CN113057609B

CN113057609B - Vital sign monitoring method and system

Info

Publication number: CN113057609B
Application number: CN202110423647.5A
Authority: CN
Inventors: 祝宇鸿; 刘滨滨; 张晓颖; 孙大洋; 程禹
Original assignee: Jilin University
Current assignee: Jilin University
Priority date: 2021-04-20
Filing date: 2021-04-20
Publication date: 2022-02-18
Anticipated expiration: 2041-04-20
Also published as: CN113057609A

Abstract

The invention relates to a vital sign monitoring method and a system, wherein a Bluetooth wearable network is formed by a wrist acquisition unit, a chest acquisition unit and a communication unit, and various motion states can be identified by a nine-axis sensor and a pneumatic sensor, so that the system can judge the vital signs of a human body by combining more motion states of the human body, and the monitoring accuracy is improved; the circuit board adopts a rigid-flexible combination design in structure, so that the monitoring system is lighter and more convenient to wear; in addition, the MCU adopts STM32WB series chips, the chips adopt a dual-core architecture, the chip power consumption is low, and moreover, the wireless cores carry a BLE5.0 protocol, so that the system power consumption is further reduced.

Description

Vital sign monitoring method and system

Technical Field

The invention relates to the technical field of data processing, in particular to a vital sign monitoring method and system.

Background

The vital sign monitoring indexes are heart rate, electrocardio, blood pressure, blood oxygen saturation, body temperature and pulse. However, with the continuous development of research, the multi-parameter vital sign monitoring system still has a deficiency in monitoring the motion state of the human body, and can only monitor the vital signs of the human body in the horizontal direction motion or static state, and cannot meet the requirements of monitoring the vital signs of the human body in motion states such as upstairs and downstairs, mountain climbing and the like.

Therefore, a method and a system for monitoring vital signs in a multi-exercise state are needed.

Disclosure of Invention

The invention aims to provide a vital sign monitoring method and a vital sign monitoring system, which can identify various motion states of a human body and accurately and effectively monitor vital sign parameters of the human body in various motion states.

In order to achieve the purpose, the invention provides the following scheme:

a vital signs monitoring system, the system comprising:

the wrist acquisition unit is arranged on the wrist of a target human body and used for acquiring blood oxygen, pulse and blood pressure of the target human body and motion state parameters of the wrist of the target human body, wherein the motion state parameters comprise angular velocity, acceleration and height variation;

the chest acquisition unit is arranged on the chest of the target human body and is used for acquiring the heart rate, the electrocardio and the body temperature of the target human body and the motion state parameters of the trunk of the target human body;

the wrist acquisition unit and the chest acquisition unit are in communication connection with an intelligent terminal through a communication unit, and the intelligent terminal is used for receiving and displaying data acquired by the wrist acquisition unit and the chest acquisition unit and outputting the current motion state of the target human body.

A vital signs monitoring method, utilizing a vital signs monitoring system as described above, the method comprising:

acquiring and displaying real-time data of blood oxygen, pulse, blood pressure, heart rate, electrocardio and body temperature of the target human body and motion state parameters of the wrist and the trunk of the target human body; the motion state parameters comprise angular velocity, acceleration and height variation;

and judging the motion state of the target human body according to the real-time data and outputting the motion state.

According to the specific embodiment provided by the invention, the invention discloses the following technical effects:

the invention provides a vital sign monitoring method and a vital sign monitoring system, wherein a chest acquisition unit and a wrist acquisition unit are matched, the motion state parameters of the wrist and the chest of a target human body are acquired, the arm action and the trunk action of the target human body are combined to judge the overall motion state of the target human body, various motion states of the human body can be identified, and the acquired real-time vital signs are conveniently analyzed in combination with the motion states, so that the accuracy of vital sign monitoring is improved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.

Fig. 1 is a block diagram of a vital signs monitoring system according to an embodiment of the present invention;

fig. 2 is a schematic view of wearing manners of portions of the vital sign monitoring system according to the embodiment of the present invention;

fig. 3 is a flowchart of a vital sign monitoring method according to an embodiment of the present invention;

FIG. 4 is a flowchart of a wrist acquisition unit initialization process provided by an embodiment of the present invention;

fig. 5 is a flowchart of initializing the breast acquisition unit according to the embodiment of the present invention;

fig. 6 is a flowchart of packet parsing according to an embodiment of the present invention;

FIG. 7 is a flowchart illustrating an embodiment of an arm movement state determination process;

fig. 8 is a flowchart of determining a motion state of a human trunk according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

To vital sign's monitoring, some companies at home and abroad have promoted some corresponding wearable products, intelligent wrist-watch or intelligent bracelet that company such as hua shi, millet released, Iwatch that the apple company released etc.. The vital sign parameters that these wearable products can monitor are comparatively single, and the precision is not high. The special monitoring devices such as a GEB30 monitoring System of GE company, BenView T5 of Mireiter company, Home-base Telemonitor-ing System of Philips company and the like can monitor various vital sign parameters, but the special monitoring devices are expensive in price, are mainly applied to hospitals and large-scale rehabilitation institutions, and are not used for family health monitoring.

Along with the continuous development of research, wearable multi-parameter vital sign monitoring system is proposed continuously, the cellular wireless multi-parameter vital sign monitoring system adopts the vital sign data that base station network deployment transmission was gathered then the active power consumption is high not enough, the multi-parameter vital sign monitoring system that adopts Wi-Fi wireless network deployment then has the high not enough of system consumption equally to multi-parameter vital sign monitoring system still is not enough at human motion state detection, can only monitor the vital sign of human motion or quiescent condition under the horizontal direction, can not satisfy the human motion state such as upstairs and downstairs, the vital sign monitoring under motion states such as climbing.

The invention aims to provide a vital sign monitoring method and a vital sign monitoring system, which can identify various motion states of a human body, so that the vital sign parameters of the human body can be conveniently analyzed by combining the motion states, the accuracy of vital sign parameter monitoring is improved, and the wearable requirement is met.

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.

Example 1

As shown in fig. 1, the present embodiment provides a vital sign parameter monitoring system, which includes:

the wrist acquisition unit M1 is arranged on the wrist of the target human body and used for acquiring blood oxygen, pulse and blood pressure of the target human body and motion state parameters of the wrist of the target human body, wherein the motion state parameters comprise angular velocity, acceleration and height variation;

the chest acquisition unit M2 is arranged on the chest of the target human body and is used for acquiring the heart rate, the electrocardio and the body temperature of the target human body and the motion state parameters of the trunk of the target human body;

the wrist collecting unit M1 and the chest collecting unit M2 are in communication connection with an intelligent terminal M4 through a communication unit M3, and the intelligent terminal M4 is used for receiving and displaying data collected by the wrist collecting unit M1 and the chest collecting unit M2 and outputting the current motion state of the target human body.

Wherein, wrist acquisition unit M1 includes:

the blood oxygen pulse sensor is connected with the first microcontroller and is used for acquiring blood oxygen, pulse and blood pressure of the target human body and sending the blood oxygen, pulse and blood pressure to the first microcontroller;

the first air pressure sensor is connected with the first microcontroller and used for collecting the height variation of the target human wrist and sending the height variation to the first microcontroller;

the first nine-axis sensor is connected with the first microcontroller and used for acquiring the angular speed and the acceleration of the wrist of the target human body and sending the angular speed and the acceleration to the first microcontroller;

the first microcontroller is in communication connection with the communication unit M3.

The chest acquisition unit M2 includes:

the heart rate and electrocardio sensor is connected with the second microcontroller and is used for collecting the heart rate and the electrocardio of the target human body and sending the heart rate and the electrocardio to the second microcontroller;

the body temperature sensor is connected with the second microcontroller and used for collecting the body temperature of the target human body and sending the body temperature to the second microcontroller;

the second nine-axis sensor is connected with the second microcontroller and used for acquiring the angular speed and the acceleration of the target human body trunk and sending the angular speed and the acceleration to the second microcontroller;

the second air pressure sensor is connected with the second microcontroller and used for collecting the height variation of the target human body trunk and sending the height variation to the second microcontroller;

the second microcontroller is in communication connection with the communication unit M3.

Referring to fig. 2, the parts of the system provided in this embodiment are worn in the corresponding positions, the wrist acquisition unit M1 is worn on the wrist, and the garment integrating the chest acquisition unit M2 and the communication unit M3 is worn on the body. The network is dressed to the bluetooth of wrist acquisition unit M1, chest acquisition unit M2 and communication unit M3 constitution to this embodiment, can discern multiple motion state through nine sensors and pneumatic sensor for the system can combine more human motion states to judge human vital sign, improves the rate of accuracy of monitoring.

In order to reduce the power consumption of the system as much as possible, in this embodiment, a corresponding low-power consumption sensor is selected according to the vital sign parameters to be acquired, and a low-power consumption MCU is selected to design an acquisition circuit schematic diagram and a PCB board diagram.

In this embodiment, the blood oxygen pulse sensor employs MAX30101, and the sensor is provided with an internal a/D converter, and can directly read the converted digital PPG signal value from the FIFO register, and calculate the blood oxygen and pulse sign data by using the lambert law. The first and second ninth sensors each employ MPU9250, and the first and second air pressure sensors each employ LPS22 HB. The first microcontroller MCU1 chooses STM32WB55 series as the main control chip, this chip wireless dual-core MCU, its M4 nuclear operation application, and BLE5.0 protocol stack is operated to M0+ nuclear. The BLE5.0 protocol has a faster transmission rate and lower power consumption than the conventional Bluetooth and BLE4.2 protocols. After networking, the wrist acquisition unit M1 and the communication unit M3 perform data frame packing on the de-noised blood oxygen, pulse, blood pressure, acceleration data and angular velocity data, and send the data frames to the communication unit M3.

The heart rate electrocardio sensor adopts an MAX30001 sensor, a heart rate algorithm is arranged in the sensor, the R _ R interval duration of the collected electrocardio is stored in an internal register, the sensor is communicated with the second microcontroller MCU2 through an SPI interface, and data are sent to the second microcontroller MCU 2. The body temperature sensor adopts a MAX30205 sensor, the sensor stores the converted body temperature data in an internal register, the sensor is communicated with the MCU2 through an I2C interface, and the second controller MCU2 can obtain the current body temperature by reading the register of the sensor. The nine-axis sensor and the air pressure sensor are used for collecting the angular velocity, the acceleration and the height change of the human body movement, the MCU2 for identifying the movement state of the human body selects an STM32WB55 series as a main control chip, and performs data frame packing on the denoised heart rate, electrocardio, body temperature, acceleration data and angular velocity data after networking with the communication unit M3 and sends the data frame packing to the communication unit M3.

Compared with the traditional Bluetooth gateway, the low-power Bluetooth gateway has the advantages of high transmission speed and low power consumption. Moreover, the Bluetooth technology has unique technical measures of self-adaptive frequency hopping, power control and the like, can avoid mutual interference with a wireless WI-FI network, Zigbee and the like, and effectively enhances the anti-interference performance of a wearable network based on a Bluetooth gateway. Therefore, the present embodiment employs bluetooth low energy to establish the wearable network. And the MCU of the communication unit selects STM32WB series as a main control chip and is used for forming a wearing network with the wrist acquisition unit M1, the chest acquisition unit M2 and the upper computer. After networking connection is successful, the communication unit M3 sends the vital sign data packets collected by the wrist collecting unit M1 and the chest collecting unit M2 to the upper computer for display. Specifically, STM32WB55 series MCU is adopted. The MCU adopts a dual-core architecture, a main processor based on an M4 kernel is responsible for various user applications, and supports a batch processing mode, so that the power consumption of a flash memory and a CPU can be reduced under a closing condition, and corresponding work can be completed at the same time. When the M0+ core processes related tasks of the BLE protocol stack, the application processor M4 core is in a sleep state, and the power consumption can be reduced to 1.8 muA, but the wake-up time of 5 mus is kept. When two cores run simultaneously, the power consumption is only 50 muA/MHz. The low power consumption characteristic of the series of MCUs can obviously reduce the power consumption of the system. When the system is powered on and starts to operate, programs operated by the M4 core initialize corresponding BLE5.0 protocol stacks on the peripheral and the M0+ core, and the system automatically enters the networking operation of the Bluetooth wearable network after the initialization is completed. And after networking is finished, the M4 core operates a corresponding sensor data reading program and a denoising program, and the processed data is packaged into a data packet according to the format of the data packet. The data packet enters the M0+ core through an IPCC channel and a mailbox (mbox) channel between the M4 core and the M0+ core, the M0+ core automatically encapsulates the data into data which accord with a BLE5.0 protocol, and the data is sent out through an antenna, so that the communication process is completed.

In the aspect of vital sign parameter acquisition, the whole of all the bioelectricity activities of the cardiac muscle cell membranes of the human body form an electrocardiosignal, and the heart rate and the electrocardio information can be detected through the periodic change of the electrocardiosignal. The method of photoplethysmography (PPG) can sense and extract pulse and blood oxygen information of a human body so as to detect pulse and blood oxygen physical sign parameters of the human body, and can also obtain blood pressure information of the human body by using pulse data. With the development of sensor technology, many medical-level sensors can extract vital sign information of a human body and store the result in a storage space inside the sensor. The vital sign data can be obtained by reading the stored data of the corresponding sensor.

An accelerometer, an angular velocity meter and a magnetometer are integrated in the first nine-axis sensor and the second nine-axis sensor in the system, so that different motion states of a human body can be identified. The first air pressure sensor and the second air pressure sensor can measure the atmospheric pressure of the environment, the current height of the human body can be obtained through the conversion of the relation between the air pressure and the height, and then the motion of the human body with the changed heights, such as going upstairs and downstairs, is monitored. The change in the acceleration of the human body, which is acquired by the nine-axis sensor of the chest acquisition unit M2, exceeds the set threshold values of different magnitude degrees, so that it can be determined whether the human body is moving in the horizontal direction, such as a stationary state, a walking state, or a running state. The height change of the human body can be indirectly measured through the air pressure data acquired by the air pressure sensor, and whether the human body goes up and down stairs or not and whether the human body does squatting movement or not can be judged by combining the acceleration and the angular velocity. For example, the height of the human body is changed, and the human body can be judged to go upstairs and downstairs only by the change of the acceleration. In addition, when a human body is in the elevator, the height of the human body is changed, and only acceleration is changed, but the speed of the elevator is higher than the speed of the human body going upstairs and downstairs, so that whether the human body is in the elevator or the human body does upstairs and downstairs movement can be distinguished according to the change of the acceleration in the vertical direction. If the acceleration and the angular velocity are changed while the height of the human body is changed, it can be determined that the human body is doing the squatting movement. The system can analyze the collected real-time vital signs by combining the motion state, and the accuracy of analysis and judgment is improved.

In order to meet the requirement of wearable, the circuit of the three-part system shown in fig. 1 is designed into a structure of rigid and flexible combination, wherein a rigid part is used for placing a chip, and the rigid part is connected through an FPC. Therefore, the circuit can deform along with clothes, and is convenient to wear.

The vital sign parameter monitoring system provided by this embodiment adopts wrist acquisition unit M1, chest acquisition unit M2 and communication unit M3 to carry out data transmission through bluetooth networking, that is, during operation of the system, wrist acquisition unit M1, chest acquisition unit M2 and communication unit M3 form a wearable network through bluetooth, and then communication unit M3 and intelligent terminal M4 carry out data transmission through bluetooth connection. The wrist acquisition unit M1 and the chest acquisition unit M2 firstly send data packets to the communication unit M3, and the communication unit M3 sends the received data to the intelligent terminal M4 for analysis and display and human motion state judgment. Compared with a structure that a single MCU is connected with a sensor in a wired mode to acquire vital signs, the structure is more convenient to wear, and compared with a structure that a wrist acquisition unit is connected with a chest acquisition unit in a wireless mode and then is interacted with the outside through one of the two wireless transmission modes, the structure is simpler, and the power consumption of the system is reduced.

Example 2

As shown in fig. 3, the present embodiment provides a vital sign monitoring method, which adopts the system described in embodiment 1, and the specific method is as follows:

step 101: acquiring and displaying real-time data of blood oxygen, pulse, blood pressure, heart rate, electrocardio and body temperature of the target human body and motion state parameters of the wrist and the trunk of the target human body; the motion state parameters comprise angular velocity, acceleration and height variation;

step 102: and judging the motion state of the target human body according to the real-time data and outputting the motion state.

In order to ensure the accuracy of the acquired data, the method also comprises the step of initializing the system before acquisition.

Fig. 4 is a flowchart of the initialization procedure of the wrist acquisition unit M1, and after the networking is successful, the MAX30101 is initialized first. The method comprises the following specific steps: reset MAX30101, write 0X40 in the register with MAX30101 address 0X09 to clear the device configuration. ② configuring the interruption setting of MAX30101, writing OXCO in the register with address 0X02 to enable FIFO interruption, and writing 0X00 in the interruption with address OX03 to disable interruption. ③ setting FIFO read-write pointer, writing 0X00 in the register with address 0X04, and writing 0X00 in the register with address 0X06, so that the read-write pointer starts from the FIFO head. Set the FIFO full threshold, write 0X0F in the register with address 0X08 to set the FIFO full threshold to 17. I.e. an interrupt will be triggered after 17 samples of data in the FIFO. Setting MAX30101 mode configuration, writing 0X03 in register with address 0X09, and setting the mode as blood oxygen and pulse measurement mode. Setting the sampling rate and the pulse width of the LED, writing 0X27 in the register with the address of OXOA, setting the sampling rate to be 100Hz and the pulse width of the LED to be 400 us. Seventh, the currents of the LED1 and the LED2 are set, 0X3F is written in a register with an address of 0X0C, the current of the LED1 is set to 21mA, 0X2F is written in a register with an address of 0X0D, and the current of the LED2 is set to 18 mA.

After the initialization of the MAX30101 is completed, the first air pressure sensor LPS22HB is initialized, specifically including the steps of: soft reset LPS22HB and restart the stored contents; setting the LPS22HB working mode as a low power consumption mode; thirdly, setting the data rate of the LPS22HB to be 75 HZ; enabling the LPS22HB device; set up the parameter of the low pass filter of LPS22 HB. The initialization of the first air pressure sensor LPS22HB is completed by these 5-step settings.

The specific steps of initializing the first nine-axis sensor MPU9250 are as follows: reset MPU 9250; secondly, waking up the MPU9250 again after delaying 100ms, and writing 0X00 in a register with the address of 0X 6B; setting the range of a gyroscope to be +/-2000 dps and setting the range of an acceleration sensor to be +/-2 g; setting the sampling rate to 1000 Hz; setting acceleration and a gyroscope to work, and writing 0X00 in a sensor with the address of 0X 6C; setting cut-off frequency of low-pass filter of gyroscope raw data and acceleration raw data. Initialization of the first nine-axis sensor MPU9250 is completed by these 6-step settings.

The MPU9250 is calibrated after initialization of the MPU9250 is completed because the MPU9250 has an initial angle after wearing, and the initial angle is the angle of movement to be subtracted after each data reading. The method is to measure a large amount of data (2000) in a static state after wearing, and then to average the data. And after the calibration is finished, sending a calibration finishing mark to be displayed on the APP of the mobile terminal.

Fig. 5 is a flowchart of initialization of the chest acquisition unit M2. First, the body temperature sensor MAX30205 is initialized, and only the temperature data format needs to be set to the normal mode, and the initialization of the body temperature sensor is completed by writing 0X00 in the register with the address of 0X 01.

Then, initializing the heart rate and heart rate electric sensor MAX30001, and specifically comprising the following steps: setting an input ECG signal not to be inverted, and not using an internal calibration signal, namely writing 0X000000 into a register with the address of 0X 14; setting the sampling clock to be 32.768KHz, enabling the ECG function, and setting the ECG impedance to be 100M omega, namely writing 0X080017 into a register with the address of 0X 10; waiting for the PLL inside the sensor to lock, and setting the 8 th bit of the state register to be 1 by hardware when the PLL locks. Setting the FIFO interruption threshold of the ECG to be 16, namely writing 0X7B0014 in a register with the address of 0X 04; setting the sampling frequency of ECG as 128bps, gain as 20V/V, cut-off frequency of internal FIR filter as 40Hz, that is, writing 0X805000 in register with address 0X 15; setting RTOR (interval between two R waves of electrocardio) sampling window width to be 96ms, gain to be 64 and average weight to be 8, namely writing 0X3FA300 in a register with address 0X1D and writing 0X202400 in a register with address 0X 1E; enabling ECG data FIFO full interrupt and R _ R data interrupt, that is, writing data 0X8000003 in the register with address 0X02, writing data 0X000403 in the register with address 0X 03; -synchronizing the ECG operation of the sensors, i.e. writing 0X000000 in the register with address 0X09 to complete the synchronization. The operation of the MAX30001 sensor is completed by the above 8 steps. The initialization of the second air pressure sensor and the second nine-axis sensor is the same as the initialization of the first air pressure sensor and the first nine-axis sensor.

When real-time data are collected, the wrist collecting unit M1 reads FIFO data of MAX30101 at the frequency f1, calculates the blood oxygen concentration and pulse at the moment according to the Langbo's law, and obtains the blood pressure value according to the physical sign equation of the blood pressure and pulse wave, thereby completing the extraction of the parameters of blood oxygen, pulse and blood pressure of the human body. Data for the first nine-axis sensor and the first air pressure sensor are collected at frequency f 2. The chest acquisition unit M2 reads the FIFO storing the ECG signals at the frequency f3, each time the sampled ECG signals occupy 3bytes, after the ECG data are read, the register with the address of 0X25 is read to obtain the sampling value of the interval time between two R wave peaks in the electrocardiosignals, the time of the interval time multiplied by 0.008 is the time between the two R wave peaks, and the heart rate value can be obtained by dividing 60 by the interval time. The second nine axis sensor data and the second barometric sensor data on the thorax acquisition unit M2 are acquired at frequency f 4.

In the embodiment, data output by the selected vital sign sensor are digital signals, noise exists in the output result, the main source of the noise is caused by insufficient contact degree between the sensor and the human body part at certain moments in the acquisition process, and the accuracy of the acquired vital signs can be interfered by the existence of the noise. To smooth out these noises, mean filtering techniques are used to smooth the data acquired by the sensors to improve the accuracy of the monitoring. And the nine-axis sensor is accompanied by the offset characteristic, and in practical use, more data noise points exist in the collected human body posture data, which causes the unsatisfactory motion state monitoring and judgment accuracy, and the collected human body posture data needs to be subjected to low-pass filtering and denoising. Therefore, the wrist acquisition unit M1 and the chest acquisition unit M2 respectively perform data frame packing after filtering and denoising the acquired real-time data, and add a wrist data header and a wrist data trailer to the real-time data acquired by the wrist acquisition unit M1 to obtain a wrist data packet; the real-time data acquired by the chest acquisition unit M2 is added with a chest data header and a chest data trailer to obtain a chest data packet.

The data packet of the wrist acquisition unit M1 is shown in table 1, and the data packet is divided into three parts, namely a data header, a data area and a data tail. Wherein, the data head contains 3bytes start bits, which are respectively 'S', 'T' and 'A', and is used for indicating the start of the data frame, and the node address is the address of the wrist Bluetooth node and is used for indicating whether the current data frame is from the wrist or the chest. Specifically, as shown in table 2, the end bits include 3bytes, which are 'E', 'N', and 'D', respectively, for indicating the end of the data frame; the data packet of the chest acquisition unit M2 is also divided into three parts, i.e., a data header, a data field and a data trailer, as shown in table 3, wherein the start bit of the data header and the end bit of the data trailer are identical to the wrist data packet, and the data of the data field is shown in table 4.

Table 1 wrist acquisition unit data packet

TABLE 2 wrist packet data zone

TABLE 3 thoracic acquisition unit data packet

TABLE 4 thoracic data packet data zone

After receiving the data packet, the intelligent terminal M4 first determines whether the frame head and the frame tail are correct, and if not, re-receives the data, and if so, determines the motion state of the target human body according to the real-time data. As shown in fig. 6, specifically, it is determined whether a node address in a data frame is an address of the wrist acquisition unit M1, and if the node address is the address of the wrist acquisition unit M1, the data frame transmitted by the wrist acquisition unit M1 is analyzed in a data area according to a format of a data frame of the wrist acquisition unit M1, so as to obtain acquired data; and if the address is not the address of the wrist node, judging whether the address is the address of the node of the chest acquisition unit M2, if the address is the address of the chest acquisition unit M2, analyzing the data area according to the format of the data frame of the chest acquisition unit M2 to further obtain the data acquired by the chest acquisition unit M2, and if the address is not the address of the chest acquisition unit M2, re-receiving the data.

And judging the motion state of the arm of the target human body according to the real-time data of the wrist and judging the motion state of the trunk of the target human body according to the real-time data of the chest after distinguishing the real-time data of the wrist and the real-time data of the chest. The variation of the acceleration of the human body, which can be acquired by the nine-axis sensor of the chest acquisition unit M2, exceeding the set threshold values of different magnitude degrees can determine how fast the human body is moving in the horizontal direction, such as a stationary state, a walking state, or a running state. The height change of the human body can be indirectly measured through the air pressure data acquired by the air pressure sensor, and whether the human body goes up and down stairs or not and whether the human body does squatting movement or not can be judged by combining the acceleration and the angular velocity. For example, the height of the human body is changed, and the human body can be judged to go upstairs and downstairs only by the change of the acceleration. In addition, when a human body is in the elevator, the height of the human body is changed, and only acceleration is changed, but the speed of the elevator is higher than the speed of the human body going upstairs and downstairs, so that whether the human body is in the elevator or the human body does upstairs and downstairs movement can be distinguished according to the change of the acceleration in the vertical direction. If the acceleration and the angular velocity are changed while the height of the human body is changed, it can be determined that the human body is doing the squatting movement.

Fig. 7 is a flow chart of judging the motion state of the arm, and first, it is judged whether the difference between the front and rear adjacent 2 heights monitored by the air pressure sensor in the wrist acquisition unit M1 is greater than a set height threshold, and if so, it indicates that the arm has moved in the vertical direction. If the acceleration change of the collected acceleration change of the nine-axis sensor is not larger than the threshold, judging whether the acceleration change of the collected acceleration change of the nine-axis sensor on the X axis, the Y axis and the Z axis is larger than the corresponding threshold of each axis, and if the acceleration change of one axis is larger than the threshold, indicating that the arm is in a static state.

Fig. 8 is a flow chart for determining the motion state of the human trunk, in which the Y axis of the nine-axis sensor of the chest acquisition unit M2 is oriented vertically downward after being worn, after the nine-axis sensor data and the air pressure sensor data acquired by the chest acquisition unit M2 are analyzed, first, whether the difference between the front and rear 2 adjacent height data monitored by the air pressure sensor is greater than a set threshold value is determined, if so, it is determined whether the change of the acceleration of the X axis is greater than the set threshold value, and if not, it is determined that the human body is in a static state at the moment. If the change of the acceleration of the X axis is larger than the set threshold value, the human body is in a state of bending down, squatting down or standing up at the moment. And then continuously judging whether the change of the acceleration of the Y axis is larger than a set threshold value, and if so, indicating that the human body is in a motion state of going upstairs and downstairs. If the human body does not move in the horizontal direction, the human body still stays in the state of bending down and squatting or standing up. And if the height difference detected by the air pressure sensor is not greater than the threshold value, continuously judging whether the change of the Z-axis acceleration is greater than the set threshold value, if so, the human body only has a motion state in the horizontal direction, namely running or walking, and if the change of the Z-axis acceleration is not greater than the threshold value, the human body is in a static motion state at the moment.

And combining the motion state of the target human body arms and the motion state of the target human body trunk to obtain the motion state of the target human body. As the vital signs of the human body are different under different exercise states, the resting heart rate of the human body is 60-100 times/minute, the average is about 75 times/minute, the exercise heart rate with medium and low intensity is 110-140 times/minute, the exercise heart rate with stronger intensity is 160-180 times/minute, and the maximum is not more than 210 times/minute. And for the sports of climbing stairs, the exercise heart rate can be kept between 120 and 160 times/minute according to different exercise intensity and different exercise time conditions.

The emphasis of each embodiment in the present specification is on the difference from the other embodiments, and the same and similar parts among the various embodiments may be referred to each other.

The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims

1. A vital signs monitoring system, the system comprising:

the wrist acquisition unit and the chest acquisition unit are in communication connection with an intelligent terminal through a communication unit, and the intelligent terminal is used for receiving and displaying data acquired by the wrist acquisition unit and the chest acquisition unit and outputting the current motion state of the target human body;

judging the motion state of the target human body arm according to the wrist real-time data and judging the motion state of the target human body trunk according to the chest real-time data;

judging whether the height variation in the wrist real-time data is larger than a preset arm height variation threshold value or not, if so, enabling the target human arm to move in the vertical direction; if not, judging whether the variation of the acceleration on the X axis, the Y axis and the Z axis in the wrist real-time data is larger than a preset arm acceleration variation threshold, if so, determining that the target human body arm moves in the direction of the axis, otherwise, determining that the target human body arm is static;

judging whether the high variation in the chest real-time data is greater than a preset trunk height threshold value or not to obtain a first judgment result, and if so, judging whether the variation of the acceleration in the chest real-time data on the x axis is greater than a preset trunk acceleration threshold value or not to obtain a second judgment result;

if the second judgment result is negative, the target human body is static; if the second judgment result is yes, the target human body is in a stooping or squatting state, whether the variation of the acceleration in the chest real-time data on the Z axis is larger than a preset trunk acceleration threshold value or not is further judged, if yes, the target human body is going upstairs or downstairs, and if not, the target human body is only in the stooping or squatting state;

if the first judgment result is negative, further judging whether the variation of the acceleration in the Y axis in the chest real-time data is larger than a preset trunk acceleration threshold value, if so, enabling the target human body to be in a walking or running state, otherwise, enabling the target human body to be static;

and combining the motion state of the target human body arms and the motion state of the target human body trunk to obtain the motion state of the target human body.

2. Vital signs monitoring system according to claim 1, wherein the wrist acquisition unit comprises:

the first microcontroller is in communication connection with the communication unit.

3. Vital signs monitoring system according to claim 1, wherein the chest acquisition unit comprises:

the second microcontroller is in communication connection with the communication unit.

4. The vital sign monitoring system of claim 1, wherein the communication unit is configured to communicate with the chest acquisition unit, the wrist acquisition unit, and the smart terminal via a BLE5.0 protocol.

5. A vital signs monitoring method, using the vital signs monitoring system of claim 1, the method comprising:

acquiring and displaying real-time data of blood oxygen, pulse, heart rate and electrocardio of the target human body and the motion state parameters of the wrist and the trunk of the target human body; the motion state parameters comprise angular velocity, acceleration and height variation;

judging the motion state of the target human body according to the real-time data and outputting the motion state;

classifying the real-time data according to the acquisition positions to obtain wrist real-time data and chest real-time data;

combining the arm movement state of the target human body with the trunk movement state of the target human body to obtain the movement state of the target human body;

judging whether the height variation in the wrist real-time data is larger than a preset arm height variation threshold value or not, if so, enabling the target human arm to move in the vertical direction;

if not, judging whether the variation of the acceleration on the X axis, the Y axis and the Z axis in the wrist real-time data is larger than a preset arm acceleration variation threshold, if so, determining that the target human body arm moves in the direction of the axis, otherwise, determining that the target human body arm is static;

if the first judgment result is negative, further judging whether the variation of the acceleration in the Y axis in the chest real-time data is larger than a preset trunk acceleration threshold, if so, judging that the target human body is in a walking or running state, otherwise, judging that the target human body is static.

6. A method for vital signs monitoring as claimed in claim 5, further comprising initializing a wrist acquisition unit and a chest acquisition unit prior to acquiring the real-time data of the target person.

7. The vital sign monitoring method of claim 5, wherein the acquiring real-time data of blood oxygen, pulse, heart rate, electrocardiogram and motion state parameters of the wrist and the trunk of the target person comprises:

adding a wrist data head and a wrist data tail to real-time data acquired by a wrist acquisition unit to obtain a wrist data packet;

and adding a chest data head and a chest data tail to the real-time data acquired by the chest acquisition unit to obtain a chest data packet.