CN112205973B - Cardiopulmonary endurance measurement method and system based on intelligent wearable equipment - Google Patents
Cardiopulmonary endurance measurement method and system based on intelligent wearable equipment Download PDFInfo
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Abstract
The invention provides a heart-lung endurance measurement method and a heart-lung endurance measurement system based on intelligent wearing equipment, wherein the method collects the resting heart rate of a user before the intelligent wearing equipment is tested; the real-time heart rate and the walking speed are collected in the test process, the quantitative index is calculated according to the PCI index formula, and the heart-lung endurance is measured and estimated. The system based on the measuring method comprises the following steps: the sensor data acquisition module is used for acquiring the real-time heart rate and the walking speed of the user in the test; the intelligent wearable device system control module is used for data processing, timing reminding and communication data analysis; a wireless communication module; and the user terminal processing module is used for calculating and evaluating heart-lung endurance and displaying data. The invention has the advantages that the index does not need a strict experimental protocol, the equipment automatically detects the motion state of the user, the data is real and effective, the cost of manual guidance and measurement in the heart-lung endurance test project can be reduced, the function of the intelligent wearing equipment is further expanded, and the practicability is improved.
Description
Technical Field
The invention relates to the field of heart-lung endurance measurement, in particular to a heart-lung endurance measurement method and system based on intelligent wearable equipment.
Background
The heart-lung endurance comprehensively reflects the oxygen uptake, transportation and utilization capacity of a human body, and relates to the functions of heart blood pumping, lung oxygen uptake and gas exchange capacity, the efficiency of carrying oxygen to various parts of the whole body by a blood circulation system, and the oxygen utilization function of tissues such as muscles. The measurement of heart and lung endurance is significant for subjects to understand their physical performance and guide exercise.
The special cardiopulmonary endurance test method comprises the steps that a subject performs extreme exercise on exercise equipment, a breathing mask continuously monitors the content and the flow rate of breathing gas, and the maximum oxygen uptake (VO) of the subject is obtained through calculation 2Max ) Cardiopulmonary endurance was assessed. The simple heart-lung endurance measuring method comprises step test, 6-minute walking, cooper twelve-minute running, fixed-distance running and the like. Professional testing methods are highly accurate, but require specialized equipment, are complex to operate, and present a risk of injury to the subject for extreme exercises. The simple test method needs manual guidance and cannot be automatically realized.
Disclosure of Invention
In order to solve the technical problems, the invention provides a cardiopulmonary endurance measurement method and system based on intelligent wearable equipment, which are used for conveniently and rapidly realizing the cardiopulmonary endurance measurement function in step tests and 6-minute walking test projects.
The invention provides a heart-lung endurance measurement method and a heart-lung endurance measurement system based on intelligent wearable equipment, which can automatically guide a user to perform a step test and a 6-minute walking test, and arrange the monitored exercise heart rate and walking speed into a physiological consumption index PCI for evaluating the heart-lung endurance level of the user. Compared with the independent judgment index in the traditional test, the PCI index is suitable for various test types, the burden of a breath test method is avoided, a brand new method for evaluating the heart-lung endurance level is added compared with other test equipment, the heart-lung endurance is measured by means of the intelligent wearing equipment, the self-measurement of a subject is facilitated, and a reference is provided for the subject to know the physical performance condition of the subject.
The technical scheme of the invention is as follows: a heart-lung endurance measurement method based on intelligent wearable equipment comprises the following steps:
step 1, a user APP logs in;
step 2, selecting test item types in the APP, wherein the item types comprise a step test and a 6-minute walking test;
step 3, before formally starting the test, the user keeps a resting state, and the intelligent wearable device automatically starts to collect the resting heart rate of the user;
step 4, clicking a movement starting option to formally start a test after preparation is finished, and the intelligent wearable device and the APP give an instruction to the action of the user according to the requirement of the item type, and simultaneously monitor whether the user finishes the action in time according to the requirement;
Step 5, acquiring exercise heart rate and walking speed data of a user in the test process;
and step 6, calculating according to a PCI index formula to obtain a quantitative index, and comparing the quantitative index with a reference standard to realize measurement and evaluation of heart-lung endurance.
Further, in the step 2, when the user selects the step test item, the step test item specifically includes:
under the prompt of the APP, the user wears the intelligent wearing equipment on the wrist normally, and meanwhile, the left hand or right hand wearing equipment is selected and confirmed in the APP; keeping a resting state before formally starting a test, and automatically starting to measure the resting heart rate of a user after the intelligent wearable device detects that the user enters a stable resting state; clicking a movement starting option to formally start a step test link after preparation is finished; the intelligent wearable device carries out short vibration prompt at a frequency of once every 0.5 seconds in the movement process, each time the user is vibrated to execute one-step action, each step action device can detect whether the user timely completes the step-up and step-down action according to the requirement, and each 4 steps are complete step-up and step-down actions; after the exercise starts, the intelligent wearable device immediately counts time and acquires the exercise heart rate and the walking speed; after the 3-minute repeated action is completed, the device sends out a long vibration prompt to finish the movement, and waits for the heart rate measurement of the last 10 seconds; after the data acquisition is finished, the data is transmitted to an APP through Bluetooth, the APP calculates to obtain a PCI index, and finally the PCI index is compared with a reference standard to evaluate the heart-lung endurance level of the user.
Further, in the step 2, when the user selects the 6-minute walk test item, the method specifically includes:
under the prompt of APP, the user wears the intelligent wearing equipment on the wrist normally; keeping a resting state before formally starting a test, and automatically starting to measure the resting heart rate of a user after the intelligent wearable device detects that the user enters a stable resting state; clicking the option of starting exercise after the preparation is finished to formally start a 6-minute walking test link; after walking, the user walks directly in the running platform or corridor; the intelligent wearing equipment immediately counts time and acquires exercise heart rate and walking speed; after 6 minutes of walking is completed, the device sends out a long vibration prompt to finish the movement, and waits for the heart rate measurement of the last 10 seconds; after the data acquisition is finished, the data is transmitted to an APP through Bluetooth, the APP calculates to obtain a PCI index, and finally the PCI index is compared with a reference standard to evaluate the heart-lung endurance level of the user.
Further, in the step 5, the intelligent wearable device collects heart rate every 10 seconds in 2 minutes, takes the average value of 12 heart rates as the resting heart rate, and collects the average value of heart rates in three time periods respectively in 30 seconds after the start of the movement, 60 seconds after the end of the movement and 10 seconds after the end of the movement in the movement process, and synchronously records the walking speed of the user.
Further, in the step 6, the specific meaning of the PCI index is: the parameters of the human body movement capability are evaluated in a walking state, and a certain linear relation exists between the parameters and the oxygen uptake. The subject is in a resting state, after the heart rate is stable, counting every 10 seconds within 2 minutes, and taking the average value of 12 heart rates as resting heart rate (resting HR); then, the exercise is performed for 6 minutes at walking speed (walking speed), the non-steady state heart rate (average value of heart rate after 30 seconds after the start of exercise), the steady state heart rate (average value of heart rate after 60 seconds after exercise) and the post-exercise heart rate (average value of heart rate after exercise) are recorded respectively, the exercise heart rate (walking HR) is represented by the average value of the three heart rates, a certain linear relation exists between the difference value of the rest heart rate of the human body and the heart rate during walking and the maximum oxygen uptake through repeated tests and data analysis, and a calculation formula of the physiological consumption index is provided:
the walking heart rate is a heart rate in an unsteady state, a heart rate in a steady state or a heart rate after exercise, and the method for indirectly testing the heart-lung function based on the physiological consumption index can basically truly reflect the heart-lung endurance level of a subject. In order to reduce the influence caused by the deviation of one of the state detection values, the average value of the non-steady state heart rate, the steady state heart rate and the post-exercise heart rate is calculated and used as the exercise heart rate.
According to another aspect of the present invention, there is provided a cardiopulmonary endurance measurement system based on an intelligent wearable device, including:
the sensor data acquisition module is used for acquiring data of heart rate and walking speed by the sensor;
the intelligent wearable equipment system control module is used for processing data acquired by the sensor, timing, reminding at regular time, monitoring the movement state and analyzing communication data;
the wireless communication module is used for realizing a Bluetooth wireless communication function between the intelligent wearable device and the mobile phone terminal;
and the user terminal processing module is used for calculating the quantization index, evaluating the calculation result and displaying the data.
Furthermore, in the step test and 6-minute walking test, the intelligent wearable device system control module sends vibration in a designated test link to prompt a user to start, end or execute action according to a certain beat. The intelligent wearable device monitors the motion state of the user at the same time, ensures that the user completes the action according to the requirement, enables the collected data to more accurately reflect the real level of the user in each state, and further enables the obtained PCI index to be more real and effective.
Further, the user terminal processing module is configured to:
Setting user information and motion parameters;
playing guiding audio, and timing reminding;
processing the exercise data, calculating a PCI index and cardiopulmonary endurance assessment;
displaying the measurement result in a software interface;
and storing data, namely storing user information and measurement data.
Further, when the step test and the 6-minute walk test are performed, the user terminal processing module controls the intelligent wearable device indicator lamp to emit light through Bluetooth communication, or the screen of the intelligent wearable device displays the current ongoing movement, so that the effect of prompting a user is achieved.
Further, when the terminal APP broadcasts a specified action to be executed in the current link during the step test and the 6-minute walk test, the user follows the instruction to complete the specified action.
Further, the terminal APP counts time according to the fixed time specified by the test, and reminds the user that the action phase is completed when the counting starts and ends.
Further, the terminal APP provides an interface supporting display of the two heart-lung endurance test items, calculates the PCI index according to heart rate and walking distance information received from the intelligent wearable equipment, compares the PCI index with a reference standard to evaluate, and finally displays the interface of the test measurement result.
Further, the user terminal processing module processes the exercise data using the cardiopulmonary endurance measurement method of one of claims 1 to 5, and performs the cardiopulmonary endurance evaluation of the step test and the 6-minute walking test.
Further, the terminal APP stores personal information added by the user and measurement results obtained after movement in a classified manner.
The beneficial effects are that:
the heart-lung endurance measuring method and system based on the intelligent wearing equipment can make up for the defects of the prior art, the heart rate and walking speed data are collected through the sensor on the intelligent wearing equipment, the movement detecting function ensures the reality and accuracy of the collected data, and the method provided by the invention is used for calculating the quantitative index so as to conveniently and rapidly realize the heart-lung endurance measuring function. The quantitative index PCI index used by the method does not need a strict experimental protocol, is theoretically suitable for various different types of heart-lung endurance test projects, and can automatically complete the measurement work in the exercise process by only wearing intelligent wearing equipment, so that the cost of manual guidance and measurement in the heart-lung endurance test projects is reduced.
Drawings
FIG. 1 is a flow chart of a heart-lung endurance measurement method based on intelligent wearable equipment;
FIG. 2 is a schematic diagram of calibration of an acceleration sensor coordinate system in a bracelet in accordance with the present invention;
FIG. 3 is a step test flow chart based on intelligent wearable equipment;
fig. 4 is a flowchart of detecting a user resting state by the intelligent wearable device provided by the invention;
FIG. 5 is a schematic diagram of the intelligent wearable device provided by the invention for detecting a step test action;
FIG. 6 is a flow chart of the intelligent wearable device detection step test method provided by the invention;
FIG. 7 is a 6-minute walk test flow chart based on intelligent wearable equipment;
fig. 8 is a structural diagram of a heart-lung endurance measurement system based on intelligent wearable equipment.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without the inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
The invention provides a heart-lung endurance measurement method and system based on intelligent wearable equipment, which are used for conveniently and quickly realizing the heart-lung endurance measurement function. The method and the system are based on the same technical conception, and because the principles of solving the problems by the method and the system are similar, the implementation of the system and the method can be mutually referred to, and the repetition is not repeated.
In the embodiment of the invention, the intelligent wearable device is a portable intelligent device. The device is at least internally provided with a heart rate sensor and a triaxial acceleration sensor, or the device can be connected with a sensor module arranged outside. Some examples of smart wearable devices are: smart phones, smart watches, smart bracelets, smart glasses, and other sports accessories or wearable accessories, etc., embodiments of the invention are not limited herein.
According to one embodiment of the invention, a heart-lung endurance measurement method based on intelligent wearable equipment is provided; as shown in fig. 1, a flow chart of a cardiopulmonary endurance measurement method based on intelligent wearable equipment is provided, in the method, a user selects a test type in a mobile phone APP, and the APP provides two test types: step test and 6-minute walk test. The intelligent wearing equipment is normally worn on the wrist under the prompt of APP, the resting state is kept, and the equipment can automatically start to collect the resting heart rate after detecting and judging the resting state. After the test is started, the user finishes the specified action according to the indication of the APP, the intelligent wearable device continuously collects the real-time heart rate of the user in the process to acquire the movement speed, monitors the movement state of the user, monitors the user to finish the action according to the requirement, calculates the received data by the mobile phone APP after the whole test is finished to obtain a quantitative index PCI index, and then compares the quantitative index PCI index with a reference standard to evaluate and measure the heart-lung endurance level of the user.
Further, the application scenarios mainly applicable to the method are as follows: step test and 6-minute walk test. These two tests differ in implementation.
First, the step test, the 6-minute walk test, and the PCI index are explained for easy understanding by those skilled in the art.
The step test is simply to rotate left and right legs to step on the steps to test the adaptation level of cardiopulmonary function. During testing, a subject stands in front of the steps vertically and moves up and down according to a certain beat. The user needs to step up and down every 2 seconds, 30 times per minute, and 3 minutes. A complete step up and down process is as follows: first one pedal goes up the step, then the other pedal goes up the step, and both legs straighten, then the foot step of step is stepped on first, and finally the other foot step. During the test, the user should rotate the left leg and the right leg, the upper body and the two legs must straighten after going up and down the steps each time, and the user cannot bend the knees. After the repeated actions are finished, the user sits on the chair immediately, records that the movement is stopped for 1 to 1 minute and a half minutes,
To represent the transformation from the geodetic coordinate system e to the carrier coordinate system b. The third column of the rotation matrix is actually a description of the z-axis direction unit vector in the b-system. And transpose of the rotation matrix
Third row vector [ c ] 13 c 23 c 33 ]Namely the original rotation matrix +.>Is included in the first column vector. Thus (S)>The third row of vectors of (a) is the description of the z-axis direction unit vector of e system, namely the gravity vector g in b system. Meanwhile, the description of the gravity vector (or the opposite direction vector thereof) in the b system is the numerical value of three directions measured by the triaxial acceleration sensor. Thus->The third row of vectors is actually the output vector of the acceleration sensor, which is the key to the pose solution.
Fig. 3 is a step test flow chart based on intelligent wearable equipment, provided by the invention, and the whole test flow comprises the following steps:
step S1: the user clicks a step test item in the mobile phone APP, the user wears the intelligent wearing equipment on the wrist normally under the prompt of the APP, meanwhile, the user clicks the left hand or right hand wearing equipment in the APP, and clicks a starting option to start a test link after the user finishes preparation;
step S2: under the prompt of the APP, the user should keep a resting state before formally starting the test, the intelligent wearable device can automatically start measuring the resting heart rate of the user, and meanwhile the APP indicates actions to be executed by the formally test to the user;
step S3: in the resting state process, after the heart rate of a user is stable, the intelligent wearing equipment collects the heart rate every 10 seconds within 2 minutes, takes the average value of 12 heart rates as the resting heart rate, the APP prompts the measurement work to be completed at the stage after the measurement is completed, clicks a starting exercise option to formally start a step test link after the user is ready, and the APP sends out voice and the equipment sends out long vibration to indicate that the exercise stage starts;
Step S4: the intelligent wearable device carries out short vibration prompt at a frequency of once every 0.5 seconds in the movement process, a vibration user should execute one-step action every time, and every 4 steps are complete step-up and step-down actions, and the device monitors whether the user completes the actions according to the requirements in real time;
step S5: after the movement starts, the intelligent wearing equipment starts timing immediately, and heart rate average values are respectively acquired in three time periods of 30 seconds after the movement starts, 60 seconds after the movement ends and 10 seconds after the movement ends, meanwhile, the intelligent wearing equipment synchronously records the walking speed of a user, and the step test is not large in difference of actual speed measurement values of each test due to fixed action frequency;
step S6: 15 seconds before the end of the stage, the APP reminds the user of preparing for the end, after the 3 minutes of repeated actions are completed, the intelligent wearable equipment sends out a long vibration prompt for ending the movement, and the user sits down according to the APP prompt;
step S7: after the data acquisition is completed, the data is transmitted to the APP through Bluetooth, the APP obtains the PCI index through the PCI calculation formula, and finally the PCI index is compared with the reference standard to obtain and display the heart-lung endurance level grade of the user on the mobile phone.
Further, fig. 4 is a flowchart of the intelligent wearable device provided by the invention for detecting the user 'S resting state, and in the step S2, the intelligent wearable device automatically detects the user' S resting state, and the specific method thereof is as follows: when the user is in a resting state, three conditions should be satisfied: the user is inactive, the heart rate is low, and heart rate variation fluctuations are small. These three conditions can be measured and judged by a triaxial accelerometer and a heart rate sensor, respectively. First the acceleration values measured by the tri-axis accelerometer are a= { ax, ay, az }, when the user is in an inactive state, the accelerations on the tri-axes should all be close to 0, then their combined accelerations However, considering that the sensor may drift, a threshold value is set to ε if the combined acceleration +.>The moment is determined to be an inactive state. The user is inactiveThe real resting heart rate can not be collected in the state, and the reason is related to psychological activities of the user during testing, when the user is in tension and excited emotion during testing, the heart rate variation fluctuation is large, and even if the user keeps sitting still, the collected heart rate is not the resting heart rate of the user. Therefore, when the heart rate data HR is smaller than or equal to E and the delta HR is smaller than or equal to delta, the user is judged to be in a resting state in the period, wherein the E is a set heart rate threshold value, the threshold value is determined by the age, sex, life habit and other characteristics, the average adult heart rate is about 75 times per minute in the resting state, and the fluctuation range of the normal adult heart rate is 60-100 per minute. When in an excited state, the heart rate is obviously increased, the range is exceeded, and the data can be used as a threshold reference value; delta is the heart rate fluctuation threshold, which is related to the maximum heart rate threshold, and the value is suggested as a percentage of the maximum heart rate. In summary, a suitable time interval T1 is set, within which, if a measured continuous combined acceleration +. >Constant establishment, simultaneous continuous measurement of heart rate HR<E and heart rate fluctuation DeltaHR<And if the delta is constant, judging that the user enters a stable resting state, and indicating that continuous collection of resting heart rate can be started. Otherwise, when the above conditions cannot be completely met in the T1, the user is indicated to not enter a stable resting state temporarily, at this time, the resting heart rate data is collected inaccurately, the APP prompts the user to keep the resting state and simultaneously perform deep breathing to relieve emotion, the intelligent wearable device discards the original data and counts time, and the detection of the new T1 time is restarted.
Further, in the step S3, the smart wearable device monitors the resting state of the user while collecting the resting heart rate, and the specific method thereof is as follows: during the acquisition process for two minutes, the triaxial accelerometer continuously measures triaxial acceleration data whenAt this time, the user is not in a resting state, and the heart rate fluctuatesWill affect the accuracy of the measurement. Of the heart rates acquired every 10 seconds, if the heart rate HR acquired this time>E or DeltaHR>Delta, it indicates that the user is now in an excited state, the heart rate acquired this time is not a true resting heart rate, and the data should be discarded. In summary, a suitable time interval T2 is set, and T2 suggests that a value of 10 seconds in synchronism with the heart rate is obtained, if the total acceleration +. >Or the heart rate HR acquired during the period>Either E or heart rate fluctuations ΔHR>Delta, judging that the current time period of the user is not in a stable resting state, prompting the user to keep the resting state by the APP, and simultaneously carrying out deep breathing to relieve emotion, wherein the intelligent wearable device can discard data in the time period; if the user fails to enter the stable resting state within n T2 time periods, the intelligent wearable device discards all the acquired data, returns to the step S2 to restart the resting state detection, and suggests a reference value of 3 according to the test condition.
Further, in step S4, the intelligent wearable device monitors in real time whether the user completes the action according to the requirement, and the specific method thereof is as follows:
when the user wears the intelligent wearing equipment on the wrist normally and the arm naturally hangs down, the carrier coordinate system b rotates 90 degrees anticlockwise around the X axis relative to the reference coordinate system e, and when the user goes up and down the step, the intelligent wearing equipment swings up and down along with the arm, and the movement process can be essentially regarded as rotary movement around the Z axis.
The rotation matrix, which is known to rotate phi about the X-axis, is expressed as:
the rotation matrix of rotation ψ about the Z-axis is expressed as:
the rotation matrix in the X-Z rotation order is expressed as:
The third row vector is the projection of the gravity vector g in the carrier coordinate system. The output of the acceleration sensor is a= { ax, ay, az } = { sin (ψ) ×sin (Φ), sin (Φ) ×cos (ψ), cos (Φ) }, where Φ= -90 °, so the value of ψ can be calculated by collecting the X-axis and Y-axis output of the acceleration sensor. In the actual step-down process, since the movement of the body in the front-back and up-down directions generates additional acceleration, the data directly output from the accelerometer cannot accurately calculate the angle of rotation about the Z-axis, and for this purpose, the acceleration generated by the body movement needs to be calculated so as to be removed from the sensor data when calculating the angle.
Fig. 5 is a schematic diagram of the intelligent wearable device provided by the invention for detecting a step test action, wherein four steps of step test actions can be divided into two processes: an upper step and a lower step; each process can be further divided into two stages. For the up step process: the first stage is to step out the left leg to climb the step, the left arm swings backwards, the right arm swings forwards, and the gravity center of the body is not raised; the body gravity center in the second stage starts to rise, the right leg also climbs the step, the left arm swings forwards, and the right arm swings backwards. For the downstairs procedure: in the first stage, the left leg descends first, the body center of gravity descends, the left arm swings backwards, and the right arm swings forwards; the right leg in the second stage also descends the step, at this time the body center of gravity is no longer lowered, the left arm swings forward, and the right arm swings backward.
Taking the example that the left arm wears intelligent wearing equipment, arms at the first stage of an upper step and the first stage of a lower step swing backwards, and acceleration generated by backward movement of the body gravity center is added in the X-axis direction and acceleration generated by descending of the body gravity center is added in the Y-axis direction in the first stage of the lower step; the arms of the second stage of the upper step and the second stage of the lower step swing forwards, and in the second stage of the upper step, the acceleration generated by the forward movement of the body gravity center is also generated in the X-axis direction, and the acceleration generated by the descending of the body gravity center is also generated in the Y-axis direction.
According to the analysis, the X-axis acceleration data measured by the intelligent wearable equipment at the first stage of the step-up is recorded asThe acceleration produced by the backward swing of the arm on the X-axis is denoted as a x 'A'; the Y-axis acceleration data measured by the device is recorded as +.>The acceleration of the arm swing in the Y-axis is denoted as a y . Then the equation is listed based on the analysis: />
X-axis acceleration data measured by intelligent wearable equipment at second stage of step-up is recorded asThe acceleration of the arm in the X-axis of forward swing is denoted as a x Acceleration due to forward movement of the body's center of gravity is denoted +.>The Y-axis acceleration data measured by the device is recorded as +. >Acceleration due to the rise of body weight is denoted +.>Then the equation is listed based on the analysis: />
X-axis acceleration data measured by intelligent wearable equipment at first stage of descending step is recorded asAcceleration due to the backward shift of the body's center of gravity is denoted +.>The Y-axis acceleration data measured by the device are respectively recorded as +.>Acceleration due to lowering of the body's centre of gravity is denoted +.>Then the equation is listed based on the analysis: />
X-axis acceleration data measured by intelligent wearable equipment at second stage of descending step is recorded asThe Y-axis acceleration data measured by the device is recorded as +.>Then the equation is listed based on the analysis: />
Simultaneous with the above equations: acceleration of the advancing of the body's centre of gravity of the upper stepAcceleration of body weight elevation ∈>Acceleration of the backward shift of the body's centre of gravity of the lower step +.>Acceleration of body weight centre lowering +.>For the first stage of the upper step and the second stage of the lower step, the data acquired by the triaxial acceleration sensor can be directly used for gesture calculation, and the acceleration generated by the change of the gravity center of the body in the data is subtracted for the first stage of the upper step and the first stage of the lower step for gesture calculation.
Taking the natural hanging of the arm as a reference of the angle of the swing arm, swinging the arm forwards to be in the positive direction of the angle, setting fixed upper and lower limit thresholds according to the collected angle change, and swinging the arm backwards to gradually reduce the angle psi when the arm is in the first stage of the upper step and the first stage of the lower step, and continuing to descend through the lower limit threshold after the angle psi is lower than the upper limit threshold and reaches the zero reference; in the second stage of the upper step and the second stage of the lower step, the arm swings forwards to gradually increase the angle psi, and after the angle psi is higher than the lower limit threshold value, the angle psi continuously rises through the upper limit threshold value after reaching the zero reference.
Fig. 6 is a flowchart of a step detection testing method for an intelligent wearable device, where when the step detection testing method is in a corresponding stage, a system will firstly analyze and judge a change process of an angle of a swing arm, specifically, detect and judge a stage action according to a change direction of the angle, whether the angle passes an upper limit threshold and a lower limit threshold or not and through a sequence, and if the requirement of the change process of the angle of the swing arm corresponding to the stage cannot be met, judge that a user cannot complete the action according to the requirement in the stage. When the arm swing angle condition is met, a corresponding threshold value is also set for the acceleration change of the body gravity center, the system can also judge the action of the user by combining the change of the body gravity center acceleration in the corresponding stage, specifically, the acceleration generated by the forward movement and the rising of the body gravity center in the second stage of the upper step is respectively larger than the respective set threshold value, and the system can judge the action of the upper step only after the condition is met; in the first stage of descending the step, the absolute values of the accelerations generated by the backward movement and the downward movement of the body center of gravity should be respectively larger than the respective set threshold values, and the system can determine the descending step action only after the condition is satisfied. Taking four phases as an action period, and when more than two phases in one action period are judged to be unsatisfactory by the system, the action period is regarded as unsatisfactory, and the system sends out a prompt to remind a user to test according to the requirements; if the test is not satisfied for all k consecutive periods, stopping the test, and restarting to execute step S4, wherein the motion data of the test is also discarded, and the value of k is recommended to be 3 according to the test condition. When the right arm wears the intelligent wearing device for testing, the direction of the swing arm is opposite to that of the left arm in each stage, but the swing arm is not different in the change of the body gravity center, so that the method for calculating the acceleration generated by the change of the body gravity center is the same as that of the left arm. When the arm swings forwards in the first stage of the upper step and the first stage of the lower step to gradually increase the angle psi, and after the angle psi is higher than the lower limit threshold value, the angle psi continuously rises to pass the upper limit threshold value after reaching the zero reference; in the second stage of the upper step and the second stage of the lower step, the arm swings backwards to gradually reduce the angle psi, and after the angle psi is lower than the upper limit threshold value, the angle psi continuously falls through the lower limit threshold value after reaching the zero reference. Therefore, in the judgment of the swing arm angle, the judgment process in the case of wearing the right arm is opposite to that of the left arm, and the judgment of the body gravity center acceleration is not different.
Fig. 7 is a 6-minute walking test flow chart based on intelligent wearable equipment, and the whole test flow comprises the following steps:
step S1: the user clicks a 6-minute walking test item in the mobile phone APP, and the intelligent wearing equipment is normally worn on the wrist by the user under the prompt of the APP;
step S2: under the prompt of the APP, the user should keep the resting state for at least 10 minutes before formally starting the test, and the APP reminds the user that the resting heart rate is to be detected and indicates to the user the action to be executed by the formal test;
step S3: in the resting state process, after the heart rate of a user is stable, the intelligent wearing equipment collects the heart rate every 10 seconds within 2 minutes, takes the average value of 12 heart rates as the resting heart rate, the APP prompts the measurement work to be completed at the stage after the measurement is completed, clicks a starting exercise option to formally start a walking test link after the user is ready, and the intelligent wearing equipment sends out long vibration to indicate the beginning of a walking stage;
step S4: after walking, the user walks directly in the running platform or the corridor, the intelligent wearing equipment starts timing, heart rate average values are respectively acquired in three time periods of 30 seconds after the start of the movement, 60 seconds after the last movement and 10 seconds after the end of the movement, and meanwhile, the intelligent wearing equipment synchronously records the walking speed of the user;
Step S5: the APP is timed when the walking test starts, the APP is reminded of making an ending preparation 15 seconds in advance, after walking for 6 minutes, the APP is used for voice broadcasting, meanwhile, the intelligent wearable equipment sends out long vibration to remind the user of ending walking, and the intelligent wearable equipment is rested, and waits for heart rate measurement of the last 10 seconds;
step S6: after the data acquisition is completed, the data is transmitted to the APP through Bluetooth, the APP obtains the PCI index through the PCI calculation formula, and finally the PCI index is compared with the reference standard to obtain and display the heart-lung endurance level grade of the user on the mobile phone.
Further, fig. 4 is a flowchart of the intelligent wearable device provided by the invention for detecting the user 'S resting state, and in the step S2, the intelligent wearable device automatically detects the user' S resting state, and the specific method thereof is as follows: when the user is in a resting state, three conditions should be satisfied: the user is inactive, the heart rate is low, and heart rate variation fluctuations are small. These three conditions can be measured and judged by a triaxial accelerometer and a heart rate sensor, respectively. First the acceleration values measured by the tri-axis accelerometer are a= { ax, ay, az }, when the user is in an inactive state, the accelerations on the tri-axes should all be close to 0, then their combined accelerations However, considering that the sensor may drift, a threshold value is set to ε if the combined acceleration +.>The moment is determined to be an inactive state. The user does not have to collect the true resting heart rate in the inactive state, and the reason for this is related to the mental activities of the user when the user is testingWhen tension and excited emotion occurs, the heart rate variation fluctuation is large, and even if the user keeps sitting still, the heart rate collected at the moment is not the resting heart rate of the user. Therefore, when the heart rate data HR is smaller than or equal to E and the delta HR is smaller than or equal to delta, the user is judged to be in a resting state in the period, wherein the E is a set heart rate threshold value, the threshold value is determined by the age, sex, life habit and other characteristics, the average adult heart rate is about 75 times per minute in the resting state, and the fluctuation range of the normal adult heart rate is 60-100 per minute. When in an excited state, the heart rate is obviously increased, the range is exceeded, and the data can be used as a threshold reference value; delta is the heart rate fluctuation threshold, which is related to the maximum heart rate threshold, and the value is suggested as a percentage of the maximum heart rate. In summary, a suitable time interval T1 is set, within which, if a measured continuous combined acceleration +. >Constant establishment, simultaneous continuous measurement of heart rate HR<=ζ and heart rate fluctuation Δhr<When δ is constant, the user is judged to enter a stable resting state, which means that continuous resting heart rate collection can be started. Otherwise, when the above conditions cannot be completely met in the T1, the user is indicated to not enter a stable resting state temporarily, at this time, the resting heart rate data is collected inaccurately, the APP prompts the user to keep the resting state and simultaneously perform deep breathing to relieve emotion, the intelligent wearable device discards the original data and counts time, and the detection of the new T1 time is restarted.
Further, in the step S3, the smart wearable device monitors the resting state of the user while collecting the resting heart rate, and the specific method thereof is as follows: during the acquisition process for two minutes, the triaxial accelerometer continuously measures triaxial acceleration data whenAnd when the user is not in a resting state, the heart rate fluctuation caused by the user is indicated to influence the accuracy of the measurement result. Of the heart rates acquired every 10 seconds, if the heart rate HR acquired this time>E or DeltaHR>Delta, indicatesThe user is now in an excited state, the heart rate acquired this time is not a true resting heart rate, and the data should be discarded. In summary, a suitable time interval T2 is set, and T2 suggests that a value of 10 seconds in synchronism with the heart rate is obtained, if the total acceleration +. >Or the heart rate HR acquired during the period>Either E or heart rate fluctuations ΔHR>Delta, judging that the current time period of the user is not in a stable resting state, prompting the user to keep the resting state by the APP, and simultaneously carrying out deep breathing to relieve emotion, wherein the intelligent wearable device can discard data in the time period; if the user fails to enter the stable resting state within n T2 time periods, the intelligent wearable device discards all the acquired data, returns to the step S2 to restart the resting state detection, and suggests a reference value of 3 according to the test condition. It should be noted that the above embodiments only enumerate some application scenarios, but those skilled in the art should understand that the present invention is not limited by the described application scenarios, because the test index and the method can be applied to other application scenarios with the same function according to the present invention, and the related scenarios are not necessarily required by the present invention.
According to another embodiment of the present invention, as shown in fig. 8, a structure diagram of a cardiopulmonary endurance measurement system based on an intelligent wearable device provided by the present invention includes:
the sensor data acquisition module is used for acquiring data of heart rate and walking speed by the sensor;
The intelligent wearable equipment system control module is used for processing data acquired by the sensor, timing, reminding at regular time, monitoring the movement state and analyzing communication data;
the wireless communication module is used for realizing a Bluetooth wireless communication function between the intelligent wearable device and the mobile phone terminal;
and the user terminal processing module is used for calculating the quantization index, evaluating the calculation result and displaying the data.
Further, the sensor data acquisition module performs software filtering on the data, performs data interaction with a communication interface of the intelligent wearable equipment central processing unit chip through the sensor, reads sensor information in real time, and performs filtering processing on the acquired data;
the intelligent wearable device system control module is used for controlling the intelligent wearable device to cooperate with data acquisition and vibration reminding functions in a specified time period in the test process so as to meet test requirements;
the intelligent wearable device system control module can control the intelligent wearable device to send out vibration to prompt the user to start and end or execute actions according to a certain beat when the user performs a test;
the intelligent wearable device bracelet system control module can continuously monitor the motion state of the user in the process of testing the user, ensure that the user completes the action according to the requirement, enable the collected data to reflect the real level of the user in each state more accurately, and further enable the obtained PCI index to be more real and effective.
The wireless communication module encapsulates the processed data in the intelligent wearable device, and performs wireless communication with the intelligent mobile phone through a Bluetooth wireless protocol to realize data interaction with the mobile phone terminal; the Bluetooth communication function with the intelligent wearable equipment is realized by calling the API service provided by the Bluetooth official, and the data interaction is carried out with the equipment;
further, the user terminal processing module is configured to:
setting user information and motion parameters;
playing guiding audio, and timing reminding;
processing the exercise data, calculating a PCI index and cardiopulmonary endurance assessment;
displaying the measurement result in a software interface;
and storing data, namely storing user information and measurement data.
The user terminal processing module controls the intelligent wearing equipment indicator lamp state and the screen display module through Bluetooth communication, and because part of old wearing equipment is not provided with a display screen, only has the indicator lamp, the intelligent wearing equipment is suitable for most intelligent wearing equipment on the market, and the equipment indicator lamp can be controlled to send out light to indicate that the equipment is in a detection state. For smart wearable devices equipped with a display screen, the device screen displays the motion currently in progress to achieve the effect of prompting the user.
The user information setting and motion parameter setting module provides a function of adding personal related information of a user and simultaneously realizes a login verification function of the APP; certain motion parameters, such as motion time, can be set;
the system comprises a guiding audio playing module, a timing reminding module and a timing reminding module, wherein the guiding audio playing module is used for controlling a terminal APP to report a specified action which should be executed in a current link in a user testing process, a user follows an instruction to finish the specified action, controls the terminal APP to count according to a fixed time specified by a test, and reminds the user that the action stage is finished when the timing starts and ends;
the heart-lung endurance assessment system comprises a heart-lung endurance assessment module, a motion data processing module, a heart-lung endurance assessment module, a motion data processing module, a heart rate assessment module, a walking speed assessment module, a heart-lung endurance assessment module and a walking speed assessment module, wherein the heart-lung endurance assessment module is used for calculating a quantification index PCI index according to heart rate and walking speed data received from intelligent wearable equipment, and comparing the heart-lung endurance assessment module with a reference standard;
displaying a measurement result module in a software interface, and displaying an interface of the test measurement result on a terminal APP;
and storing data, namely storing user information and a measurement data module, and storing personal information added by a user and measurement results obtained after movement in a classified manner.
It should be noted that, for simplicity of description, the foregoing method embodiments or examples are all expressed as a series of combinations of actions, but it should be understood by those skilled in the art that the present invention is not limited by the order of actions described, as some steps may be performed in other order or simultaneously according to the present invention. Further, those skilled in the art will also appreciate that the implementations or embodiments described in the specification are presently preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (10)
1. A heart-lung endurance measurement method based on intelligent wearable equipment comprises the following steps:
step 1, a user APP logs in;
step 2, selecting test item types in the APP, wherein the item types comprise a step test and a 6-minute walking test;
step 3, before formally starting the test, the user keeps a resting state, and the intelligent wearable device automatically starts to collect the resting heart rate of the user;
step 4, clicking a movement starting option to formally start a test after preparation is finished, and the intelligent wearable device and the APP give an instruction to the action of the user according to the requirement of the item type, and simultaneously monitor whether the user finishes the action in time according to the requirement;
step 5, acquiring exercise heart rate and walking speed data of a user in the test process;
step 6, calculating according to a PCI index formula to obtain a quantitative index, and comparing the quantitative index with a reference standard to realize measurement and evaluation of heart-lung endurance;
In the step 4, monitoring whether the user completes the action in time according to the requirement specifically includes:
x-axis acceleration data measured by intelligent wearable equipment at first stage of step up is recorded asThe acceleration produced by the backward swing of the arm on the X-axis is denoted as a x 'A'; the Y-axis acceleration data measured by the device is recorded as +.>The acceleration of the arm swing in the Y-axis is denoted as a y The method comprises the steps of carrying out a first treatment on the surface of the Then the equation is listed based on the analysis: />
X-axis acceleration data measured by intelligent wearable equipment at second stage of step-up is recorded asThe acceleration of the arm in the X-axis of forward swing is denoted as a x Acceleration due to forward movement of the body's center of gravity is denoted +.>The Y-axis acceleration data measured by the device is recorded asAcceleration due to the rise of body weight is denoted +.>Then the equation is listed based on the analysis: />
X-axis acceleration data measured by intelligent wearable equipment at first stage of descending step is recorded asAcceleration due to the backward shift of the body's center of gravity is denoted +.>The Y-axis acceleration data measured by the device are respectively recorded as +.>Acceleration due to lowering of the body's centre of gravity is denoted +.>Then the equation is listed based on the analysis: />
X-axis acceleration data measured by intelligent wearable equipment at second stage of descending step is recorded asThe Y-axis acceleration data measured by the device is recorded as +.>Then the equation is listed based on the analysis: />
Simultaneous with the above equations: acceleration of the advancing of the body's centre of gravity of the upper step Acceleration of body weight elevation ∈>Acceleration of the backward shift of the body's centre of gravity of the lower step +.>Acceleration of body weight centre lowering +.>For the first stage of the upper step and the second stage of the lower step, the data acquired by the triaxial acceleration sensor can be directly used for gesture calculation, and the acceleration generated by the change of the gravity center of the body in the data is needed to be subtracted for gesture calculation in the second stage of the upper step and the first stage of the lower step;
in the step 4, monitoring whether the user completes the action in time according to the requirement, and further comprising:
when the system is in the corresponding stage, the system firstly analyzes and judges the change process of the angle of the swing arm, specifically detects and judges the stage action according to the change direction of the angle, whether the angle passes through an upper limit threshold value and a lower limit threshold value or not and the sequence, and if the requirement of the change process of the angle of the swing arm corresponding to the stage cannot be met, the system judges that the user cannot finish the action according to the requirement in time in the stage; when the arm swing angle condition is met, a corresponding threshold value is also set for the acceleration change of the body gravity center, the system can also judge the action of the user by combining the change of the body gravity center acceleration in the corresponding stage, specifically, the acceleration generated by the forward movement and the rising of the body gravity center in the second stage of the upper step is respectively larger than the respective set threshold value, and the system can judge the action of the upper step only after the condition is met; in the first stage of descending the step, the absolute values of acceleration generated by backward movement and descending of the body center of gravity are respectively larger than the respective set threshold values, and the system can judge the descending step action only after the condition is met; taking four phases as an action period, and when more than two phases in one action period are judged to be unsatisfactory by the system, the action period is regarded as unsatisfactory, and the system sends out a prompt to remind a user to test according to the requirements; if the test is determined to be unsatisfactory in all the k consecutive periods, stopping the test, restarting the execution of the step 4, and discarding the motion data of the test.
2. The method for measuring the heart-lung endurance based on the intelligent wearable device according to claim 1, wherein in the step 2, when the user selects the step test item, specifically comprising:
under the prompt of the APP, the user wears the intelligent wearing equipment on the wrist normally, and meanwhile, the left hand or right hand wearing equipment is selected and confirmed in the APP; keeping a resting state before formally starting a test, and automatically starting to measure the resting heart rate of a user after the intelligent wearable device detects that the user enters a stable resting state; clicking a movement starting option to formally start a step test link after preparation is finished; the intelligent wearable device carries out short vibration prompt at a frequency of once every 0.5 seconds in the movement process, each time the user is vibrated to execute one-step action, each step action device can detect whether the user timely completes the step-up and step-down action according to the requirement, and each 4 steps are complete step-up and step-down actions; after the exercise starts, the intelligent wearable device immediately counts time and acquires the exercise heart rate and the walking speed; after the 3-minute repeated action is completed, the device sends out a long vibration prompt to finish the movement, and waits for the heart rate measurement of the last 10 seconds; after the data acquisition is finished, the data is transmitted to an APP through Bluetooth, the APP calculates to obtain a PCI index, and finally the PCI index is compared with a reference standard to evaluate the heart-lung endurance level of the user.
3. The method for measuring heart and lung endurance based on intelligent wearable equipment according to claim 1, wherein in the step 2, when the user selects a 6-minute walking test item, specifically comprising:
under the prompt of APP, the user wears the intelligent wearing equipment on the wrist normally; keeping a resting state before formally starting a test, and automatically starting to measure the resting heart rate of a user after the intelligent wearable device detects that the user enters a stable resting state; clicking the option of starting exercise after the preparation is finished to formally start a 6-minute walking test link; after walking, the user walks directly in the running platform or corridor; the intelligent wearing equipment immediately counts time and acquires exercise heart rate and walking speed; after 6 minutes of walking is completed, the device sends out a long vibration prompt to finish the movement, and waits for the heart rate measurement of the last 10 seconds; after the data acquisition is finished, the data is transmitted to an APP through Bluetooth, the APP calculates to obtain a PCI index, and finally the PCI index is compared with a reference standard to evaluate the heart-lung endurance level of the user.
4. The method for measuring heart-lung endurance based on the intelligent wearable device according to claim 1, wherein in the step 5, the intelligent wearable device collects heart rate every 10 seconds in 2 minutes in a resting state, takes an average value of 12 heart rates as a resting heart rate, and collects heart rate average values respectively in 30 seconds after the start of the exercise, 60 seconds after the last of the exercise and 10 seconds after the end of the exercise during the exercise, and simultaneously records walking speeds of the user synchronously.
5. The method for measuring heart-lung endurance based on intelligent wearable equipment according to claim 1, wherein in the step 6, the specific meaning of the PCI index is: the method comprises the steps of evaluating parameters of the movement capacity of a human body in a walking state, wherein a certain linear relation exists between the parameters and oxygen uptake, a subject is in a resting state, counting every 10 seconds within 2 minutes after the heart rate is stable, and taking an average value of 12 heart rates as resting heart rate resting HR; then, walking speed is used for 6 minutes, a heart rate average value of unsteady state heart rate, namely 30 seconds after starting exercise, a heart rate average value of steady state heart rate, namely the last 60 seconds of exercise and a heart rate average value of post exercise, namely 10 seconds of exercise are respectively recorded, the average value of the three heart rates is used for representing walking HR, and a calculation formula of physiological consumption index is provided:
6. the method for measuring heart-lung endurance based on the intelligent wearable device according to claim 1, wherein in the step 3, the intelligent wearable device automatically starts to collect the resting heart rate of the user comprises:
when the user is in a resting state, three conditions should be satisfied: user inactivity, heart rate below a threshold, heart rate variability fluctuations less than the threshold; the three conditions are respectively measured and judged by a triaxial accelerometer and a heart rate sensor;
First the acceleration values measured by the tri-axis accelerometer are a= { ax, ay, az }, when the user is in an inactive state, the accelerations on the tri-axes should all be close to 0, then their combined accelerationsConsidering that the sensor will drift, a threshold value is set as epsilon,if the combined acceleration->Then the moment is determined to be an inactive state; when heart rate data HR is smaller than or equal to an E and delta HR is smaller than or equal to delta, judging that a user is in a resting state in the time period, wherein the E is a set heart rate threshold; delta is the heart rate fluctuation threshold, a time interval T1 is set, within which, if the measured continuous combined acceleration +.>Constant establishment, simultaneous continuous measurement of heart rate HR<E and heart rate fluctuation DeltaHR<If the delta is constant, judging that the user enters a stable resting state, and indicating that continuous resting heart rate collection can be started; otherwise, when the above conditions cannot be completely met in the T1, the user is indicated to not enter a stable resting state temporarily, at this time, the resting heart rate data is collected inaccurately, the APP prompts the user to keep the resting state and simultaneously perform deep breathing to relieve emotion, the intelligent wearable device discards the original data and counts time, and the detection of the new T1 time is restarted.
7. Cardiopulmonary endurance measurement system based on intelligent wearing equipment, characterized by comprising:
the sensor data acquisition module is used for acquiring data of heart rate and walking speed by the sensor;
the intelligent wearable equipment system control module is used for processing data acquired by the sensor, timing, reminding at regular time, monitoring the movement state and analyzing communication data;
the wireless communication module is used for realizing a Bluetooth wireless communication function between the intelligent wearable device and the mobile phone terminal;
the user terminal processing module is used for calculating the quantization index, evaluating the calculation result and displaying the data;
the motion state monitoring, the motion state of the user is monitored, and the user is ensured to finish the actions according to the requirements, including:
first step of up stepX-axis acceleration data measured by the intelligent wearable equipment are recorded asThe acceleration produced by the backward swing of the arm on the X-axis is denoted as a x 'A'; the Y-axis acceleration data measured by the device is recorded as +.>The acceleration of the arm swing in the Y-axis is denoted as a y The method comprises the steps of carrying out a first treatment on the surface of the Then the equation is listed based on the analysis: />
X-axis acceleration data measured by intelligent wearable equipment at second stage of step-up is recorded asThe acceleration of the arm in the X-axis of forward swing is denoted as a x Acceleration due to forward movement of the body's center of gravity is denoted +. >The Y-axis acceleration data measured by the device is recorded asAcceleration due to the rise of body weight is denoted +.>Then the equation is listed based on the analysis: />
X-axis acceleration data measured by intelligent wearable equipment at first stage of descending step is recorded asAcceleration by rearward movement of body's centre of gravityThe degree is recorded as->The Y-axis acceleration data measured by the device are respectively recorded as +.>Acceleration due to lowering of the body's centre of gravity is denoted +.>Then the equation is listed based on the analysis: />
X-axis acceleration data measured by intelligent wearable equipment at second stage of descending step is recorded asThe Y-axis acceleration data measured by the device is recorded as +.>Then the equation is listed based on the analysis: />
Simultaneous with the above equations: acceleration of the advancing of the body's centre of gravity of the upper stepAcceleration of body weight elevation ∈>Acceleration of the backward shift of the body's centre of gravity of the lower step +.>Acceleration of body weight centre lowering +.>For the first stage of the upper step and the second stage of the lower step, the data acquired by the triaxial acceleration sensor can be directly used for gesture calculation, and the acceleration generated by the change of the gravity center of the body in the data is needed to be subtracted for gesture calculation in the second stage of the upper step and the first stage of the lower step;
the motion state monitoring, the monitoring user motion state, ensure that the user accomplishes the action according to the requirement, further include:
When the system is in the corresponding stage, the system firstly analyzes and judges the change process of the angle of the swing arm, specifically detects and judges the stage action according to the change direction of the angle, whether the angle passes through an upper limit threshold value and a lower limit threshold value or not and the sequence, and if the requirement of the change process of the angle of the swing arm corresponding to the stage cannot be met, the system judges that the user cannot finish the action according to the requirement in time in the stage; when the arm swing angle condition is met, a corresponding threshold value is also set for the acceleration change of the body gravity center, the system can also judge the action of the user by combining the change of the body gravity center acceleration in the corresponding stage, specifically, the acceleration generated by the forward movement and the rising of the body gravity center in the second stage of the upper step is respectively larger than the respective set threshold value, and the system can judge the action of the upper step only after the condition is met; in the first stage of descending the step, the absolute values of acceleration generated by backward movement and descending of the body center of gravity are respectively larger than the respective set threshold values, and the system can judge the descending step action only after the condition is met; taking four phases as an action period, and when more than two phases in one action period are judged to be unsatisfactory by the system, the action period is regarded as unsatisfactory, and the system sends out a prompt to remind a user to test according to the requirements; when the test is judged to be unsatisfactory in all the k continuous periods, the test is stopped, the test is restarted, and the motion data of the test are also discarded.
8. The system for measuring heart-lung endurance based on intelligent wearable equipment according to claim 7, wherein the system control module of the intelligent wearable equipment sends vibration to prompt a user to start, end or execute actions according to a certain beat in a specified test link in the step test and 6-minute walk test process.
9. The smart wearable device-based cardiopulmonary endurance measurement system of claim 7, wherein the user terminal processing module is configured to:
setting user information and motion parameters;
playing guiding audio, and timing reminding;
processing the exercise data, calculating a PCI index and cardiopulmonary endurance assessment;
displaying the measurement result in a software interface;
and storing data, namely storing user information and measurement data.
10. The heart-lung endurance measurement system based on the intelligent wearable device according to claim 7, wherein the user terminal processing module controls the intelligent wearable device indicator to emit light through bluetooth communication or the intelligent wearable device screen to display the currently ongoing movement in the process of performing the step test and the 6-minute walk test so as to achieve the effect of prompting the user;
The terminal APP broadcasts the appointed action which should be executed by the current link, and the user follows the action of finishing the prescription; timing according to the fixed time specified by the test, and reminding the user that the action phase is completed when the timing starts and ends;
the terminal APP provides an interface supporting displaying the two heart-lung endurance test projects, and calculates the PCI index according to heart rate and walking distance information received from the intelligent wearable device.
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