CN112138361B

CN112138361B - Cardio-pulmonary endurance measurement method and system based on oxygen uptake calculation

Info

Publication number: CN112138361B
Application number: CN202011096901.7A
Authority: CN
Inventors: 高理升; 李关东; 叶玉琪; 王艺桦; 张瑞骐; 马祖长; 许杨; 王涛; 王辉; 李云龙; 胡天骄; 孙怡宁; 杨先军; 陈焱焱; 周旭; 孙少明
Original assignee: Hefei Institutes of Physical Science of CAS
Current assignee: Hefei Institutes of Physical Science of CAS
Priority date: 2020-10-14
Filing date: 2020-10-14
Publication date: 2022-02-11
Anticipated expiration: 2040-10-14
Also published as: CN112138361A

Abstract

The invention provides a method and a system for measuring cardiopulmonary endurance based on oxygen uptake calculation, wherein the method adopts intelligent wearable equipment for pre-test acquisitionCollecting the resting heart rate of the user; collecting real-time heart rate and walking speed in the test process according to the VO_2MaxCalculating the formula to obtain the maximum oxygen intake, and measuring and evaluating the cardiopulmonary endurance through the maximum oxygen intake. The invention does not need expensive measuring equipment, automatically detects the motion state of the user, ensures the truth and the effectiveness of data, can reduce the cost of manual guidance and measurement in the cardiopulmonary endurance test project, further expands the functions of intelligent wearable equipment and improves the practicability of the intelligent wearable equipment.

Description

Cardio-pulmonary endurance measurement method and system based on oxygen uptake calculation

Technical Field

The invention relates to the field of cardiopulmonary endurance measurement, in particular to a cardiopulmonary endurance measurement method and system based on oxygen uptake calculation.

Background

The heart and lung endurance comprehensively reflects the capability of human body to take, transport and utilize oxygen, and relates to the functions of heart pumping blood, lung oxygen uptake and gas exchange, the efficiency of blood circulation system carrying oxygen to all parts of the body, and the function of muscle and other tissues to utilize oxygen. The measurement of the cardiopulmonary endurance has important significance for the subject to know the physical condition of the subject and guide the body-building training.

The professional 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) And evaluating the heart and lung endurance. Simple cardiorespiratory endurance measurement methods include step test, 6-minute walking, Cooper twelve-minute running, fixed-distance running, and the like. The professional test method has high accuracy, but needs professional instruments, is complex to operate, and has injury risk when a subject performs extreme sports. The simple test method needs manual guidance, is simple to operate, can reflect the cardiopulmonary endurance level of a subject even though the result precision is not high compared with the professional test result precision, and is suitable for testing the central pulmonary endurance of the physical fitness test.

Disclosure of Invention

In order to solve the technical problems, the invention provides a method and a system for measuring the cardiopulmonary endurance based on oxygen uptake calculation, which are used for conveniently and quickly realizing the cardiopulmonary endurance measurement function in step tests and 6-minute walking test projects. The invention guides the user to carry out the cardiopulmonary endurance level test, and the monitored exercise heart rate and walking speed are measured according to VO_2MaxThe maximum oxygen consumption of the user is calculated by a calculation formula to evaluate the cardiopulmonary endurance level of the user, and compared with other wearable devices, a brand-new method for evaluating the cardiopulmonary endurance level is added. VO of the invention_2MaxVO is directly measured by calculation method_2MaxAvoid callingThe suction test instrument burdens the user with exercise. The intelligent wearable device is used for measuring the cardiopulmonary endurance, so that the testee can measure the cardiopulmonary endurance by himself conveniently, and reference is provided for the testee to know the physical ability of the testee.

The technical scheme of the invention is a cardiopulmonary endurance measurement method and system based on oxygen uptake calculation, comprising the following steps:

step 1, a user selects a test item type from an APP to prepare for starting a test; the item types comprise a step test and a 6-minute walk test;

step 2, before the test is formally started, the user keeps a rest state, and the intelligent wearable device automatically starts to acquire the rest heart rate of the user;

step 3, after the test is started, the intelligent wearable equipment provides an indication for the action of the user, and meanwhile, whether the user completes the action in time according to the requirement is monitored;

step 4, collecting exercise heart rate and walking speed data of a user in the test process;

step 5, calculating the metabolic equivalent of the user movement according to the walking speed;

step 6, combining VO according to metabolic equivalent and user exercise heart rate_2MaxCalculating a formula to obtain the maximum oxygen uptake, and comparing the maximum oxygen uptake with a reference standard to realize measurement and evaluation of the cardiopulmonary endurance.

Further, in step 1, when the user selects a step test item, the method specifically includes:

under the prompt of the APP, the user normally wears the intelligent wearable device on the wrist, and meanwhile selects and confirms the left-hand or right-hand wearable device in the APP; the resting state is kept before the formal test is started, and the resting heart rate of the user is automatically measured after the intelligent wearable device detects that the user enters the stable resting state; after the preparation is finished, clicking the starting exercise option to formally start a step testing link; the intelligent wearable device performs short vibration prompt at the frequency of once every 0.5 second in the movement process, the user performs one step of action each time the user vibrates, each step of action device can detect whether the user completes the step-up and step-down action in time according to the requirement, and each 4 steps of action is a complete step-up and step-down action; after the exercise starts, the intelligent wearable equipment immediately counts time and collects exercise heart rate and walking speed; after the 3-minute repeated action is finished, the equipment sends out long vibration to prompt the end of the movement; data acquisition finishes the back, sends data to APP through the bluetooth, and APP calculates and obtains the biggest oxygen uptake, compares with reference standard at last, makes the aassessment to user's cardiopulmonary endurance level.

Further, in step 1, when the user selects the 6-minute walking test item, the method specifically includes:

under the prompt of the APP, the user normally wears the intelligent wearable device on the wrist; the resting state is kept before the formal test is started, and the resting heart rate of the user can be automatically measured after the intelligent wearable device detects that the user enters the stable resting state; after the preparation is finished, clicking the starting exercise option to formally start a 6-minute walking test link; after walking begins, the user walks directly in a running platform or a corridor; the intelligent wearable equipment is used for timing and collecting the exercise heart rate and the walking speed; after walking for 6 minutes, the equipment sends out long vibration to prompt the end of the exercise; data acquisition finishes the back, sends data to APP through the bluetooth, and APP calculates and obtains the biggest oxygen uptake, compares with reference standard at last, makes the aassessment to user's cardiopulmonary endurance level.

Further, in step 3, the intelligent wearable device in a resting state collects the heart rate every 10 seconds within 2 minutes, an average value of the heart rates of 12 times is taken as the resting heart rate, and in the exercise process, the intelligent wearable device collects the heart rate average value in 20-30 seconds after the exercise starts and in the last 20-30 seconds of the exercise respectively, and meanwhile, the intelligent wearable device synchronously records the walking speed of the user.

Further, in step 5, when the movement is an up-down step, the method for calculating the metabolic equivalent comprises the following steps:

GrossVO₂＝3.5+0.2×v+1.33×1.8×v×h

wherein v represents the speed of going up and down steps, the unit is one minute, and the step is four steps each time; h represents the step height in meters;

when the exercise is walking, the metabolic equivalent inference formula is specifically:

GrossVO₂＝3.5+0.1×v+1.8×v×ratio

wherein v represents walking speed in meters per minute (m/min); ratio represents the percent grade, which is typically 0 in a conventional walk test.

Further, in step 6, the maximum oxygen uptake estimation formula is as follows:

Gross VO₂the maximum oxygen intake is the oxygen content which can be taken in when the human body does exercise with maximum intensity; HR1, HR2 are two heart rate values during exercise, HRrest is resting heart rate, HRmax is maximum heart rate.

Further, in step 2, the automatic collection user's rest heart rate that begins of intelligent wearing equipment includes:

the user should satisfy three conditions when in a resting state: user inactivity, heart rate below a threshold, heart rate variation fluctuation less than a threshold; the three conditions are measured and judged by a triaxial accelerometer and a heart rate sensor respectively;

first, the acceleration value measured by the three-axis accelerometer is a ═ { ax, ay, az }, and when the user is in an inactive state, the accelerations in the three axes should all be close to 0, so that the combined acceleration is the sum of the accelerations

Considering that the sensor will drift, a threshold value is set to epsilon, if acceleration is combined

Then the time is determined to be inactive; when the heart rate data HR acquired by the heart rate sensor is less than or equal to AE and meanwhile the Delta HR is less than or equal to Delta, determining that the user in the time slot is in a resting state, wherein the AE is a set heart rate threshold; delta is the heart rate fluctuation threshold, and a time interval T1 is set, if in this intervalMeasured continuous resultant acceleration

Constant, continuous measurement of the heart rate HR<≦ Ε and heart rate fluctuation Δ HR<If the value is not more than δ, judging that the user enters a stable resting state, and indicating that the user can start to continuously acquire resting heart rate; on the contrary, when above-mentioned condition can not satisfy in T1 completely, then indicate that this user does not enter stable resting state for a while, gather resting heart rate data this moment and will be inaccurate, APP suggestion user keeps resting state and carries out deep breathing simultaneously and alleviates the mood, and original data and timing are abandoned to intelligent wearing equipment, the detection of a new round of T1 time is restarted.

Further, in step 3, monitoring whether the user completes the action in time according to the requirement includes:

recording X-axis acceleration data measured by intelligent wearable equipment at first stage of going up step

The acceleration generated in the X-axis by swinging the arm backward is recorded as a'_x(ii) a The measured Y-axis acceleration data of the apparatus is recorded as

The acceleration of the arm swing on the Y axis is denoted as a_y(ii) a Then the equation is listed according to the analysis:

recording X-axis acceleration data measured by intelligent wearable equipment at second stage of climbing steps

The acceleration of the arm swinging forward on the X axis is denoted as a_xAcceleration due to forward movement of body center of gravity is recorded

The measured Y-axis acceleration data of the apparatus is recorded as

The acceleration due to the rise of the body's center of gravity is recorded as

Then the equation is listed according to the analysis:

recording X-axis acceleration data measured by intelligent wearable equipment at first stage of lower step

The acceleration caused by the backward shift of the body's center of gravity is recorded as

The measured Y-axis acceleration data of the device are respectively recorded as

The acceleration due to the lowering of the body's centre of gravity is recorded as

Then the equation is listed according to the analysis:

recording X-axis acceleration data measured by intelligent wearable equipment at second stage of lower step

The measured Y-axis acceleration data of the apparatus is recorded as

Then the equation is listed according to the analysis:

the above equation is combined to calculate: before the center of gravity of the body of the upper stepAcceleration of movement

Acceleration of body center of gravity rise

Acceleration of downward step body center of gravity backward movement

Acceleration of lowering of body center of gravity

For the first stage of the upper step and the second stage of the lower step, the data acquired by the three-axis acceleration sensor can be directly used for attitude calculation, and the acceleration generated by the body weight center change in the data needs to be subtracted for attitude calculation in the second stage of the upper step and the first stage of the lower step.

Further, in step 3, monitoring whether the user completes the action in time according to the requirement, further comprising:

when the swing arm angle is in a corresponding stage, the system firstly analyzes and judges the change process of the swing arm angle, specifically, the change direction of the angle, whether the change process passes through upper and lower limit thresholds and the action of the stage is detected and judged through the sequence, if the change process requirement of the swing arm angle corresponding to the stage cannot be met, the user is judged that the action of the stage cannot be completed as required in time; after the arm swing angle condition is met, corresponding threshold values are set aiming at the acceleration change of the body gravity center, the system can judge the action of the user in the corresponding stage by combining the change of the body gravity center acceleration, specifically, in the second stage of the upper step, the acceleration generated by the forward movement and the rising of the body gravity center is respectively larger than the respective set threshold values, and the system can judge the action of the upper step only after the condition is met; in the first stage of descending steps, the absolute values of the acceleration generated by the backward movement and the descending of the gravity center of the body are respectively greater than the respective set threshold values, and the system can judge the motion of descending steps only after the condition is met; taking four stages as an action period, and when more than two stages in the action period are judged to be not in accordance with the action by the system, the action period is considered to be not in accordance with the requirement, and the system sends out a prompt to remind a user to test according to the requirement; and when the test is judged to be not satisfactory in k continuous periods, stopping the test, and restarting to execute the step 4, wherein the motion data of the test is also discarded.

According to another aspect of the present invention, there is also provided a cardiopulmonary endurance measurement system based on oxygen uptake calculation, comprising:

the sensor data acquisition module is used for acquiring data of heart rate and walking speed by a sensor;

the intelligent wearable equipment system control module is used for processing, timing reminding, motion state monitoring and analyzing communication data acquired by the sensor;

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 according to the acquired walking speed and the acquired metabolic equivalent and calculating the maximum oxygen uptake by combining the exercise heart rate and the metabolic equivalent.

When the movement is up and down steps, the method for calculating the metabolic equivalent comprises the following steps:

GrossVO₂＝3.5+0.2×v+1.33×1.8×v×h

GrossVO₂＝3.5+0.1×v+1.8×v×ratio

wherein v represents walking speed in meters per minute (m/min); ratio represents the percent grade, which in a conventional walk test is typically 0;

the maximum oxygen uptake calculation formula is as follows:

Has the advantages that:

the method and the system for measuring the cardio-pulmonary endurance based on oxygen uptake calculation can make up for the defects of the prior art, the heart rate and walking speed data are acquired through the sensor on the intelligent wearable device, the reality and the accuracy of the acquired data are ensured through the motion detection function, the maximum oxygen uptake is calculated by using the method provided by the invention, and the function of measuring the cardio-pulmonary endurance is conveniently and quickly realized. VO of the invention_2MaxVO is directly measured by calculation method_2MaxThe exercise burden of the breath test instrument on the user is avoided. The intelligent wearable device is used for measuring the cardiopulmonary endurance, so that the testee can measure the cardiopulmonary endurance by himself conveniently, and reference is provided for the testee to know the physical ability of the testee. Expensive test equipment and strict test requirements are not needed, the device is theoretically suitable for cardiopulmonary endurance test items of various different types, a user only needs to wear intelligent wearable equipment, measurement can be automatically completed in the exercise process, and the cost of manual guiding and measuring in the cardiopulmonary endurance test items is reduced.

Drawings

FIG. 1 is a flow chart of a method for measuring cardiopulmonary endurance based on oxygen uptake calculation according to the present invention;

FIG. 2 is a schematic diagram of the calibration of an acceleration sensor coordinate system in a bracelet according to the present invention;

FIG. 3 is a flow chart of a step test provided by the present invention;

FIG. 4 is a flow chart of the cardiopulmonary endurance measurement system based on oxygen uptake calculation to detect the resting state of a user;

FIG. 5 is a schematic diagram of a step test action detected by the cardiopulmonary endurance measurement system based on oxygen uptake calculation provided by the present invention;

FIG. 6 is a flow chart of a method for testing steps of a cardiopulmonary endurance measurement system based on oxygen uptake calculation according to the present invention;

FIG. 7 is a flow chart of a 6 minute walk test provided by the present invention;

FIG. 8 is a block diagram of a cardiopulmonary endurance measurement system based on oxygen uptake calculations provided by the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying 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, rather than all embodiments, and all other embodiments obtained by a person skilled in the art based on the embodiments of the present invention belong to the protection scope of the present invention without creative efforts.

The invention provides a method and a system for measuring the cardio-pulmonary endurance based on oxygen uptake calculation, which are used for conveniently and quickly realizing the function of measuring the cardio-pulmonary endurance. The method and the system are based on the same technical conception, and because the principle of solving the problems of the method and the system is similar, the implementation of the system and the method can be mutually referred, and repeated parts are not repeated.

In the scheme provided by the embodiment of the invention, the intelligent wearable equipment is portable intelligent equipment. The device is at least built-in with a heart rate sensor, a triaxial acceleration sensor, or the device can also be connected with an externally deployed sensor module. Some examples of smart wearable devices are: smart phones, smart watches, smart bracelets, smart glasses, and other sports accessories or wearable accessories, and the like, which are not limited herein.

According to one embodiment of the invention, a cardiopulmonary endurance measurement method based on oxygen uptake calculation is provided; as shown in fig. 1, a flowchart of a cardiopulmonary endurance measurement method based on an intelligent wearable device is provided in the present invention, in the method, a user selects a test type in a mobile phone APP, and the APP mainly provides two test types: bench test and 6 minute walk test. Normally wear intelligent wearing equipment on the wrist under APP's suggestion, keep the rest state, equipment detects and judges can begin to gather the rest rhythm of the heart automatically after for the rest state. After beginning the experiment, the user follows the instruction of APP and accomplishes the regulation action, constantly gathers user's real-time rhythm of the heart at this in-process intelligent wearing equipment, acquires the rate of motion, monitors user's motion state simultaneously, supervises the user and accomplishes the action as required, and after whole experiment, cell-phone APP calculates the data of receiving and handles, obtains the biggest oxygen uptake volume, compares with reference standard afterwards, realizes the aassessment measurement to this user's cardiopulmonary endurance level.

Further, the method is mainly applicable to the following application scenarios: bench test and 6 minute walk test. These two tests differ in their implementation.

First, the bench test, the 6-minute walk test, and the maximum oxygen uptake are explained to facilitate understanding by those skilled in the art.

The step test is simply that the left leg and the right leg are alternately stepped on the steps to test the adaptation level of the cardiopulmonary function. During testing, a subject stands vertically in front of the steps and moves up and down according to a certain beat. The user has to step up and down each time every 2 seconds, 30 times per minute for 3 minutes. A complete up-down step process comprises the following steps: firstly, one foot steps on the steps, then the other foot steps on the steps, the two legs are straightened, then the foot steps of the steps are stepped on, and finally the other foot steps are stepped on. When the user needs to do the tests by turns with the left leg and the right leg, the upper body and the two legs must be straightened after going up and down the steps each time, and the knees cannot be bent. After the repeated actions are finished, the user sits on the chair immediately and statically, and the heart rate of three times of 1 minute to 1 minute and half minute, 2 minutes to 2 minutes and 3 minutes to 3 minutes and half minute after the movement stops is recorded. The evaluation index is calculated by the formula: the evaluation index is the duration of the ascending step movement(s) × 100/(2 × sum of 3 pulses in the recovery period).

The 6 minute walk test is a relatively simple cardiorespiratory endurance exercise test. The user had a rest at the start point long enough before the trial. The timer was set to 6 minutes, and the user walked in a straight corridor or on a treadmill as quickly as possible, and the walking distance was measured for six minutes, and the level of cardiopulmonary endurance was estimated from the length of the walking distance.

In the two tests, the cardiopulmonary endurance level has respective evaluation criteria, but the change of the step evaluation index and the walking distance cannot truly and accurately reflect the relationship with the cardiopulmonary endurance level. The invention adopts the maximum oxygen uptake as an evaluation index in the two tests, wherein the index is a gold standard for measuring and evaluating the cardiopulmonary endurance, and the index can be applied to different types of cardiopulmonary endurance test items by the estimation method.

The maximum oxygen intake is the amount of oxygen that can be taken in by the human body during the maximum intensity of exercise. The metabolism of substances and energy is the basis of the skill and activity of various tissues and organs in the body, and the motor ability is the centralized expression of various functional activities of the body. The exercise capacity can be divided into aerobic exercise and anaerobic exercise according to the energy mode. The ability to provide aerobic energy is fundamental and has been studied by a large number of scholars, with maximum oxygen uptake being the most common and effective method of assessing aerobic capacity. As one of the important material selection bases for endurance athletes, high-level maximum oxygen intake is the basis of high-level aerobic exercise capacity.

The maximum load test is the gold standard of the cardiopulmonary endurance test, but has the defects of expensive equipment, long operation time, safety risk and the like. Therefore, people further develop a secondary load test, and the basic principle of the secondary load test is that the square of the speed, the power and the heart rate of human motion are in a linear relation in a certain intensity range. Therefore, only two heart rate values (HR1, HR2) and corresponding two oxygen uptake Values (VO) during exercise are measured_{2_1}、VO_{2_2}) And calculating the maximum oxygen uptake by combining the resting heart rate HRrest and the presumed maximum heart rate HRmax according to the following formula:

the principle of the wearable heart-lung endurance test is consistent with the principle of the secondary load test, butVO2 was not measured in real time but was replaced by metabolic equivalents (Mets), both in ml/(kg min), converted to VO2max 3.5 Mets. ACSM designs the total oxygen consumption (Gross VO) for walking, running, fixing bicycles, steps₂) The metabolic equivalent equation of (c). The maximum oxygen uptake prediction formula becomes:

in a typical test scenario, the variables of the metabolic equivalent formula are related only to velocity. Therefore, VO can be obtained by only obtaining two speed values and heart rate values in the user motion process_2max. If more points can be obtained, more accurate measurement results can be obtained through quadratic curve fitting.

Fig. 2 is a schematic diagram of the acceleration sensor coordinate system calibrated in the intelligent wearable device according to the present invention, taking a bracelet as an example, the common bracelet has a long strip shape, based on the right-hand coordinate system, the X-axis of the acceleration sensor is parallel to the longer side of the bracelet, the Y-axis is parallel to the shorter side, and the Z-axis is perpendicular to the front of the bracelet and faces upward.

First, the mathematical basis for use in motion detection is explained to facilitate understanding by those skilled in the art. And taking a geodetic coordinate system as a reference coordinate system e, wherein the coordinate system of the intelligent wearable device is called a carrier coordinate system b, and the initial direction is the same as that of the system e. Then use the rotation matrix

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 the description of the z-axis unit vector in system b. And the transposition of the rotation matrix

Third row vector [ c ]₁₃ c₂₃ c₃₃]I.e. the original rotation matrix

The third column of vectors. Therefore, the temperature of the molten metal is controlled,

i.e. the description of the e-system z-axis direction unit vector, i.e. the gravity vector g, in the b-system. Meanwhile, the description of the gravity vector (or the vector in the opposite direction) in the b system is the values of the three directions measured by the three-axis acceleration sensor. Thus, it is possible to provide

The third row vector is actually the output vector of the acceleration sensor, which is the key of the attitude calculation. Fig. 3 is a step test flow chart based on the intelligent wearable device provided by the invention, and the whole test flow includes the following steps:

step S1: the user clicks a step test item in a mobile phone APP, the user normally wears the intelligent wearable device on the wrist under the prompt of the APP, meanwhile, the user clicks and confirms that the device is worn by the left hand or the right hand in the APP, and after the preparation is finished, a starting option is clicked to start a test link;

step S2: under the prompt of the APP, the user should keep a resting state before the formal test is started, the intelligent wearable device automatically starts to measure the resting heart rate of the user, and meanwhile, the APP explains the action to be executed by the formal test to the user;

step S3: in the process of a rest state, after the heart rate of a user is stable, the intelligent wearable device collects the heart rate every 10 seconds within 2 minutes, the average value of the heart rates of 12 times is taken as the rest heart rate, after the measurement is finished, the APP can prompt the completion of the measurement work in the stage, after the user finishes the preparation, the user clicks a starting exercise option to formally start a step test link, the APP sends out voice, and the device sends out long vibration to indicate that an exercise stage starts;

step S4: the intelligent wearable device performs short vibration prompt at the frequency of once every 0.5 second in the movement process, the user should perform one-step action by vibration every time, complete step-up and step-down actions are performed every 4 steps, and the device monitors whether the user completes the actions according to the requirements or not in real time;

step S5: after the exercise starts, timing is started immediately by the intelligent wearable device, heart rate mean values are collected in two time periods of 20-30 seconds after the exercise starts and 20-30 seconds after the exercise starts, and meanwhile the intelligent wearable device can synchronously record the speed of going up and down steps of the user;

step S6: the APP reminds the user of preparing for finishing 15 seconds before the stage is finished, and after 3 minutes of repeated actions are finished, the intelligent wearable equipment sends out long vibration to prompt the user to finish the movement;

step S7: data acquisition finishes, sends data to APP through the bluetooth, APP rethread metabolism equivalent and VO_2MaxCalculating a formula to obtain the maximum oxygen uptake, and finally comparing the maximum oxygen uptake with a reference standard to obtain and display the level grade of the cardiopulmonary endurance of the user on the mobile phone.

Further, fig. 4 is a flowchart of detecting the resting state of the user by the intelligent wearable device provided by the present invention, and the specific method of automatically detecting the resting state of the user by the intelligent wearable device in step S2 includes: the user should satisfy three conditions when in a resting state: user inactivity, low heart rate, less fluctuation in heart rate variation. These three conditions can be measured and determined by the tri-axial accelerometer and heart rate sensor, respectively. First, the acceleration value measured by the three-axis accelerometer is a ═ { ax, ay, az }, and when the user is in an inactive state, the accelerations in the three axes should all be close to 0, so that the combined acceleration is the sum of the accelerations

However, considering that the sensor may drift, a threshold value is set to ε, if the acceleration is combined

Then the time is determined to be inactive. The user can not necessarily acquire the true resting heart rate in an inactive state, the reason is related to the psychological activity of the user during testing, when the user is in nervous and excited emotion during testing, the heart rate change fluctuation is large, and even if the user keeps sitting still, the acquired heart rate is not the resting heart rate of the user. Therefore, care should be takenHeart rate data HR collected by the rate sensor is less than or equal to AE, and meanwhile when Delta HR is less than or equal to Delta, the user in the time period is judged to be in a resting state, wherein the AE is a set heart rate threshold value, the threshold value is determined by characteristics such as age, gender and life habits, the average adult heart rate is about 75 times/minute in the resting state, and the fluctuation range of the normal adult heart rate is 60-100 times/minute. When in an excited state, the heart rate is obviously increased and exceeds the range, and the data can be used as a threshold reference value; delta is a heart rate fluctuation threshold, which is related to a maximum heart rate threshold, and the value is suggested as a percentage of the maximum heart rate. In summary, an appropriate time interval T1 is set, during which the acceleration, if any, is measured continuously

It is established that heart rate HR ≤ Ε and heart rate fluctuation Δ HR ≤ δ are continuously measured simultaneously, then it is determined that the user enters a stable resting state, representing that the work of continuously acquiring the resting heart rate can be started. On the contrary, when above-mentioned condition can not satisfy in T1 completely, then indicate that this user does not enter stable resting state for a while, gather resting heart rate data this moment and will be inaccurate, APP suggestion user keeps resting state and carries out deep breathing simultaneously and alleviates the mood, and original data and timing are abandoned to intelligent wearing equipment, the detection of a new round of T1 time is restarted.

Further, the intelligent wearable device monitors the resting state of the user while collecting the resting heart rate in the step S3, and the specific method thereof is as follows: during the two-minute acquisition process, the triaxial accelerometer continuously measures the triaxial acceleration data

The time indicates that the user is not in a resting state at the moment, and the heart rate fluctuation brought along with the resting state affects the accuracy of the measurement result. In the heart rate collected every 10 seconds, if the heart rate HR collected is the same as the heart rate HR collected>E or Δ HR>Delta, indicating that the user is now excited, the heart rate collected is not the true resting heart rate and the data should be discarded. In summary, an appropriate time interval T2, T2 is set for suggestionCan take a value of 10 seconds in synchronism with the acquisition of the heart rate if a resultant acceleration is detected during that time

Or the heart rate HR acquired during the period>eAE or heart rate fluctuation Δ HR>If 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 and simultaneously performing deep breathing to relieve emotion by the APP, and discarding data in the time period by the intelligent wearable device; if the user is judged not to enter the stable rest state within n T2 time periods, the intelligent wearable device discards all the collected data and returns to the step S2 to restart the detection of the rest state, and the value of n is determined according to the test condition and suggests that the reference value is 3.

Further, the intelligent wearable device monitors whether the user completes the action as required in real time in step S4, and the specific method includes:

when a user normally wears the intelligent wearable device on the wrist 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, when the user goes up and down steps, the intelligent wearable device swings up and down along with the arm, and the movement process can be regarded as rotation movement around the Z axis.

The rotation matrix for a known rotation phi about the X axis is represented as

The rotation matrix of the rotation psi about the Z-axis is represented as

The rotation matrix in the X-Z rotation order is represented 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 is-90 °, so that the value of ψ can be calculated by collecting the X-axis and Y-axis output quantities of the acceleration sensor. In practice, during the step descending process, the data directly output by the accelerometer cannot accurately calculate the angle of rotation around the Z axis because the motion of the body in the front-back and up-down directions generates additional acceleration, and therefore, the acceleration generated by the body motion needs to be calculated so as to be removed from the sensor data when the angle is calculated.

Fig. 5 is a schematic diagram of step testing actions of the intelligent wearable device provided by the invention, and the four-step actions of the step testing can be divided into two processes: an upper step and a lower step; each process can be divided into two stages. For the stair climbing process: in the first stage, the left leg is stepped up, the left arm swings backwards, the right arm swings forwards, and the gravity center of the body does not rise; in the second stage, the gravity center of the body begins to rise, the right leg also ascends the step, the left arm swings forwards, and the right arm swings backwards. For the following step procedure: in the first stage, the left leg firstly goes down the step, the gravity center of the body also descends, the left arm swings backwards, and the right arm swings forwards; in the second stage, the right leg also goes down the step, at the moment, the gravity center of the body does not descend any more, the left arm swings forwards, and the right arm swings backwards.

Taking a left arm wearing intelligent wearing equipment as an example, the arms swing backwards in the first stage of the upper step and the first stage of the lower step, and in the first stage of the lower step, in addition to the acceleration generated by the swinging of the arms, the acceleration generated by the backward movement of the gravity center of the body is also arranged in the X-axis direction, and the acceleration generated by the descending of the gravity center of the body is also arranged in the Y-axis direction; the arms swing forwards in the second stage of the upper step and the second stage of the lower step, and in the second stage of the upper step, the acceleration generated by the swinging of the arms, the acceleration generated by the forward movement of the gravity center of the body and the acceleration generated by the descending of the gravity center of the body are added in the X-axis direction and the Y-axis direction.

According to the analysis, X-axis acceleration data measured by the intelligent wearable equipment in the first stage of the upper step is recorded as

The acceleration generated in the X-axis by swinging the arm backward is recorded as a'_x(ii) a Is provided withRecording the measured Y-axis acceleration data

The acceleration of the arm swing on the Y axis is denoted as a_y. Then the equation is listed according to the analysis:

The measured Y-axis acceleration data of the apparatus is recorded as

The acceleration due to the rise of the body's center of gravity is recorded as

Then the equation is listed according to the analysis:

Then the equation is listed according to the analysis:

The measured Y-axis acceleration data of the apparatus is recorded as

Then the equation is listed according to the analysis:

the above equation is combined to calculate: acceleration of upper step body gravity center forward movement

Acceleration of body center of gravity rise

Acceleration of downward step body center of gravity backward movement

Acceleration of lowering of body center of gravity

For the first stage of the upper step and the second stage of the lower step, the data acquired by the three-axis acceleration sensor can be directly used for attitude calculation, and the acceleration generated by the body weight center change in the data needs to be subtracted in the second stage of the upper step and the first stage of the lower step to be used for attitude calculation.

The method comprises the following steps that an arm naturally hangs down to serve as a reference of an angle of a swing arm, a fixed upper limit threshold and a fixed lower limit threshold are set according to collected angle changes, when the arm swings backwards in a first stage of an upper step and a first stage of a lower step, an angle psi is gradually reduced, and when the angle psi is lower than the upper limit threshold, the arm continues to descend to pass through the lower limit threshold after reaching a zero reference; and in the second stage of the upper step and the second stage of the lower step, the arm swings forwards to enable the angle psi to be gradually increased, and after the angle psi is higher than the lower limit threshold value, the arm continuously rises to pass through the upper limit threshold value after reaching the zero reference.

Fig. 6 is a flowchart of a testing method for detecting steps of an intelligent wearable device provided by the present invention, when the intelligent wearable device is in a corresponding stage, the system will firstly analyze and judge the change process of the swing arm angle, specifically, detect and judge the action of the stage according to the change direction of the angle, whether the angle passes through the upper and lower threshold values and the sequence, and if the change process requirement of the swing arm angle corresponding to the stage cannot be met, judge that the user cannot complete the action in the stage as required in time. After the arm swing angle condition is met, corresponding threshold values are set aiming at the acceleration change of the body gravity center, the system can judge the action of the user in the corresponding stage by combining the change of the body gravity center acceleration, specifically, in the second stage of the upper step, the acceleration generated by the forward movement and the rising of the body gravity center is respectively larger than the respective set threshold values, and the system can judge the action of the upper step only after the condition is met; in the first stage of the downstairs, the absolute values of the acceleration generated by the backward movement and the descending of the gravity center of the body are respectively larger than the respective set threshold values, and the system can judge the downstairs movement only after the condition is met. Taking four stages as an action period, and when more than two stages in the action period are judged to be not in accordance with the action by the system, the action period is considered to be not in accordance with the requirement, and the system sends out a prompt to remind a user to test according to the requirement; and when the test is judged to be not satisfactory in k continuous periods, stopping the test, and restarting to execute the step S4, wherein the motion data of the test is also discarded, and the value of k is determined according to the test condition, and the recommended reference value is 3. When the right arm wears the intelligent wearing equipment for testing, the swing arm direction is opposite to that of the left arm in each stage, but no difference exists in the change of the body gravity center, so that the acceleration calculation method generated by the change of the body gravity center is the same as that of the left arm. When the first stage of the upper step and the first stage of the lower step are carried out, the arm swings forwards to enable the angle psi to be gradually increased, and after the angle psi is higher than the lower limit threshold value, the arm continuously rises to pass through the upper limit threshold value after reaching the zero reference; and in the second stage of the upper step and the second stage of the lower step, the arm swings backwards to enable the angle psi to be gradually reduced, and after the angle psi is lower than the upper limit threshold value, the arm continues to descend to pass through the lower limit threshold value after reaching the zero reference. Therefore, the judgment process of the swing arm angle in the right arm wearing condition is opposite to that of the left arm, and the body gravity center acceleration judgment is not different.

Further, the inference formula of the metabolic equivalent of the upper step and the lower step in step S7 is specifically:

GrossVO₂＝3.5+0.2×v+1.33×1.8×v×h

wherein v represents the speed of the upper step and the lower step, the unit is time/min, and the step is four steps each time; h represents the step height in meters (m), which should be input into the system in advance as a known quantity.

Fig. 7 is a flowchart of a 6-minute walking test based on an intelligent wearable device, where the whole test flow includes the following steps:

step S1: the user clicks a 6-minute walking test item in the mobile phone APP, and the user normally wears the intelligent wearable device on the wrist under the prompt of the APP;

step S2: under the prompt of the APP, the user should keep a rest state for at least 10 minutes before the formal test is started, and the APP reminds the user that the rest heart rate is to be detected and explains the action to be executed by the formal test to the user;

step S3: in the process of a rest state, after the heart rate of a user is stable, the intelligent wearable device collects the heart rate every 10 seconds within 2 minutes, the average value of the heart rates of 12 times is taken as the rest heart rate, the APP can prompt the completion of the measurement work in the stage after the measurement is finished, a user clicks a starting exercise option to formally start a walking test link after the preparation of the user is finished, and the intelligent wearable device sends long vibration to indicate that a walking stage starts;

step S4: after walking starts, the user directly walks in a running platform or a corridor, the intelligent wearable device starts timing, heart rate mean values are respectively collected in two time periods of 20-30 seconds after the movement starts and 20-30 seconds after the movement is finished, and meanwhile the intelligent wearable device synchronously records the walking speed of the user;

step S5: timing when the APP starts a walking test, reminding a user of completing preparation in advance for 15 seconds, and after walking for 6 minutes, enabling the APP to broadcast in a voice mode and meanwhile enabling the intelligent wearable device to send long vibration to remind the user of finishing walking;

step S6: data acquisition finishes, sends data to APP through the bluetooth, APP rethread metabolism equivalent and VO_2MaxCalculating a formula to obtain the maximum oxygen uptake, and finally comparing the maximum oxygen uptake with a reference standard to obtain and display the level grade of the cardiopulmonary endurance of the user on the mobile phone.

Then the time is determined to be inactive. The user can not necessarily acquire the true resting heart rate in an inactive state, the reason is related to the psychological activity of the user during testing, when the user is in nervous and excited emotion during testing, the heart rate change fluctuation is large, and even if the user keeps sitting still, the acquired heart rate is not the resting heart rate of the user. Therefore, when the heart rate data HR acquired by the heart rate sensor is less than or equal to AE and meanwhile the Delta HR is less than or equal to Delta, the user position in the time period is determinedIn a resting state, Ε is a set heart rate threshold whose value is determined by characteristics such as age, sex, life habit, and the like, the average adult heart rate is about 75 times/minute in the resting state, and the fluctuation range of the normal adult heart rate is 60-100/minute. When in an excited state, the heart rate is obviously increased and exceeds the range, and the data can be used as a threshold reference value; delta is a heart rate fluctuation threshold, which is related to a maximum heart rate threshold, and the value is suggested as a percentage of the maximum heart rate. In summary, an appropriate time interval T1 is set, during which the acceleration, if any, is measured continuously

The time indicates that the user is not in a resting state at the moment, and the heart rate fluctuation brought along with the resting state affects the accuracy of the measurement result. In the heart rate collected every 10 seconds, if the heart rate HR collected is the same as the heart rate HR collected>E or Δ HR>Delta, indicating that the user is now excited, the heart rate collected is not the true resting heart rate and the data should be discarded. In summary, setting a suitable time interval T2, T2 suggests that the time interval may be 10 seconds in synchronization with the heart rate acquisition, if a combined acceleration is detected during this time intervalDegree of rotation

Or the heart rate HR acquired during the period>eAE or heart rate fluctuation Δ HR>If 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 and simultaneously performing deep breathing to relieve emotion by the APP, and discarding data in the time period by the intelligent wearable device; if the user is judged not to enter the stable rest state within n T2 time periods, the intelligent wearable device discards all the collected data and returns to the step S2 to restart the detection of the rest state, and the value of n is determined according to the test condition and suggests that the reference value is 3. Further, the walking metabolic equivalent inference formula in step S7 is specifically:

GrossVO₂＝3.5+0.1×v+1.8×v×ratio

It should be noted that the above embodiments only exemplify some application scenarios, but those skilled in the art should understand that the present invention is not limited to the described application scenarios, because the test indexes and methods 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 structural diagram of a cardiopulmonary endurance measurement system based on oxygen uptake calculation according to the present invention includes:

GrossVO₂＝3.5+0.2×v+1.33×1.8×v×h

GrossVO₂＝3.5+0.1×v+1.8×v×ratio

wherein v represents walking speed in meters per minute; ratio represents the percent grade, which in a conventional walk test is typically 0;

the maximum oxygen uptake calculation formula is as follows:

Furthermore, the sensor data acquisition module performs software filtering on data, performs data interaction with a communication interface of a central processing unit chip of the intelligent wearable device through the sensor, reads sensor information in real time, and performs filtering processing on the acquired data;

the intelligent wearable device system control module controls the intelligent wearable device to match with data acquisition and vibration reminding functions within a specified time period in the test process so as to meet the test requirements;

the intelligent wearable device system control module can control the intelligent wearable device to emit vibration to prompt the user to start and end or execute actions according to a certain beat when the intelligent wearable device is in a specified test ring;

intelligence wearing equipment bracelet system control module can continuously monitor user motion state at the user in-process of testing, ensures that the user accomplishes the action as required for the data of gathering can reflect the true level under each state of user more accurately, and then makes the biggest oxygen uptake volume that obtains more true effective.

The wireless communication module is used for packaging the processed data in the intelligent wearable device and wirelessly communicating with the intelligent mobile phone through a Bluetooth wireless protocol to realize data interaction with the mobile phone end; the Bluetooth communication function with the intelligent wearable equipment is realized by calling API service provided by a Bluetooth official party, and data interaction with the equipment is realized;

further, the user terminal processing module is configured to:

setting user information and setting motion parameters;

playing guidance audio and timing reminding;

processing exercise data, and calculating maximum oxygen uptake and assessment of cardiopulmonary endurance;

displaying the measurement result in a software interface;

and storing data, namely storing user information and measurement data.

User terminal processing module through bluetooth communication control intelligence wearing equipment pilot lamp state and screen display module, because part old-fashioned wearing equipment is not equipped with the display screen, only has the pilot lamp, consequently for most intelligent wearing equipment on the adaptation market, can control this type of equipment pilot lamp and send bright representation equipment and be in the detection state. For the intelligent wearable device equipped with the display screen, the screen of the device displays the current ongoing movement so as to prompt the user.

The user information setting and motion parameter setting module is used for providing a function of adding personal related information of a user and realizing the login verification function of the APP; certain motion parameters can be set, such as motion time and the like;

playing guidance audio, timing and reminding a module, controlling a terminal APP to broadcast a specified action to be executed in a current link in a user test process, controlling the terminal APP to time according to fixed time specified by a test after the user follows the specified action, and reminding the user that the action stage is finished when the time is started and finished;

the exercise data is processed, a maximum oxygen uptake and cardiopulmonary endurance evaluation module is calculated, the maximum oxygen uptake is calculated according to the heart rate and walking speed data received from the intelligent wearable equipment, and meanwhile, the maximum oxygen uptake and the walking speed data are compared with a reference standard to evaluate the cardiopulmonary endurance;

a measurement result module is displayed in a software interface, and an interface of the measurement result of the test is displayed on a terminal APP;

and the data storage module is used for storing user information and measurement data 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 above-mentioned method embodiments or examples are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts described, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments or examples described in this specification are presently preferred, and that the acts and modules illustrated are not necessarily required to practice the invention.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.

Claims

1. A cardiopulmonary endurance measurement method based on oxygen uptake calculation is characterized by comprising the following steps:

in step 2, the automatic collection user rest heart rate that begins of intelligence wearing equipment includes:

Determining that the user is in an inactive state; when heart rate data HR acquired by a heart rate sensor is less than or equal to AE and meanwhile delta HR is less than or equal to delta, determining that a user is in a resting state, wherein the AE is a set heart rate threshold; delta is the heart rate fluctuation threshold, and a time interval T1 is set during which the acceleration is continuously measured if the combined acceleration is continuously measured

Constant, continuous measurement of the heart rate HR<≦ Ε and heart rate fluctuation Δ HR<If the value is not more than δ, judging that the user enters a stable resting state, and indicating that the user can start to continuously acquire resting heart rate; on the contrary, when above-mentioned condition can not satisfy in T1 completely, then indicate that this user does not enter stable resting state for a while, gather resting heart rate data this moment and will be inaccurate, APP suggestion user keeps resting state and carries out deep breathing simultaneously and alleviate the mood, and original data are abandoned to intelligent wearing equipmentTiming, and restarting a new round of T1 time detection;

2. The method for measuring cardiorespiratory endurance capacity based on oxygen uptake calculation according to claim 1, wherein in step 1, when the user selects the step test item, the method specifically comprises:

3. The method for measuring cardiorespiratory endurance capacity based on oxygen uptake calculation of claim 1, wherein in step 1, when the user selects the 6-minute walking test item, the method specifically comprises:

4. The method of claim 1, wherein the method of measuring cardiorespiratory endurance capacity based on oxygen uptake calculation,

in the step 4, the intelligent wearable device collects the heart rate every 10 seconds in 2 minutes in a resting state, the average value of the heart rates of 12 times is taken as the resting heart rate, the device collects the heart rate average value in two time periods of 20-30 seconds after the start of exercise and 20-30 seconds after the end of exercise in the exercise process, and meanwhile the device synchronously records the walking speed of the user.

5. The method for measuring cardiorespiratory endurance capacity based on oxygen uptake calculation of claim 1, wherein in step 5, when the movement is up and down steps, the method for calculating metabolic equivalent is as follows:

GrossVO₂＝3.5+0.2×v+1.33×1.8×v×h

GrossVO₂＝3.5+0.1×v+1.8×v×ratio

6. The method of claim 1, wherein in step 6, the maximum oxygen uptake is calculated according to the following formula:

7. The method for measuring cardiorespiratory endurance capacity based on oxygen uptake calculation according to claim 1, wherein in the step 3, monitoring whether the user timely completes the action according to the requirement specifically comprises:

second stage intelligence to stepX-axis acceleration data measured by wearable equipment is recorded as

The measured Y-axis acceleration data of the apparatus is recorded as

The acceleration due to the rise of the body's center of gravity is recorded as

Then the equation is listed according to the analysis:

Then the equation is listed according to the analysis:

intelligent threading device for second stage of lower stepX-axis acceleration data measured by the wearing device is recorded as

The measured Y-axis acceleration data of the apparatus is recorded as

Then the equation is listed according to the analysis:

Acceleration of body center of gravity rise

Acceleration of downward step body center of gravity backward movement

Acceleration of lowering of body center of gravity

8. The method of claim 1, wherein the step 3 of monitoring whether the user performs the action on demand further comprises:

9. A cardio pulmonary endurance measurement system based on oxygen uptake calculations, comprising:

automatic user's rest rhythm of heart that begins to gather of intelligence wearing equipment includes:

first three-axis accelerationThe accelerometer measures acceleration a ═ { ax, ay, az }, and when the user is in an inactive state, the accelerations in the three axes should all be close to 0, so their combined acceleration is equal to 0

Determining that the user is in an inactive state; when the heart rate data HR acquired by the heart rate sensor is less than or equal to AE and meanwhile the Delta HR is less than or equal to Delta, the user is judged to be in a resting state, wherein the AE is a set heart rate threshold; delta is the heart rate fluctuation threshold, and a time interval T1 is set during which the acceleration is continuously measured if the combined acceleration is continuously measured

Constant, continuous measurement of the heart rate HR<≦ Ε and heart rate fluctuation Δ HR<If the value is not more than δ, judging that the user enters a stable resting state, and indicating that the user can start to continuously acquire resting heart rate; on the contrary, when above-mentioned condition can not satisfy in T1 completely, then indicate that this user does not enter stable resting state for a while, gather resting heart rate data this moment and will be inaccurate, APP suggestion user keeps resting state and carries out deep breathing simultaneously and alleviate the mood, and original data and timing are abandoned to intelligent wearing equipment, the detection of a new round of T1 time restarts

and the user terminal processing module is used for calculating according to the collected walking speed and metabolic equivalent and calculating the maximum oxygen uptake by combining the exercise heart rate and the metabolic equivalent.

10. The system of claim 9, wherein the heart-lung endurance measurement system based on oxygen uptake calculation,

GrossVO₂＝3.5+0.2×v+1.33×1.8×v×h

GrossVO₂＝3.5+0.1×v+1.8×v×ratio

the maximum oxygen uptake calculation formula is as follows: