JPH06190079A - Measuring method and apparatus for physical power index, and training apparatus - Google Patents

Abstract

PURPOSE:To obtain a physical index with high precision keeping higher safety with a small burden on a person to be inspected by employing a multiple variation model formula using a neural method having at least a pulse rate and an added value of the person to be inspected as input variable in the measurement of the physical index with the application of an exercising load to the person to be inspected. CONSTITUTION:In the measurement of a physical index by applying an exercising load to the person to be inspected, a multiple variation model formula MC is used by a neural method having at least a pulse rate and an added value of the person to be inspected as input variable. At least one of sex, age, a physical weight, elapsed time, a changing rate of pulse rate and a physical index forecast previously is used as the input variable of the multiple variation model formula MC by the neural method in addition to the pulse rate and the added value. In the case of a multi-stage addition system, the exercising load of the subsequent stage is determined based on an estimated value of the physical power index obtained as output variable O at each stage to obtain the subsequent input-variable I and the results are put into the multiple variation model type MC to obtain the physical power index again. This operation is repeated.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a physical fitness index measuring method and a measuring device for measuring a physical fitness index, and a training device using the same.

[0002]

2. Description of the Related Art Physical strength is an aerobic work capacity, which represents the tenacity of a person who can continue exercising for a long time, and anoxic, which represents the strength of supplying the energy required for exercise in a short time without using oxygen. It is divided into work ability, and as the physical fitness index showing the former, the maximum oxygen uptake amount, the maximum exercise ability (the load value for the maximum oxygen uptake amount of each individual, and in the physical fitness measurement, the load at the maximum pulse rate ( WWC), PWC170, PWC150, PWC75% HRmax (PWC
Is a physical work ability, and is expressed as a work rate (W) when a certain physiological reaction is observed in the body to exercise, P
The pulse rates of WC170 and PWC150 are 170 beats / minute and 15 beats, respectively.
The work rate at 0 beats / minute, and the PWC 75% HRmax is generally the work rate at 75% of the maximum pulse rate estimated from the age and sex of the individual).

To measure such a physical strength index, an exercise load device having a variable load value is used, starting from a light exercise and gradually increasing in strength, and when a predetermined target pulse rate is reached, the end of that amount is reached. There is a maximum underload method, which is a physical strength measurement method that ends exercise after continuing exercise, and a maximum load method, which is a physical strength measurement method that starts with a light exercise and gradually strengthens to increase to the maximum limit of physical strength. Generally, the maximum underload method is used to obtain a physical fitness index from a regression line, in which the test subject is subjected to three-stage load for 3 to 4 minutes, and the final pulse rate and load of each stage are set as three sets of data. .

Note that the exercise load device includes a treadmill and a bicycle ergometer. The former is designed to allow walking or running motion on a belt-shaped floor surface, and the amount of exercise load is made variable by adjusting the speed and inclination angle at which the belt-shaped floor surface is moved. As shown in FIG. 3, the latter has a bicycle shape composed of a handle 4, a saddle 3, a pedal 2 and a main body 1, and also has a variable load for the operation of stepping on the pedal 2. It can be adjusted. Reference numeral 5 in the figure denotes a pulse rate sensor attached to the exerciser's ear to measure the exerciser's pulse rate.

Further, conventionally, an appropriate exercise pulse rate is estimated from the maximum pulse rate required for sex and used for training, or first, physical fitness is measured to obtain a physical fitness index, and an appropriate load intensity is calculated based on the physical fitness index. It is common to set up and perform training. The maximum pulse rate is the maximum pulse rate for each individual, and to be precise, the maximum oxygen uptake (VO2 m
ax: The maximum amount of oxygen taken into the body per minute during exercise, which is often expressed as the maximum amount of oxygen taken per unit weight per unit time. Usually, the maximum amount of oxygen that can be taken per kg body weight is given. (represented in ml), but is often determined by a regression equation according to the statistically determined sex and age, and various regression equations have been proposed. 209-0.69 x age (beats / min) Female: 205-0.75 x age (beats / min) is used, and based on this maximum pulse rate, (maximum pulse rate-resting pulse rate) x The exercise rate + resting pulse rate is often used to set the pulse rate during exercise. A value of 30% to 70% (0.3 to 0.7) is usually used as the exercise intensity.

[0006]

When the physical work capacity is measured by the maximum load method, the exercise load is increased to the exercise tolerance capacity (all out) of the person, and the burden on the subject is large, which is dangerous. Sometimes. Even if the maximum-underload method is used to estimate the physical fitness index with a regression line, if the accuracy of the measurement results is expected to some extent, the burden on the subject must be considerably large.

On the other hand, due to the increase in health-orientedness, there is a growing need for a person with a low physical strength level to measure the physical strength safely and accurately. However, ensuring the safety in measuring the physical strength and the accuracy of the measurement result It is difficult to achieve both at the same time, and physical fitness measurement in places where the emergency system is not perfect, such as fitness clubs and homes, often involves physical fitness measuring devices that do not increase the load strength in order to prioritize safety. Is low.

Further, in the conventional training, when the pulse rate is kept constant, the set pulse rate which is the basis of the training is based on the statistically calculated maximum pulse rate. It is not a reflection of this, and the training is often not appropriate for the individual.
Even when exercising with a load set based on the physical strength measurement results, the load setting is based on past physical strength, so the physical strength of the individual who constantly changes, such as physical condition, is correctly reflected. It is not always training.

The present invention has been made in view of the above points, and an object of the present invention is to obtain a highly accurate physical strength index despite a small burden on a subject and high safety. (EN) Provided are a physical fitness index measuring method and device, and a training device capable of effective training.

[0010]

According to the method for measuring a physical fitness index according to the present invention, at least the pulse rate and the load value of the subject are used as input variables when the physical fitness index is measured by applying an exercise load to the subject. The main feature of the present invention is that it is performed by using a multivariate model equation based on a neuro method, and the physical fitness index measuring device according to the present invention measures at least the physical fitness index by applying an exercise load to the subject. Is characterized by including a measuring means using a multivariate model formula by a neuro method with the pulse rate and the load value as input variables, and the training apparatus further includes at least the pulse rate and the load value of the exerciser. A measuring means using a multivariate model formula by a neuro method as an input variable and a setting means for setting an exercise load according to the physical strength index estimated by this measuring means are provided. It has a feature that is e.

[0011]

According to the present invention, since the physical strength index of the subject can be obtained at an early stage, it is not necessary to subject the subject to an exercise load for a long time. At this time, as an input variable of the multivariate model equation, in addition to the pulse rate and the load value, the exerciser's sex, age, weight, elapsed time, the amount of change in pulse rate,
Using at least one of physical fitness indexes such as the amount of change in the load value and the previously predicted maximum oxygen uptake amount and maximum exercise capacity will increase the safety in increasing the load and further increasing the measurement accuracy of the physical fitness index. It can be ensured. When a bicycle ergometer is used as an exercise load device for exerting an exercise load, using the pedal rotation speed as an input variable is effective in measuring a physical fitness index.

The multivariate model formula (formula) referred to here
Since the target variate is non-linear, even if it is impossible to relate the target variate with a multivariate analysis that automatically associates a large number of explanatory variates as mathematical expressions, the Means a formula obtained by using a neuro method that provides an effective mathematical formula, and such a multivariate model formula is usually created based on a large number of experimental data. When performing measurements using the created multivariate model formula, it is often difficult to directly use the multivariate model formula due to the relation of the calculation speed. The present invention also includes a control table that is used and that uses a simplified model formula obtained from a simulation result. The simplified model formula can be obtained, for example, as a value obtained by adding or subtracting a value obtained by multiplying each input variable by a predetermined value, when the maximum oxygen intake amount is used as the output variable. The control table is used when the number of control variables is small.It is a method of expressing the control relationship of each variable in the table and obtaining the required value from the table when necessary. The fuzzy method is used when the number of control variables increases. Is also a method of obtaining the output by inferring and calculating the output using the membership function of each control variable.

The multivariate model formulas used in the examples described below were prepared using experimental data obtained when the physical fitness index was measured by the maximum underload method using a bicycle ergometer. The subjects at this time were 7 healthy men and women in their 20s to 60s.
There are 0 people, and the exercise load (protocol) is 4
Exercise was performed for a total of 16 minutes at 0% to 70% exercise intensity, and a regression line was obtained from the four sets of data obtained at each stage to calculate a physical fitness index.

Then, at least the pulse rate and the load value generated from various data obtained at the time of measuring the physical strength are used as input variables (preferably, in addition to the both, sex, age, height, weight, body Fat mass, body fat percentage, obesity, blood pressure,
Breathing rate, elapsed time, pulse rate change, load value change,
Outputs the maximum oxygen intake or maximum exercise capacity predicted previously, at least one of the average maximum oxygen intake or maximum exercise capacity of the stratum to which the subject belongs as an input variable), maximum oxygen intake or maximum exercise capacity A multivariate model formula for variables was obtained.

The load applied in the measurement using this multivariate model formula MC may be the fixed load system shown in FIG. 2 (a) or the multi-stage load system shown in FIG. 2 (b). A so-called lamp load system in which the strength is continuously increased at a constant rate may be used. In case of multi-stage load system,
As shown in FIG. 1, the exercise load at the next stage is determined based on the estimated value of the physical fitness index of the subject obtained as the output variable O at each stage, and the next input variable I is obtained. If the physical strength index is measured again by repeatedly entering the physical strength index into the formula MC and measuring the physical strength index again, the measurement accuracy can be further increased. For example, when estimating the maximum exercise ability as a physical fitness index at the end of each stage, the estimated maximum exercise ability is multiplied by a coefficient to set the next load, and the multiplication coefficient is 0.3 (30% exercise intensity) to 0. Approximately 8 (80% exercise intensity) and gradually increase the coefficient to a reasonable level.

The load setting during training is also performed by multiplying the estimated maximum exercise ability by a coefficient when the maximum exercise ability is estimated by the multivariate model formula. As the multiplication coefficient, a value (exercise intensity) suitable for the target training may be selected, and 40% to 60% exercise intensity is effective for maintaining physical strength and weight loss. When estimating the maximum oxygen uptake by the multivariate model formula, the maximum oxygen uptake (ml / kg / min) × body weight (kg) = 233 + 1
An appropriate load can be calculated by converting the maximum exercise capacity from the empirical formula of 3.08 × load (W) and multiplying by the exercise intensity.

Although the submaximal loading method was used here for the analysis of the experimental data, it is of course possible to use the maximal loading method, and if the breath analysis is also used, the multivariate model equation with higher accuracy can be obtained. Can be created.

[0018]

EXAMPLES The present invention will be described below with reference to the measurement of physical fitness using the bicycle ergometer shown in FIG. Example 1 The maximum exercise capacity was measured as a physical fitness index by using a multi-step loading method in 5 steps for a total of 12 minutes. The determination of the next load at each stage was performed by estimating the maximum exercise capacity using the multivariate model formula and multiplying it by a coefficient.
The load from the start to 1 minute is 20 to 40 W according to age and sex, and the load at 1 to 4 minutes is estimated by the multivariate model formula with the pulse rate and the load value as input variables at the 1st minute. The load of 40% exercise intensity of the maximum exercise capacity, the load of 5 to 8 minutes was the load of 50% exercise intensity of the maximum exercise capacity estimated in the 4th minute, and the load of 9 to 12 minutes was the 8th minute The load of 60% exercise intensity of the maximum exercise ability estimated in step 1 was set. In the model formula based on the neuro method used, the result of learning 40,000 times with the output variable O as the maximum exercise capacity was used. As a result, the average error (W) of the estimated maximum exercise capacity with respect to the measured value was as follows. As the measurement result of the maximum exercise capacity, the estimated value at 12 minutes was used.

[0019] In addition, the measurement result of the maximum athletic ability is obtained by calculating the regression line from the data of the three sets of load and pulse measured at 4 minutes, 8 minutes, and 12 minutes, and calculating the maximum pulse rate of the subject by sex and age. The maximum pulse rate may be obtained, and the load at that maximum pulse rate may be obtained as the maximum exercise capacity.

Example 2 In measuring the maximum exercise capacity by the multi-step load method, the initial input variable I is age, sex, and weight, and the input variable I at the time of prediction thereafter is age, sex, weight, and pulse rate. The load value, the pulse rate integrated value, the load integrated value, and the maximum exercise capacity at the time of the previous prediction, and the multiplication coefficient is the load of 30% of the maximum exercise capacity estimated from age, sex, and weight in the initial stage.
The load in 4 minutes is 40 of the maximum exercise capacity estimated in the 1st minute.
% Exercise intensity load, load at 5 to 8 minutes was 50% of maximum exercise capacity estimated at the 4th minute, and load was 9 to 12
As the load in minutes, a load of 60% exercise intensity of the maximum exercise capacity estimated in the 8th minute was set. The model formula based on the used neuro method uses the result of learning 40,000 times with the output variable O as the maximum exercise capacity, as in the above embodiment.
The average error of the estimated maximum exercise capacity from the measured value at this time was as follows. Maximum exercise capacity is 1
The estimate at 2 minutes was used.

[0021] Example 3 The initial input variable I is age, sex, and weight, and the input variable I at the time of subsequent prediction is added to age, sex, and weight, and the pulse rate, load value, integral value of pulse rate, and integral value of load. The maximum oxygen uptake amount was estimated as the output variable O under the same conditions as in Example 1 except that the maximum oxygen uptake amount at the time of the previous prediction was used. The load was set by increasing the coefficient for each step from 30% to 60% to the maximum exercise capacity converted from the maximum oxygen intake. Here, the model formula by the neuro method was not directly used, but a simplified model formula was created and used from the result of learning 40,000 times. The simplified model formula has a form in which each input variable I is multiplied by a constant and added. The mean squared error of the estimated maximum oxygen uptake at this time with respect to the measured value was as follows.
As the measurement result of the maximum oxygen uptake, the estimated value at 12 minutes was used.

[0022] Example 4 A maximum load of oxygen was measured by applying a load for 1 minute by the fixed load method. The load was calculated by converting the maximum oxygen uptake estimated by the multivariate model equation by the neuro method using the input variable I as the input variable I at the start of measurement and multiplying it by 0.3. The exercise load device used is a bicycle ergometer. In the estimation by the neuro method, the input variable I at each prediction time point is shown in Table 4, and the output variable is used as the maximum oxygen uptake amount, and the result of 40,000 times of learning is used. The mean squared error of the estimated maximum oxygen uptake at this time with respect to the measured value was as follows. The estimated value at 1 minute was used as the measurement result of the maximum oxygen uptake.

[0023] Embodiment 5 This embodiment is different from Embodiment 3 except that the input variable I is different.
The maximum oxygen uptake was estimated under the same conditions as above, and the load at each stage was set. The mean squared error of the estimated maximum oxygen uptake at this time with respect to the measured value was as follows. As the measurement result of the maximum oxygen uptake, the estimated value at 12 minutes was used.

[0024] Sixth Embodiment This embodiment is also the third embodiment except that the input variable I is different.
The maximum oxygen uptake was estimated under the same conditions as above, and the load at each stage was set. The mean squared error of the estimated maximum oxygen uptake at this time with respect to the measured value was as follows. As the measurement result of the maximum oxygen uptake, the estimated value at 12 minutes was used.

[0025] Example 7 The initial input variable I was age, sex, and weight, and the input variable I at the time of prediction thereafter was classified into classes (classified as under 45 years old and above 45 years old), sex, and class weight (male). : Less than 60kg and more than 60kg, female: 50k
less than g and 50 kg or more), pulse rate, load value, integrated value of pulse rate, integrated value of load, and maximum oxygen uptake at the time of the previous prediction, except under the same conditions as in Example 3. Maximum oxygen uptake was estimated and load was set. Average of the estimated maximum oxygen uptake at this time 2
The power error was as follows. As the measurement result of the maximum oxygen uptake, the estimated value at 12 minutes was used.

[0026] In the above description, only the case of measuring the physical strength has been described, but it can be used as a training apparatus depending on the determination of the load based on the measured physical strength index. For example, the maximum exercise capacity (maximum oxygen uptake) is estimated by a multivariate model formula by the neuro method at the final stage of the exercise that also serves as a warm-up for 4 minutes, and the load that gives a predetermined exercise intensity to the estimated maximum exercise capacity is calculated. Set it and continue to exercise. 4 for training suitable for weight loss and improving endurance
It is said that it is preferable to perform exercise with 5% exercise intensity. In this case, the load setting at the start of exercise is set to the maximum exercise ability estimated from the weight, sex, and age as in Example 2, for example. The maximum exercise capacity is estimated using the same input variable as that of the second embodiment 1 minute after the start of exercise, and the load is set to a value obtained by multiplying this by 0.4. Then, warming up for 3 minutes is performed, and the maximum exercise capacity estimated at 4 minutes is multiplied by 0.45 to set the load. If the maximum exercise capacity is estimated after that, the load setting becomes more accurate because the maximum exercise capacity can be measured more accurately. In addition, if the physical condition of the day is not good, the load setting reflects this, so that safe training can be always performed without undue burden. The case where the measuring method of the second embodiment is used has been described, but it goes without saying that the method is not limited to this.

[0027]

As described above, according to the present invention, in order to measure the physical fitness index using the multivariate model equation in which at least the pulse rate and the load value of the subject are used as input variables, the conventional maximum maximum load method is used. It is not always necessary to collect data from several points, so the time required to measure physical strength can be shortened,
In addition to reducing the burden on the test subject, it is possible to obtain measurement results with good accuracy without setting a high load intensity, so that a person with a low physical strength level is not forced to a high load, and It is possible to measure according to the level, and it is possible to measure a physical strength index having high safety and reliability.

Further, since the training device for setting the load based on the measurement of the physical fitness index takes into consideration the training effect and the physical condition of the subject, effective and safe training can be performed.

[Brief description of drawings]

FIG. 1 is a block diagram of an embodiment.

2A is an explanatory diagram of a fixed load system, and FIG. 2B is an explanatory diagram of a multi-stage load system.

FIG. 3 is a perspective view showing an example of a bicycle ergometer.

[Explanation of symbols]

 MC Multivariate model formula I Input variable O Output variable

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[Procedure amendment]

[Submission date] September 20, 1993

[Procedure Amendment 1]

[Document name to be amended] Statement

[Correction target item name] 0013

[Correction method] Change

[Correction content]

The multivariate model formulas used in the examples described below were prepared using experimental data obtained when the physical fitness index was measured by the maximum underload method using a bicycle ergometer. The subjects at this time were 7 healthy men and women in their 20s to 60s.
There are 0 people, and the exercise load (protocol) is 4
Exercise was performed for 4 minutes in 4 stages at 0% to 70% exercise intensity for a total of 16 minutes, and a regression line was obtained from the 4 sets of data obtained at each stage to calculate a physical fitness index.

[Procedure Amendment 2]

[Document name to be amended] Statement

[Correction target item name] 0018

[Correction method] Change

[Correction content]

[0018]

EXAMPLES The present invention will be described below with reference to the measurement of physical fitness using the bicycle ergometer shown in FIG. Example 1 When measuring the maximum exercise capacity as a physical fitness index, a multi-stage loading method was performed in four stages for a total of 12 minutes. The determination of the next load at each stage was performed by estimating the maximum exercise capacity using the multivariate model formula and multiplying it by a coefficient.
The load from the start to 1 minute is 20 to 40 W according to age and sex, and the load at 1 to 4 minutes is estimated by the multivariate model formula with the pulse rate and the load value as input variables at the 1st minute. The load of 40% exercise intensity of the maximum exercise capacity, the load at 4 to 8 minutes was the load of 50% exercise intensity of the maximum exercise capacity estimated at 4 minutes, and the load at 8 to 12 minutes was the 8th minute The load of 60% exercise intensity of the maximum exercise ability estimated in step 1 was set. In the model formula based on the neuro method used, the result of learning 40,000 times with the output variable O as the maximum exercise capacity was used. As a result, the average error (W) of the estimated maximum exercise capacity with respect to the measured value was as follows. As the measurement result of the maximum exercise capacity, the estimated value at 12 minutes was used.

[Procedure 3]

[Document name to be amended] Statement

[Correction target item name] 0020

[Correction method] Change

[Correction content]

Example 2 In measuring the maximum exercise capacity by the multi-step load method, the initial input variable I is age, sex, and weight, and the input variable I at the time of prediction thereafter is age, sex, weight, and pulse rate. The load value, the pulse rate integrated value, the load integrated value, and the maximum exercise capacity at the time of the previous prediction, and the multiplication coefficient is the load of 30% of the maximum exercise capacity estimated from age, sex, and weight in the initial stage.
The load in 4 minutes is 40 of the maximum exercise capacity estimated in the 1st minute.
% Exercise intensity load, load at 4 to 8 minutes was 50% exercise load of maximum exercise capacity estimated at 4 minutes, 8 to 12
As the load in minutes, a load of 60% exercise intensity of the maximum exercise capacity estimated in the 8th minute was set. The model formula based on the used neuro method uses the result of learning 40,000 times with the output variable O as the maximum exercise capacity, as in the above embodiment.
The average error of the estimated maximum exercise capacity from the measured value at this time was as follows. Maximum exercise capacity is 1
The estimate at 2 minutes was used.

[Procedure amendment 4]

[Document name to be amended] Statement

[Name of item to be corrected] 0022

[Correction method] Change

[Correction content]

[0022] Example 4 A maximum load of oxygen was measured by applying a load for 1 minute by the fixed load method. The load was determined by converting the maximum oxygen uptake estimated by the multivariate model equation by the neuro method using the input variable I as the input variable I at the start of measurement and multiplying it by 0.3. The exercise load device used is a bicycle ergometer. In the estimation by the neuro method, the input variable I at each prediction time point is shown in Table 4, and the output variable is used as the maximum oxygen uptake amount, and the result of 40,000 times of learning is used. The mean squared error of the estimated maximum oxygen uptake at this time with respect to the measured value was as follows. The estimated value at 1 minute was used as the measurement result of the maximum oxygen uptake.

Front page continuation (72) Inventor Satsuki Saeki 1048, Kadoma, Kadoma City, Osaka Prefecture Matsushita Electric Works Co., Ltd.

Claims (8)

[Claims] 1. A physical fitness, characterized in that, when an exercise load is applied to a subject to measure a physical fitness index, a multivariate model formula by a neuro method using at least the pulse rate and the load value of the subject as input variables is used. How to measure the index. 2. As input variables of the multivariate model formula by the neuro method, in addition to pulse rate and load value, sex, age,
The method for measuring a physical fitness index according to claim 1, wherein at least one of the weight, the elapsed time, the amount of change in pulse rate, the amount of change in load value, and the previously predicted physical fitness index is used. 3. The method for measuring a physical fitness index according to claim 1, wherein the exercise load is applied by a fixed load method. 4. The method for measuring a physical fitness index according to claim 1, wherein the exercise load is applied by a multi-stage load method in which the load is gradually increased. 5. The physical fitness index according to claim 4, wherein the physical fitness index for each stage is estimated by a multivariate model formula, and the exercise load of the next stage is determined based on the estimated physical fitness index. Measuring method. 6. The method for measuring a physical fitness index according to claim 5, wherein the physical fitness index for each stage is calculated by obtaining a regression line from the characteristic quantities of the pulse and the load at each stage. 7. A physical fitness measuring device for measuring a physical fitness index by exerting an exercise load on a subject, the measuring means using a multivariate model formula by a neuro method in which at least the pulse rate and the load value of the subject are used as input variables. A device for measuring a physical fitness index, which is characterized by being provided. 8. A training device for exerting an exercise load on a subject, comprising: a measuring means using a multivariate model formula by a neuro method in which at least the pulse rate and the load value of the exerciser are input variables; and the measuring means. A training device comprising: a setting unit configured to set an exercise load according to the estimated physical fitness index.