CN103315741B - Respiration monitoring method based on wireless link information - Google Patents

Abstract

The invention relates to a breath monitoring method based on wireless link information, which belongs to the fields of health monitoring, patient and elderly monitoring, wireless communication and wireless network, and relates to a non-contact breath monitoring method based on wireless link information. The monitoring method uses multiple wireless links formed by multiple wireless scanning nodes arranged around the bed, and estimates the change period of the wireless link signal strength by monitoring the periodic impact of human respiration on the signal strength of multiple wireless links. Then estimate the breathing rate of the human body. The system used in the monitoring method is composed of N wireless scanning nodes, wireless sink nodes, general PC, respiratory frequency estimation algorithm and a bed for carrying the human body. The invention belongs to non-contact monitoring, without installing any facilities on the monitored human body, making the monitoring system more humane, suitable for monitoring infants and postoperative patients, and this non-contact monitoring technology has a wider application prospect.

Description

Respiratory Monitoring Method Based on Wireless Link Information

technical field

The invention belongs to the fields of health monitoring, patient and elderly monitoring, wireless communication and wireless network, and relates to a non-contact respiratory monitoring method based on wireless link information.

Background technique

Respiratory frequency monitoring has very important application value in people's daily life. A notable feature of many critically ill patients is that the respiratory rate fluctuates greatly. Therefore, hospitals need to monitor the respiratory rate of critically ill patients so as to effectively monitor the occurrence of emergencies. Obstructive sleep apnea syndrome is a common chronic disease with high morbidity and great harm. Using monitoring equipment to reliably monitor the patient's respiratory rate is of great significance to the clinical prevention and treatment of this disease. Dyspnea caused by turning over is an important factor that puts the baby's life at risk. Therefore, it is necessary for the family to monitor the baby's breathing so as to better understand the baby's sleep state. Respiratory monitoring has broad application prospects in the field of medical and health monitoring.

At present, the existing breathing monitoring technology is based on the carbon dioxide gas monitoring technology. This solution installs the straw to the nostril of the ward, and uses the change of the carbon dioxide gas when the human body breathes to monitor the breathing rate of the human body. Related work, such as P.Sebel, M.Stoddart, et al. "Respiration: The Breath of Life", Torstar Books, 1985; Ouqiong et al. "Comparison of two methods of portable sleep monitoring and polysomnography", Foreign Medicine, Respiratory System Volume, 2005, Volume 25, pages P562-P565. The monitoring results of this type of technology are relatively accurate, but it is necessary to install a breathing tube to the nose of the person, which is inconvenient to install and difficult for babies to use.

In recent years, with the development of wireless technology, low-cost wireless nodes have been more and more widely used. Due to the inherent characteristics of radio waves, radio waves will be shielded when passing through the human body, resulting in changes in the received signal strength. When the volume of the human body changes, the shaded wireless link will change, thus causing the link signal strength to change. Human breathing causes periodic changes in the volume of the human body, which in turn causes periodic changes in the signal strength of the wireless link passing through the human body; therefore, the periodic law of link changes can be estimated by using the periodic change characteristics of the signal strength of the wireless link to realize Estimation of respiratory rate.

Contents of the invention

The purpose of the present invention is to overcome the defect of prior art, invent a kind of non-contact breathing state monitoring method based on wireless link information, utilize the wireless measurement signal between each wireless node as input information, by estimating wireless link signal strength Periodic variation law realizes the technique of estimating human respiratory frequency. In order to use multiple wireless link signals passing through the human body to perceive the respiratory frequency information of the human body, and realize non-contact monitoring of the sleep status of patients and babies.

The technical solution of the present invention is a breathing monitoring method based on wireless link information, which is characterized in that the monitoring method utilizes multiple wireless links formed by N wireless scanning nodes arranged around the bed, and monitors human breathing to multiple The periodical influence of the signal strength of the wireless link is estimated to estimate the change period of the signal strength of the wireless link, and then the breathing frequency of the human body is estimated; the specific steps of the breathing monitoring method are:

1) Arrange N wireless scanning nodes J1-JN around the bed at a height higher than the bed so that wireless signals can pass through the human body; multiple wireless scanning nodes are responsible for scanning each pair of wireless nodes in turn, and the measured The signal strength information of the wireless link is sent to the wireless convergence node 1; the wireless communication operating frequency and power of the wireless scanning node can be adjusted, powered by batteries and external power sources, broadband omnidirectional antennas, and the height from the ground can be adjusted from 0.3m to 1.5m;

2) The wireless aggregation node 1 collects all wireless link measurement information and sends it to the general PC 2; the respiratory frequency estimation algorithm running on the general PC 2 judges the respiratory frequency based on the link measurement information; the wireless aggregation node 1 The working frequency of wireless communication can be adjusted, powered by battery and external power supply, broadband omnidirectional antenna, with USB interface or universal serial interface for data transmission with general PC, and the height can be adjusted from 0.3m to 1.5m from the ground;

3) Use the periodic change characteristics of the wireless link signal strength to estimate the periodic law of the link change, and realize the estimation of the respiratory frequency; the respiratory frequency estimation algorithm: the algorithm uses the received signal strength information of the wireless link as input information, and estimates the signal strength change period, output the estimated respiratory rate; assuming that each wireless scanning node forms a total of L wireless links at the current moment, and the signal strength of each link changes as A total of N signal strength data are collected for each link, and the human respiratory frequency F at the current moment can be estimated by the following formula:

$\text{<span class="notranslate"> F=arg</span>}{\mathrm{<span\; class="notranslate">\; max</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}\underset{\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; L</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; |</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;FTn</span>}}<span\; class="notranslate">\; |</span>$

Among them, F _max > F > F _min , F _max is the maximum breathing frequency, usually 50, F _min is the minimum breathing frequency, usually 8; the breathing frequency estimation algorithm performs Fourier transform on the signal strength information of multiple wireless links Transform and analyze its frequency-domain spectrum characteristics to find out the frequency value corresponding to the power spectrum peak, so as to find out the breathing frequency of the human body;

4) The respiratory frequency estimation algorithm running on a general-purpose PC takes the received signal strength information of the wireless link as input information, estimates the signal strength change period by analyzing the frequency domain spectrum characteristics of the signal strength information of multiple wireless links, and outputs the estimated respiratory rate Frequency Find out the frequency value corresponding to the power spectrum peak; use a general-purpose PC with a main frequency of 1GHz or more, a memory of 256M or more, and a USB interface or a general-purpose serial interface for data transmission with the wireless aggregation node;

5) Workflow: The general-purpose PC sends a command to start monitoring to the wireless convergence node 1, and the wireless convergence node 1 orders each wireless scanning node to start working, and then each wireless scanning node starts to perform wireless scanning, and transmits the signals of each wireless link The strength information is sent to the wireless aggregation node; the wireless aggregation node sends the signal strength information of each wireless link to a general-purpose PC, and the respiratory frequency estimation software running on the general-purpose PC estimates the human respiratory frequency based on the signal strength information of the wireless link , to realize the estimation of human respiratory rate.

The respiration monitoring method based on wireless link information is characterized in that: the system adopted by the monitoring method consists of N wireless scanning nodes J1...JN, wireless convergence node 1, general-purpose PC 2, respiratory frequency estimation algorithm and for carrying The bed of the human body is composed of 3, and the breathing frequency estimation algorithm is installed on a general-purpose PC (2).

The beneficial effects of the present invention are: 1) The wireless measurement signal can be used to estimate the human respiratory frequency; 2) The non-contact respiratory frequency measurement can be realized; 3) The monitoring system is convenient for quick installation and disassembly; 4) The equipment consists of multiple It is composed of a cheap wireless node, and the cost is low.

Description of drawings

Fig. 1 is a system structure block diagram of the present invention, among the figure: J1-the 1st wireless scanning node, J2-the 2nd wireless scanning node, J3-the 3rd wireless scanning node, J4-the 4th wireless scanning node, J5-the 5th wireless scanning node Scanning node, J6-6th wireless scanning node, J7-7th wireless scanning node, J8-8th wireless scanning node, J9-9th wireless scanning node, J10-10th wireless scanning node, 1-wireless convergence node, 2 -Universal PC, 3-bed,

Figure 2 is a flow chart of the respiratory rate estimation algorithm.

Detailed ways

The implementation of the present invention will be described in detail below in conjunction with the technical solutions and accompanying drawings, but the present invention is not limited to specific embodiments. The invention discloses a method for monitoring the breathing frequency of a human body by using wireless signals. The breathing frequency of the human body is estimated by analyzing the periodic variation law of the signal strength information of the wireless links by using the signal strength information of multiple wireless links.

Embodiment: accompanying drawing 1 is a system structural block diagram of the present invention, and system has 10 wireless scanning nodes J1, ... J10, wireless scanning node and wireless convergence node 1 are all based on the CC2520 chip design of Texas Instruments, meet the Zigbee industry standard, work In the 2.45GHz frequency band, it is powered by batteries, supported by a tripod, and the height can be adjusted from 0.3m to 1.5m. Place 10 wireless scanning nodes on the four sides of the bed 3 used to carry the human body, with a distance of 0.6m and a height of 13cm slightly higher than the bed, so as to ensure that the wireless signal passes through the human body as much as possible. The wireless aggregation node 1 and the general PC 2 are placed 3m away from the bed, and the wireless aggregation node 1 collects all the wireless link measurement information and sends them to the general PC 2; the respiratory frequency estimation algorithm running on the general PC 2 is based on link The respiratory rate is judged based on the road measurement information. The working frequency of the wireless communication of the wireless convergence node 1 can be adjusted. It is powered by a battery and an external power supply and has a broadband omnidirectional antenna. It has a USB interface or a universal serial interface for data transmission with a general PC, and its height can be adjusted from 0.3m to 1.5m from the ground. Each wireless scanning node J1...J10 forms a queue, and broadcasts and sends wireless signals sequentially, and other wireless scanning nodes J1...J10 that do not send signals receive the wireless signal sent by the sending node at the current moment, and measure the received signal strength information. The information broadcast by each wireless scanning node J1...J10 is the received signal strength information of other wireless scanning nodes received by each wireless scanning node in the previous round of scanning, that is, the signal strength information of each wireless link at the previous moment.

Accompanying drawing 2 is the algorithm flow chart of respiration frequency estimation, when general-purpose PC 2 sends the order that starts monitoring to wireless convergence node 1, wireless convergence node commands each wireless scanning node to start working, and then each wireless scanning node starts to carry out wireless scanning, and will The signal strength information of each wireless link is sent to the wireless aggregation node; the wireless aggregation node sends the signal strength information of each wireless link to a general-purpose PC, and the breathing frequency estimation software running on the general-purpose PC is based on the signal strength of the wireless link The information performs Fourier transform on the wireless link information to search for the most significant periodic change characteristics, and then estimate the human respiratory rate to realize the estimation of the human respiratory rate.

The wireless aggregation node 1 listens to the wireless signal information of each wireless link, and sends the received signal strength information of each wireless link to the general PC 2 . The breathing frequency estimation algorithm running on the general PC 2 takes the wireless link measurement information as input, performs spectral transformation analysis on the signal, estimates the power spectrum of the wireless link measurement signal, and finds out the frequency corresponding to the peak of the power spectrum. The frequency is is the human respiratory rate. The breathing frequency estimation algorithm outputs the calculated human breathing frequency information to the screen of the general-purpose PC 2 .

According to the respiratory frequency estimation algorithm, the algorithm takes the received signal strength information of the wireless link as input information, estimates the signal strength change period, and outputs the estimated respiratory frequency; assuming that each wireless scanning node forms a total of L wireless links at the current moment, each link The signal strength of the A total of N signal strength data are collected for each link, and the human respiratory frequency F at the current moment can be estimated by the following formula:

$\text{<span class="notranslate"> F=arg</span>}{\mathrm{<span\; class="notranslate">\; max</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}\underset{\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; L</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; |</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;FTn</span>}}<span\; class="notranslate">\; |</span>$

Among them, F _max > F > F _min , F _max is the maximum breathing frequency, usually 50, F _min is the minimum breathing frequency, usually 8; the breathing frequency estimation algorithm performs Fourier transform on the signal strength information of multiple wireless links Transform, analyze its frequency-domain spectrum characteristics, find out the frequency value corresponding to the power spectrum peak, and then find out the breathing frequency of the human body. The test shows that the efficient monitoring of human respiratory rate can be realized by arranging 10 nodes. Experiments were conducted on adults and children respectively. The monitoring result of the adult whose real respiratory rate is 14 times/min is 13.8 times/min, and the real respiratory rate is 37. The baby monitoring result of times/minute was 37.3 times/minute.

The invention utilizes the frequency domain characteristics of the wireless link signal intensity variation to realize the estimation of the human body's respiration frequency. Changes in human breathing will cause periodic changes in body volume, which in turn will cause changes in the signal strength of the wireless link passing through the human body. Therefore, the variation characteristics of the signal strength of the wireless link can be used to estimate the respiratory frequency. The respiratory status monitoring technology based on wireless link information is non-contact monitoring, without installing any facilities on the monitored human body, which makes the monitoring system more humane, especially suitable for monitoring infants and postoperative patients. These characteristics determine that the non-contact monitoring technology of respiratory status based on wireless link information has a wider application prospect.

Claims (2)

1. the monitoring of respiration method based on wireless link information, it is characterized in that, the multi wireless links that this monitoring method utilizes a plurality of wireless scan nodes that are arranged in a surrounding to form, by monitoring human breathing, the periodicity of multi wireless links signal intensity is affected, estimate the period of change of wireless link signals intensity, and then estimate the respiratory frequency of human body; The concrete steps of monitoring of respiration method are:

1) by N wireless scan node (J1 ... JN) be arranged in the surrounding of bed (3), placing height is higher than bed, so that wireless signal passes from human body; N wireless scan node be responsible for taking turns each radio node of scan flow to and each wireless link signals strength information measuring is sent to wireless aggregation node (1); The radio communication operating frequency of wireless scan node, power can regulate, battery and external power power supply, and wideband omnidirectional antenna, apart from ground, regulating is highly 0.3m to 1.5m;

2) wireless aggregation node (1) is collected all wireless link metrical informations, and is sent to general purpose PC (2); The respiratory frequency algorithm for estimating of the upper operation of general purpose PC (2), judges respiratory frequency based on link metrical information; The radio communication operating frequency of wireless aggregation node (1) can regulate, battery and external power power supply, wideband omnidirectional antenna, has USB interface or the USB (universal serial bus) of carrying out transfer of data with general purpose PC (2), apart from ground, regulates height 0.3m to 1.5m;

3) general purpose PC (2) send start monitoring order to wireless aggregation node (1), each wireless scan node of wireless aggregation node order is started working, each wireless scan node starts to carry out without line sweep afterwards, and the signal strength information of each wireless links is sent to wireless aggregation node; Wireless aggregation node is sent to general purpose PC (2) by the signal strength information of each wireless links, the signal strength information of the respiratory frequency Estimation Software of the upper operation of general purpose PC (2) based on wireless link, estimate human body respiration frequency, realize the estimation to human body respiration frequency;

Above-mentioned three steps have been described the workflow of system; The respiratory frequency algorithm for estimating of the upper operation of general purpose PC (2) is usingd wireless link received signal strength information as input message, by analyzing the frequency domain spectral characteristic of multi wireless links signal strength information, estimate the change in signal strength cycle, the respiratory frequency of output estimation, finds out the frequency numerical value that power spectrum peak is corresponding; Suppose that each wireless scan node of current time forms L wireless links altogether, the change in signal strength of each link is every link has gathered N signal strength data altogether, and the human body respiration frequency F of current time can adopt following formula to estimate:

$F={\arg \max }_F\underset{l=1}{\overset{L}{\mathrm{\&Sigma;}}}\left|\underset{n=i-N+1}{\overset{i}{\mathrm{\&Sigma;}}}{R}_l^n{e}^{-j2\mathrm{\&pi;FTn}}\right|$

Wherein, F _max> F > F _min, F _maxfor maximum breathing frequency is 50, F _minfor minimum respiratory frequency is 8; Respiratory frequency algorithm for estimating, by multi wireless links signal strength information is done to Fourier transformation, is analyzed its frequency domain spectral characteristic, finds out the frequency numerical value that power spectrum peak is corresponding, thereby finds out the respiratory frequency of human body; The dominant frequency of the general purpose PC (2) adopting is more than 1GHz, more than inside saving as 256M, has USB interface or the USB (universal serial bus) of carrying out transfer of data with wireless aggregation node.

2. the monitoring of respiration method based on wireless link information according to claim 1, it is characterized in that: the system that monitoring method adopts is by N wireless scan node (J1 ... JN), wireless aggregation node (1), general purpose PC (2), respiratory frequency algorithm for estimating and form for carrying the bed (3) of human body, respiratory frequency algorithm for estimating is arranged on general purpose PC (2).