Deep learning technology has been widely used in edges . However,  the limited storage and computing resources of mobile devices cannot meet the real-time requirements of deep neural network computing. In this paper , we propose a safer respiratory rate monitoring system using a three-tier architecture : robot layers, edge layers, and cloud layers. We decompose the task into a three-tier architecture of a lightweight network according to the computing resources of different devices, including mobile robots, edges, and clouds. We deploy feature extraction tasks, Spatio-temporal feature tracking tasks, and signal extraction and preprocessing tasks to robot layers, edge layers, and cloud layers , respectively. We have deployed this non-contact respiratory monitoring system in the Second Affiliated Hospital of the Anhui Medical University of China . Experimental results  show that the proposed approach in this paper significantly outperforms other approaches. It is supported by the computation time cost of robot+edge+cloud architecture, which are 2.26 ms per frame, 27.48 ms per frame, 0.78 seconds for  processing one-minute length respiratory signals, respectively. Furthermore, the computation time costs of using the proposed system to calculate the respiratory rate are less than that of edge+cloud architecture and cloud architecture  .
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 .
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safer respiratory rate monitoring system using a
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: robot layers, edge layers, and cloud layers. We decompose the task into a three-tier architecture of a lightweight network according to the computing resources of different devices, including mobile robots, edges, and clouds. We deploy feature extraction tasks, Spatio-temporal feature tracking tasks, and signal extraction and preprocessing tasks to
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in the Second Affiliated Hospital of the Anhui Medical University of China
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convolution operation, similarity calculation,
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. 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