KR20090061230A - A method for controlling workload by measuring heart rate in sub-maximal exercise test and a system for controlling workload of fitness apparatus - Google Patents

Abstract

A method for controlling the exercise load of a sporting apparatus by calculating the heart rate at the submaximal exercise inspection, and a system for controlling the exercise load of a sporting apparatus by using the method are provided to allow the exercise load suitable for the estimated physical strength to be controlled simply and automatically without using an expensive equipment. A method for controlling the exercise load of a sporting apparatus by calculating the heart rate at the submaximal exercise inspection comprises the steps of (S10) calculating the regression equation for estimating the index control variable obtained by the regression analysis using the heart rate data measured, calculated and collected at the submaximal exercise state under the stepwise incremental exercise condition to a plurality of measurement targets as an independent variable and the index control variable data obtained at the maximal exercise as a dependent variable; (S20) measuring the heat rate at the initial stabilization stage and the each load step at the submaximal exercise state to the individual user performed under the stepwise incremental exercise condition identical to the stepwise incremental exercise condition used in the regression analysis, and calculating and collecting the change of the heart rate between the each steps; (S30) inputting the collected heart rate value or change to the independent variable of the index control variable regression equation to output the estimated value of the index control variable as an dependent variable; (S40) determining the sporting apparatus target control value corresponding to the exercise load intensity and the retention time according to the kind of the exercise load recipe from the estimated value of the index control variable; and (S50) transferring the exercise apparatus target control value to the apparatus control part of a sporting apparatus to control the operation state of the operation part.

Description

A method for controlling workload by measuring heart rate in sub-maximal exercise test and a system for controlling workload of exercise equipment Fitness Apparatus}

The present invention relates to an exercise load control method through the heart rate measurement in the sub-maximum exercise test and an exercise load control system for implementing the method in the exercise equipment, more specifically in the sub-maximal exercise test process Based on the measured heart rate measurements of the user, the control variable estimates that can be obtained from the maximum exercise test, such as anoxic threshold, are estimated, and the various exercise methods required for each user are determined according to these control variable estimates. Calculates the exercise intensity and duration according to the present invention, and transmits a control signal to the machine control unit of the exercise machine to achieve the calculated exercise intensity, and the machine control unit of the exercise machine loads and loads on the basis of the transmitted control signal. By providing a method of adjusting the duration and the like and a system implementing such a method, Relieve the burden of using expensive equipment that can measure Lactate Threshold (LT) and Ventilatory Threshold (VT) to measure Anaerobic Threshold (AT) in (Load) Exercise In addition to personal fitness, users can perform fitness measurements easily and in a short time without excessive exercise that can exceed their own physical ability. The present invention relates to an exercise load adjustment method for providing an exercise load adjustment means for using an exercise device, and an exercise load adjustment system for implementing such a method in an exercise device.

In general, health-related fitness consists of four elements: cardiorespiratory, muscle strength / endurance, flexibility, and body composition, among which the whole body endurance becomes a key component.

Here, systemic endurance is comprehensively related to oxygen absorption system such as lung, heart, blood, capillaries, oxygen utilization system of transport system or skeletal muscle tissue, and the superiority of this systemic endurance is physical work ability or oxygen intake depending on aerobic energy. As the age increases, the overall endurance decreases gradually, but the main factors besides the increase of age are body fat and weight gain, muscle loss, decrease in the amount of physical activity in daily life, and the increase in disease morbidity. There is this.

Because of this, there are methods to check the physical strength of the individual, such as student fitness test, exercise load test, basic fitness test, and whole-body endurance measurement, such as five sports (50m running, long jump, sit up front) Student physical test of bending, sit-ups, long running, walking) is a method that does not take into account the individual's physique (height, weight). In the case of the test, although it is an accurate test method using various expensive equipment that measures the presence or absence of a heart attack, blood pressure, oxygen intake, heart rate, respiratory rate, lung capacity, and ventilation during exercise, it is inferior in economy and simplicity. Of the above measurement methods, in the case of the basic physical fitness test is more than the exercise load test described above This test takes a lot of time because the scan that requires inspection items and there is a drawback falling simplicity.

On the other hand, the whole body endurance measurement method is an indirect measurement method through the heart rate and body fat measurement using a low-cost measuring instrument in a short time can be a simple and comparatively accurate fitness measurement, but this indirect whole body endurance measurement method so far Most of them perform exercise tests according to the maximum exercise load such as 12 minute running test, 1500m running test, 20m shuttle run test, 10m shuttle run stamina test, etc. The whole body endurance was measured, and in this way, there is a problem that the physical burden and the mental burden are forced to keep running at a set time or distance by electric power when measured by a weak person such as an elderly person or a patient. Even big problems occur In addition, individual deviations in the power used as a measurement standard also remain a problem that cannot be ignored.

In order to solve this problem, a measurement method that satisfies the following basic conditions is required, which requires 1) safety to be able to measure under load without the user performing power, and 2) a high level of technical expertise. The key factors are simplicity that does not take much time for measurement, and 3) economics that does not require expensive equipment and requires much manpower, and 4) non-invasiveness that does not collect blood. Can be.

On the other hand, there is an anaerobic threshold to evaluate the degree and effect of endurance training corresponding to whole body endurance and to estimate the endurance performance. To estimate the anaerobic threshold, the lactate threshold is used to estimate the anaerobic threshold. LT) or ventilation threshold (VT) should be measured. The criteria for determining lactic acid threshold (LT) according to various studies are that blood lactate concentration is higher than resting concentration (Wasserman, 1973), and blood lactate concentration is 2 mmol. / L point (Hagberg, 1984), blood lactate concentration of 4mmol / L (MacDougall et al., 1977), and the rapid increase in the slope of lactic acid concentration with increasing exercise intensity (Keul et al., 1979) And the criteria for determining the ventilation threshold (VT) are that VE is out of linear proportional to exercise intensity (Wasserman et al., 1973), and that VCO ₂ is out of linear proportional to VO ₂ . (Wasserman et al., 1973; Beaver et al., 1973), and the respiratory constant increases rapidly (Wasserman et al., 1973). However, in order to measure Lactate Threshold (LT) or Ventilatory Threshold (VT), expensive equipment is required, and it is difficult to manage to maintain the reliability of the measurement. A study of estimating anoxic threshold using heart rate, a physiological variable, is being attempted.

The present invention is to solve the problems of the prior art as described above, the object of the present invention is a simple measurement method in the sub-maximum exercise test state is not the maximum exercise load state of the subject to be measured as the user of the present invention According to these estimated index control variables, it provides a method for estimating anaerobic threshold as an index control variable that can be obtained from the maximum exercise test using the heart rate measurement according to the Calculating the exercise load intensity and / or duration according to various exercise methods required for each user, and transmitting a control signal to the machine control unit of the exercise machine to achieve the calculated exercise load intensity and duration, and The instrument control unit adjusts the load and load duration of the instrument operation unit based on the transmitted control signal. Including the steps of: to provide a system for implementing the exercise load control method, and made this way.

In addition, when using the whole body endurance measuring method according to the present invention, the user's mental burden for performing a vigorous exercise up to the user's physical fitness limit as in the conventional method for the maximum exercise load for measuring the user's fitness In addition to eliminating the need for physical fitness, the measurement equipment required for physical fitness is simple, increasing the convenience and economy, and in addition, providing a means to adjust the exercise load according to the exercise load prescription required by the user in a simple manner. It is aimed at.

The present invention to achieve the above object,

Using the heart rate data measured and calculated under sub-maximal exercise under Stepwise Incremental Exercise conditions with multiple subjects as an independent variable and using the maximum exercise A regression analysis step of calculating a regression equation for estimating the indicator control variable obtained through the regression analysis using the indicator control variable data obtained in the) state as the dependent variable;

Measure the heart rate measurements of the initial ballast and each load step in sub-maximal exercise conditions for individual users under the step load increase exercise conditions of the same conditions as the step load increase conditions used in the regression analysis. A heart rate data collection step of calculating and collecting heart rate change values;

Inputting the heart rate measurement and / or heart rate change collected in the heart rate data collection step into an independent variable of the set index control variable regression equation and outputting an estimate of the index control variable in the maximum exercise state as a dependent variable value Estimating an estimate of the indicator control variable;

An exercise machine target control value determining step of determining an exercise machine target control value corresponding to the exercise load intensity and duration according to the exercise load prescription type from the estimated value of the indicator control variable estimated in the estimating value of the indicator control variable; And

An exercise machine operation control step of transmitting the exercise machine target control value to the machine control unit of the exercise machine to control an operating state of the machine operation part; Provide a load regulation method.

The present invention may further include a user information input step of inputting information such as a user's age or gender, and the user information input through the user information input step may include the index control variable regression equation. It is desirable to be used as an additional variable for selecting, wherein the user information may also be used as a criterion for determining the exercise apparatus target control value.

And the indicator control variable in the present invention, the maximum oxygen intake (VO ₂ max), the heart rate of the anaerobic threshold level (AT-HR), the maximum heart rate (HR _max ), the oxygen intake of the anaerobic threshold level (AT-VO ₂ / kg), maximum power (Max-Power, power at maximum oxygen intake), and power at oxygen free threshold (AT-Power). Here, the maximum oxygen intake (VO ₂ max), maximum heart rate (HR _max ), and maximum work rate (Max-Power) are the maximum values of the exercise load intensity to ensure the safety of the user during the exercise. Can be used as a criterion to calculate the heart rate at the anaerobic threshold level (AT-HR), the oxygen intake at the anaerobic threshold level (AT-VO ₂ / kg), and the power at the anaerobic threshold (AT-Power). ) Is the reference value (eg Vs. aerobic exercise for weight control or exercise load for cardiopulmonary endurance improvement).

In addition, the present invention provides an exercise load control system for implementing the exercise load control method of the exercise device through the heart rate measurement in the sub-maximal exercise test as an aspect of the device invention in a form different from the method invention described above. , More specifically,

Using the heart rate data measured and calculated in the sub-maximal exercise condition in the stepwise incremental exercise condition for a large number of subjects as an independent variable and using the maximum exercise A control memory unit for storing a regression equation for estimating an indicator control variable obtained through regression analysis using the indicator control variable data obtained in a state as a dependent variable;

The initial ballast and each load in the sub-maximum exercise state for the individual user performed under the step load increase exercise condition of the same condition as the step load increase condition used in the regression analysis to obtain the regression equation stored in the control memory unit 100 A heart rate data collection unit for measuring a heart rate measurement value of a step and calculating and collecting a heart rate change value between each load step;

The heart rate measurement value and / or the heart rate change value collected by the heart rate data collection unit are input to the independent variable of the index control variable regression equation of the control memory unit to estimate the index control variable in the maximum exercise state. An estimation value calculating unit of the indicator control variable to be output;

According to the type of exercise load prescription, an exercise machine target control value storing an exercise machine target control value for defining an exercise load intensity and duration of an exercise machine corresponding to the estimated value of the indicator control variable estimated by the estimation calculator of the indicator control variable. A database; And

The exercise machine target control value outputted from the exercise machine target control value database includes an exercise machine operation control unit for controlling the operation state of the machine operating unit by transmitting the exercise machine target control value to the machine control unit of the exercise machine. It provides an exercise load control system of exercise equipment through heart rate measurement.

The present invention preferably further includes a user information input unit for inputting information such as age or gender of the user, and the user information input through the user information input unit selects the indicator control variable regression equation in the control memory. It provides an exercise load control system, characterized in that used as an additional variable. Here, the user information input through the user information input unit may be used as a selection item of the exercise apparatus target control value database.

According to the method invention and the device system invention according to the present invention, it is possible to indirectly estimate the user's fitness only by the heart rate measurement data of the sub-exercise state, and further, to simply and automatically adjust the exercise load appropriately to the estimated fitness level. High cost to measure Lactate Threshold (LT) and Ventilatory Threshold (VT) to measure Anaerobic Threshold (AT) at maximum (load) exercise In addition to relieving the burden of using the equipment of the user, it is possible to perform the fitness measurement in a short time in a simple and short time without excessive exercise that can exceed his or her physical ability in the personal aspect. Safe use of exercise equipment with appropriate exercise load intensity and duration It provides the effect of allowing.

Hereinafter, specific embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a flowchart illustrating the steps performed by the exercise load adjustment method through the heart rate measurement in the sub-maximum exercise test according to the present invention, and FIG. 2 is a block diagram showing the main components of the system invention of the present invention. It is shown in the form.

First, the steps of the exercise load adjustment method by measuring the heart rate in the sub-maximum exercise test of the present invention, as shown in Figure 1

Using the heart rate data measured and calculated under sub-maximal exercise under Stepwise Incremental Exercise conditions with multiple subjects as an independent variable and using the maximum exercise A regression analysis step (S10) of calculating a regression equation for estimating the indicator control variable obtained through the regression analysis using the indicator control variable data obtained in the) state as the dependent variable;

Measure the heart rate measurements of the initial ballast and each load step in sub-maximal exercise conditions for individual users under the step load increase exercise conditions of the same conditions as the step load increase conditions used in the regression analysis. A heart rate data collection step of calculating and collecting a heart rate change value (S20);

Inputting the heart rate measurement and / or heart rate change collected in the heart rate data collection step into an independent variable of the set index control variable regression equation and outputting an estimate of the index control variable in the maximum exercise state as a dependent variable value Estimating an estimated value of the indicator control variable (S30);

An exercise machine target control value determining step (S40) of determining an exercise machine target control value corresponding to the exercise load intensity and duration according to the exercise load prescription type from the estimated value of the indicator control variable estimated in the estimating step of the indicator control variable ; And

An exercise load through the heart rate measurement in the sub-exercise test, comprising: an exercise machine operation control step (S50) of transmitting the exercise machine target control value to the machine control unit of the exercise machine to control the operation state of the machine operation unit; Provide an adjustment method.

In addition, the present invention may further include a user information input step (S15) for inputting information such as the age or gender of the user, as shown in Figure 2, and the user information input step (S15) Preferably, the user information input through the user information may be used as an additional variable for selecting the index control variable regression equation, and the user information may also be used as a criterion for determining the target apparatus control value.

And the indicator control variable in the present invention, the maximum oxygen intake (VO ₂ max), the heart rate of the anaerobic threshold level (AT-HR), the maximum heart rate (HR _max ), the oxygen intake of the anaerobic threshold level (AT-VO ₂ / kg), maximum power (Max-Power, power at maximum oxygen intake), and power at oxygen free threshold (AT-Power). Here, the maximum oxygen intake (VO ₂ max), maximum heart rate (HR _max ), and maximum work rate (Max-Power) are the maximum values of the exercise load intensity to ensure the safety of the user during the exercise. Can be used as a criterion to calculate the heart rate at the anaerobic threshold level (AT-HR), the oxygen intake at the anaerobic threshold level (AT-VO ₂ / kg), and the power at the anaerobic threshold (AT-Power). ) Is a reference based on the type of exercise you want (e.g., , It can be used as the basis for calculating the reference value) of the exercise of the aerobic exercise or enhance cardiorespiratory endurance of weight control purpose object.

Indicator control variables used in the present invention, namely maximum oxygen intake (VO ₂ max), anoxic threshold level heart rate (AT-HR), maximum heart rate (HR _max ), anoxic threshold level oxygen intake (AT-VO ₂ / kg), maximum power (Max-Power, power at maximum oxygen intake), and power at anaerobic threshold (AT-Power), etc., from independent heart rate and heart rate change values at Estimated as a dependent variable, in order to understand the interrelationship between the independent variable and the dependent variable and to predict the change of the dependent variable for the independent variable, the submaximal motion as illustrated in the following Table 1 for a large number of experimental subjects Multiple regression of heart rate measurements (see FIG. 4) and measurement results from the maximum exercise test (see FIG. 5) in accordance with the protocol for estimating the gradual load increase in the sub-maximal exercise test ) All possible regression was used as the variable selection method, and all independent and dependent variables were correlated before regression analysis, and only independent variables having significant correlation with the dependent variables were used in the regression analysis. , but oxygen uptake (VO ₂₎ the value of the maximum oxygen intake amount at the maximum exercise test (Maximal test) in the test is at rest heart rate of the measurement target person, or at least 100 times a submaximal exercise test (Sub-maximal exercise test) ( VO 2 Only data except the subjects who were larger than max) were used.

Stage 1 25 W Exercise load : (150kgm-min ^-1 , 0.5kg), 60rpm hold for 3 minutes Tier 2 100 W exercise load : (600kgmmin- ¹ , 2.0kg), 60rpm hold for 3 minutes Tier 3 125 W Exercise load : (750kgmmin ^-1 , 2.5kg), 60rpm hold for 3 minutes 4 steps 150 W Exercise load : (900kgmmin ^-1 , 3.0kg), 60rpm hold for 3 minutes

In the present invention, a multiple regression analysis of six dependent variables (indicator control variables) from the experimental results obtained through the above experiments was carried out as a preferred embodiment.

① In the regression analysis for the estimation of VO ₂ max, the dependent variable is the VO ₂ max, and the independent variable is the initial heart rate and the sub-maximal exercise test at rest (rest). Heart rate (HR1, HR2, HR3, HR4) at each load phase (Load phase; 25W, 100W, 125W, 150W) and the change in heart rate at each load phase in the exercise test) (I.e., the difference in heart rate for each load step; HR01, HR12, HR23, HR34, HR40) and summarized as shown in Table 2 below.

HR0: Heart rate at rest HR01: HR1-HR0 HR1: heart rate at 25W HR12: HR2-HR1 HR2: heart rate at 100W HR23: HR3-HR2 HR3: heart rate at 125W HR34: HR3-HR3 HR4: heart rate at 150W HR40: HR3-HR0

The regression analysis results are shown in Table 3 below.

Modified R ² SEE F P value 0.431 3.38 4.029 0.027 model Non-standardized coefficient Normalization factor t Significance B Standard error beta (a constant) 26.995 12.469 2.245 0.044 HR1 -0.053 0.125 -0.104 -0.420 0.682 HR3 0.221 0.111 0.755 1.983 0.071 HR23 -0.809 0.312 -0.943 -2.598 0.023 HR34 -0.200 0.182 -0.253 -1.097 0.294

According to the correlation analysis between the maximum oxygen intake (VO ₂ max) and 10 independent variables (HR0, HR1, HR2, HR3, HR4, HR01, HR12, HR23, HR34, HR40), HR1, HR2, HR3, HR23, HR34 was significantly correlated with the maximum oxygen uptake (VO ₂ max). Based on the results of this correlation analysis, the dependent variable was the maximum oxygen uptake (VO ₂ max), and the independent variables were HR1, HR2, HR3, HR23. , Regression analysis using HR34 to estimate the maximum oxygen uptake (VO ₂ max), which was calculated by the following equation, with a significant probability of 0.027 (<0.05), modified R ² of 0.431, and standard error of the estimated value ( SEE) showed 3.38 ml / min / kg.

VO ₂ max = 26.995-0.053 x HR1 + 0.221 x HR3-0.809 x HR23-0.200 x HR34

Here, HR2 among the 10 independent variables was excluded from the independent variable due to collinearity (which occurs when the correlation between the independent variables becomes high and in this case, the accuracy of the dependent variable estimation becomes low). The absolute value of the denormalization coefficient of B was highest at 0.809, indicating that the difference between HR2 (heart rate at 100W) and HR3 (heart rate at 125W) (HR23) had the greatest effect on the maximum oxygen uptake (VO ₂ max).

② Regression analysis was performed to estimate the heart rate (AT-HR) at the anaerobic threshold level, and the following equation was calculated as the regression equation to estimate the heart rate (AT-HR) at the anaerobic threshold level.

AT-HR = 229.644-0.004 x HR0-0.569 x HR1-0.643 x H12-0.691 x HR23-0.3060 x HR40

③ Maximum heart rate (HR _max) by performing a regression analysis for the estimation, the regression equation was calculated by the following equation in which the estimated maximum heart rate (HR _max).

HR _max = 90.106 + 0.121 x HR1 + 0.439 x H2 + 1.251 x HR23

④ by performing a regression analysis for the anaerobic estimated oxygen uptake (AT-VO ₂ / kg) of the threshold level, the anaerobic threshold level of oxygen uptake (AT-VO ₂ / kg) estimated by regression the following equation in which the Calculated.

AT-VO ₂ / kg = 88.848 + 0.398 × HR0-0.305 × HR1-0.544 × HR2-0.933 × HR23 + 0.18 × HR40

⑤ By performing a regression analysis on the maximum power (Max-Power) estimation, the following equation was calculated as a regression equation to estimate the oxygen intake (AT-VO ₂ / kg) of the anaerobic threshold level.

    Max-Power = 392.059 + 0.688 × HR0-1.915 × HR1 + 1.456 × HR2-1.286 × HR3-2.378 × HR34

(6) The following equation was calculated as a regression equation for estimating the AT-Power at the anaerobic threshold by performing a regression analysis on the AT-Power at the anaerobic threshold.

    AT-Power = 484.032 + 1.092 × HR0-3.166 × HR1-0.199 × HR4 + 1.214 × HR12-4.014 × HR23

When the regression analysis step (S10) described above ends, the heart rate data collection step (S20) is the maximum under the step load increase exercise conditions (same conditions as the step load increase conditions used in the regression analysis) for the individual user In the lower exercise state, the heart rate measurement values of the initial ballast and each load stage are measured and the heart rate change between each load stage is calculated and collected.In the step of estimating the estimated value of the indicator control variable (S30), the heart rate data collection stage (S20) is performed. The collected heart rate measurement value and / or heart rate change value are input to the independent variable of the index control variable regression equation which is set, and the estimated value of the index control variable is output as the dependent variable value.

Furthermore, from the estimated value of the indicator control variable estimated in the estimating step of the indicator control variable, the exercise load prescription type (for example, the heart rate (AT-HR) of the estimated anaerobic threshold level or the 70-80% level of the maximum heart rate) is progressed. Exercise device target control values (e.g., in the case of treadmills), corresponding to the exercise load intensity and duration according to the cardiopulmonary endurance exercise, aerobic exercise progressing to 60-70% of the estimated maximum heart rate, After passing through the exercise apparatus target control value determining step (S40) for determining the traveling speed, etc., the exercise apparatus target control value is transmitted to the machine controller of the exercise machine to perform the operation control step (S50). do.

With reference to the accompanying drawings, a preferred embodiment of the exercise load control system to actually implement the exercise load adjustment method through the heart rate measurement in the sub-exercise test according to the present invention as described above with reference to Figure 3, the present invention Exercise load control system according to the control memory unit 100, the heart rate data collection unit 200, the estimated value calculation unit 300 of the indicator control variable, the exercise equipment target control value database 400, as shown in FIG. An exercise device operation control unit 500 is included. Preferably, the apparatus further includes a user information input unit 150 as shown. More preferably, a display unit for outputting related control data and the like in a screen form. Not).

The control memory unit 100 is an independent variable for heart rate data that is measured, calculated, and collected in a sub-maximal exercise state under a stepwise incremental exercise condition against a plurality of subjects. and used as a maximal exercise (Maximal Indicator control variable data obtained during exercise (maximum oxygen intake (VO ₂ max), heart rate at anaerobic threshold level (AT-HR), maximum heart rate (HR _max ), oxygen intake at anaerobic threshold level (AT-VO ₂₎ / kg), maximum power (Max-Power, power at maximum oxygen intake), and power at anaerobic threshold (AT-Power, etc.) as regression for estimating indicator control variables obtained through regression analysis Equations (1, 2, 3, 4, 5, 6) are stored.

In addition, the heart rate data collector 200 may be configured to perform the same condition as the step load increase condition used in the regression analysis for obtaining the regression equation stored in the control memory unit 100 for the individual user who is actually receiving the exercise prescription. In the submaximal exercise condition performed under the step load increase exercise condition, the heart rate measurement values of the initial stabilizer and each load step are measured, and the heart rate change between each load step is calculated and collected. 300 is input to the independent variable of the indicator control variable regression equation.

The estimated value calculator 300 of the indicator control variable inputs the heart rate measurement value and / or heart rate change values collected by the heart rate data collector 200 into an independent variable of the indicator control variable regression equation of the control memory 100. The estimate of the indicator control variable is output as the dependent variable value.

The exercise machine target control value database 400 defines an exercise load intensity and a duration of an exercise machine corresponding to the estimated value of the indicator control variable estimated by the estimation calculator of the indicator control variable according to the set exercise load prescription type. The target control value is stored, and thus, the exercise machine target control value output from the exercise machine target control value database 400 is transmitted to the machine control unit 600 of the exercise machine through the exercise machine operation control unit 500, thereby to implement the machine. The operating state of the actuating part is controlled, where the actuating state control of the mechanism actuating part adjusts the strength and load duration of the rotating load of the rotating body controlled by the magnetic braking system, for example in the case of a health bike or a health cycle. In the case of the tread mill, It will also be understood to mean that the control controls the control or the running speed, running time and the like.

Although the preferred embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements of those skilled in the art using the basic concepts of the present invention defined in the following claims are also provided. It belongs to the scope of rights.

1 is a flow chart of a method invention embodiment according to the present invention.

2 shows a flow chart of another embodiment of a method invention according to the invention.

3 shows the construction and operating state of the device invention according to the invention.

FIG. 4 is a graphical representation of heart rate measurements for subjects subject to incremental loading step measurement protocols in submaximal exercise testing. FIG.

5 is a diagram showing the results of the maximum exercise load test for the measurement subjects.

Explanation of symbols on the main parts of the drawings

S10: Regression Analysis Step S20: Heart Rate Data Collection Phase

S30: estimating the estimated value of the indicator control variable

S40: determination of the exercise apparatus target control value

S50: exercise equipment operation control step

100: control memory 200: heart rate data collection unit

300: indicator control variable estimation unit

400: fitness equipment target control value database

500: exercise machine operation control unit 600: mechanism control unit

Claims (9)

Using the heart rate data measured and calculated under sub-maximal exercise under Stepwise Incremental Exercise conditions with multiple subjects as an independent variable and using the maximum exercise A regression analysis step (S10) of calculating a regression equation for estimating the indicator control variable obtained through the regression analysis using the indicator control variable data obtained in the) state as the dependent variable; Measure the heart rate measurements of the initial ballast and each load step in sub-maximal exercise conditions for individual users under the step load increase exercise conditions of the same conditions as the step load increase conditions used in the regression analysis. A heart rate data collection step of calculating and collecting a heart rate change value (S20); Estimation step of estimating the index control variable for inputting the heart rate measurement value and / or heart rate change value collected in the heart rate data collection step into the independent variable of the set index control variable regression equation to output the estimated value of the index control variable as a dependent variable (S30) ); An exercise machine target control value determining step (S40) of determining an exercise machine target control value corresponding to the exercise load intensity and duration according to the exercise load prescription type from the estimated value of the indicator control variable estimated in the estimating step of the indicator control variable ; And The exercise device through the heart rate measurement in the sub-exercise test, characterized in that it comprises an exercise device operation control step (S50) for transmitting the exercise device target control value to the device control unit of the exercise device to control the operation state of the device operating unit. Exercise load adjustment method. The method of claim 1, further comprising a user information input step (S15) of inputting information such as age or gender of the user. According to claim 1 or 2, wherein the indicator control variable, the maximum oxygen intake (VO ₂ max), anoxic threshold level heart rate (AT-HR), maximum heart rate (HR _max ), oxygen-free threshold level oxygen One or more indicator control variables selected from the group including intake (AT-VO ₂ / kg), maximum work (Max-Power, work at maximum oxygen intake), and work at anaerobic threshold (AT-Power). Exercise load control method of the exercise equipment by measuring the heart rate in the sub-maximal exercise test, characterized in that made. According to claim 1 or 2, wherein the heart rate measurements collected in the heart rate data collection step (S20) is the initial heart rate at rest and heart rate in each load step of increasing load in the sub-maximal exercise test, The heart rate change value calculated and collected in the heart rate data collection step (S20) is the exercise load adjustment method of the exercise equipment by heart rate measurement in the sub-maximal exercise test, characterized in that the difference in the heart rate of each load step. Using the heart rate data measured and calculated in the sub-maximal exercise condition in the stepwise incremental exercise condition for a large number of subjects as an independent variable and using the maximum exercise A control memory unit 100 for storing a regression equation for estimating an indicator control variable obtained through regression analysis using the indicator control variable data obtained in a state as a dependent variable; The initial ballast and each load in the sub-maximum exercise state for the individual user performed under the step load increase exercise condition of the same condition as the step load increase condition used in the regression analysis to obtain the regression equation stored in the control memory unit 100 A heart rate data collector 200 for measuring a heart rate measurement value of the stage and calculating and collecting a heart rate change value between each load stage; Indicator control to input the heart rate measurement value and / or heart rate change value collected by the heart rate data collection unit to the independent variable of the indicator control variable regression equation of the control memory unit 100 to output the estimated value of the indicator control variable as a dependent variable value An estimate calculator 300 of the variable; According to the type of exercise load prescription, an exercise machine target control value storing an exercise machine target control value for defining an exercise load intensity and duration of an exercise machine corresponding to the estimated value of the indicator control variable estimated by the estimation calculator of the indicator control variable. A database 400; And And an exercise machine operation control unit 500 for controlling the operation state of the machine operation unit by transmitting the exercise machine target control value output from the exercise machine target control value database to the machine control unit 600 of the exercise machine. Exercise load control system of exercise equipment through heart rate measurement in submaximal exercise test. The system of claim 5, further comprising a user information input unit (150) for inputting information such as age or gender of the user. 7. The maximum value of claim 6, wherein the user information input through the user information input unit 150 is configured to be used as an additional variable for selecting the indicator control variable regression equation in the control memory 100. Exercise load control system of exercise equipment by measuring heart rate in exercise test. According to claim 6, The user information input through the user information input unit 150 is heart rate measurement in the sub-exercise test, characterized in that configured to be used as a selection item of the exercise device target control value database 400 Load control system of exercise equipment through The method of claim 5, wherein the indicator control variable includes: maximum oxygen intake (VO ₂ max), anoxic threshold level (AT-HR), maximum heart rate (HR _max ), anoxic One selected from the group consisting of oxygen intake at the sex threshold level (AT-VO ₂ / kg), maximum power (Max-Power, power at the maximum oxygen intake), and power at the anaerobic threshold (AT-Power). Exercise load control system of the exercise equipment through the heart rate measurement in the sub-maximal exercise test, characterized in that consisting of the above indicator control variables.

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