1 research outputs found
The Role of Edge Robotics As-a-Service in Monitoring COVID-19 Infection
Deep learning technology has been widely used in edge computing. However,
pandemics like covid-19 require deep learning capabilities at mobile devices
(detect respiratory rate using mobile robotics or conduct CT scan using a
mobile scanner), which are severely constrained by the limited storage and
computation resources at the device level. To solve this problem, we propose a
three-tier architecture, including robot layers, edge layers, and cloud layers.
We adopt this architecture to design a non-contact respiratory monitoring
system to break down respiratory rate calculation tasks. Experimental results
of respiratory rate monitoring show that the proposed approach in this paper
significantly outperforms other approaches. It is supported by computation time
costs with 2.26 ms per frame, 27.48 ms per frame, 0.78 seconds for convolution
operation, similarity calculation, processing one-minute length respiratory
signals, respectively. And the computation time costs of our three-tier
architecture are less than that of edge+cloud architecture and cloud
architecture. Moreover, we use our three-tire architecture for CT image
diagnosis task decomposition. The evaluation of a CT image dataset of COVID-19
proves that our three-tire architecture is useful for resolving tasks on deep
learning networks by edge equipment. There are broad application scenarios in
smart hospitals in the future