Academic Journal of Computing & Information Science, 2020, 3(1); doi: 10.25236/AJCIS.2020.030113.
Tao Yuan1, a, Huo Shihang2, b
1 School of Harbin University of Science and Technology, Harbin 150080, China
2 School of Harbin University of Science and Technology, Harbin 150080, China
a [email protected], b [email protected]
Based on depth learning, a wearable elastic band embedded with grating strips is tied to the chest or abdominal cavity of a patient. Due to volume changes generated during breathing, the position changes of grating strips in the elastic band are caused. Accurate detection of the position changes of grating strips through grating sensors can realize monitoring of respiratory process. Firstly, the application program is developed based on LabVIEW virtual software platform, and the remote monitoring and control functions are realized by using the network mode based on Browser/Server (B/S). Then, the impedance signals and blood oxygen saturation of human body are collected through simulation experiments for system test verification. The system realizes the wireless transmission and monitoring of respiratory signals, which enables patients to get rid of the bondage of electrode connection and does not affect sleep quality. With the characteristics of small volume and low power consumption, it provides a new idea for community medical remote monitoring.
in-depth study, Rip, Rbm, blood oxygen saturation, Sleep apnea syndrome, B/s
Tao Yuan, Huo Shihang. Intelligent Remote Respiratory Monitoring System Based on Deep Learning. Academic Journal of Computing & Information Science (2020), Vol. 3, Issue 1: 122-130. https://doi.org/10.25236/AJCIS.2020.030113.
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