Cloud-based non-invasive cognitive breath monitoring system for patients in health-care system

International Journal of Data Science and Analytics - Trang 1-12 - 2023
Mukesh Soni1, Mohammad Shabaz2, Renato R. Maaliw3, Ismail Keshta4, Rasool Altaee5, Sanju Das6
1Department of CSE, University Centre for Research & Development Chandigarh University, Mohali, India
2Model Institute of Engineering and Technology, Jammu, India
3College of Engineering, Southern Luzon State University, Lucban, Philippines
4Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
5Medical Laboratories Techniques Department, Al-Mustaqbal University College, Hillah, Iraq
6Department of Computer Science, Assam University, Silchar, India

Tóm tắt

The health-care industry is seeing a dynamic ease of use of low-cost issue solutions that improve patient care, cut down on problems, increase efficacy, and provide health-care decision-makers access to intelligence information at the point of treatment. During sleep, breathing and apnea are recorded using a non-invasive sleeping breath monitoring system (NSBS), which is suggested in this study. The system gathers information about the movement of the chest and abdomen while sleeping around the pressure-sensitive sensor belt, determines the type of breathing being done, records the presence of apnea, and transmits the information wirelessly via Bluetooth to a mobile device. Real-time waveforms may be drawn and displayed by the mobile app, and the data can subsequently be uploaded to a cloud platform by the mobile device. To enable user sleep nursing monitoring, the PC terminal downloads data from the cloud to create the waveform and show the respiratory status record when the user is sleeping. Getting the pressure signal produced by the body's movement while you sleep is the fundamental tenet of the non-invasive sleep monitoring system. The FSR408 piezoresistive sensor from Interlink is utilized in this design. The sensor is 600-mm long, 16-mm wide, and 1-mm thick. The sensor's maximum range is 10 kg, its precision in force resolution is greater than 0.5%, and its reaction time is 1–2 ms, which satisfies the requirements for the acquisition of various metrics when a person is asleep. When computing the typical breathing signal, the algorithm's correlation degree similarity is greater than 90%. When an apnea frame is discovered, the resemblance to the usual frame is noticeably decreased, and the lesser the similarity, the longer the apnea period.

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International Journal of Data Science and Analytics

1-12

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