Abstract

This research is about new advances in the application of remote bio-signal monitoring technology. An unobtrusive IoT bio-signal measurement system is attached to a bed using a very thin strip sensor, then the user’s sleep efficiency and respiration rate can be measured with accuracy similar to that of an existing FDA-approved sleep tracker. In particular, in this study, we propose a ubiquitous central monitoring system that links an existing, personal use, unobtrusive measurement system to cloud-based systems via WiFi transmission. The proposed monitoring system simultaneously collects, stores, and displays the data from multiple devices using a web server as well as PC and mobile platforms such as personal smart devices. In this study, we implemented a system for the real-time transmission and display of data from multiple unobtrusive systems and validated that there were no problems associated with sending and receiving data at distances of 300 km with around a one-second delay. In addition, we evaluated the tele-monitoring system’s data processing time, CPU usage, and memory usage as the number of users was increased. Each user transmits an average of 810 bytes of data including information such as user id, time stamp, data for each channel, respiration rate and sleep status. We observed that the average data processing time was 0.15 seconds, average CPU usage was 5.01%, average memory usage was 0.1% assuming 10 users connected simultaneously. These results are expected to be useful in guiding future similar personal, public, and clinical applications of this technology.

Highlights

  • According to the United Nations, globally, there were 962 million people aged 60 years or over in 2017, this represents an increase of 152 percent over the 383 million older people there were in 1980

  • SLEEP EFFICIENCY AND RESPIRATION RATE MEASUREMENT Our previous research confirmed that the developed unobtrusive monitoring system has tenable performance in measuring sleep efficiency and respiratory rate that is comparable with that of a polysomnography (PSG) or wearable sleep trackers

  • It was confirmed that the FSR system had an error of only 0.3 bpm and the Polyvinylidene fluoride (PVDF) system had an error of 1.7 bpm for respiratory rate measurements

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Summary

Introduction

According to the United Nations, globally, there were 962 million people aged 60 years or over in 2017, this represents an increase of 152 percent over the 383 million older people there were in 1980. This number is projected to grow to 1.4 billion by 2030 and, by 2050, to nearly 2.1 billion [1], doubling its 2017 size. Over the last 10 years, RPM has been rapidly extended to cover the concept of monitoring and treating a wide range of people, including chronic, sick, elderly, and postoperative patients [6], [8]. Many researchers have studied wireless remote monitoring of heart and blood-related diseases, activity including fall detection and mobility related

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