Context‐aware emergency detection method for edge computing‐based healthcare monitoring system. Issue 6 (11th October 2020)

• Record Type: Journal Article
• Authors: Wang, Lei; Xu, Boyi; Cai, Hongming; Zhang, Pengzhu
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 33:Issue 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-11
• Subjects: Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.4128
• Languages: English
• ISSNs: 2161-5748

Abstract

Abstract: When taking physical exercise, healthcare monitoring systems are needed to detect emergencies, such as sychnosphygmia, and so on. However, heterogeneity and privacy make it more challenging to collect and process multi‐source healthcare data in a real time manner. To support healthcare monitoring, in this research, a multi‐layer edge computing‐based framework is designed to detect emergencies in low latency and secure ways. First, a universal data model is defined to process heterogeneous data from multi‐sources such as sensors or smart devices. Then, Gated Recurrent Units‐based (GRU) model is utilized to capture and process physiological data series. Fine‐grained detection models are built and distinguished according to scenario information and individual health persona. In the proposed edge computing‐based framework, detection algorithm computation and physiological index data storage are close to end user sides to support low latency. Meanwhile encryption algorithm is used to protect data privacy. Finally, a case study in the Physical Education Class scenario is implemented to demonstrate the feasibility of our methods. The experimental result shows that our GRU model is more accurate and is three times faster for emergency identification compared with Support Vector Classification model. Abstract : When taking physical exercise, people in suboptimal health status are more likely to be exposed to sudden dangers such as sychnosphygmia. To support real‐time healthcare monitoring, we propose a multi‐layer edge computing‐based framework to detect emergencies in people's daily routines. A universal data model is defined for data heterogeneity problem, and Gated Recurrent Units‐based (GRU) model is used to analyze physiological data series in the awareness of context information. Meanwhile encryption algorithm is used to protect data privacy.