JP2005230340A - Energy consumption estimation apparatus, energy consumption estimation system, and database - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an energy consumption estimation apparatus and an energy consumption estimation system that can obtain the amount of energy consumption at a simple and high time resolution. <P>SOLUTION: The apparatus comprises an acceleration sensor 11 detecting variations on acceleration of a subject, an air pressure sensor 12 detecting a change in air pressure of the subject and an air pressure differentiation processor 13, a moving style discriminator 16 discriminating a type of moving style of the subject on the basis of a detection value by the acceleration sensor 11 and a time differentiation value by the air pressure sensor 12, a moving speed estimation section 17 estimating a moving speed of the subject on the basis of the detection value of the acceleration sensor 11, and an energy consumption estimation section 18 estimating energy consumption of the subject on the basis of a formula between the moving speed corresponding to each type of moving style discriminated by the moving style discriminator 16 and the energy consumption. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to an energy consumption estimation device, an energy consumption estimation system, and a database that can obtain an energy consumption corresponding to the behavior of a subject simply and with high time resolution.

  In recent years, exercise prescriptions have been performed for the purpose of improving or preventing lifestyle-related diseases such as diabetes and obesity and heart diseases. In order to maximize exercise effectiveness and safety, it is necessary to personally determine the quantity and quality of exercise. The physical activity amount in daily life is used as the exercise amount index, and the exercise intensity corresponding to the activity amount per unit time is used as the quality indicator. In particular, elderly people not only have a decline in motor function due to aging, but have a greater variation in physical fitness level than younger people, reflecting their exercise habits and life history. Therefore, when performing exercise prescription, applying standard values for each generation may lack appropriateness in terms of both effectiveness and safety. Therefore, it is required to indicate the amount of physical activity and activity intensity as accurately as possible individually. It is also important not only to give quantitative instructions, but also to know whether exercise is being performed properly. In fact, daily physical activity and total energy consumption have been reported to be associated with cardiovascular disease and death.

  In general, the amount of physical activity is expressed as the amount of energy consumed during physical exercise or work. Currently, physical activity is measured in various ways. For high measurement accuracy, there are a breath gas analysis method and a double labeled water method, but they are cumbersome and highly restrictive, and the burden on the patient is great. In order to solve these problems, in recent years, a method has been proposed in which various small sensors are used to measure the behavior and running speed of a subject without restriction to evaluate the amount of physical activity.

Japanese Patent Laid-Open No. 11-347021

  However, the above-described methods for evaluating the amount of physical activity cannot evaluate vertical movements such as stair climbing, such as walking on a flat ground or climbing a staircase that is said to have a physical load that is more than three times greater than walking on a flat ground. There was a problem that the difference in walking form could not be determined.

  In addition, since the physical load varies depending on the walking speed, it is necessary to consider the walking speed in order to perform an accurate physical activity evaluation. Thus, for young people, a simple estimation method such as gait × standard stride assuming a standard body has been shown, but it has also been reported that walking speed decreases with age. It has been pointed out that this is due to a decrease in stride due to a decrease in hip joint extension during walking and a decrease in low dorsiflexion during heel contact. There is a large individual difference in such a decline in motor function associated with aging, and walking ability varies even in the same age and the same physique. For this reason, there has been a problem that sufficient specification cannot be made by a method such as pace / standard stride that assumes a standard body like a young person.

  Here, a method of changing the regression equation for each individual is also conceivable. However, the method of changing the regression equation for each individual has poor versatility, and there is a problem that sufficient estimation cannot be performed for walking of elderly people, patients, and the like.

  In general, the energy consumption is estimated by a reference value of exercise intensity for each action type published by the Medical Society. However, recent studies have pointed out that this reference value is not necessarily optimal for the behavior of daily living behavior. At this time, what is important is the dynamics of oxygen intake with respect to exercise load, and if the dynamics of oxygen intake can be known, it is very useful for cardiac function diagnosis and exercise prescription. However, at present, there is no means to know this other than direct measurement with a highly restrictive oxygen consumption meter, and energy consumption is estimated in consideration of the amount of excess oxygen intake after exercise (EPOC: Excess Post-exercise Oxygen Consumption). There was a problem that there was no simple method to do.

  The present invention has been made in view of the above, and an object of the present invention is to provide an energy consumption estimation device, an energy consumption estimation system, and a database that can obtain an energy consumption with simple and high time resolution. .

  In order to solve the above-described problems and achieve the object, an energy consumption estimation apparatus according to claim 1 includes an acceleration sensor that detects a change in a subject's acceleration, a pressure sensor that detects a change in the pressure of the subject, A movement form determination means for determining the type of movement form of the subject based on the detection value of the acceleration sensor and the time differential value of the barometric sensor, and the movement speed of the subject based on the detection value of the acceleration sensor A moving speed estimating means for estimating, and an energy consumption estimating means for estimating the energy consumption of the subject based on a relational expression between a moving speed and an energy consumption corresponding to various moving forms determined by the moving form determining means And.

  The energy consumption estimation apparatus according to claim 2 is characterized in that, in the above invention, the energy consumption is a value estimated based on a dynamic model of oxygen intake.

  The energy consumption estimation apparatus according to claim 3 is characterized in that, in the above invention, the relational expression is an expression that takes into account the personal attributes of the subject.

  According to a fourth aspect of the present invention, there is provided an energy consumption estimation system comprising: an acceleration sensor that detects a change in acceleration of a subject; a barometric sensor that detects a change in pressure of the subject; a detection value of the acceleration sensor; A moving form determining means for determining the type of moving form of the subject based on the value; a moving speed estimating means for estimating the moving speed of the subject based on a detection value of the acceleration sensor; and the moving form determining Energy consumption estimation means for estimating the energy consumption amount of the subject based on a relational expression between the movement speed and energy consumption corresponding to various movement forms determined by the means, and first communication means for communicating with the outside A relational database that holds a portable device carried by the subject and at least a relation between the movement form and the relational expression in a database. When, characterized in that and a database apparatus having a second communication means for communicating, the between the portable device.

  The energy consumption estimation system according to claim 5 is characterized in that, in the above invention, the energy consumption is a value estimated based on a dynamic model of oxygen intake.

  The energy consumption estimation system according to claim 6 is characterized in that, in the above invention, the relational expression is an expression that takes into account the personal attributes of the subject.

  The database according to claim 7 is a database used when obtaining the energy consumption of the subject, and is associated with the type of movement form of the subject, the type of movement form, and the movement speed of the subject. A relational expression indicating a relationship with an energy consumption estimated based on a dynamic model of oxygen intake is stored.

  The database according to claim 8 is characterized in that, in the above invention, the personal attribute of the subject is further stored, and the relational expression is associated with a combination of the personal attribute and the type of movement form. To do.

  In the energy consumption estimation apparatus, energy consumption estimation system, and database according to the present invention, the movement form is determined and the movement speed is estimated using the acceleration sensor and the atmospheric pressure sensor, and the movement speed corresponding to the various movement forms determined as the movement form is determined. Since the subject's energy consumption is estimated based on the relational expression between the energy consumption and the energy consumption, there is an effect that the energy consumption can be estimated easily, with high accuracy and with high time resolution. In particular, it is possible to reliably perform health management and disease prevention management by estimating the energy consumption with such simple and high time resolution.

  Hereinafter, an energy consumption estimation device, an energy consumption estimation system, and a database that are the best mode for carrying out the present invention will be described.

  FIG. 1 is a block diagram showing a schematic configuration of an energy consumption estimation system according to an embodiment of the present invention. In FIG. 1, the energy consumption estimation system includes a portable device 10 carried by a subject and a database device 20 wirelessly connected to the portable device 10. As shown in FIG. 2, the portable device 10 is attached to the waist of the subject. The portable device 10 includes an acceleration sensor 11, an atmospheric pressure sensor 12, an atmospheric pressure differential processing unit 13 that secondarily differentiates an output from the atmospheric pressure sensor 12, a memory 15, and a communication interface (I / F) 19. The control unit 14 includes a movement form determination unit 16 that determines the movement form of the subject based on the detection value of the acceleration sensor 11 and the output value from the atmospheric pressure differential processing unit 13, and the movement of the subject based on the detection value of the acceleration sensor 11. A movement speed estimation unit 17 that estimates a speed, and an energy consumption estimation unit that estimates energy consumption based on a relational expression between the movement speed and the energy consumption corresponding to the movement form determined by the movement form determination unit 16 18. The memory 15 mainly stores values output by the acceleration sensor 11 and the atmospheric pressure differential processing unit 13. The communication I / F 19 performs a wireless communication process with the database device 20.

  The database device 20 includes a database 21, a control unit 22, and a communication I / F 23. The database 21 includes, for each movement form, a movement form energy consumption relation part 24 that stores a relational expression between the movement speed and the energy consumption, and a personal file part 25 that stores specific information for each subject. The communication I / F 23 performs wireless communication processing with the mobile device 10 side. The control unit 22 controls the database 21 and the communication I / F 23.

  Here, with reference to the flowchart shown in FIG. 3, the energy consumption estimation processing procedure by the energy consumption estimation system will be described. In FIG. 3, first, the movement form discrimination unit 16 is based on the detected value of the acceleration sensor 11 and the output value of the atmospheric pressure differential processing unit 13, stationary, flat ground walking, climbing stairs, descending stairs, climbing stairs, climbing down stairs. , Such as jogging, elevator climbing, elevator descending, etc. are discriminated (step S101).

  Further, the moving speed estimation unit 17 estimates the moving speed of the subject based on the detection value of the acceleration sensor 11 (step S102). For example, the moving speed is estimated by calculating the number of steps based on the acceleration in the vertical (z) direction.

  Thereafter, the control unit 14 accesses the database device 20 side, and acquires a relational expression between the movement speed and the energy consumption corresponding to the movement form determined by the movement form determination unit 16 (step S103).

  After that, the energy consumption estimation unit 18 applies the movement speed estimated by the movement speed estimation unit 17 to the relational expression between the obtained movement speed and the energy consumption, and calculates the energy consumption (step S104). Exit.

  It should be noted that the relational expression held by the movement form energy consumption relation unit 24 may be corrected by specific information stored in the personal file unit 25. This correction may be performed by the control unit 22 or may be performed by the control unit 14. This makes it possible to estimate the energy consumption with higher accuracy.

  Here, estimation of energy consumption will be described. First, energy consumed during exercise generates energy substance ATP in the cell in order to acquire kinetic energy (contraction) during exercise, but it is divided into two when viewed from the use of oxygen. One is an aerobic metabolic process and the other is an anaerobic metabolic process. Aerobic metabolic processes are oxidative phosphorylation pathways that break down carbohydrates (glucose) or fats (fatty acids) using oxygen. The other anaerobic metabolic processes are ATP creatine system and glycolysis system that perform incomplete decomposition of creatine phosphate or glucose (generate lactic acid) without using oxygen. The energy substance ATP is synthesized by one of these two processes and supplied to the contracting device. However, each muscle has different proportions of slow muscle fibers that mainly use aerobic metabolic processes, fast muscle fibers that mainly use anaerobic metabolic processes, and intermediate fibers that can use both processes. Included. As the muscle output increases, these fibers participate in contraction in the order of slow muscle → intermediate type → fast muscle. It should be noted that in daily life, fast muscles that acquire energy anaerobically are rarely used for a long period of time, so for convenience, oxygen intake during exercise is converted into energy consumption.

  In addition, the calculation of the energy consumption or energy consumption rate (energy consumption per unit time) of the human body takes in double labeled water, which is a stable isotope, as described above, and calculates the isotope in the urine to be discharged. There are a method for determining the ratio of metabolite water and double labeled water not included, and a human calorie counter for living in a closed space and measuring the total amount of ingested oxygen and exhausted carbon dioxide. However, these are used for measuring energy consumption including exercise in one day as described above, and are not suitable for measuring behavior in minutes because of low time resolution.

Therefore, in general, a method of estimating energy consumption is performed based on a database of energy consumption of various exercises provided by researchers until now, which is published by the American Sports Medicine Association. The information that makes up this database is mostly based on research data that measure oxygen uptake using Douglas bags (sealed bags that collect exhaled breath) or breath gas analyzers. In most cases, the energy consumption rate during each action or exercise is taken as the energy consumption per unit time of the action when the exercise continues and the oxygen intake reaches a steady state. . That is, as shown in FIG. 4, the oxygen intake EEx when the exercise continued for time a is assumed to be E, where the oxygen intake per unit time is E.
EEx = E × a
This corresponds to the area of the portion surrounded by the oblique lines in FIG.

  However, in reality, at the start of exercise, in the aerobic metabolism, the energy supply is not in time, and the rise of oxygen intake is delayed. The region A deficient due to this delay is compensated by anaerobic metabolism, but oxygen recovery in the region B continues even after the end of exercise because of the recovery of the consumed creatine phosphate and the treatment of the produced lactic acid. When the intensity of exercise is not high, the areas of the area A and the area B are equal.

  However, even in the same exercise, when the exercise time is short and the oxygen uptake ends without reaching a steady state, as shown in FIG. 5, the product of the observed maximum oxygen intake per unit time E ′ and the exercise time a ′ E ′ · a ′, that is, the area surrounded by diagonal lines, is smaller than the oxygen intake E · a ′ necessary for actual exercise.

  In addition, as shown in FIG. 6, strong exercise with a high proportion of anaerobic metabolism is the product F ′ · b ′ of the maximum observed oxygen intake F ′ and exercise time b ′, That is, the area surrounded by the oblique lines is smaller than the oxygen intake amount F · b ′ actually required for exercise. That is, in the case of the exercise shown in FIGS. 5 and 6, if the energy consumption is calculated from the oxygen intake at the end of the exercise, the actual energy consumption will be underestimated. FIG. 7 shows changes in the amount of energy consumed when going down the stairs (marked with ○) and when going up the stairs (marked with □), and the state shown in FIG. 5 or 6 actually occurs. Understandable.

  Therefore, we focused on the shortage of oxygen intake at the start of exercise (oxygen borrowing) and the amount of oxygen (oxygen debt) that is continuously consumed after the end of exercise. In the less moderate exercise shown in FIG. 4, the areas A and B are almost the same, so there is no problem. However, as shown in FIGS. 5 and 6, the area A ′ obtained from the maximum oxygen intake is larger than the area A ′. Area B increases. Here, the inventors pay attention to the possibility that the area B (oxygen debt) becomes equal to the oxygen borrowing (A + A ′) estimated from the actually required energy amount, and obtain the energy consumption from the oxygen debt. I made it. However, since it is difficult to measure the oxygen debt precisely, we decided to calculate the energy consumption from the post-exercise excess oxygen intake (EPOC) including the oxygen debt.

  That is, the energy consumption amount is obtained from the oxygen intake amount, but when the oxygen intake during exercise is expressed with time, it is as shown in FIG. Let α be the standing oxygen intake equivalent to 1.2 times the basal metabolic rate, β be the value obtained by subtracting α from the oxygen intake during exercise, and γ be the oxygen intake after exercise. Therefore, the energy consumption is obtained as α + β + γ. In the past, the value of α + β + δ was used as the energy consumption during exercise. The energy consumption (kcal / kg / min) is almost equal to 0.005 times the oxygen intake (ml / kg / min).

Therefore, when the relationship between the stair ascending / descending speed and the energy consumption (α + β + γ) in the case where the behavior form is during stair ascending / descending, the relationship shown in FIG. 9 was obtained. In FIG. 9, the energy consumption rate (kcal / kg / min) corresponding to the energy consumption is shown on the vertical axis (y), and the horizontal axis (x) is a step rate (walking) that reflects the moving speed when moving up and down the stairs. Rate or pace) (steps / min). From this relationship, the regression equation for stairs up (□) is
^{y = 0.0000185x 2 -0.00185x + 0.162 ...} (A1)
The regression equation for stairs down (○ mark) is
^{y = 0.0000110x 2 -0.00167x + 0.115 ...} (A2)
Is obtained as a relational expression. A relational expression between such speed and energy consumption is obtained for each action form and stored in the movement form energy consumption relation part 24 of the database 21.

For example, as a relational expression between speed and energy consumption for other behavioral forms, there are the following. First, the relational expression when walking on a flat ground and traveling on a flat ground is expressed by the following expression.
y = 0.001X + 0.012 (B1)
Here, y represents the energy consumption rate (kcal / kg / min) similarly to the expressions A1 and A2, and X represents the walking speed (m / min) corresponding to x.

Similarly, the relational expression at the time of walking on a hill is obtained for inclinations of 3 degrees, 6 degrees, and 9 degrees, and is represented by the following expressions C1 to C3.
y = 0.00091X + 0.012 (3-degree tilt) (C1)
y = 0.00130X + 0.0087 (slope of 6 degrees) (C2)
y = 0.00180X + 0.0012 (9 degree inclination) (C3)
Note that since the inclination angle can measure the absolute altitude with high accuracy using the atmospheric pressure sensor 12 and the atmospheric pressure differential processing unit 13, the inclination can be obtained by using the absolute altitude difference. Are classified as detailed behavioral forms. Further, when the behavioral form is a stationary state, the basal metabolic rate in that state is calculated as the energy consumption amount. This arithmetic expression may be described as a database, or the energy consumption estimation unit 18 may immediately calculate the energy consumption without searching the database.

  Such relational expressions between various movement forms and energy consumption rates are stored in the movement form energy consumption relation section 24 as a database as shown in FIG. As an example of behavior, the energy consumption related section 24 is provided with flat ground walking / running, 3rd, 6th, 9th ascending hill walking, descending hill walking, stair climbing, stair climbing, and stationary state, Relational expressions as attribute values corresponding to the respective items are described. Therefore, when it is determined as an action form of flatland walking / running, a relational expression B1 corresponding to this action form is acquired, and the energy consumption estimation unit 18 calculates an energy consumption using this relational expression B1. . As described above, the energy consumption estimation unit 18 obtains an energy consumption rate by substituting the speed estimated by the movement speed estimation unit 17 into the acquired relational expression B1, and consequently calculates the energy consumption. To do.

  By the way, the arithmetic expression described above is merely an example of an expression obtained based on data of males in their twenties, and further, arithmetic expressions corresponding to age or gender are obtained and stored as a database. This makes it possible to obtain a more accurate energy consumption. This personal data including age, sex, and height is stored in the personal file section 25, and this personal data can be used. In other words, the personal attributes may be extracted from or associated with the age and sex from the value of the personal data. As a result, the number of arithmetic expressions is the number of combinations of the above-described action form items and personal attributes. Since personal attributes can be set in advance, it is a static condition for determining an arithmetic expression, and an action form is determined each time, so it can be said that it is a dynamic condition for determining an arithmetic expression. . In addition, each coefficient value of the relational expression such as the regression equation described above can be updated by further detailed data accumulation, and thereby, energy consumption with higher accuracy can be calculated.

  In this way, the energy consumption (energy consumption rate) corresponding to various behavior forms such as walking on a flat ground, climbing stairs, and descending stairs as shown in FIG. 11 can be obtained. However, as described above, it is necessary to obtain the moving speed used in the arithmetic expression.

Therefore, the movement estimation process by the movement speed estimation unit 17 will be described. The moving speed estimation unit 17 obtains the walking speed from the vertical (z) direction acceleration detected by the acceleration sensor 11. The vertical acceleration during walking shows a periodic acceleration change as shown in FIG. Therefore, the power spectrum is obtained as shown in FIG. In addition, a continuous line is a walking result of a young person, and a broken line is a walking result of an elderly person. As a result, it can be said that the peak position near 2 Hz is the frequency band of the walking motion. From Parseval's theorem, the equivalent signal energy in the time domain signal included in the frequency band can be calculated from the power of the spectrum in a specific frequency band. FIG. 14 shows the result. From this result, the relationship between the walking speed and the signal energy can be obtained by the following equation regardless of the type of the young person and the old person. That is,
y = 0.1722ln (x) -0.3346
It is. As a result, the walking speed (m / s) with respect to the height (m) at that time can be calculated from the signal energy S of the acceleration sensor. Therefore, to obtain walking speed, height is required as personal data. This personal data may be stored in advance in the personal file unit 25, and the personal data may be obtained by accessing the portable device 10 when used.

Next, the determination process of the movement form of the subject by the movement form determination unit 16 will be described. This process is almost the same as the contents described in Patent Document 1. The atmospheric pressure sensor 12 is a semiconductor pressure sensor, and the output voltage V ₀₁ has the following relationship with respect to the absolute atmospheric pressure P (kPs).
V ₀₁ = V _S (0.009P−0.095) ± (pressure error × 0.0009V _S C _T )
Here, the rated voltage V _S = 5.0 (V). Also, the C _T at temperature error constant, at 0 ° C. to 85 ° C., 1. Assuming that the atmospheric pressure change per 1 m of height difference is about 1.18 × 10 ⁻² (kPs), the sensor output changes about 530 μV per 1 m.

Therefore, when extracting only the change in absolute atmospheric pressure in a short time, such as when going up and down stairs, it is possible to measure with a large amplification factor by using time change information, and the influence of altitude of the measurement location and changes in weather. Less. Therefore, the atmospheric pressure differential processing unit 13 performs the following signal processing on the input output voltage V _{01 and} outputs it as the voltage V ₀₂ .
1) First, high-frequency noise is removed by LPF. When the cutoff frequency is 10 Hz, ω ₁ = 20π (rad / s), and ζ ₁ = 0.707, the transfer function G ₁ (s) is
G ₁ (s) = ω ² ₁ / (s ² + 2ζ ₁ ω ₁ s + ω ² ₁ )
2) Next, 10-fold amplification is performed by an inverting amplifier circuit.
G ₂ (s) = − 10
3) Further, a Kalman filter process (sign inversion) is performed.
G ₃ (s) = − 3.553 s / (s ² +2.666 s + 3.553)
4) Further, 100-fold amplification is performed by an inverting amplifier circuit.
G ₄ (s) = − 100
By such processing, a time change signal V _{02 of} atmospheric pressure is obtained. This is represented by the product of transfer functions G ₁ (s) to G ₁ (s), as shown in FIG. This atmospheric pressure processing system has a differential characteristic in a frequency range lower than 0.3 Hz, and has an LPF characteristic in a higher frequency range. The gain characteristic at 10 Hz or more is −60 (dB / dec).

Here, acceleration and atmospheric pressure changes were measured for men in their twenties during walking, jogging, running, stair climbing, and elevator lifting in a five-story building (height difference 14.58 m). The staircase has an angle of about 30 degrees, a step height of 180 mm, and there is a landing for every 10 steps, reaching the next floor in 20 steps. Further, the elevator ascending / descending speed is about 1.8 m / s on average. FIG. 16 shows an example of the atmospheric pressure information V ₀₂ and the acceleration α _{Z in} the head and foot direction when running on a flat ground, moving up and down stairs, and moving up and down by an elevator. In the ascending and descending by the elevator, the atmospheric pressure information V ₀₂ rises and falls by about 0.4 V around 2.5 V, respectively. The acceleration α _Z is constant at approximately 0 m / s ² in both cases. Ascending and descending by stairs, the atmospheric pressure information V ₀₂ rises and falls by about 0.2 V, respectively. In addition, the acceleration α _Z constantly varies with walking motion. In flat ground walking, the atmospheric pressure information V ₀₂ is substantially constant around 22.5 V, and the acceleration α _Z is constantly oscillating. From this, it is possible to estimate the _{up /} down movement discrimination reference values P _up and P _down , and to estimate that the pressure information V ₀₂ rises if the pressure information V ₀₂ exceeds P _up and falls if the pressure information falls below P _down .

The left diagram of FIG. 17 shows the acceleration α _Z during walking (lower diagram) and the variance S ² _Z per ^second (upper diagram). Similarly, the right diagram in FIG. 17 shows the results when jogging and running. Assuming that the determination reference values for running and jogging are S ² _W and S ² _J , from these figures, when S ² _Z <S ² _W , the vehicle is stopped, and when S ² _Z > S ² _J , jogging or It can be said that it is traveling.

Therefore, the movement form discrimination | determination part 16 can perform the following movement form discrimination | determination processes. In walking, the moving form is determined for each step, and the moving form is determined every second when stationary.
1) First, it is distinguished whether the exercise state is a stationary state or a stepping state.
In the case of 0 <S ² _Z <S ² _W , it is determined as a stationary state (S ² _W = 1.92 (m / s ² ) ² ),
When S ² _W <S ² _Z , it is determined that the vehicle is in the stepped state.
2) In the case of the stepping state, in order to estimate the movement form of one step, the acceleration waveform for one step is cut out from the acceleration waveform for every second. The acceleration waveform α _Z at the time of walking has a periodic peak due to the impact at the time of landing of the foot. Assuming that the human walking cycle is about 0.5 s per step, in walking or jogging, it can be considered that there are two or more peaks per second as shown in FIG. Here, the peak satisfying the following conditions is defined as the moment of touching the foot, and the acceleration waveform for each step is cut out. And
Peak value> α _P (α _P = 2.45 m / s ² )
Peak is maximum in dt seconds before and after (dt = 0.3s)
If there is no peak in the acceleration waveform every second to be detected, the motion state is corrected from the stepping state to the stationary state.
3) When a still state, with an average value Vm of the atmospheric pressure information V ₀₂ for each processing unit section performs flat, raised, the determination of the falling. The processing unit section is a section for one second in a stationary state and one step waveform in a stepping state. Referring to FIG. 16, the vertical movement is determined by providing appropriate determination reference values P _up and P _down for the average value Vm. That is,
When Vm> P _up (= 2.57 V), it is determined that the voltage has risen.
When P _down (= 2.43 V) <Vm <P _up , it is determined as a flat ground.
If Vm <P _down , it is determined that the vehicle is descending.
In this way, six types of behavior forms as shown in FIG. 19 can be discriminated.

  By the way, when underestimation of energy consumption as shown in FIGS. 5 and 6 is performed, a problem arises in health management. For example, it is recommended that the amount of energy consumed in daily exercise is 300 kcal or more (equivalent to 5 kcal energy consumption per liter of oxygen intake) in Health Japan 21 which is a health promotion guideline of the Ministry of Health, Labor and Welfare. This is based on the fact that there was a significant difference in the mortality rate and the incidence of ischemic heart disease between energy consumption of 300 kcal or more and less. Although there is a lot of value overseas, almost the same trend has been obtained. Thus, grasping energy consumption in daily life is an important index for health management. Recently, as a result of an epidemiological analysis of the fatality rate when the muscle mass is thin and thin and fat and the muscle mass is high, the death rate is higher when the muscle mass is lower Has been reported to be higher. In the past, even the 1978 Harvard University alumni study reported that the mortality rate of stair users was lower than that of non-users. It is extremely important to accurately grasp the energy consumption of short-time and high-intensity exercises that have been reported to have high health effects in this way. The usefulness of this energy consumption estimation device can be understood from the fact that this energy consumption can be obtained correctly and simply.

  On the other hand, not only simply measuring the energy consumption, but also quantifying the exercise intensity (corresponding to the energy consumption per unit time). Training effects by exercise, such as adaptation of skeletal muscles, respiratory / circulatory system, etc. cannot be expected unless the exercise intensity becomes moderate or higher. For this reason, this energy consumption estimation apparatus can be effectively used as an index of appropriate health promotion exercises.

  Furthermore, the oxygen debt contained in EPOC leads to acidification in the body, causing metabolic acidosis in patients with reduced reserves such as heart failure, respiratory failure, renal failure, etc., and triggers deterioration of the pathological condition for these patients. Therefore, this energy consumption estimation apparatus that can accurately and easily obtain the amount of oxygen debt in daily life with high time resolution is useful for managing the life of patients.

  In the above-described embodiment, the relational expression of the energy consumption with respect to the movement speed for each movement form is stored on the database apparatus 20 side. However, the present invention is not limited to this, and is stored on the portable apparatus 10 side. May be. The personal data stored in the age and personal file 25 may also be set in the mobile device 10 so that the memory 15 is removable, so that the energy consumption amount can be estimated only by the mobile device 10.

1 is a block diagram showing a schematic configuration of an energy consumption estimation system according to an embodiment of the present invention. It is a figure which shows the position where a portable apparatus is mounted | worn. It is a flowchart which shows the energy consumption amount estimation processing procedure by an energy consumption amount estimation system. It is a figure which shows the time change of the oxygen uptake per unit time with respect to the following moderate exercise | movement. It is a figure which shows the time change of the oxygen uptake per unit time with respect to the exercise | movement for a short time. It is a figure which shows the time change of the oxygen uptake per unit time with respect to a big exercise | movement. It is a figure which shows the time change of the specific energy consumption amount in rising and falling of stairs. It is a model figure which shows the time change of energy consumption. It is a figure which shows the speed dependence of an energy consumption rate. It is a figure which shows an example of a database. It is a figure which shows the time change of the energy consumption with respect to various action forms. It is a figure which shows the time change of an acceleration. It is a figure which shows the power spectrum of acceleration. It is a figure which shows the relationship of the walking speed per height with respect to the signal energy of acceleration. It is a Bode diagram of an atmospheric pressure differential processing part. It is a figure which shows the relative relationship of the change of atmospheric pressure and acceleration. It is a figure which shows the relationship between an acceleration and its standard deviation. It is a figure which shows the waveform at the time of a walk. It is a figure which shows an example of the action form discriminated.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Portable apparatus 11 Acceleration sensor 12 Atmospheric pressure sensor 13 Atmospheric pressure differential processing part 14,22 Control part 15 Memory 16 Movement form discrimination | determination part 17 Movement speed estimation part 18 Energy consumption estimation part 19,23 Communication I / F
20 Database Device 21 Database 24 Mobile Form Energy Consumption Department 25 Personal File Department

Claims (8)

An acceleration sensor for detecting a change in acceleration of the subject;
A pressure sensor for detecting a change in pressure of the subject;
A moving form determining means for determining the type of moving form of the subject based on the detected value of the acceleration sensor and the time differential value of the barometric sensor;
A moving speed estimating means for estimating a moving speed of the subject based on a detection value of the acceleration sensor;
Energy consumption estimation means for estimating the energy consumption of the subject based on a relational expression between movement speed and energy consumption corresponding to various movement forms determined by the movement form determination means;
An energy consumption estimation device comprising:   The energy consumption estimation apparatus according to claim 1, wherein the energy consumption is a value estimated based on a dynamic model of oxygen intake.   The energy consumption estimation apparatus according to claim 1, wherein the relational expression is an expression that takes into account the personal attributes of the subject. An acceleration sensor for detecting a change in acceleration of the subject;
A pressure sensor for detecting a change in pressure of the subject;
A moving form determining means for determining the type of moving form of the subject based on the detected value of the acceleration sensor and the time differential value of the barometric sensor;
A moving speed estimating means for estimating a moving speed of the subject based on a detection value of the acceleration sensor;
Energy consumption estimation means for estimating the energy consumption of the subject based on the relational expression between the movement speed and energy consumption corresponding to various movement forms determined by the movement form determination means;
A first communication means for communicating with the outside;
A portable device carried by the subject,
A relational database that holds at least the relation between the movement form and the relational expression as a database;
Second communication means for performing communication with the portable device;
A database device having
An energy consumption estimation system comprising:   The energy consumption estimation system according to claim 4, wherein the energy consumption is a value estimated based on a dynamic model of oxygen intake.   6. The energy consumption estimation system according to claim 4, wherein the relational expression is an expression that takes into account the personal attributes of the subject. A database used to determine the energy consumption of a subject,
The type of movement form of the subject,
A relational expression indicating the relationship between the movement speed of the subject and the energy consumption estimated based on the dynamic model of the oxygen intake, associated with the type of movement form,
A database characterized by storing.   The database according to claim 7, further storing the personal attribute of the subject, wherein the relational expression is associated with a combination of the personal attribute and the type of movement form.

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