةالعرض

لا توجد ملفات لها صلة بهذه التسجيلة.

هذه التسجيلة تظهر في المجموعات التالية

عرض بسيط للتسجيلة