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Edge-AI in LoRa-based Health Monitoring: Fall Detection System with Fog Computing and LSTM Recurrent Neural Networks
Univ Turku, Dept Future Technol, Turku, Finland..
Univ Turku, Dept Future Technol, Turku, Finland..
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electronics and Embedded systems.
Univ Turku, Dept Future Technol, Turku, Finland..
2019 (English)In: 2019 42ND INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP), IEEE , 2019, p. 601-604Conference paper, Published paper (Refereed)
Abstract [en]

Remote healthcare monitoring has exponentially grown over the past decade together with the increasing penetration of Internet of Things (IoT) platforms. IoT-based health systems help to improve the quality of healthcare services through real-time data acquisition and processing. However, traditional IoT architectures have some limitations. For instance, they cannot properly function in areas with poor or unstable Internet. Low power wide area network (LPWAN) technologies, including long-range communication protocols such as LoRa, are a potential candidate to overcome the lacking network infrastructure. Nevertheless, LPWANs have limited transmission bandwidth not suitable for high data rate applications such as fall detection systems or electrocardiography monitoring. Therefore, data processing and compression are required at the edge of the network. We propose a system architecture with integrated artificial intelligence that combines Edge and Fog computing, LPWAN technology, IoT and deep learning algorithms to perform health monitoring tasks. In particular, we demonstrate the feasibility and effectiveness of this architecture via a use case of fall detection using recurrent neural networks. We have implemented a fall detection system from the sensor node and Edge gateway to cloud services and end-user applications. The system uses inertial data as input and achieves an average precision of over 90% and an average recall over 95% in fall detection.

Place, publisher, year, edition, pages
IEEE , 2019. p. 601-604
Keywords [en]
IoT, Edge Computing, Healthcare Monitoring, LoRa, LPWAN, RNN, LSTM, Fall Detection
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-264289DOI: 10.1109/TSP.2019.8768883ISI: 000493442800131Scopus ID: 2-s2.0-85071062873OAI: oai:DiVA.org:kth-264289DiVA, id: diva2:1374852
Conference
2019 42ND INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP)
Note

QC 20191203. QC 20200107

Available from: 2019-12-03 Created: 2019-12-03 Last updated: 2020-01-07Bibliographically approved

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Tenhunen, Hannu

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