JP3984253B2 - Health care equipment - Google Patents

Description

  The present invention relates to a health care apparatus. More specifically, the present invention relates to a health management device for efficiently supporting the health management of a mobile user by accurately and efficiently estimating the motion pattern of the mobile user.

A method for attaching a motion sensor to a human body, detecting the motion of the body, estimating the type and intensity of the motion from the motion pattern and size, calculating calorie consumption, etc. and using it as health maintenance data is described in the following document. As disclosed, it has been widely known.
For example, the motion detection unit of the portable information processing apparatus includes an acceleration sensor (detects an acceleration waveform in each of the three orthogonal axes) and a gyro (detects a rotational angular velocity waveform around each of the three orthogonal axes). This is a technique for specifying a behavior pattern of a wearer in an action recognition unit and displaying the result on a display unit (for example, Patent Document 1).
In addition, the user wears a portable terminal, automatically measures the amount of exercise of the user from the acceleration, and transmits the data together with related information such as calorie intake to an external center computer using a telephone line. And it is the technique which analyzes the transmitted data, performs health checkup, and returns the diagnosis result to the portable terminal. In addition, as a portable terminal, it has a function which preserve | saves data and displays according to a user's request | requirement, The example which mounted | wore the user's waist | hip | lumbar part with the said portable terminal is illustrated (for example, patent document) 2).
Japanese Patent Laid-Open No. 10-24026 (Claims) JP-A-10-295651 (Claims)

However, Patent Document 1 does not mention how to mount an acceleration or angular velocity sensor on which part of the body of the carrier, and how can the behavior pattern of the carrier be identified from the detected waveform. Is not clear.
Further, Patent Document 2 exemplifies wearing on the waist, but does not show how to detect acceleration in which direction on the waist and how to use it to calculate calorie consumption. In addition, it cannot be used when a wheelchair user or a leg restrainer wants to detect an upper body movement during rehabilitation. In other words, in the prior art, it is difficult to say that a consistent and practical technique including a preferred method of using a motion sensor and a device configuration for knowing the form of necessary motion has been established. It is done.
Furthermore, the movement pattern of the carrier, such as when the arm swinging motion is large due to individual differences of the carrier, when walking with a hand in the pocket, or when carrying luggage, etc. There was a problem that it could not be estimated accurately.
On the other hand, the conventional portable information processing apparatus generally performs Fourier transform for noise reduction processing on body behavior data including acceleration (G) and angular velocity (ω), and has a large number of processing steps. The problem of becoming was seen. Therefore, in order to increase the data processing speed, it is necessary to increase the clock frequency of the control circuit. As a result, there is a problem that the power consumption is large even though the information processing apparatus is a portable information processing apparatus.

Therefore, as a result of intensive studies on such a problem, the present inventors have determined [acceleration (G)] and [angular velocity (ω)] measured by the motion sensor, and [step data] measured by the identification means. The present inventors have found that an exercise pattern can be estimated accurately and efficiently only by considering a data processing method, regardless of individual differences among carriers.
That is, the present invention uses a mapping data composed of [acceleration (G) / angular velocity (ω)] and [step count data] created in advance, and is practically used to accurately and efficiently estimate a user's movement pattern. The purpose is to provide a healthy health management device (system).

  According to the present invention, there is provided a health management device that is worn by a wearer and for estimating an exercise pattern from the wearer's physical behavior data, the acceleration (G ) And angular velocity (ω) as body behavior data, and identification for calculating step count data by extracting the acceleration (G) and angular velocity (ω) or one of the periodicities And mapping data for collating with the detected physical action data, the mapping data including [acceleration (G) / angular velocity (ω)] and [step data] created in advance. A featured health care device is provided to solve the above-mentioned problems.

Further, in configuring the health management device of the present invention, it is preferable to further include means for transmitting body behavior data to an external computer.
That is, by using an external computer, it is preferable that a part of the processing work of the health care apparatus is borne or more advanced processing is performed.

In configuring the health management device of the present invention, it is preferable to include a band-pass filter and / or a low-pass filter for performing analog noise reduction processing on the body behavior data.
Note that the acceleration (G) is subjected to noise reduction processing using a band-pass filter, and the angular velocity (ω) is subjected to noise reduction processing using a low-pass filter, and then the periodicity of each motion pattern is determined. preferable.

In configuring the health management device of the present invention, it is preferable to provide a digital filter for performing digital noise reduction processing on the body behavior data.
In other words, the body behavior data is analog-reduced using a band-pass filter or low-pass filter, then A / D converted, and data other than the predetermined value is sharply deleted using a digital filter. It is preferable to perform noise reduction processing.

In configuring the health care device of the present invention, it is preferable to set the clock frequency of the control circuit to a value within the range of 0.01 to 5 MHz.
That is, it is preferable to limit the clock frequency of the control circuit within a predetermined range as the number of data processes and processing steps decrease.

In configuring the health care device of the present invention, it is preferable to provide a sleep function for stopping the operation for detecting the angular velocity (ω) for a predetermined time or an arbitrary period.
That is, regarding the angular velocity (ω), the motion sensor is provided with a sleep function, and, for example, only when the acceleration (G) exceeds a predetermined value, the motion sensor is stopped for a predetermined time or between the measurement of a plurality of accelerations (G). It is preferable to stop only for an arbitrary period.

Further, when configuring the health management device of the present invention, it is preferable that a plurality of mapping data is provided, and the user selects in advance one of the mapping data before collating with the physical behavior data.
That is, for example, two mapping data consisting of [acceleration (G) / angular velocity (ω)] and [step data] are divided into a case where the carrier owns the luggage and a case where the carrier does not own the luggage. It is preferable to use any mapping data that has been created in advance and selected by the user for data verification.

In configuring the health management device of the present invention, it is preferable that a plurality of mapping data is provided and the mapping data is automatically selected in synchronization with the physical behavior data.
That is, for example, when the carrier is an elderly person and wants light exercise for the purpose of rehabilitation, or when the carrier is an adolescent and wants heavy exercise for the purpose of intense physical training, respectively. Correspondingly, it is preferable to automatically select and use one of a plurality of mapping data created in advance in synchronization with the measured physical behavior data.

In configuring the health care device of the present invention, it is preferable that the stride / height data of the exercise pattern of the wearer can be estimated from the angular velocity (ω).
That is, it has been found that the angular velocity (ω) is proportional to the stride / height data of the portable person under a predetermined condition, and it is preferable to estimate the stride / height data of the portable person using this.

Further, in configuring the health management device of the present invention, it is preferable that the mobile person's stride can be calculated from the stride / height data of the mobile person's exercise pattern by taking into account separately input height data. .
That is, since it is possible to estimate the stride / height data of the portable person under a predetermined condition, it is preferable to calculate the stride of the portable person taking into account information of height data that has been separately identified.

Further, in configuring the health care device of the present invention, walking from the step length of the wearer and the step count data calculated by extracting the acceleration (G) and the angular velocity (ω) or one of the periodicity. A configuration capable of calculating the speed is preferable.
That is, since the step length of the portable person can be calculated under a predetermined condition, a configuration in which the walking speed of the portable person can be calculated in consideration of information on step count data separately measured is preferable.

In configuring the health care device of the present invention, it is preferable that the average calorie consumption in the exercise pattern of the wearer can be calculated.
That is, it is preferable that the average calorie consumption in the exercise pattern of the wearer can be calculated from the estimated exercise pattern and an exercise coefficient separately determined corresponding thereto.

In addition, when configuring the health care device of the present invention, if the carrier carries a baggage of a predetermined weight, the weight of the baggage is added to the weight of the carrier, and the average calorie consumption in the exercise pattern The structure which can calculate is preferable.
In other words, it is preferable that the average calorie consumption can be accurately calculated from the estimated exercise pattern and a coefficient of motion determined separately corresponding to the estimated exercise pattern even when the wearer carries a predetermined weight of luggage. .

Further, when configuring the health management device of the present invention, the apparatus further includes a sensor for measuring at least one of a carrier's heart rate, heart rate interval, blood pressure, blood flow rate, oxygen consumption, blood glucose level, and body temperature. preferable.
That is, it is preferable to measure physical behavior data other than acceleration (G), angular velocity (ω), and step count data in the exercise pattern of the wearer by using a dedicated sensor.

According to the health management device of the present invention, by comparing physical action data with mapping data composed of [acceleration (G) / angular velocity (ω)] and [step count data] created in advance, The motion pattern can be estimated with high accuracy.
In addition, since the movement pattern of the wearer can be estimated with high accuracy, the measurement frequency and measurement time of the body behavior data can be reduced. Therefore, it is possible to reduce the clock frequency of the control circuit as the number of data processing decreases, and as a result, it is possible to reduce the power consumption of the health management device.

  In addition, according to the health management device of the present invention, by further including a means for transmitting body behavior data to an external computer, the external computer can be charged with a part of processing work of the health management device, or more sophisticated. Can be carried out.

In addition, according to the health management device of the present invention, it is possible to effectively reduce the power consumption of the health management device by providing the specific filter for analog noise reduction processing on the body behavior data. be able to.
That is, unlike the conventional noise reduction process using the Fourier transform method, the noise reduction process using a specific filter process can greatly reduce the number of processing steps. Therefore, the clock frequency of the control circuit can be reduced, and the power saving of the health management device can be effectively achieved.
Since sensing of acceleration (G) of the carrier requires data in a predetermined frequency region, noise reduction processing is performed using a bandpass filter, and the angular velocity (ω) of the carrier is equal to or lower than the predetermined frequency. Therefore, the periodicity such as the angular velocity (ω) in the motion pattern can be determined with high accuracy by performing noise reduction processing using a low-pass filter.

In addition, according to the health management device of the present invention, it is possible to effectively reduce the power consumption of the health management device by further performing noise reduction processing on the body behavior data using a digital filter.
That is, in addition to the noise reduction process using a specific analog filter, a desired value can be taken out sharply by performing the noise reduction process using a digital filter. Therefore, while it is possible to accurately estimate the movement pattern of the wearer, it is possible to further effectively save power.

Further, according to the health management device of the present invention, it is possible to further effectively reduce the power consumption of the health management device by limiting the clock frequency of the control circuit to a value within a predetermined range.
That is, the clock frequency of the control circuit can be limited within a predetermined range as the number of data processes and processing steps decrease.

In addition, according to the health care device of the present invention, regarding the measurement of the angular velocity (ω), the sleep function is provided, and the motion sensor is stopped for a predetermined time or an arbitrary period, thereby reducing the power consumption of the health care device. Effectively.
That is, regarding the measurement of the angular velocity (ω), there is a fact that the power consumption of the health care device is about 100 times that of the acceleration (G). Therefore, it is possible to effectively reduce the power consumption of the health management device by providing the health management device with a sleep function and stopping the detection of the angular velocity (ω) for a predetermined time or for an arbitrary period. .

In addition, according to the health management device of the present invention, by providing a plurality of mapping data composed of [acceleration (G) / angular velocity (ω)] and [step count data], it is possible to estimate the exercise pattern of the wearer with higher accuracy. can do.
That is, when exercising in a state where the movement of the wearer is restricted, the estimation accuracy of the exercise pattern may be reduced. Therefore, for example, when a carrier possesses a package, a plurality of mapping data is created in advance, and one of the mapping data selected by the carrier is used for data collation. Thus, it is possible to accurately estimate the movement pattern of the wearer.

In addition, according to the health management device of the present invention, a plurality of mapping data is provided, and one of the mapping data is automatically selected in synchronization with the physical behavior data, thereby further improving the accuracy of the movement pattern of the wearer. It can be estimated well.
In other words, according to the type and request of the user, the movement pattern of the user can be more accurately selected by automatically selecting one of a plurality of mapping data created in advance in synchronization with the measured physical behavior data. It can be estimated well.

In addition, according to the health care device of the present invention, it is possible to estimate the exercise pattern of the portable person with higher accuracy by estimating the stride / height data of the portable person from the angular velocity (ω).
In other words, since the angular velocity (ω) is proportional to the step / height data of the user under a predetermined condition, the user's step / height data is accurately calculated and used to calculate the exercise pattern of the user. Can be estimated with higher accuracy.

In addition, according to the health management device of the present invention, by taking into account separately inputted height data, the stride of the exercise pattern of the wearer is calculated from the stride / height data of the exercise pattern of the wearer, and the Can be estimated with higher accuracy.
In other words, since the angular velocity (ω) is proportional to the stride / height data of the user under a predetermined condition, the stride of the user can be calculated with high accuracy, and the exercise pattern of the user can be more accurately used. It can be estimated well.

Further, according to the health management device of the present invention, the walking speed can be calculated from the step length of the portable person and the step count data measured by the motion sensor, and the movement pattern of the portable person can be estimated with higher accuracy. .
In other words, since the angular velocity (ω) is proportional to the stride / height data of the user under a predetermined condition, the user's walking speed is accurately calculated, and using this, the movement pattern of the user is further calculated. It can be estimated with high accuracy.

In addition, according to the health management device of the present invention, it is possible to calculate the average calorie consumption in the exercise pattern of the wearer, and to support the wearer's health management more efficiently.
In addition, this average calorie consumption can be computed from Formula (1) mentioned later as an example.

Further, according to the health care device of the present invention, when the carrier carries a predetermined weight of the baggage, the weight of the baggage is added to the weight of the carrier, and the average calorie consumption in the exercise pattern is calculated. It can be calculated more accurately.
That is, in the equation (1) described later, the average calorie consumption is calculated even when the wearer carries a baggage of a predetermined weight based on the estimated exercise pattern and a separately determined exercise coefficient. Can be calculated accurately.

  In addition, according to the health management device of the present invention, by further comprising a sensor for measuring at least one of the heart rate, heart rate interval, blood pressure, blood flow rate, oxygen consumption, blood glucose level, and body temperature of the carrier, It is possible to support the health management of the carrier more efficiently.

Embodiments relating to the health management device of the present invention will be specifically described below with reference to the drawings as appropriate.
First, FIG. 1 shows mapping data composed of [acceleration (G) / angular velocity (ω)] and [step count data] created in advance. The abscissa indicates the number of instantaneous steps (steps / minute), and the ordinate indicates the acceleration (G) / angular velocity (ω) ratio (m / (rad · second)). As can be easily understood from FIG. 1, the exercise pattern of the wearer can be classified into, for example, six regions corresponding to the exercise intensity in relation to the boundary line (L) and the number of instantaneous steps. .
More specifically, an area below the boundary line (L) represented by the straight line [G / ω = −0.05 × (instantaneous step count) +9], where the instantaneous step count is less than 100 steps / min. A can be made to correspond to the walking pattern (walking pattern 1) having the lowest exercise intensity. Also, a region B below the adjacent boundary line (L) and having an instantaneous step count of less than 100 to 110 steps / minute corresponds to a walking pattern (walking pattern 2) with a relatively low exercise intensity. Can be made. In addition, the region C below the adjacent boundary line (L) and having an instantaneous step number of less than 110 to 120 steps / minute corresponds to a walking pattern (walking pattern 3) with a relatively medium exercise intensity. Can be made. Further, a region D below the adjacent boundary line (L) where the number of instantaneous steps is 120 or more can be made to correspond to a walking pattern (walking pattern 4) having an intermediate exercise intensity.
On the other hand, the region E above the boundary line (L) and having an instantaneous step count of less than 160 steps / minute can correspond to a travel pattern (travel pattern 1) having a relatively high exercise intensity. Furthermore, the region F above the boundary line (L) on the right side and having an instantaneous step count of 160 steps / minute or more can be made to correspond to a travel pattern with a high exercise intensity (travel pattern 2). .
In addition, it is also preferable to add / subtract a predetermined constant to acceleration (G) / angular velocity (ω), multiply a predetermined coefficient, or perform numerical processing such as logarithmic averaging.
Further, the angular velocity (ω) can also be calculated from the acceleration (G) by performing a predetermined calculation process, for example, an arithmetic process such as integrating the acceleration (G) under a certain condition and multiplying the length. .

As described above, the areas A to F displayed in the mapping correspond to the walking patterns 1, 2, 3, 4 and the running patterns 1, 2 having different exercise intensities. It is possible to correspond to a plurality of walking patterns and a plurality of running patterns defined in No. 3 (fourth revision “Japanese nutrition requirement”).
More specifically, the walking pattern 1 can substantially correspond to walking at a walking speed of 60 m / min. Moreover, the walking pattern 2 can be made to substantially correspond to walking with a walking speed of 70 m / min. Moreover, the walking pattern 3 can be made to substantially correspond to walking with a walking speed of 80 m / min. Moreover, the walking pattern 4 can be made to substantially correspond to walking with a walking speed of 90 m / min or more. Moreover, the running pattern 1 can be made to substantially correspond to light jogging. Furthermore, the running pattern 2 can be made to substantially correspond to strong jogging.

FIG. 2 is a diagram in which the measurement data of [acceleration (G)] and [angular velocity (ω)] shown in FIG. 1 are mapped according to a conventional classification method. More specifically, the horizontal axis represents the carrier's angular velocity (ω), and the vertical axis represents the carrier's acceleration (G).
As can be easily understood from FIG. 2, it may be difficult to classify the movement pattern of the user with high accuracy. That is, the data corresponding to the walking patterns 1 to 4 shown in FIG. 1 are indicated by black circles (●), and the data corresponding to the running patterns 1 to 2 shown in FIG. 1 are indicated by white triangles (Δ). In some cases, a circle (●) and a white triangle (Δ) are partially mixed.
Thus, unlike the conventional classification method as shown in FIG. 2, the mapping data consisting of [acceleration (G) / angular velocity (ω)] and [step data] as shown in FIG. By comparing the acceleration (G) and the angular velocity (ω) with the step count data measured by the identification means such as a microprocessor, it is possible to estimate the movement pattern of the carrier accurately and efficiently.

Here, the difference between the estimation result of the exercise pattern when using the health care device of the present invention in Table 1 and the estimation result of the exercise pattern when using the conventional health management device is shown in Table 2. Show. Walking and running as exercise patterns actually performed are taken in the horizontal columns, respectively, and walking and running as exercise patterns estimated using the health care device are taken in the vertical columns.
According to the present invention, mapping data composed of [acceleration (G) / angular velocity (ω)] and [step count data] and acceleration (G) measured by the motion sensor are apparent when these estimation results are compared. Further, by collating the angular velocity (ω) and the step count data measured by the discriminating means, it is possible to estimate the movement pattern of the wearer more accurately and efficiently. On the other hand, it can be understood that when a conventional health care device is used, the exercise pattern of the wearer is often erroneously estimated.

Next, referring to FIG. 3, the number of steps calculated using the health management device of the present invention and the conventional health management when the carrier travels a certain standard (corresponding to 100 steps) (number of repetitions: 47 samples). The result of comparison with the number of steps calculated using the device for an operation will be described.
That is, in FIG. 3, the horizontal axis indicates the number of steps calculated using the health management device, and the vertical axis indicates the frequency of the number of repetitions: 47 samples. The shaded bar graph corresponds to the number of steps calculated using the health management device of the present invention, and the white bar graph corresponds to the number of steps calculated using the conventional health management device.
As shown in FIG. 3, the number of steps calculated by using the health management device of the invention has a smaller variation in measurement. More specifically, in the case of the number of steps calculated using the health management device of the invention, the value is within the range of 80 to 110 steps, whereas it is calculated using the conventional health management device. The number of steps is a value within the range of 60 to 110 steps.
Therefore, it can be said that in the running operation, the number of steps calculated using the health management device of the present invention is measured more accurately than the number of steps calculated using the conventional health management device.

Next, referring to FIG. 4, the number of steps calculated using the health management device of the present invention and the conventional health management when the carrier walks with a certain standard (corresponding to 100 steps) (repetition number: 47 samples). The result of comparison with the number of steps calculated using the device for an operation will be described.
That is, in FIG. 4, the horizontal axis indicates the number of steps calculated using the health management device, and the vertical axis indicates the frequency of the number of repetitions: 47 samples. The shaded bar graph corresponds to the number of steps calculated using the health management device of the present invention, and the white bar graph corresponds to the number of steps calculated using the conventional health management device.
As shown in FIG. 4, the number of steps calculated using the health management device of the invention has a smaller variation in measurement, and the frequency of 100 steps, which is a true value, is higher. More specifically, in the case of the number of steps calculated using the health management device of the invention, the value is within the range of 80 to 120 steps, and the frequency of 100 steps which is a true value is 33. On the other hand, the number of steps calculated using a conventional health management device is a value within the range of 30 to 110 steps, and the frequency of 100 steps as a true value is 28.
Therefore, it can be said that in walking motion, the number of steps calculated using the health management device of the present invention is measured with higher accuracy than the number of steps calculated using the conventional health management device.

Next, referring to FIG. 5, the health care device of the present invention when the carrier walks for a certain standard (corresponding to 100 steps) with a baggage weighing 5 kg (repetition number: 47 samples). A comparison result between the number of steps calculated using the method and the number of steps calculated using a conventional health management device will be described.
That is, in FIG. 5, the horizontal axis indicates the number of steps calculated using the health management device, and the vertical axis indicates the frequency of the number of repetitions: 47 samples. The shaded bar graph corresponds to the number of steps calculated using the health management device of the present invention, and the white bar graph corresponds to the number of steps calculated using the conventional health management device.
As shown in FIG. 5, the number of steps calculated using the health management device of the invention has a smaller variation in measurement and the frequency of 100 steps, which is a true value, is significantly higher. More specifically, in the case of the number of steps calculated using the health management device of the invention, the value is within the range of 90 to 110 steps, and the frequency of 100 steps as a true value is 33. On the other hand, the number of steps calculated using a conventional health care device is a value within the range of 0 to 110 steps, and the frequency of 100 steps as a true value is 0.
Therefore, according to the number of steps calculated using the health management device of the present invention in the walking motion with the baggage carried, it is compared with the number of steps calculated using the conventional health management device as shown in FIG. Thus, it can be said that the measurement is extremely accurate.

In the case where the behavior of the portable person is restricted in this way, a plurality of mapping data is provided, and the portable person selects in advance one of the mapping data before collating the physical action data. Is preferably used.
That is, for example, two mapping data are created in advance for the case where the carrier owns the baggage and the case where the carrier does not own the baggage, and one of the ones selected by the carrier during the data matching It is preferable to use the mapping data.
In addition, for example, two mapping data are created in advance for the case where the mobile phone is operating with the hand in the pocket and the case where the mobile phone is operating with the hand out of the pocket. In addition, it is preferable to use any mapping data selected by the carrier for data collation.
In addition, although it has been recognized that light exercise is effective for rehabilitation and brain activation in the elderly, the health management device of the present invention is a minute change as such light exercise. However, it can be detected. Therefore, a plurality of mapping data composed of [acceleration (G) / angular velocity (ω)] and [step count data] prepared in advance in synchronization with the measured physical behavior data in accordance with the type and request of the user. By automatically selecting, it is possible to estimate the movement pattern of the wearer with higher accuracy.
Further, for example, mapping data corresponding to three cases is divided into a case where the user's arm swing is large, a case where the carrier's arm swing is medium, and a case where the carrier's arm swing is small. It is also preferable to create in advance and use any mapping data selected by the carrier for data verification.

Next, the relationship between the angular velocity (ω) calculated using the health management device of the present invention and the stride / height data will be described with reference to FIG.
That is, FIG. 6 shows the angular velocity (ω) calculated using the health management device of the present invention when a plurality of mobile users (total number of measurements: 18 samples) walk on the horizontal axis on a constant reference (corresponding to 100 steps). The stride / height data (−) for a plurality of users is taken on the vertical axis.
As can be understood from FIG. 6, the angular velocity (ω) calculated using the health care device of the present invention is within a predetermined range, more specifically, the angular velocity (ω) is in the range of about 300 to 3300 rad / sec. If there is, it is understood that there is a proportional relationship with the stride / height data (−) for a plurality of mobile users.
Therefore, in the case of the health care device of the present invention, it can be said that the stride / height data (−) for a plurality of carriers can be calculated with high accuracy from the angular velocity (ω) calculated using the device.
In addition, since the angular velocity (ω) and the step length / height data of the portable person are proportional to each other under the predetermined conditions as described above, the portable person's stride / height data is accurately calculated and used to carry the portable person. Can be estimated with higher accuracy. Therefore, the average calorie consumption in the exercise pattern of the carrier can be calculated with higher accuracy from the appropriately estimated exercise pattern, and as a result, the health management of the carrier can be supported more efficiently.

Next, with reference to FIG. 7 and FIG. 8, a portable measuring instrument and its configuration in the health care apparatus of the present invention will be specifically described.
7A is a diagram showing a state where the wristwatch-type portable measuring device 100 is attached to the wrist 107, and FIG. 7B is a side view of the wristwatch-type portable measuring device 100. FIG. FIG. 8 is a diagram for explaining a method of mounting the portable measuring device 100 on the adapter 4 for connection to an external computer (not shown).
Therefore, as shown in FIG. 7 (b), the portable measuring device 100 has a wrist-like band 103 as a health management device, as shown in FIG. As shown in FIG. 7A, it can be worn on the wrist 107 of the wearer. However, such a portable measuring device may be arranged in a pendant style or worn on the waist or legs.
The portable measuring device 100 includes a motion sensor 110, a display device 111, a communication circuit module 105 with an external computer, a power source 112, and operation switches 106 and 109 as main components.

The motion sensor 110 is preferably disposed so as to be parallel to the display device 111. This is because the portable measuring device 100 is mounted like a wristwatch so that the display surface is on the back side or palm side of the wrist, and when the upper body is upright and the elbow is naturally bent and stretched, This is because it is parallel to the display surface of the portable measuring device 100, that is, the display device 111. More specifically, the optimum method for detecting the motion pattern is to measure the acceleration in the vertical motion of the body in the vertical (vertical) direction, that is, in the X direction shown in FIG. As for the rotation angular velocity, the rotation in a plane including both the vertical direction and the front-rear direction of the body (ω direction in the figure), that is, the horizontal rotation axis (parallel to the Z axis in the drawing) that faces the horizontal direction of the body. This is to measure the rotational movement around. That is, if there is a thin angular velocity sensor having a rotation detection surface parallel to the widest surface, it is preferable to dispose the motion sensor 110 including the sensor in parallel with the display device 111.
Further, the means for transmitting body behavior data from the portable measuring device 100 to the external computer is not particularly limited. For example, the portable measuring device and the external computer are connected by cable, or the mobile phone communication system is connected. It is preferable to use a wireless connection to transmit data. More specifically, it is possible to use a communication means using a wireless communication standard in the 2.4 GHz frequency band called a Bluetooth (Bluetooth ( registered trademark) ) standard.
However, since the overall configuration can be further reduced, as shown in FIG. 8, the wristwatch-type portable measuring device 100 is removed by the user and attached to the adapter 4 for connecting it to an external computer. Thus, it is also preferable to transmit data. If comprised in this way, the power supply part in a portable measuring device can be made small, and a measurement function can be exhibited, charging suitably.

Next, with reference to FIG. 9, an example of a motion sensor mounted on a portable measuring device as the health management device of the present invention will be described.
That is, the motion sensor 200 includes an outer container 240 held in a box-like airtight state, and a number of hermetic end pins 241 that penetrate the bottom of the outer container 240. Each pin 242 is electrically connected to each of the electrode film groups on the motion sensor vibrating body 50 by, for example, a wire bonding method, but the electrode films and bonding wires are not shown. The motion sensor vibrating body 250 is formed of a single piezoelectric material flat plate, and is formed by integrating an acceleration sensor unit and an angular velocity sensor unit. That is, in the motion sensor vibrating body 250, the fixed portion A252 (shaded portion) on the back side of the total base portion 251 and the back surface of the small-area fixed portion B264 (shaded portion) are on the base (not shown) on the container 240 side. It is electrically connected and firmly supported.

Further, the angular velocity sensor unit 250 of the motion sensor 200 is a so-called tripod tuning fork-shaped portion, and includes an L-shaped outer leg A 253, outer leg B 255, middle leg C 254, tuning fork base 256, and fulcrum 257. Has been. The outer leg A253 and the outer leg B255 are included in the angular velocity measurement circuit so that each of the outer legs A253 and the outer leg B255 is cantilevered and oscillates symmetrically with respect to a symmetry axis (not shown) in the same manner as a normal biped tuning fork. It is excited with a constant amplitude by an excitation circuit (oscillation circuit).
Further, the middle leg C254 is not excited, but includes a surface electrode (not shown) for detecting the bending thereof. Further, regions 258A, 258B, and 258C shown with hatching different from the fixed portion are respectively loaded masses, and are composed of a metal thick plating layer applied to the leg tip portion in order to lower the natural frequency and equalize each other. ing. However, the natural frequency of the middle leg C254 may be configured to be appropriately different from the natural frequency of both outer legs.
When the angular velocity sensor unit 250 of the motion sensor 200 rotates at an angular velocity (ω) around the rotation direction parallel to the Z axis perpendicular to the drawing surface, ie, the Z axis, the angular velocity is applied to the outer vibrating legs. Coriolis force proportional to The direction is the longitudinal direction of the leg, and if a force toward the distal end of the leg acts on the outer leg A253 at a certain moment, a force toward the base of the leg acts on the outer leg B255. Therefore, the direction of the force changes sinusoidally in synchronism with the vibration of the leg and reverses periodically. In addition, since both the outer legs are parallel to each other and the eccentric direction of the load mass is opposite to the outer leg axis, the two forces constitute a couple, swing the tuning fork base 256, and move around the fulcrum 257. Invoke minute rotational vibrations. By sensing the vibration of the tuning fork base 256 caused by the moment due to the Coriolis force, the middle leg C254 vibrates with an amplitude proportional to the Coriolis force. That is, the vibration voltage extracted by the detection electrode provided on the middle leg C254 is the detection signal of the angular velocity (ω).

On the other hand, the acceleration sensor unit 260 of the motion sensor 200 is composed of a pair of parallel vibrating bars 261 and 262 and a load mass 265. Further, rods 261 and 262, which are spring portions, a load mass 265, two support springs 263 (a member for allowing a minute displacement in the X direction in the figure while supporting the load mass 265), and a fixing portion 264 (load mass 265) In particular, a portion for supporting and fixing so as not to be greatly displaced in the X direction. The rods 261 and 262, which are fixed at both ends, are excited in the oscillation circuit in a vibration form having a bow shape symmetric with respect to the symmetry axis of the acceleration sensor unit 260.
The oscillation frequency is normally constant, but when the acceleration (G) in the X direction shown in the figure acts on the load mass 265, the load mass 265 compresses the rods 261 and 262 in the longitudinal direction with a force proportional to the magnitude. Will pull. Therefore, the oscillation frequency increases or decreases depending on the direction and magnitude of the force. Thus, the acceleration (G) in the X-axis direction can be obtained by comparing a reference frequency separately provided with the oscillation frequency and knowing the direction and amount of change in the oscillation frequency.
In other words, the greatest advantage of such a motion sensor is that it is thin and placed parallel to the largest surface (display surface) of the wristwatch-type device to detect important acceleration (G) and angular velocity (ω). It is possible.

In order to operate such a motion sensor 200, it is preferable to provide a sleep function for stopping the motion sensor only for a predetermined time or an arbitrary period in the operation of detecting the angular velocity (ω).
That is, regarding the angular velocity (ω), the motion sensor is provided with a sleep function, and for example, only when the acceleration (G) exceeds a predetermined value, the motion sensor is stopped for a predetermined time or between a plurality of acceleration (G) measurements. It is preferable to stop only for an arbitrary period.
The reason for this is that it is possible to effectively reduce the power consumption of the health management device by stopping the sensing of the angular velocity (ω) for a predetermined time or for an arbitrary period. More specifically, the measurement of the angular velocity (ω) whose power consumption is 100 times larger is used for health management when the acceleration (G) exceeds a predetermined value or only in a preset time delay. This is because power saving of the apparatus can be effectively achieved.
More specifically, by providing such a sleep function, the battery life (required charging time) can be extended from 2 days to 10 days or more even if the clock frequency of the control circuit remains 10 MHz. Is known.

Next, with reference to FIG. 10 and FIG. 11, a method for determining the periodicity of body measurement data in the health care device of the present invention will be described.
That is, based on the acceleration (G) and angular velocity (ω) measured by the motion sensor, or any one of the body measurement data, the identification means appropriately extracts them, and if a predetermined periodicity is seen It can be estimated whether the carrier is in a walking state or in a running state.
More specifically, as shown in FIGS. 10 and 11, interrupt processing is performed at S1 and S1 ′, and then angular velocity (ω) or acceleration (G) is specifically measured by a motion sensor at S2 and S2 ′. To do. Next, in S3 and S3 ′, the obtained angular velocity (ω) and acceleration (G) or any one of the body measurement data is subjected to noise reduction processing using an analog filter. At that time, it is more preferable to perform a noise reduction process of a predetermined high frequency range using the low pass filter for the angular velocity (ω), and a band pass filter for the acceleration (G). More preferably, noise reduction processing is performed. That is, since data of a predetermined frequency region is necessary for the acceleration (G) of the carrier, noise reduction processing is performed using a bandpass filter, and the angular velocity (ω) of the carrier is equal to or lower than the predetermined frequency. Therefore, it is possible to accurately determine the periodicity of the motion pattern by performing noise reduction processing using a low-pass filter.
In addition, when a carrier is a human being, it turns out that the peak of angular velocity ((omega)) or acceleration (G) corresponding to a motion pattern generate | occur | produces in the low frequency band of 0.01-5 Hz, for example. Therefore, the periodicity of the motion pattern can be accurately estimated by analyzing the peak of the angular velocity (ω) or acceleration (G) and the zero crossing point in the low frequency band and extracting them appropriately.

Next, in S4 and S4 ′, it is preferable to perform A / D conversion on the body measurement data of the angular velocity (ω) or acceleration (G) subjected to noise reduction processing using an analog filter. And it is preferable to implement a noise reduction process using a digital filter for the angular velocity (ω) or acceleration (G) subjected to A / D conversion.
In other words, in addition to noise reduction processing using a specific analog filter, only desired values can be taken out sharply by performing noise reduction processing using a digital filter.
Next, in S5 and S5 ′, a zero cross point at which the minus data changes to the plus data is determined. Therefore, the periodicity of the angular velocity (ω) and the acceleration (G) on which the noise reduction processing has been performed can be clearly determined using the determined zero cross point as a reference.
Next, when the period (T _ω or T _g ) between zero-cross points of the body measurement data of the angular velocity (ω) or acceleration (G) is equal to or less than a predetermined value, it may be determined that the body measurement data has a predetermined periodicity. it can. Conversely, if the period ( _Tω or _Tg ) between the zero-cross points is equal to or less than a predetermined value, it can be said that the wearer is performing an exercise having a predetermined periodicity.
On the other hand, when the period (T _ω or T _g ) between zero cross points of the body measurement data of the angular velocity (ω) or acceleration (G) exceeds a predetermined value, it is determined that the body measurement data has no periodicity. Can do. That is, it can be said that the carrier does not carry out a predetermined exercise pattern for a predetermined time or longer. Therefore, in that case, as shown in FIG. 12, it can be determined that the wearer is in a state of static motion, for example, desk work.
If it is determined that the user is carrying out a predetermined motion pattern having periodicity, twice the zero crossing point frequency of the measured angular velocity (ω) × reference measurement time (for example, 2 seconds) Can be counted as the step count data in the reference measurement time.
Note that if there is periodicity in either the angular velocity (ω) or acceleration (G) in the motion pattern, it can be determined that the wearer is carrying out the motion pattern having periodicity. Even if no sex is observed, it is sufficient if periodicity is observed on the other side. Therefore, for example, the periodicity of the angular velocity (ω) is measured first, and if there is periodicity, the wearer carries out the motion pattern having periodicity without measuring the periodicity of acceleration (G). It can be judged. On the other hand, if the periodicity of the angular velocity (ω) is measured first, and the periodicity of the acceleration (G) is observed even if the periodicity of the angular velocity (ω) is not measured, the movement pattern having the periodicity is observed. It can be determined that

Next, a method for recognizing an exercise pattern using the health care device of the present invention will be described with reference to FIG.
That is, according to the flowcharts of FIGS. 10 and 11, when it is determined that the angular velocity (ω) in the motion pattern has periodicity, the acceleration (G), angular velocity (ω), and step count data of the wearer are taken into account. Then, by collating with mapping data composed of [acceleration (G) / angular velocity (ω)] and [step count data] created in advance, it is possible to estimate the movement pattern of the wearer.
More specifically, it is possible to determine whether the carrier is in a running state or a walking state by checking with the mapping data depending on whether it is located above or below the boundary line (L). .
Then, when it is determined that the carrier is in the traveling state, it is possible to determine, for example, whether the traveling pattern 1 or the traveling pattern 2 is in consideration of the step count data.
On the other hand, even when it is determined that the carrier is in a walking state, it is possible to determine whether the walking pattern is in one of the walking patterns 1 to 4 in consideration of the step count data.

Note that when determining periodicity when recognizing an exercise pattern, noise reduction processing is performed on body behavior data using a bandpass filter and / or a lowpass filter. Therefore, compared with the conventional noise reduction processing by Fourier transform, there is a feature that the processing steps can be remarkably reduced.
More specifically, the number of processing steps that took about 100 steps in the conventional noise reduction processing by Fourier transform can be reduced to about 1/10 in the noise reduction processing by the analog filter.
Further, by performing noise reduction processing on the body behavior data using a digital filter, it is possible to achieve significant power saving of the health management device.
That is, in addition to the noise reduction process using a specific analog filter, a desired value can be taken out sharply by performing the noise reduction process using a digital filter.
On the other hand, in the conventional main frequency extraction process by Fourier transform, the clock frequency of the control circuit has to be increased in order to increase the data processing speed, and specifically, it must be at least 10 MHz or more. On the other hand, according to the noise reduction processing by the specific filter of the present invention, the amount of data processing can be remarkably reduced. Therefore, the clock frequency of the control circuit can be reduced, specifically, 2 MHz or less.
Therefore, by carrying out noise reduction processing using an analog filter and further noise reduction processing using a digital filter in this way, it is possible to accurately estimate the periodicity and the number of steps of the user's movement pattern, Power saving can be achieved more effectively. It has been found that when the clock frequency of the control circuit is reduced from 10 MHz to 2 MHz, the battery life (required charging time) of the health care device can be extended from 2 days to 10 days or more.

Finally, with reference to FIG. 13, a system using the health care device of the present invention will be described.
That is, FIG. 13 is a diagram showing a detailed system of the health management device. Such a health management apparatus system includes a portable measuring device 100 for measuring a user's physical behavior data and estimating an exercise pattern from the user's physical behavior data, an estimated exercise pattern of the user, and the like. The computer is basically composed of, for example, a computer device 8 as an external computer for calculating an average calorie consumption from separately inputted behavioral coefficients, correction coefficients, and the like.
A charging / communication adapter 4 that communicates between the portable measuring device 100 and the computer device 8; a wireless LAN router 5 that establishes a route and establishes a network with a plurality of wireless LAN terminals; A wireless LAN weight scale 6 for measuring the body weight, a wireless LAN body fat scale and a wireless LAN visceral fat scale 7 for measuring the body fat and visceral fat of the body, and a charging / communication adapter 4 further includes a monitor 9 for showing data relating to health care after calculation relating to calorie consumption based on the exercise pattern data received via the line 4c from the portable measuring device 100 mounted on 4 It is preferable that
For example, in order to identify a person on a weight scale, a wireless tag is attached to the health management device of the present invention, and a tag reader incorporated in a body fat scale or a visceral fat scale is used as an ID as personal identification information. It is also preferable to add a system for reading the data and transmitting it to the computer device together with data such as weight.
In addition, the portable measuring device 100 includes an acceleration sensor 11 that detects acceleration in a specific direction and a rotation parallel to a specific plane in order to detect acceleration of a part of the body (not shown) of the user. The angular velocity sensor 12 for detecting the angular velocity and the sensors that are mechanical vibrators are respectively excited (the drive signals are P130 and P140), and the acceleration and angular velocity or one of the detection signals P11 and P12 is extracted for detection and amplification. It is preferable to include an acceleration measurement circuit 13 that outputs a voltage proportional to the detected value and an angular velocity measurement circuit 14.

Here, the acceleration output P13 and the angular velocity output P14 are subjected to predetermined calculations by the acceleration calculation circuit 15 and the angular velocity calculation circuit 16, respectively. This predetermined calculation is to convert the signal by processing the waveforms of the signals P13 and P14. For example, the peak value of the input waveform is extracted, rectified and smoothed and averaged, a predetermined period This means obtaining the dispersion value of the peak value of the waveform appearing in the waveform, finely sampling the signal for a predetermined period to obtain the dispersion value, performing other mathematical processing, obtaining the period of the oscillating waveform, and the like. The motion data which is those outputs means the acceleration calculation output P15 and the angular velocity calculation output P16.
The motion determination circuit 17 receives internal signals P15, P16, P265 and the like from the acceleration calculation circuit 15, the angular velocity calculation circuit 16, and the control circuit 26, and outputs the acceleration calculation output and the angular velocity calculation output included therein. The two types of information are compared with respective numerical ranges that have been experimentally obtained for some types of exercise in advance, and the type and intensity of exercise performed by the wearer within a certain period are determined.
The determination result signal P17 including these pieces of information is stored in the storage device 19 and sent to the display device 18 (including necessary circuits), and its contents (type of exercise, intensity, evaluation thereof), etc. are registered in advance. It is displayed together with the personal information and nutrition intake data of the user who has been used, and enables diagnosis and judgment by data managers including medical personnel. Further, the storage signal P19 including the contents stored in the storage device 19 is reproduced as needed by the reproduction circuit 20 as the reproduction signal P20 and displayed on the display device 18.
Further, the control circuit 26 acts on each circuit in the portable measuring instrument 100 to generate control signals P261, P62, P263, P264, and P265, and adjusts the operation timing of each circuit and the cooperative operation between the circuits. It is preferable.

The output from the motion determination circuit 17 is transmitted by the communication control circuit 27 to the communication control circuit 36 provided in the computer device 8 via the communication cable 4c.
Therefore, as shown in FIG. 2, the computer device 8 includes a CPU 31 that performs calculation and control, a ROM 32 that stores BIOS, a RAM 33 that boots and writes OS software and application software from the hard disk HD 34, OS software, and application software. Display data on the monitor 9 by drawing the display data on the hard disk HD 34 and the portable measuring device 100 including a calorie calculation unit 34 a made of software and a display control unit 34 b for controlling and displaying predetermined information. Display control circuit 35, communication control circuit 36 for controlling serial communication with the COM port and USB port, keyboard 37 for inputting a key code by key operation, moving the cursor position displayed on the screen and clicking Mouse 38 for inputting information, it is preferably composed of a wireless LAN control circuit 39 that performs wireless LAN communication with the wireless LAN router 5.
Although not shown, the portable measuring device 100 is a measuring device other than the acceleration (G) and the angular velocity (ω) in at least one direction of a predetermined portion of the body of the portable person. It is preferable to separately provide a sensor that measures at least one of heart rate (pulse rate), heart rate interval, blood pressure, blood flow rate, oxygen consumption, blood glucose level, and body temperature.
That is, by measuring and adding physical behavior data other than acceleration (G), angular velocity (ω), and step count data in the mobile person's motion pattern with a dedicated sensor, the mobile person's motion pattern can be estimated more accurately. As well as improving, health management can be supported more efficiently.

The measured acceleration (G) and angular velocity (ω) data are processed in various ways and can be used for identification of motion patterns and calculation of energy consumption. In that case, it is preferable to transfer the motion data of acceleration (G) and angular velocity (ω) to the computer device 8, and the computer device 8 calculates and visualizes it, and records it in a predetermined manner.
That is, based on a wide range of conventional research data, for example, the energy consumption per unit weight (kg) for each of the various behavior forms based on a male aged 20 to 29 as shown in Table 3 is a “behavioral coefficient”. It is given as and can be used (by Japan Sports Association Sports Science Committee)
For subjects (users) of different ages and genders, correction coefficients related to energy consumption as shown in Table 4 are given and can be used (4th revision “Japanese nutrition requirements”). Depends on the quantity.)
Furthermore, the exercise pattern of the wearer can be divided into several stages corresponding to the exercise intensity according to the amount indicating the magnitude of acceleration (G) or angular velocity (ω). Furthermore, when it is determined that the vehicle is in the walking state or the running state, the number of steps, the step length, and the like can be easily counted from the periodicity of the data.
Therefore, once the exercise pattern and the type of exercise estimated to be performed by the mobile phone are determined, the short-term average energy consumption (basal metabolic energy) in the action pattern is based on the basic data obtained by such an external organization. Can be calculated from the following formula (1).
Average energy consumption = Behavioral coefficient [kcal / kg / min] x Body weight [kg] x Time [min] x Correction coefficient (1)

The long-time energy consumption can be calculated by integrating the short-time energy consumption that changes with time. Alternatively, the motion sensor is operated intermittently rather than constantly, and the type and intensity of the motion pattern identified from the data during operation is maintained during the interval period of intermittent operation, for example, several minutes to several tens of minutes. The estimated energy consumption may be integrated by estimation.
Furthermore, when the carrier carries a predetermined weight of luggage, the weight of the luggage can be added to the weight of the portable person to calculate the average calorie consumption in the exercise pattern more accurately.
That is, for example, when carrying a 5 kg baggage, the average calorie consumption in the exercise pattern is accurately calculated by adding the weight of the baggage to the weight of the carrier in the above-described equation (1). Can do.

According to the health care device of the present invention, the measured body behavior data is collated with mapping data composed of [acceleration (G) / angular velocity (ω)] and [step data] created in advance. The person's movement pattern can be estimated accurately and efficiently.
Therefore, while it is possible to accurately estimate the periodicity and the number of steps of the user's movement pattern, it is possible to reduce the clock frequency of the control circuit, so that power saving can be further effectively achieved. The battery life of the health care device can be extended to more than 10 days.
On the other hand, according to the health care device of the present invention, it is possible to calculate the stride of the exercise pattern of the wearer by taking into account separately input height data. Therefore, it can be used for estimation of the exercise pattern of the wearer, and can support the wearer's health management more efficiently.

It is a figure provided in order to explain the mapping data which consists of [acceleration (G) / angular velocity (ω)] and [step count data]. It is a figure provided in order to demonstrate the mapping data which consists of the conventional classification method. It is the figure which compared the step count calculated using the health management apparatus of this invention in driving | running | working operation | movement with the step count calculated using the conventional health management apparatus. It is the figure which compared the step count calculated using the health management apparatus of this invention in walking movement, and the step count calculated using the conventional health management apparatus. It is the figure which compared the step count calculated using the health management apparatus of this invention in the walk operation in the state of carrying a load, and the step count calculated using the conventional health management apparatus. It is a figure for demonstrating the relationship between the angular velocity ((omega)) calculated using the health management apparatus of this invention, and stride / height data. (A) is a figure which shows the mounting | wearing state to the wrist of a wristwatch type portable measuring device, (b) is a side view of a wristwatch type portable measuring device. It is a figure for demonstrating the mounting method of the portable measuring device to the adapter for connecting with an external computer. It is a figure provided in order to demonstrate the motion sensor mounted in a portable measuring device. It is a flowchart for demonstrating the determination method of the periodicity by measurement of angular velocity ((omega)) in a portable measuring device. It is a flowchart for demonstrating the determination method of the periodicity by the measurement of the acceleration (G) in a portable measuring device. It is an algorithm for recognizing an exercise pattern using the health care device of the present invention. It is a figure for demonstrating the system using the apparatus for health management of this invention.

Explanation of symbols

4: Charging / communication adapter 6: Wireless LAN scale 7: Wireless LAN fat scale / visceral fat scale 8: External computer (computer device)
9: Monitor 100: Portable measuring device (health management device)
200: Motion sensor 250: Angular velocity sensor 260: Acceleration sensor

Claims (14)

A health management device for wearing a wearer and estimating an exercise pattern from the user's physical behavior data,
A motion sensor for detecting acceleration (G) and angular velocity (ω) in at least one direction of the predetermined portion of the user as the body behavior data;
And an identification means for calculating the step count data by extracting the acceleration (G) and the angular velocity (ω), or any one of the periodicities,
Mapping data for collating with the detected physical action data, comprising mapping data composed in advance of [acceleration (G) / angular velocity (ω)] and [step count data] apparatus.   The health management apparatus according to claim 1, further comprising means for transmitting body behavior data to an external computer.   The health management device according to claim 1, further comprising a bandpass filter and a lowpass filter for performing analog noise reduction processing on the physical behavior data, or any one of the filters. .   The health management device according to any one of claims 1 to 3, further comprising a digital filter for digitally performing noise reduction processing on the physical behavior data.   The health management device according to any one of claims 1 to 4, wherein the clock frequency of the control circuit is set to a value within a range of 0.01 to 5 MHz.   The health management device according to any one of claims 1 to 5, further comprising a sleep function for stopping the operation of detecting the angular velocity (ω) for a predetermined time or an arbitrary period.   The health management according to any one of claims 1 to 6, wherein a plurality of the mapping data are provided, and a user selects in advance one of the mapping data before collation with the physical behavior data. Equipment.   The health management device according to any one of claims 1 to 6, wherein a plurality of the mapping data are provided, and the mapping data is automatically selected in synchronization with the physical behavior data.   The health management device according to any one of claims 1 to 8, wherein stride / height data of the exercise pattern of the wearer is estimated from the angular velocity (ω).   10. The health management step according to claim 9, wherein a step length of the exercise pattern of the portable person is calculated from a step length / height data of the exercise pattern of the portable person by taking into account separately inputted height data. apparatus.   11. The health management device according to claim 10, wherein a walking speed is calculated from a step length of the exercise pattern of the wearer and step count data measured by the exercise sensor.   The health care device according to any one of claims 1 to 11, wherein an average calorie consumption in the exercise pattern of the wearer is calculated.   The average calorie consumption in the exercise pattern is calculated by adding the weight of the load to the weight of the loader when the loader is carrying a load of a predetermined weight. The health care device according to any one of 12 above.   14. The sensor according to claim 1, further comprising a sensor that measures at least one of a heart rate, a heart rate interval, a blood pressure, a blood flow rate, an oxygen consumption amount, a blood glucose level, and a body temperature of the carrier. The health management device according to 1.

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