A Real-Time Patient Monitoring Framework for Fall Detection
Joint Authors
Ajerla, Dharmitha
Mahfuz, Sazia
Zulkernine, Farhana
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-09-22
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Information Technology and Computer Science
Abstract EN
Fall detection is a major problem in the healthcare department.
Elderly people are more prone to fall than others.
There are more than 50% of injury-related hospitalizations in people aged over 65.
Commercial fall detection devices are expensive and charge a monthly fee for their services.
A more affordable and adaptable system is necessary for retirement homes and clinics to build a smart city powered by IoT and artificial intelligence.
An effective fall detection system would detect a fall and send an alarm to the appropriate authorities.
We propose a framework that uses edge computing where instead of sending data to the cloud, wearable devices send data to a nearby edge device like a laptop or mobile device for real-time analysis.
We use cheap wearable sensor devices from MbientLab, an open source streaming engine called Apache Flink for streaming data analytics, and a long short-term memory (LSTM) network model for fall classification.
The model is trained using a published dataset called “MobiAct.” Using the trained model, we analyse optimal sampling rates, sensor placement, and multistream data correction.
Our edge computing framework can perform real-time streaming data analytics to detect falls with an accuracy of 95.8%.
American Psychological Association (APA)
Ajerla, Dharmitha& Mahfuz, Sazia& Zulkernine, Farhana. 2019. A Real-Time Patient Monitoring Framework for Fall Detection. Wireless Communications and Mobile Computing،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1212328
Modern Language Association (MLA)
Ajerla, Dharmitha…[et al.]. A Real-Time Patient Monitoring Framework for Fall Detection. Wireless Communications and Mobile Computing No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1212328
American Medical Association (AMA)
Ajerla, Dharmitha& Mahfuz, Sazia& Zulkernine, Farhana. A Real-Time Patient Monitoring Framework for Fall Detection. Wireless Communications and Mobile Computing. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1212328
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1212328