WO2018030742A1

WO2018030742A1 - Method and apparatus for recognizing exercise

Info

Publication number: WO2018030742A1
Application number: PCT/KR2017/008533
Authority: WO
Inventors: 정주호; 정창근
Original assignee: 주식회사 비플렉스
Priority date: 2016-08-09
Filing date: 2017-08-08
Publication date: 2018-02-15
Also published as: JP2021098027A; JP7101835B2; CN109328094B; JP2019528105A; CN109328094A

Abstract

Disclosed are a method for recognizing exercise and an apparatus for same comprising: an acceleration sensor unit for measuring the acceleration value of three axial directions including upward and downward, left and right, and forward and backward directions; an angular velocity sensor unit for measuring the angular velocity value of three axial directions including upward and downward, left and right, and forward and backward directions; a processing unit for generating a first exercising state value based on the acceleration value of the three axial directions and the angular velocity value of the three axial directions; and a user interface unit for controlling a sleep mode or an alive mode of the processing unit.

Description

Exercise recognition method and device

The present invention relates to an exercise recognition method and apparatus, and more particularly, to an exercise recognition method and apparatus for collecting and analyzing walking and driving exercise data of a user.

In general, it has been pointed out that the amount of exercise in daily life of modern people is quite insufficient to maintain proper physical health. Accordingly, there is a growing interest in systematic exercise methods that effectively promote health. Specifically, exercises such as exercises to quickly and systematically train the body, correction of postures to promote health in the long term, and exercises suitable for elderly people whose physical ability has deteriorated with a long lifespan Interest in sports is increasing. As one of the exercise methods to meet such various needs, there is a walking exercise that anyone can easily do.

Most people walk in unconsciously accustomed postures because anyone who walks without physical problems can walk. However, because the human body is not perfectly symmetrical, in most cases the walking takes place in an unbalanced and incorrect position. Continued walking in this wrong posture can skew muscles and skeletons, and can also cause a variety of systemic pain. In the general public, such a poor walking posture causes a problem of deteriorating physical health, and especially in a growing child or an elderly person whose physical ability is deteriorated, body distortion and deterioration of health are more severe. On the other hand, professionals such as athletes and dancers need more physical skills than the general public, there is a problem such as generating a limit to improve the physical ability.

As such, correct walking posture is important from the general public to professionals, and thus, various studies on how to effectively correct the walking posture have been conducted.

According to the prior art, a pressure sensor mounted on a shoe or a footrest is mostly used to detect walking. However, in the case of recognizing and analyzing the walking posture according to the prior art, there is a risk that the pressure sensor may be damaged in the process of being continuously pressurized for a long period of time. Can cause. In addition, the size of a person's foot varies greatly depending on the individual, and accordingly, shoes having a pressure sensor must be produced in various sizes, and thus, there is a problem in that production efficiency and economy are considerably inferior. In addition, there is a problem that the family can not share a single walking posture correction device and must be purchased separately according to the size of the foot to increase the economic burden.

As such, in the technology of recognizing, detecting, and analyzing walking in order to correct a walking posture, there is an increasing demand for a technology capable of effectively detecting, detecting, and analyzing walking in a manner other than a pressure sensor.

SUMMARY OF THE INVENTION The present invention has been made in view of the above-described technical problem, and an object of the present invention is to substantially compensate for various problems caused by the limitations and disadvantages of the prior art. An object of the present invention is to provide a motion recognition method and apparatus for collecting and analyzing the same, and to provide a computer-readable recording medium having recorded thereon a program for executing the method.

According to an embodiment of the present invention, the motion recognition method of the first device includes: measuring acceleration values in three axes including up, down, left, and right directions by an acceleration sensor unit; Measuring a three-axis angular velocity value including up, down, left, and right directions by the angular velocity sensor unit; Generating a first motion state value based on the triaxial acceleration value and the triaxial angular velocity value by a processing unit; And controlling a sleep mode or an alive mode of the processing unit by a user interface unit.

According to an embodiment of the present invention, the method may further include transmitting the first exercise state value to a second device.

According to an embodiment of the present disclosure, the transmitting of the first exercise state value to the second device may include transmitting at least one of Bluetooth, Wi-Fi, and NFC.

According to an embodiment of the present invention, the method may further include transmitting the first exercise state value to a server.

According to an embodiment of the present invention, the method may further include measuring a user position value.

According to an embodiment of the present invention, the method may further include generating a second exercise state value based on at least one of the first exercise state value, the user position value, and the user profile.

According to an embodiment of the present invention, the method may further include transmitting at least one of the first exercise state value and the second exercise state value to a server.

According to an embodiment of the present invention, the second exercise state value is at least one of distance, speed, calorie consumption amount, altitude, and stride length.

According to an embodiment of the present invention, the method may further include generating posture correction information by comparing each predetermined reference value with at least one of the first exercise state value and the second exercise state value.

According to an embodiment of the present invention, the method may further include outputting the posture correction information to at least one of sound, illustration, image, and vibration.

According to an embodiment of the present disclosure, the measuring of the three-axis acceleration value and the measuring of the three-axis acceleration value may include: when the processing unit is changed to the live mode by the user interface unit, the second device; Establish a connection, and generate the triaxial acceleration value and the triaxial angular velocity value, respectively, based on commands from the second device or the user interface portion.

According to an embodiment of the present invention, the acceleration sensor unit and the angular velocity sensor unit store the triaxial acceleration value and the triaxial angular velocity value in a first-in first-out queue; The processing unit is in a sleep mode when the storage space of the first-in, first-out queue is less than a predetermined threshold, and the processing unit is in the alive mode when the storage space of the first-in, first-out queue is above a predetermined threshold.

According to an embodiment of the present invention, the first exercise state value includes: exercise time, exercise step, minute / minute, interpolation, interpolation, head angle, ground support time, airborne time, ground support time to airborne time ratio, maximum At least one of vertical force, average vertical force load ratio, maximum vertical force load ratio, left and right balance, and left and right uniformity.

According to an embodiment of the present invention, the first device has a band worn on the head and waist, a form attached to the head and waist in a clip form, a form provided in a hat, a form attached to a belt, a glasses form, a helmet form, and an ear. It consists of one of the form, the form to be attached to the garment and the form to be worn as clothing.

According to an embodiment of the present invention, the glasses form is made of one of augmented reality glasses, glasses frames and sunglasses, the form attached to the ear is made of one of a hands-free earpiece, headphones and earphones, the form worn on the garment is a vest And a harness.

On the other hand, according to an embodiment of the present invention, the motion recognition method of the second device comprises the steps of receiving a first motion state value generated based on the three-axis acceleration value and the three-axis angular velocity value from the first device; Measuring a user position value; And generating a second exercise state value based on at least one of the first exercise state value, the user position value, and the user profile.

According to an embodiment of the present disclosure, the receiving of the first exercise state value from the first device may be performed using at least one of Bluetooth, Wi-Fi, and NFC.

In addition, according to an embodiment of the present invention includes a computer-readable recording medium on which a program for performing the method is recorded.

In addition, according to an embodiment of the present invention, the motion recognition first apparatus includes: an acceleration sensor unit measuring three-axis acceleration values including up, down, left, and right; An angular velocity sensor unit for measuring triaxial angular velocity values including vertical and horizontal directions; A processing unit configured to generate a first motion state value based on the triaxial acceleration value and the triaxial angular velocity value; It includes a user interface for controlling the sleep mode or the alive mode of the processing unit.

In addition, according to an embodiment of the present invention, the motion recognition second device includes: a first communication unit configured to receive a first motion state value generated based on a 3-axis acceleration value and a 3-axis angular velocity value from the first device; A position sensor unit measuring a user position value; And a processing unit configured to generate a second exercise state value based on at least one of the first exercise state value, the user position value, and the user profile.

According to the present invention, by using the characteristic analysis algorithm of the present invention, such as measuring acceleration, position, etc. in the user's body (e.g., head or waist), and converting it into a center of mass motion state value, it is effective and accurate. Recognize, detect, and analyze walking.

In addition, according to the present invention, by using the characteristic analysis algorithm of the present invention, such as measuring the acceleration, position, etc. in the user's body, converting it to the center of mass motion state value, and estimating the pressure center path therefrom, It can recognize, detect and analyze walking in an accurate and accurate way. In particular, the present invention uses the acceleration measured at the position most similar to the movement of the center of mass of the user's body (specifically, the acceleration in the left and right directions is measured at the head side, and the acceleration and the position in the front and rear directions are measured at the waist side). Measurement, and the acceleration in the up and down direction is made to measure at the head side or the waist side), more accurate acceleration and position can be measured.

In addition, according to the present invention, there is an advantage in that only a sensor for measuring a dynamic physical quantity of a user, such as an acceleration sensor and a position sensor, can be used in terms of device configuration. That is, by using a pressure sensor that recognizes walking by being pressed by the user's foot, there are various problems such as device durability and life deterioration problem, and production and use of a separate device according to the user's body size. However, in the case of the present invention, since the technical configuration itself of arranging the pressure sensor which is the cause of this problem on the foot part is completely excluded, various problems as described above are fundamentally eliminated. In addition, it is also possible to obtain effects such as improved user convenience and economic efficiency in each user or producer.

1 illustrates use states of the exercise posture deriving apparatus according to an embodiment of the present invention.

Figure 2 shows a schematic diagram of the exercise posture derivation apparatus according to an embodiment of the present invention.

3 is a flowchart illustrating a method of deriving an exercise posture according to an exemplary embodiment of the present invention.

4 shows a relationship diagram between a center of mass and a center of pressure in accordance with one embodiment of the present invention.

5 illustrates pressure center direction determination and position inference according to an embodiment of the present invention.

6 illustrates an example of an estimated pressure center path in accordance with an embodiment of the present invention.

7 illustrates an example of an up and down acceleration graph with respect to time during walking and driving according to an exemplary embodiment of the present invention.

8 illustrates an example of an acceleration signal measurement result according to an embodiment of the present invention.

9 is a flowchart illustrating a motion recognition method according to another embodiment of the present invention.

10 is a detailed flowchart of a data collection and exercise recognition step according to another embodiment of the present invention.

11 shows an example of an acceleration signal measurement result according to another embodiment of the present invention.

12 is a detailed flowchart of an acceleration-based exercise state value derivation step according to another embodiment of the present invention.

13 is a view illustrating usage states of the exercise posture deriving apparatus according to another embodiment of the present invention.

14 is a schematic diagram of an apparatus for deriving an exercise posture according to another embodiment of the present invention.

15 shows a schematic diagram of an apparatus for quantifying injury risk according to another embodiment of the present invention.

16 is a flowchart of a method of quantifying an injury risk according to another embodiment of the present invention.

FIG. 17 is a graph illustrating acceleration in up and down directions according to another embodiment of the present invention. FIG.

18 is a view illustrating an inclination on a vertical acceleration graph when driving in accordance with another embodiment of the present invention.

19 illustrates an impact amount on an up and down acceleration graph while driving according to another embodiment of the present invention.

20 is a view illustrating a motion recognition first device according to another embodiment of the present invention.

21 is a view illustrating a second exercise recognizing apparatus according to another embodiment of the present invention.

22 is a flowchart illustrating a motion recognition method according to another embodiment of the present invention.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. Like reference numerals in the drawings denote like elements, and the size of each element in the drawings may be exaggerated for clarity.

The exercise posture deriving apparatus 100 according to an exemplary embodiment of the present invention is worn on a user's head as shown in FIG. 1. The exercise posture deriving apparatus 100 according to the present exemplary embodiment may be formed in a band or a hat worn on a head as shown in FIG. 1, and may be made in a form of being plugged into an ear like an earphone by making it more compact. It will be apparent to those skilled in the art that the present invention may be in other forms.

The exercise posture deriving apparatus 100 according to the present exemplary embodiment includes a sensor signal collecting unit 110 and an exercise posture deriving unit 121 as illustrated in FIG. 2. The exercise posture deriving apparatus 100 according to the present exemplary embodiment further includes an exercise correction generator 122 and a correction information output unit 130.

The sensor signal collection unit 110 includes a three-axis acceleration sensor 111 including up, down, left, right, front and rear, and a position measuring sensor 112 for measuring a user position. The three-axis acceleration sensor 111 may be generally employed by selecting an appropriate one from among sensors used to measure acceleration in the three-axis direction, such as a form in which a gyroscope is embedded. Position measuring sensor 112 is for measuring the absolute position of the user, for example, it can be made to measure the user's position using a GPS signal, or the high-precision satellite navigation technology that is more accurate than the recent GPS has been developed Sensors to which this technique is applied may also be used. In addition, the sensor signal collector 110 may further include a three-axis angular velocity sensor 113 as shown in FIG. 2 to increase the accuracy in the motion recognition and analysis process which will be described in more detail below.

The sensor signal collection unit 110 according to the present embodiment is worn on the user's head as shown in FIG. 1 to measure the dynamic physical quantity of the user such as acceleration, speed, and position. Conventionally, a pressure sensor provided in a shoe and a footrest that is directly pressed by a foot for monitoring a gait is used. Accordingly, there is a problem that damage to the sensor occurs quickly, the device durability and life is shortened. In addition, there is a problem such as deterioration of walking recognition and analysis accuracy due to damage to the device during use, convenience and economical degradation due to frequent device replacement. In addition, when such a device is provided in a shoe, a separate device is required for each user according to the size of the user's foot, which increases the user's convenience and economical deterioration. There was a problem.

However, the present embodiment completely escapes the concept of using the pressure pressed by the foot in gait recognition, and measures the dynamic physical quantity of the user such as acceleration, speed and position measured in the user's head as shown in FIG. The characteristic analysis algorithm of the present invention to be described is applied to realize recognition, detection and analysis of gait. As such, the present embodiment differs from the prior art in the measurement position and measurement physical quantity. At this time, the root cause of the various problems pointed out in the prior art comes from the technical configuration of 'positioning the pressure sensor on the foot', according to the present invention, only the configuration alone eliminates the various problems as described above. Can be.

The exercise posture deriving unit 121 receives a signal from the sensor signal collecting unit 110 and uses a three-axis acceleration and position signal to include a walking or driving motion state value including acceleration, speed, and position of the user's center of mass. Derivation, and analyzes the walking or driving motion state value to derive a walking or driving posture. Specifically, the exercise posture deriving unit 121 estimates a pressure center path from the walking or driving motion state value, and analyzes the pressure center path to derive a walking or driving posture. Since the analysis algorithm of the present invention used by the exercise posture derivation unit 121 will be described in more detail later, the description is omitted here.

Meanwhile, the exercise posture deriving unit 121 may be formed in an integrated circuit form that may perform various calculations, and may be formed on the sensor signal collecting unit 110 and one substrate, or may be formed in a form of a separate computer. . In this case, when the exercise posture derivation unit 121 is formed separately from the sensor signal collection unit 110, as illustrated in FIG. 2 for transmitting a signal between the sensor signal collection unit 110 and the exercise posture derivation unit 121. The communication unit 114 may be provided as well. The communication unit 114 may be made by wire or wireless communication. The wireless communication may use Bluetooth, Wi-Fi and NFC technologies, but it is apparent to those skilled in the art that other wireless communication technologies may be used.

The exercise correction generator 122 compares the walking posture derived by the exercise posture deriving unit 121 and the reference posture to generate posture correction information. As described above, the exercise posture deriving unit 121 derives a walking or driving posture of the user based on the signal collected by the sensor signal collection unit 110. From this, the stride, which is one of the elements of the walking posture, can be obtained. In this case, the exercise correction generator 122 includes the optimal key stride relationship data for each walking and traveling speed, and compares the walking posture information of the corresponding user with an excessively wider stride than the height of the corresponding user. It is easy to determine whether it is narrow and to calculate the amount of stride correction that should be reduced or increased if it is outside the optimum range.

The correction information output unit 130 converts and outputs the posture correction information generated by the exercise correction generator 122 as information recognizable by a user including sound, illustration, and image. For example, when it is necessary to reduce the stride length by calculating the stride length correction amount, a voice such as "Reduce stride length" is output through a speaker provided on the exercise posture derivation apparatus 100, or a warning sound is sounded. The user may be not aware of the optimal stride length and may be encouraged to change the walking posture. Alternatively, the present invention may be realized in various forms, such as connected to a smartphone, a computer, a dedicated display, or the like, so that accurate calibration information may be output as an illustration or an image.

In addition, the exercise posture deriving apparatus 100 may transmit and store the walking posture derived by the exercise posture deriving unit 121 to an external database 140 to accumulate the posture. A user who needs such walking or driving motion analysis may be a general person who performs daily walking or jogging to promote health, or an expert who trains to improve physical ability. Naturally, it is desirable to be able to see the change. In addition, when the exercise analysis data is accumulated and stored in a large amount as described above, such data may be used as big data and used for various statistics or analysis, and thus various applications are possible.

In the exercise posture deriving method according to an exemplary embodiment of the present invention, the exercise posture deriving apparatus 100 performs an analysis such as detecting a user's exercise and determining whether the user is walking or driving. At this time, as described above, the analysis algorithm used in the present invention uses a dynamic physical quantity measured in the user's head, the exercise pose derivation apparatus 100 is a three-axis acceleration including at least up, down, left, right, front and rear What is necessary is to include the sensor 111 and the position measurement sensor 112 for measuring a user position, and the analysis algorithm to be described below may be performed in the exercise pose derivation unit 121. In addition, the exercise posture deriving apparatus 100 may further include various additional components described above for improving the function of the apparatus.

3 is a flowchart illustrating a method of deriving an exercise posture according to an exemplary embodiment of the present invention. The exercise posture deriving method according to the present embodiment includes a pressure center path estimating step, an exercise type determination step, and an exercise posture derivation step, as shown. Each step will be described in more detail below.

In the step of estimating the pressure center path, the mass is obtained by using the motion state values of the user's center of mass calculated using the triaxial accelerations a _x , a _y , and a _z collected by the exercise posture deriving apparatus 100. The pressure center path is estimated by projecting onto the ground in the direction of the acceleration vector from the center position.

In the exercise posture deriving apparatus 100, the three-axis acceleration sensors 111 may be collected by using the three-axis acceleration sensor 111, and may be integrated by using the three-axis acceleration sensor 111, or collected using the position measurement sensor 112 per hour. The location information can be used to find both speed and location. On the other hand, in general, when analyzing the movement of a certain object is analyzed based on the movement of the center of mass of the object, since the exercise posture deriving apparatus 100 is provided in the user's head, the values measured here It is desirable to convert the exercise state value to an analysis. The conversion of the value measured at the user's head position to the value at the user's center of mass can be easily derived by appropriately multiplying a gain value previously obtained using body information such as user key information.

By deriving the motion state values of the center of mass (per hour acceleration / velocity / position for each direction, frequency analysis, etc.), the pressure center path can be estimated therefrom. The human body behaves by using the reaction pressure applied to the supporting foot during walking or driving. The sum of these reaction pressures is called the ground reaction force (GRF), and the center of this pressure is called the center of pressure (COP). It is found that the ground reaction generated at this time has a characteristic from the center of pressure to the center of mass (COM).

In the present invention, this biomechanical property is used inversely, and the pressure center is inferred by projecting onto the ground in the vector direction of the force measured at the center of mass.

The direction of pressure center refers to the direction from the center of mass toward the pressure center. The step of estimating the pressure center path may be configured to first determine the pressure center direction, and then project in this direction to infer the pressure center position. More specifically, first, in the pressure center direction determination step, as shown in FIG. 5, the ratio of the left and right accelerations (a _x ) to the sum of the up and down accelerations (a _z ) and the gravitational acceleration (g) and the up and down accelerations are shown. The direction of the pressure center is determined by the ratio of the forward and backward acceleration (a _y ) to the sum of (a _z ) and gravity acceleration (g). When the direction of the pressure center is determined as described above, in the next pressure center position inference step, it is assumed that the center of mass is located at a height determined by multiplying a predetermined user key information by a predetermined inference constant. Projection to the ground in the direction determined in the direction determination step infers the pressure center position. The analogy constant here refers to the height of the center of mass according to the height of the user. In general, it is well known that a child's center of mass is proportionally higher than an adult's center of mass, and that a male's center of mass is proportionally higher than a woman's center of mass. For example, it is known that the center of mass of an adult male is on average 55.27% of the height, in which case the analogy constant is 0.5527. Thus, for example, by inputting the child / adult and the male / female classification information when the user key information is input, an appropriate analogy constant can be selected and used for calculation.

In order to further increase the accuracy of the pressure center position thus obtained, a pressure center position correction step may be further performed, wherein the pressure center position inferred in the pressure center position inference step is corrected by a value obtained by multiplying predetermined constants for forward and backward and left and right directions. . Here, the front-rear and left-right direction correction constants are constants capable of statistically matching the pressure center position obtained by the projection method as described above with the actual front-rear and left-right pressure centers.

In the movement type determination step, it is determined whether the vehicle is walking or traveling from the pattern of the up-down acceleration a _z graph.

6 is an example of a landing place pattern obtained as an estimated pressure center path. As shown, it can be seen that the left and right feet are alternately supporting the ground.

On the other hand, the distinction between walking and driving is that one or both feet always touch the ground in walking, while one or both feet always float from the ground in walking.

7 illustrates an example of an up and down acceleration graph with respect to time during walking and driving. In the case of the graph shown in FIG. 7 (A), it can be seen that a peak occurs when both feet touch the ground. In the case of the graph shown in FIG. 7 (B), the moment when both feet float from the ground It can be seen that a constant value section in which the vertical acceleration a _z becomes the minimum value exists. In this case, when the walking and the driving are different from each other, the patterns of the up and down acceleration (a _z ) graphs may be different from each other to determine whether the current movement of the user is walking or driving.

In the step of deriving the movement posture, the posture information including the stride length, interpolation, interpolation angle, and left and right asymmetry is derived based on the estimated pressure center path and the three-axis acceleration (a _x , a _y , a _z ). 6 will be described in more detail with reference to the example of the pressure center path of FIG. 6 and the example of the up and down acceleration during walking or driving of FIG. 7.

First, it has been described above that the appearance of the user's exercise is slightly different when walking and driving, and of course, there are also common items. As described above, in walking, one or both feet always touch the ground, and in driving, one or both feet always float from the ground. That is, there is a section in which only one foot is supported in common when walking and driving. In consideration of these points, the exercise posture derivation step includes a step of determining an intermediate support point for first determining an intermediate support point, and a step classification determination step for determining a bipedal support section, a single support section, and an airborne section, Basic information for deriving a posture while distinguishing walking and driving is formed.

First, the walking motion is described as follows. At the moment when the heel of one foot hits the ground, the toe of the other foot does not fall off the ground, ie the feet are supported. In this state, only one foot supports the ground while the other foot falls off the ground, and the other foot moves forward while slicing the air and the human body also moves forward. At the moment when the heel of the other foot touches the ground, the foot of one foot is not separated from the ground, that is, the state where both feet are supported is reestablished. In this process, while the human body is moving forward with only one foot, the person's head does not shake significantly in the vertical direction (a local minimum is formed at the vertical acceleration (a _z )). At the moment, the largest shakes in the vertical direction (a peak value is formed at the vertical acceleration a _z ).

That is, the walking motion can be divided into sections in which both feet are on the ground, sections in which only one foot is on the ground, and shaking in the vertical direction is the least while only one foot is on the ground. This aspect of the motion is well illustrated in FIG. 7A and as shown in this example, in the mid-point determination step, the local at the vertical acceleration a _z measured in the time domain when the user's motion is walking. Define the minimum as the middle support point. Also, in the section classification determining step, when the user's motion is walking, the section in which the peak value is formed in the vertical acceleration (a _z ) measured in the time domain is determined as the double support section, and the remaining section is determined as the one support section.

Next, the driving movement is solved and described as follows. Start with the moment one foot forwards to the ground (the other foot floats in the air at this moment). In this state, one foot spurts the ground and the human body moves forward with both feet floating in the air, and with both feet stir in the air, the front and rear are changed and the other foot comes forward. As soon as the other foot comes out to touch the ground, the moment of spurting the ground is reestablished. In this process, the person's head shakes most in the vertical direction at the moment of spurting the ground with one foot (the local maximum is formed at the acceleration (a _z ) in the vertical direction), while in the air while moving in the air almost in the vertical direction. It is not shaken (a constant value is formed at the vertical acceleration a _z ).

That is, the driving motion may be divided into sections in which both feet are in the air and sections in which only one foot is on the ground, and the shaking in the vertical direction is the least while both feet are in the air. This aspect of the exercise is well illustrated in FIG. 7B and as shown in this example, in the intermediate support point determination step, the local at the vertical acceleration a _z measured in the time domain when the user's movement is running. Define the maximum as the middle support point. Also, in the section classification determining step, when the user's movement is driving, the section that is represented by the constant value in the vertical acceleration a _z measured in the time domain is determined as the floating section, and the remaining section is determined as the one foot support section. Herein, the constant value appearing in the floating section is a predetermined value of the signal level level when the accelerometer does not apply any external force other than gravity, and may be appropriately determined as a value close to approximately zero. In other words, the constant value is a reference value for determining the current stance. In this sense, the constant value may be called a stance phase constant. If the interval is large, it is determined as one foot support interval.

Thus, when the basic information for deriving the attitude is derived, it is possible to derive a walking or driving posture such as stride length, interpolation, interpolation, left and right asymmetry.

Stride length: First, the average speed is calculated by measuring user location information at predetermined time intervals. Next, the walking frequency is calculated by measuring the number of intermediate supporting points during the time interval. Finally, the user's stride length can be accurately calculated by dividing the average speed by the walking frequency.

Interpolation: Interpolation in the left and right directions can be calculated using the pressure center position value corresponding to the intermediate support point. That is, by applying the time value corresponding to the intermediate support point shown in FIG. 7 (A) or (B) to the pressure center position value shown in FIG. 6, when the pressure center position corresponding to this time value is found, the left foot depresses the ground. The position and the position of the right foot are out of the ground, and the distance between them can be measured to accurately calculate the user's interpolation.

Compensation angle: The complementary angle may be calculated using a pressure center position value corresponding to a start point of the one foot support section and a pressure center position value corresponding to an end point of the one foot support section. To solve the problem, the heel of the foot support at the beginning of the foot, and the foot of the foot at the end of the foot to the ground.

In other words, to obtain the angle between the pressure center positions as described above, to obtain the angle of the heel position and the toe position of the moment when the ground is taken, that is, the angle of view, that is, the user's angle of view can be calculated accurately in this way. .

Left and right asymmetry: First, identify the foot supporting the sign of the left and right acceleration (a _x ) measured in the time domain. Next, by comparing the difference between the peak value, golgap and two of the vertical acceleration (a _z) measured in the time domain. In other words, by comparing the peak value, the valley value, and the like when the left foot is supported with the right foot, the left and right asymmetry of the user can be accurately calculated. It is also possible to calculate the repeatability of walking or driving in the same way.

FIG. 8 is an example of the acceleration signal measurement result, and it can be seen that the left and right asymmetry of the vertical acceleration a _z is strongly shown in the bottom graph of FIG. 8.

By deriving a walking or driving posture such as stride length, interpolation, interpolation, left and right asymmetry, and the like, the user can monitor in real time whether the user is walking or driving in the correct posture. Of course, at this time, the stride, interpolation, interpolation, and left and right asymmetry values corresponding to the optimal posture may be stored in advance, and the calibration amount may be calculated by comparing the current posture values currently being monitored. By notifying or storing the information to the user in real time so that the user can check later, the user can effectively correct his posture by walking or driving to a more correct posture.

9 is a flowchart illustrating a motion recognition method for walking and driving monitoring according to another embodiment of the present invention.

The motion recognition method according to the present embodiment is largely composed of two steps, that is, a data collection and motion recognition step of determining whether to walk and drive by collecting and analyzing three-axis accelerations (a _x , a _y , a _z ); And an acceleration-based motion state value derivation step of calculating the motion state values of the user's center of mass using the collected three-axis accelerations (a _x , a _y , a _z ). Hereinafter, the detailed steps of each step will be described in more detail.

As shown in FIG. 10, the data collection and motion recognition step includes a vertical acceleration collection step, a peak detection step, a motion detection step, a triaxial acceleration collection step, a Fourier transform step, and a motion shape determination step. The data collection and exercise recognition step recognizes whether the exercise is taking place in the user, and if the exercise corresponds to walking or driving.

As shown in FIG. 10, data variables to be collected are initially initialized to prepare for performing exercise recognition.

In the up-and-down acceleration collection step, instead of collecting all of the three-axis acceleration (a _x , a _y , a _z ), the up-down acceleration a _z is collected once. The collected up-and-down acceleration a _z may be used as it is, but it is more preferable to pass a noise removing step of removing noise by passing through a predetermined band pass filter. At this time, the band pass filter may be formed, for example, 0.1 to 5 Hz corresponding to the walking or running frequency of a general person, but it is apparent to those skilled in the art that this range may be appropriately changed.

In the peak detection step, the peaks of the vertical acceleration a _z thus collected are detected, and in the motion detection step, the motion occurs by determining whether the vertical acceleration a _z peak value is greater than or equal to a predetermined threshold. Judge whether or not burned. If it is determined that the exercise did not occur in the motion detection step, the process returns to the initial preparation step again, the variable is initialized.

In other words, in performing the analysis in the motion recognition apparatus 100, when it is made to always collect the three-axis acceleration (a _x , a _y , a _z ), unnecessary calculation load is generated when the motion is not performed, thereby consuming power. Problems such as fever may occur. On the other hand, the most significant difference in the case of the user sitting or moving backwards, or walking or running, is the degree that the user shakes up and down, that is, the vertical acceleration (a _z ). Therefore, by first collecting the vertical acceleration (a _z ) of the user wearing the motion recognition device 100, if the value is above a certain threshold value is determined that the user is walking or running to start the full-scale motion detection from then on Therefore, the unnecessary computational load problem described above can be prevented.

In the triaxial acceleration collection step, as described above, if the vertical acceleration _az peak value is equal to or greater than a predetermined threshold value, the triaxial acceleration a _x , a _y , a _z are collected. Here, likewise, the collected three-axis accelerations (a _x , a _y , a _z ) may be used as they are, but it is more preferable to pass a noise removing step of removing noise by passing through a predetermined band pass filter. The band pass filter at this time may be formed in the same manner as the band pass filter previously used to remove the vertical acceleration a _z noise, or may be appropriately changed and set.

In the Fourier transform step, the frequency response graph is derived by Fourier transforming the three-axis accelerations (a _x , a _y , a _z ), and the frequency response graph is determined based on a predetermined frequency response modification or magnitude in the motion type determination step. In comparison with the step of determining whether the walking and driving movement or other movement. If it is determined that the exercise corresponding to the walking and driving movement has not occurred in the exercise type determination step, the process returns to the initial preparation stage again and the parameter initialization is performed. Otherwise, if it is determined that the walking and driving movement has occurred, the acceleration-based movement state value is derived. Steps will be performed.

As such, in the exercise type determination step, it is determined whether the exercise of the user is walking and driving.

11 illustrates an example of an acceleration signal measurement result when walking according to another exemplary embodiment of the present invention. On the left side of FIG. 11 is a time domain acceleration graph in which the horizontal axis is time / vertical axis (x), front and rear (y), and vertical axis (z), respectively, in three-axis accelerations (a _x , a _y , a _z ). On the right side, there is shown a frequency response graph derived through the Fourier transform step in which the horizontal axis represents the frequency and the vertical axis represents the magnitude as described above.

Of course, during walking or running, periodically shakes up and down, front and rear, left and right direction, that is, a periodic signal is generated as shown on the left side of FIG. At this time, since the left and right feet are alternately walking or running, the frequency of the periodic signal in the left and right directions has a value 1/2 of the frequency of the periodic signal in the up and down directions and the front and rear directions. It's easy to see in the graph on the right. On the other hand, one foot or both feet are always in contact with the ground when walking, while one or both feet are floating from the ground when driving. That is, when the walking or running is made, large shaking in the head must occur periodically.

In consideration of the above-described points, the exercise type determination step may be performed to determine the user's movement as a walking and driving state if the following equation is satisfied, and to determine other movements. In other words, when the following relation is easily solved and explained, it is determined that walking or driving is performed when the degree of periodic shaking in the vertical direction and the left and right directions is greater than a certain level.

M _{z, p} / M _{z, other} > c _z and M _{x, p} / M _{x, other} > c _x

(From here,

a _z : acceleration in the vertical direction,

f _p : frequency with the maximum magnitude in the Fourier transform result of the up-down acceleration a _z ,

M _{z, p} : Vertical acceleration in a Fourier transform result of a _z a f _p to the center frequency and the total energy of the frequency components in the vertical direction based on the band with a bandwidth of less than 1Hz,

M _{z, other} : Sum of energy of remaining frequency components excluding the vertical reference band from the Fourier transform result of vertical acceleration a _z ,

c _z : a predetermined vertical reference threshold,

a _x : the horizontal acceleration,

M _{x, p:} the Fourier transform results in the lateral direction acceleration a _x in f _p / 2 of the center frequency and the total energy of the frequency components in the left and right directions based on the band with a bandwidth of less than 1Hz,

M _{x, other} : Sum of energy of remaining frequency components excluding the left and right reference bands from the Fourier transform result of left and right acceleration a _x ,

c _x : predetermined left and right reference threshold)

When it is determined that the user exercise is walking and driving by satisfying the above equation, it is now necessary to determine whether the exercise is walking or driving. At this time, as described above, one foot or both feet are always in contact with the ground during walking, and one or both feet are floating from the ground during driving. Here, at the time when both feet are lifted up from the ground while driving, there is no external force exerted on the user in the up and down direction because both feet are flying in the air, so the up and down acceleration (a _z ) is the minimum constant value at this time. Will be formed.

In consideration of the points described above, the step of determining the movement type determines that the user's motion is a driving state if there is a section satisfying the following equation in the Fourier transform result of the up-down acceleration a _z , otherwise walking It is made to judge the state.

a _z <k

(From here,

k: stance determination constant)

Here, the stance phase constant is a predetermined value of the signal level level when the accelerometer does not apply any external force other than gravity, and may be appropriately determined as a value close to approximately zero.

When the user's walking or driving motion is detected through the data collection and motion recognition step including the detailed steps as described above, an acceleration-based exercise state value derivation step is performed using the collected variables.

As shown, the acceleration-based motion state value derivation step includes a mass center acceleration derivation step, a center of mass velocity and a location derivation step.

In the derivation of the center of gravity acceleration, the acceleration of the user center of mass is derived by multiplying each of the three-axis accelerations a _x , a _y , a _z by a predetermined gain value. In general, when analyzing the motion of an object, it is analyzed based on the motion of the center of mass of the object. Since all the variables used in the analysis are measured in the user's head, it is referred to as the state of motion of the center of mass. To convert. This gain value may be represented by a constant vector γ, and may be previously obtained using body information such as user key information.

In the center of mass velocity and position derivation step, the speed and position of the center of mass of the user are derived using previously measured user key information, user position information, and center of mass acceleration. That is, it is possible to obtain the velocity and position of the center of mass (integrated by the integral constant value) by integrating the center of mass acceleration obtained as described above, or the velocity of the center of mass using the user position information measured in time by the position measuring sensor. You can also get the location. There is an error as much as the integral constant between these two calculated values. By comparing them properly, the velocity and position of the center of mass can be accurately calculated.

As described above, according to the present invention, it is possible to accurately determine whether the user is walking or driving by using the acceleration, the position, and the like measured at the user's head. You can see exactly how you are moving (ie how the acceleration, velocity, and position of the center of mass are represented). Therefore, based on this, it is possible to derive various elements of walking or driving posture and to use it for correcting posture.

As shown in FIG. 13, the exercise posture deriving apparatus 1300 according to the present exemplary embodiment is configured to be divided into a user's body, more specifically, a head and a waist. That is, in the exercise posture deriving apparatus 1300 according to the present embodiment, as shown in the schematic diagram of FIG. 13, the head sensor signal collecting unit 1310H worn on the head is formed to be inserted into the ear like an earphone, and the waist The waist sensor signal collector 1310W worn on the side may be formed to be plugged into a belt. Of course, the present invention is not limited thereto, and for example, the head sensor signal collecting unit 1310H may be variously modified, such as a headband shape, a glasses shape, a shape to be attached to a separate hat, a helmet shape, and the like. Of course.

Exercise posture deriving apparatus 1300 according to another embodiment of the present invention, as shown in Figure 14, the head sensor signal collector 1310H, the waist sensor signal collector 1310W and exercise posture derivation unit 1442. The exercise posture deriving apparatus 1300 further includes an exercise correction generator 1422 and a correction information output unit 1430.

The head sensor signal collection unit 1310H includes a head side triaxial acceleration sensor 1411H including up, down, left and right, front and rear, and the waist side sensor signal collection unit 1310W is configured to be vertical, horizontal, horizontal, And a position measuring sensor 1412W for measuring a user's position and a waist side three-axis acceleration sensor 1411W including front and rear.

The head side and waist side three-

axis acceleration sensors

1411H and 1411W may be appropriately selected and used among sensors generally used for measuring acceleration in three axes, such as a form in which a gyroscope is incorporated. The position measuring sensor 1412W is for measuring an absolute position of a user. For example, the position measuring sensor 1412W may be configured to measure a user's position using a GPS signal. Sensors to which this technique is applied may also be used. In addition, the head and waist

sensor signal collectors

1310H and 1310W are three-axis angular velocity sensors 1412H as shown in FIG. 14 to increase the accuracy in the motion recognition and analysis process, which will be described in more detail below. ) May be further included.

In the exercise posture deriving apparatus 1300 according to the present exemplary embodiment, the head and waist sensor

signal collecting units

1310H and 1310W are worn on the head and waist of the user as shown in FIG. Measure the user's dynamic physical quantity. In particular, in the present embodiment, when measuring the dynamic physical quantity of the user for the posture derivation, it is made to use the value measured at the position that appears most similar to the movement of the center of mass of the user's body. For example, in the present invention, the acceleration in the left and right directions is measured at the head side, the acceleration and the position in the front and rear directions are measured at the waist side, and the acceleration in the vertical direction is measured at the head side or the waist side. More specifically, in the case of the up-down acceleration, the up-down acceleration is quite accurate even when measured from the head side or the waist side, so that the value measured at either the head side or the waist side can be selectively used. Alternatively, the average value of the measured values on both sides may be used.

Of course, in general, the acceleration sensor is made to measure the acceleration in the three axis direction, that is, up and down, left and right, front and rear, and thus collected in the head sensor signal collector 1310H alone or the waist sensor signal collector 1310W alone. Of course, it is also possible to perform various calculations to be described later using the up, down, left, and right directions. However, when walking and driving are performed, the left and right movements in the head and the left and right movements of the center of mass of the user's body appear more similar, and the forward and backward movements in the waist and the front and rear movements of the center of mass of the user's body are more similar. Appears. In addition, the vertical movement is similar in the head, waist and center of mass. On the other hand, in the motion recognition method of the present invention to be described later, the motion recognition or posture derivation is made using the dynamic physical quantity at the center of mass of the user's body. Putting these things together, the left and right accelerations are measured on the head side, the front and back accelerations are measured on the waist side, and the head or waist side is appropriately selected to measure the vertical acceleration or both sides. By calculating the acceleration in the vertical direction as a value obtained by the average value, it is possible to obtain the effect that the final motion recognition, posture derivation, and the like are much more accurate.

The exercise posture deriving unit 1421 receives a signal from the head and waist sensor

signal collecting units

1310H and 1310W, and uses the 3-axis acceleration and position signals to determine the acceleration, velocity, and position of the user's center of mass. Deriving a walking or driving exercise state value, and analyzing the walking or driving exercise state value serves to derive a walking or driving posture. Specifically, the exercise posture deriving unit 1421 estimates a pressure center path from the walking or driving motion state value, and derives a walking or driving posture by analyzing the pressure center path. The analysis algorithm of the present invention used by the exercise posture deriving unit 1421 will be described in more detail later, and thus description thereof is omitted.

Meanwhile, the exercise posture deriving unit 1421 may be formed in an integrated circuit that may perform various calculations, and may be formed on one substrate as one body with the waist sensor signal collecting unit 1310W, or may be a separate computer. It may also be in the form. In addition, the head and waist sensor

signal collecting unit

1310H and 1310W may be provided with a head communication unit 1413H and a waist communication unit 1413W, respectively, for signal transmission with the exercise posture deriving unit 1421. . When the waist sensor signal collecting unit 1310W is integrally formed with the exercise posture deriving unit 1421, the waist side communication unit 1310W may be directly connected to the exercise posture deriving unit 1421 to transmit a signal, or The waist communication unit 1413W may serve to receive a signal transmitted from the head communication unit 1413H and transmit the signal to the exercise posture deriving unit 1421. Head and waist communication unit (1413H) (1413W) may be made of a wire, or may be made to transmit a signal using at least one wireless communication selected from Bluetooth, Wi-Fi, NFC to increase user convenience. .

The exercise correction generating unit 1422 generates the posture correction information by comparing the walking posture derived by the exercise posture deriving unit 1421 and the reference posture. The exercise posture deriving unit 1421 derives a walking or driving posture of the user based on the signals collected by the head and waist sensor

signal collecting units

1310H and 1310W as described above. The driving direction, the speed, and the like of the user can be derived while driving, and the stride length, which is one of the elements of the walking posture, can be obtained from this. In this case, the exercise correction generator 1422 embeds optimal key stride relationship data for each walking and traveling speed, and compares the walking posture information of the corresponding user with an excessively wide stride length compared to the height of the corresponding user. It is easy to determine whether it is narrow and to calculate the amount of stride correction that should be reduced or increased if it is out of the optimum range.

The correction information output unit 1430 converts and outputs the posture correction information generated by the exercise correction generator 1422 as information recognizable by a user including sound, illustration, and image. For example, if it is necessary to reduce the stride length by calculating the stride length correction amount, a voice such as "Reduce stride length" is output through a speaker provided on the exercise posture derivation device 1300, or a warning sound sounds. The user may be not aware of the optimal stride length and may be encouraged to change the walking posture. In particular, in this configuration, since the calibration information output unit 1430 is integrally formed with the head sensor signal collection unit 1310H, it is preferable that the calibration information output unit 1430 is disposed close to the user's information collection organ, that is, the eye, the ear, and the like, to transmit the information. Do. That is, as a specific example, when the head sensor signal collector 1310H is formed in the form of earphone plugged in the ear as shown in FIG. Can be. Alternatively, the present invention may be realized in various forms, such as connected to a smartphone, a computer, a dedicated display, or the like, so that accurate calibration information may be output as an illustration or an image.

In addition, the exercise posture deriving apparatus 1300 may be configured to transmit and store the walking posture derived by the exercise posture deriving unit 1421 to an external database 1440 to accumulate the posture. A user who needs such walking or driving motion analysis may be a general person who performs daily walking or jogging to promote health, or an expert who trains to improve physical ability. Naturally, it is desirable to be able to see the change. In addition, when the exercise analysis data is accumulated and stored in a large amount as described above, such data may be used as big data and used for various statistics or analysis, and thus various applications are possible.

In the exercise posture derivation apparatus 1300 according to the present exemplary embodiment as described above, specific and comprehensive examples of the movement information recognition through the exercise posture derivation and the output of the correction information are as follows. First, the left and right acceleration is collected at the head side, the front and rear acceleration and the position are collected at the waist side, and the up and down acceleration is collected at the head side or the waist side, similar to the movement of the center of mass of the user's body as described above. .

The waist sensor signal collector 1310W may be integrally formed with the exercise pose derivation unit 1421, and thus, the physical quantities collected by the head sensor signal collector 1310H may be derived through the head communication unit 1413H. It is transmitted to the part 1421 side. At this time, the waist side communication unit 1413W provided in the waist sensor signal collecting unit 1310W receives the signal and transmits the signal to the exercise posture deriving unit 1421.

Using the physical quantities such as the acceleration and the position collected in this way, the exercise posture deriving unit 1421 derives the walking and driving postures of the user. The exercise correction generator 1422 generates the posture correction information by comparing the derived actual priest with the ideal reference posture. The exercise posture deriving unit 1421 and the exercise correction generating unit 1422 may also be integrally formed, that is, they are integrally formed with the waist sensor signal collecting unit 1310W.

In order for the posture correction information thus generated to be effectively delivered to the user, it is preferable that the information is transmitted from the head side close to the eyes, ears, and the like, which are the user's information collection organs. As described above, when the head sensor signal collection unit 1310H and the calibration information output unit 1430 are integrally formed, the generated posture correction information sequentially processes the waist communication unit 1413W and the head communication unit 1413H. The calibration information output unit 1430 is delivered to the calibration information output unit 1430 to effectively transmit the calibration information in the form of delivering a voice message to the user's ear.

According to another exemplary embodiment of the present invention, a method for deriving an exercise posture may be performed by using the exercise posture derivation apparatus 1300 to detect a user's exercise and determine whether the user is walking or driving. At this time, as described above, the analysis algorithm used in the present invention uses a dynamic physical quantity measured in the user's head and waist bar, the exercise posture derivation apparatus 1300 is at least a head including a top, bottom, left and right, front and rear The three-axis direction acceleration sensor 1411H side, the three-axis direction acceleration sensor 1411W, including the up, down, left, right, front and rear, and a position measuring sensor 1412W for measuring the user's position, the exercise posture In the deriving unit 1421, an analysis algorithm to be described below may be performed. In addition, the exercise posture deriving apparatus 1300 may further include various additional components described above for improving the function of the apparatus.

Injury risk quantification device 1500 according to the present embodiment is a device for informing the user of the risk of injury that may occur during walking or driving. More specifically, it is as follows. When walking or driving, it is well known that a variety of reasons, such as a bad posture or a hard ground, can cause a strain on the ankle, knee, waist, etc., which may lead to injury. In order to prevent such a risk, conventionally, only a measure such as wearing a functional sneaker such as shock absorbing, etc., has not been an accurate indicator to know how much a risk of injury actually occurs. In the present embodiment, the risk of injury is quantified as a determination index, and when the risk of injury rises above a certain level, the user is notified of the degree of danger as an alarm. This allows the user to take appropriate measures to stop walking or driving properly, correct posture, change shoes, change walking or driving course before injury, and ultimately occur during walking or driving. This can greatly reduce the risk of injury.

The injury risk quantification device 1500 according to the present embodiment includes a sensor signal collection unit 1510, a controller 1520, and an alarm unit 1530. The injury risk quantification device 1500 may further include a database 1540.

The sensor signal collector 1510 includes an acceleration sensor 1511 and is worn on the upper body except the user's arm. The sensor signal collection unit 1510 may be a single dog or a plurality of dogs. The sensor signal collector 1510 may be formed in two and may be worn on each of the user's head and waist. In this case, the sensor signal collector that is worn on the user's head may be the head sensor signal collector 1510H and the user's waist. The sensor signal collector worn on the side may be divided into the waist sensor signal collector 1510W. As a specific example of the wearing state, the head sensor signal collection unit 1510H worn on the head side is made of an ear plug like earphone, and the waist sensor signal collector 1510W worn on the waist side is plugged into a belt. It may be made in the form. Of course, the present invention is not limited thereto, and for example, the head sensor signal collecting unit 1510H may be variously modified, such as a headband shape, a glasses shape, a form attached to a separate hat, a helmet shape, and the like. Of course. In addition, although not shown in the drawing, the sensor signal collector 1510 may be worn anywhere except the user's arm, for example, to be worn or inserted into the breast pocket of the garment to be worn on the chest, Various modifications can be made, such as a form worn using a separate vest or harness.

The sensor signal collector 1510 includes an acceleration sensor 1511 as described above. The acceleration sensor 1511 may generally employ and select an appropriate one from among sensors used to measure acceleration in the three axis direction, such as a form in which a gyroscope is embedded. On the other hand, the sensor signal collector 1510 may be provided with a controller 1520 that performs a role such as performing a calculation and controlling the acceleration data signal collected by the acceleration sensor 1511 directly. Alternatively, the controller 1520 may be implemented in various ways, such as to be implemented in the form of an app on a conventionally used smartphone. That is, when the control unit 1520 is implemented as a separate device from the sensor signal collection unit 1510, the sensor signal so that the acceleration data signal collected by the acceleration sensor 1511 can be smoothly transmitted to the control unit 1520. The collection unit 1510 may further include a communication unit 1512. Such signal transmission may be made by wired communication through wiring, or may be made by wireless communication such as Bluetooth, Wi-Fi, NFC, etc., and may be adopted by selecting an appropriate form according to required conditions or required performance. .

Although it will be described in more detail in the description of the injury risk quantification method according to the present embodiment, the present embodiment uses the vertical acceleration in determining the risk of injury.

In addition, the present embodiment uses the vertical acceleration to quantify the risk of injury. In general, when traveling, the left and right movements in the head and the left and right movements of the center of mass of the user's body appear more similarly, and the back and forth movements in the waist and the forward and backward movements of the center of mass of the user's body are relatively similar. appear. In addition, the vertical movement is similar in both the upper body and the center of mass, including the head to the waist. However, the arm part of the upper body, except for the movement of the center of mass in addition to the movement in the forward and backward direction, the arm is excluded. In consideration of this point, the acceleration in the vertical direction may be measured in any of the upper body except the arm. Incidentally, the acceleration in the up and down direction is quite accurate even when measured in the upper body except the arm, so that the value measured at either the head side or the waist side may be selectively used, or the measured value at both sides may be The average value may be appropriately selected.

The control unit 1520 receives the signal from the sensor signal collection unit 1510, derives at least one injury risk determination index calculated based on the vertical acceleration (a _z ), and using the injury risk determination index It determines and controls the occurrence of alarm. More specifically, the control unit 1520 derives at least one is a risk judgment indicators to the portion, the average slope, the selection of the maximum slope, the maximum impact force, the impulse of the vertical acceleration (a _z) in the vertical direction acceleration (a _z) Through this, the risk of injury is quantified and the degree of danger is determined. Derivation of the injury risk determination index performed by the controller 1520 will be described in more detail with reference to the method of quantifying injury risk according to the present embodiment.

The actual implementation form of the controller 1520 may be formed in various ways depending on the need or purpose. That is, the controller 1520 may be formed on one substrate as an integrated circuit form capable of performing various calculations and integrally with the sensor signal collector 1510, or a separate dedicated device (that is, for quantifying injury risk) It may be made in the form of an independent device (manufactured only as a) or a separate computer, etc.), or may be implemented in the form of an app on a smart phone that is being used as described above. As described above, when the controller 1520 is integrally formed with the sensor signal collector 1510, the controller 1520 may be configured to receive a signal directly from the acceleration sensor 1511. On the other hand, if the control unit 1520 is formed independently of the sensor signal collection unit 1510, such as in the form of a separate device or a smart phone app is made to receive a signal from the acceleration sensor 1511 by wire or wireless communication Can be.

The alarm unit 1530 receives an alarm generation control signal from the control unit 1520, and serves to alert the user of a risk of injury. The controller 1520 derives at least one injury risk determination index calculated based on the up and down acceleration a _z and uses the same to determine whether an alarm has occurred. In this case, the user is notified of the danger by controlling the alarm to be generated.

The alarm unit 1530 outputs an alarm signal as information that can be recognized by a user including sound, illustration, and image. For example, when the alarm unit 1530 is in the form of a speaker that outputs sound, a warning sound may be sounded if the risk of injury is greater than a reference level. Alternatively, when the device according to the present embodiment is applied to augmented reality glasses such as Google Glass, the alarm unit 1530 outputs a red warning figure or an image flickering on the augmented reality glasses, or "the risk of injury is several percent. It is also possible to output a message such as "." Alternatively, the alarm unit 1530 may be implemented as a thermoelectric element and may be in direct or indirect contact with the user's skin. If the risk of injury is greater than or equal to the standard, the alarm unit 1530 may alert the user by being cold or hot. As another example, when the user is visually impaired, the alarm unit 1530 may be configured in a form that can be changed by braille to be recognized by the sense of touch. As such, the alarm unit may be formed in any form as long as the alarm unit can output the alarm signal as information that can be recognized by the user.

In addition, the injury risk quantification device 1500 may be configured to accumulate and store injury risk data including an injury risk alarm occurrence time point and an injury risk determination index value at the corresponding time point to an external database 1540. A user who needs such walking or driving motion analysis may be a general person who performs daily walking or jogging to promote health, or an expert who trains to improve physical ability. Naturally, it is desirable to be able to see the change. In addition, when the exercise analysis data is accumulated and stored in a large amount as described above, such data may be used as big data and used for various statistics or analysis, and thus various applications are possible.

The injury risk quantification method according to the present embodiment includes an acceleration sensor 1511 as described above, and is measured by using at least one sensor signal collector 1510 worn on the upper body except the user's arm. Injury risk determination indicators are used to quantify the risk of injury using directional acceleration (a _z ). To this end, the injury risk quantification method according to the present embodiment includes a data collection step, a determination index derivation step, an injury risk determination step, and an injury risk warning step. In addition, the method may further include a noise removal step to increase the accuracy of derivation of the injury risk determination index. Referring to each step shown in Figure 16 in more detail as follows.

In the data collection step, the up-down acceleration a _z measured by the sensor signal collector 1510 is collected. The collected up-and-down acceleration a _z may be used as it is, but it is more preferable to pass a noise removing step of removing noise by passing through a predetermined band pass filter. In this case, the band pass filter may be formed at, for example, 0.1 to 5 Hz, which corresponds to a walking or running frequency of a general person, but, of course, the range may be appropriately determined.

In the determination index deriving step, at least one injury risk determination index calculated based on the vertical acceleration a _z is derived. At this time the injury risk judgment indicators may be a vertical direction acceleration (a _z) the average slope, maximum slope of the vertical acceleration (a _z), the maximum impact force, at least one selected from the amount of impact. Each judgment indicator will be described in more detail later.

In the injury risk determination step, it is determined whether the injury risk determination index is greater than a predetermined criterion. At this time, the injury risk determination index may be a plurality as described above, may generate an alarm when any one of the multiple determination index is above the reference, may generate an alarm when all above the reference, or appropriately prioritized You can also rank and raise alarms in stages. In the injury risk determination step, if the injury risk determination index is smaller than a predetermined criterion, the alarm returns to the data collection step without generating an alarm. In addition, in the injury risk determination step, it is preferable to make the determination using the injury risk determination index calculated by collecting data of at least two cycles with respect to the up-down acceleration (a _z ) data represented by the periodic signal. .

In the injury risk warning step, when at least one of the injury risk determination indexes is greater than a predetermined criterion, the user is alerted of the injury risk. As described above, the warning form of the injury risk can be in various forms such as sound, illustration, image, and the like, and the user is actively alerted to the action to reduce the risk of injury (end of exercise, posture correction, shoe replacement, course change). Etc.) can ultimately greatly reduce the risk of injury.

Hereinafter will be described in more detail the process of deriving several examples and each of the injury risk determination indicators used in the present invention.

As shown, the up-and-down acceleration a _z appears in a periodic form with respect to time (which is natural because walking or driving itself is a periodic movement). The driving motion is described as follows. Start with the moment one foot forwards to the ground (the other foot floats in the air at this moment). In this state, one foot spurts the ground and the body of the person moves forward with both feet floating in the air, and with both feet stir up in the air, the other foot changes forward and the other foot comes forward. As soon as the other foot comes out to touch the ground, the moment of spurting the ground is reestablished. In this process, the person's head shakes most in the vertical direction at the moment of spurting the ground with one foot (the local maximum is formed at the acceleration (a _z ) in the vertical direction), while in the air while moving in the air almost in the vertical direction. It is not shaken (a constant value is formed at the vertical acceleration a _z ).

The moment the foot hits the ground, the most impact is applied to the joint, and this impact is shown in the form of the first peak in the vertical acceleration graph as shown in FIG. 17. The risk of injury will vary depending on the degree of impact at this time, and in the present invention, it was used as the basis of quantified judgment by indexing it. As such a surface is determined, in the present invention, as described above, it uses the maximum slope, the maximum impact force, the impulse of the vertical direction acceleration average slope, vertical acceleration (a _z) of (a _z).

18 is a view illustrating an inclination on a vertical acceleration graph when driving in accordance with another embodiment of the present invention. This will be described the process of deriving the average slope and the maximum slope of the vertical acceleration (a _z ).

First, when the injury risk determination index is selected as the average inclination value of the vertical acceleration (a _z ), the injury risk determination index is calculated using the following equation.

(Where, a _z : up and down acceleration, mean: average value calculation function, i: index number, t _i : i-th time, t _i _-1 : i-1 th time, t _c : shock start time, t _m : Impact end time)

The impact start time actually means the moment the foot lands on the ground. This may be determined as a time when the vertical acceleration a _z rises upward through a predetermined reference value (for example, 0.3 m / s ² ) near zero at a value of zero or less. Herein, the specific value of the reference value for determining the impact start time may be appropriately determined from a value of 0.5 m / s ² or less as in the above-described example. The impact end time is the point at which the first peak value appears and can be easily seen intuitively on the graph. The index i is an index of times digitized by dividing the time from the impact start time to the impact end time by n, and n may be appropriately determined as necessary.

The average slope value is thus the average value of the n slope values obtained at each interval when n is divided from the impact start time to the impact end time. FIG. 18 illustrates a vertical acceleration a _z graph in one period, and the average slope value as described above may be obtained in one such period. On the other hand, as shown in FIG. 17, while driving, the graph of the form as shown in FIG. 18 is repeatedly repeated, and the average slope value as described above may be obtained for each period (that is, for each step). At this time, in the determination index deriving step, an average vertical loading rate calculated as a product of the user mass m and the average slope may be further calculated.

On the other hand, when the injury risk determination index is selected as the maximum inclination value of the vertical acceleration (a _z ), the injury risk determination index is calculated using the following equation.

i = 1, 2,... , n,

t ₀ = t _c , t _n = t _m

(Where, a _z : up and down acceleration, max: maximum calculation function, i: index number, t _i : i time, t _i _-1 : i-1 time, t _c : shock start time, t _m : Impact end time)

The maximum slope is the maximum of n slope values obtained between the shock start time and the impact end time in one period (one step) as described in the description of the average slope. At this time, in the determination index derivation step, the instantaneous vertical loading rate calculated as the product of the user mass m and the maximum slope may be further calculated.

19 illustrates an impact amount on an up and down acceleration graph while driving according to another embodiment of the present invention. This section describes the process of deriving the maximum impact force and the amount of impact.

First, when the injury risk determination index is selected as the maximum impact force value, the injury risk determination index is calculated using the following equation.

(Here, _az : acceleration in the up and down direction, m: user mass, t _m : end time of impact)

As described above, since the impact end time is the time at which the first peak value appears, the time at which the maximum impact force is naturally shown is the impact end time. In FIG. 19, the first peak (1st peak) of the up-down acceleration a _z is indicated, and the value multiplied by the user's mass (m) is the maximum impact force value.

On the other hand, when the injury risk determination index is selected as the impact amount value, the injury risk determination index is calculated using the following equation.

(Here, a _z : up and down acceleration, m: user mass, t _c : shock start time, t _m : shock end time)

In FIG. 19, the up-down acceleration a _z graph area between the impact start time and the impact end time is shown, and the value obtained by multiplying the area by the user's mass (m) is the impact amount value.

The exercise recognition first device 2000 (hereinafter, referred to as a first device) according to the present embodiment includes an acceleration sensor unit 2010, an angular velocity sensor unit 2020, a processing unit 2040, and a user interface unit 2050. . The first device 2000 according to the present exemplary embodiment is worn on the user's body and measures the user's dynamic physical quantity such as acceleration and angular velocity, thereby analyzing the user's exercise state such as walking and driving. As shown in the example shown in Figure 1, the first device 2000 is a band worn on the head and waist, the form attached to the head and waist in a clip form, the form provided in the hat, the form of the belt, glasses, helmet form , Can be attached to the ear, the form attached to the garment, it can be made in the form of wearing using a separate vest or harness. Specifically, the glasses may be in the form of Augmented Reality (AR) glasses, glasses frames, sunglasses, or the like. Attaching to the ear may be in the form of a handsfree earpiece, headphones and earphones. In addition, it will be apparent to those skilled in the art that the first apparatus 2000 may be made in various modified forms. The first device 2000 may be formed on one substrate in the form of an integrated circuit capable of performing various calculations.

The acceleration sensor unit 2010 measures three-axis acceleration values including up, down, left and right, and front and rear.

The angular velocity sensor unit 2020 measures triaxial angular velocity values including up, down, left and right, and front and rear.

The processing unit 2040 generates a first motion state value based on the triaxial acceleration value and the triaxial angular velocity value. The first exercise state value is exercise time, exercise step, minute per minute, interpolation, interpolation, head angle, ground support time, air float time, air support time to ground support time ratio, maximum vertical force, average vertical force load ratio, At least one of the maximum vertical force load ratio, left and right balance, left and right uniformity. The first device 2000 may determine the exercise state of the user through the first exercise state value. In the meaning of each of the first exercise state values, the reward per minute is the number of steps per minute, the interpolation is the average distance between the legs, the interpolation angle is the leg angle average, the head angle is the upper and lower head angle average, and the ground support time is the support time touching the ground. The levitation time is the average time all legs are not in contact with the ground, the maximum vertical force is the maximum value of the ground reaction force, the average vertical force load rate is the average of the initial slope of the support zone of the left and right ground reaction forces, and the maximum vertical force load rate is left And the maximum inclination of the initial slope of the support section of the right ground reaction force.

Stability refers to whether the state of movement is consistently maintained for each leg of the left and right foot in time, strength, etc., and expressed in% using the coefficient of variation (CV) of each leg. Obtain through

Stability (Left) = 1-std (Left indices) / mean (Left indices)

Stability (Right) = 1-std (Right indices) / mean (Right indices)

Values that can be used as indexes for evaluation indexes include vertical force maximum, vertical acceleration maximum, support section impact, support time, stray time, average vertical force load rate and maximum vertical force load rate.

Balance of left and right (Balance) represents the left and right unbalance (%), and is obtained by the following equation.

Balance = Left index / (Left index + Right index) * 100%

The user interface 2050 controls the sleep mode or the alive mode of the processing unit 2040. The user interface 2050 may be implemented in software or hardware form. For example, the user interface 2050 may be implemented as a push button in the form of software or hardware. A flowchart of the exercise recognition method disclosed from the user input of the user interface 2050 will be described in detail later with reference to FIG. 22.

Meanwhile, the first device 2000 according to the present embodiment may further include a first communication unit 2070. The first communication unit 2070 transmits the first exercise state value to the second device 2100. The first communicator 2070 may transmit the first exercise state value to the second device 2100 at predetermined intervals, which may be implemented in various transmission schemes. The second device 2100 according to the present embodiment may be various types of devices such as a computer, a mobile terminal, and a watch.

Meanwhile, the first device 2000 according to the present embodiment may further include a second communication unit 2080. The second communication unit 2080 transmits the first exercise state value to the server 2200.

Meanwhile, the first device 2000 according to the present embodiment may further include a position sensor unit 2030.

The position sensor unit 2030 measures a user position value. The position sensor unit 2030 measures the position value of the user based on GPS or ultra-precision satellite navigation technology, etc., but it is apparent to those skilled in the art that other techniques may be used.

When the first device 2000 further includes the position sensor unit 2030, the processing unit 2040 may perform a second exercise based on at least one of the first exercise state value, the user position value, and the user profile. Create a state value. The second exercise state value is at least one of exercise distance, exercise speed, calorie consumption amount, altitude, and stride length. In the meaning of each of the second exercise state values, the altitude means the vertical height moved during the exercise, and the stride length means the distance moved forward during the ground support section and the levitation section. The user profile includes personal information such as a user's height and weight.

In addition, the processing unit 2040 may further generate posture correction information by comparing each predetermined reference value with at least one of the first exercise state value and the second exercise state value. For example, the processor 2040 stores the optimal key stride relationship data for each exercise speed, and is the stride length not too wide or narrow compared to the height of the user based on the stride length among the second exercise state values. Judge. When the stride is out of the optimum range, the processing unit 2040 generates the stride length correction information to be reduced or increased as posture correction information.

Meanwhile, the first device 2000 according to the present embodiment may further include an output unit 2060. The output unit 2060 converts and outputs the posture correction information as information that can be recognized by a user, which is at least one of sound, illustration, image, and vibration. For example, if the stride correction amount is calculated and you need to reduce the stride length, the speaker may output a voice such as "Reduce stride length" or a warning sound to alert you that you are not optimal stride length. Can be induced to change Alternatively, the first device 2000 may be connected to an external device such as a mobile terminal, a watch, a computer, a dedicated display, and output the calibration information to at least one of sound, illustration, image, and vibration.

When the first device 2000 further includes the position sensor unit 2030, the first device 2000 may further include a third communication unit which transmits the second exercise state value to the server 2200. The server 2200 accumulates and stores the second exercise state value in a database. The server 2200 provides statistical data based on the second exercise state value stored in the database. The statistical data includes a maximum value, a minimum value, and an average value for each of the second exercise state values for a predetermined exercise section. A user who needs an exercise analysis may be provided with the statistical data through the server 2200, and may use it in various ways such as improving his or her exercise habits. A user who needs exercise analysis may be a general person who walks or jogs every day to promote health, or a professional who trains to improve physical ability. In addition, the server 2200 stores the second exercise state value for each user and provides a big data service in which the second exercise state value is analyzed relationally and statistically between users.

The first communication unit 2070, the second communication unit 2080, and the third communication unit are configured of at least one of wireless communication including Bluetooth, Wi-Fi, and NFC, and wired communication through wiring, but other wired / wireless communication technologies may be used. Is apparent to those skilled in the art. In addition, the first communication unit 2070, the second communication unit 2080, and the third communication unit may be physically configured as a single interface or a plurality of interfaces.

The exercise recognition second device 2100 (hereinafter referred to as a second device) according to the present exemplary embodiment includes a first communication unit 2110, a processing unit 2150, and a position sensor unit 2170. The second device 2100 according to the present embodiment may be various types of devices such as a computer, a mobile terminal, and a watch.

The first communication unit 2110 receives the first exercise state value generated based on the triaxial acceleration value and the triaxial angular velocity value from the first device 2000.

The position sensor unit 2170 measures a user position value. The position sensor unit 2030 measures the position value of the user based on GPS or ultra-precision satellite navigation technology, etc., but it is apparent to those skilled in the art that other techniques may be used.

The processing unit 2150 generates a second exercise state value based on at least one of the first exercise state value, the user position value, and the user profile. The second exercise state value is at least one of distance, speed, calorie consumption, altitude, and stride length. The user profile includes personal information such as a user's height and weight.

Meanwhile, the processing unit 2150 according to the present exemplary embodiment may further generate exercise posture correction information by comparing each predetermined reference value with at least one of the first exercise state value and the second exercise state value. Can be. For example, the processor 2150 stores the optimal key stride relationship data for each exercise speed, and is the stride length not too wide or narrow compared to the height of the user based on the stride length among the second exercise state values. Judge. When the stride is out of the optimum range, the processing unit 2150 generates the stride length correction information to be reduced or increased as posture correction information.

Meanwhile, the second device 2100 according to the present embodiment may further include an output unit 2190. The output unit 2190 converts and outputs the posture correction information as information recognizable by a user, which is at least one of sound, illustration, image, and vibration. For example, if the stride correction amount is calculated and you need to reduce the stride length, the speaker may output a voice such as "Reduce stride length" or a warning sound to alert you that you are not optimal stride length. Can be induced to change

Meanwhile, the second device 2100 according to the present embodiment may further include a second communication unit 2150. The second communication unit 2150 transmits the second exercise state value to the server 2200. The server 2200 accumulates and stores the second exercise state value in a database. The server 2200 provides statistical data based on the second exercise state value stored in the database. The statistical data includes a maximum value, a minimum value, and an average value for each of the second exercise state values for a predetermined exercise section. A user who needs an exercise analysis may be provided with the statistical data through the server 2200, and may use it in various ways such as improving his or her exercise habits. In addition, the server 2200 stores the second exercise state value for each user and provides a big data service in which the second exercise state value is analyzed relationally and statistically between users.

Although the first communication unit 2110 and the second communication unit 2130 are configured with at least one of wireless communication including Bluetooth, Wi-Fi, and NFC and wired communication through wiring, it will be apparent to those skilled in the art that other wired / wireless communication technologies may be used. Do. In addition, the first communication unit 2110 and the second communication unit 2130 may be physically configured as a single interface or a plurality of interfaces.

In operation 2210, the user interface 2050 of the first device 2000 changes the processing unit 2040 to the alive mode.

In operation 2220, the first device 2000 establishes a connection with the second device 2100 through the first communication unit 2070.

When the connection with the second device 2100 is established, in operation 2230, the first device 2000 receives a command from the user interface 2050 or the second device 2100.

In operation 2240, the first device 2000 measures the 3-axis acceleration value and the 3-axis angular velocity value through the acceleration sensor unit 2010 and the angular velocity sensor unit 2020 based on the command. According to an embodiment of the present invention, the acceleration sensor unit 2010 and the angular velocity sensor unit 2020 store the three-axis acceleration value and the three-axis angular velocity value in a first in first out (FIFO) queue. do. The first device 2000 changes the processing unit 2040 to the sleep mode when the storage space of the first-in, first-out queue is less than a predetermined threshold, and alives the processing unit 2040 when the storage space of the first-in, first-out queue is greater than or equal to a predetermined threshold. By changing to the mode, the device can be driven with low power.

In operation 2250, the first device 2000 generates a first motion state value based on the triaxial acceleration value and the triaxial angular velocity value. The first exercise state value is exercise time, exercise step, minute per minute, interpolation, interpolation, head angle, ground support time, air float time, air support time to ground support time ratio, maximum vertical force, average vertical force load ratio, At least one of the maximum vertical force load ratio, left and right balance, left and right uniformity.

In operation 2260, the first device 2000 transmits the first exercise state value to the second device 2100.

In operation 2270, the second device 2100 measures a user location value.

In operation 2280, the second device 2100 generates a second exercise state value based on at least one of the first exercise state value, the user position value, and the user profile. The second exercise state value is at least one of distance, speed, calorie consumption, altitude, and stride length. The user profile includes personal information such as a user's height and weight.

The second device 2100 may optionally further generate exercise posture correction information by comparing each predetermined reference value with at least one of the first exercise state value and the second exercise state value. For example, the second device 2100 stores the optimal key stride relation data for each exercise speed, and the stride length is too wide or narrow compared to the height of the user based on the stride length among the second exercise state values. Determine whether or not. If the stride length is out of the optimum range, the second device 2100 generates the stride length correction information to be reduced or increased as posture correction information. The second device 2100 converts and outputs the posture correction information as information recognizable by a user, which is at least one of sound, illustration, image, and vibration. For example, if the stride correction amount is calculated and you need to reduce the stride length, the speaker may output a voice such as "Reduce stride length" or a warning sound to alert you that you are not optimal stride length. Can be induced to change

In operation 2290, the second device 2100 transmits the second exercise state value to the server 2200. The server 2200 accumulates and stores the second exercise state value in a database. The server 2200 provides statistical data based on the second exercise state value stored in the database. The statistical data includes a maximum value, a minimum value, and an average value for each of the second exercise state values for a predetermined exercise section. A user who needs an exercise analysis may be provided with the statistical data through the server 2200, and may use it in various ways such as improving his or her exercise habits. In addition, the server 2200 stores the second exercise state value for each user and provides a big data service in which the second exercise state value is analyzed relationally and statistically between users.

Although the preferred embodiment of the present invention has been described in detail above, the scope of the present invention is not limited thereto, and various modifications and equivalent other embodiments are possible. Therefore, the true technical protection scope of the present invention will be defined by the appended claims.

For example, an apparatus according to an exemplary embodiment of the present invention may include a bus coupled to units of each of the apparatus as shown, at least one processor coupled to the bus, and a command, received And a memory coupled to the bus for storing messages or generated messages, and coupled to at least one processor for performing instructions as described above.

In addition, the system according to the present invention can be embodied as computer readable codes on a computer readable recording medium. The computer-readable recording medium includes all kinds of recording devices in which data that can be read by a computer system is stored. The computer-readable recording medium may include a magnetic storage medium (eg, ROM, floppy disk, hard disk, etc.) and an optical reading medium (eg, CD-ROM, DVD, etc.). The computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion.

Claims

An acceleration sensor unit measuring a three-axis acceleration value including up, down, left and right, and front and rear;

An angular velocity sensor unit for measuring triaxial angular velocity values including vertical and horizontal directions;

A processing unit configured to generate a first motion state value based on the triaxial acceleration value and the triaxial angular velocity value;

And a user interface configured to control a sleep mode or an alive mode of the processing unit.
The method of claim 1,

The first device further comprises a first communication unit for transmitting the first exercise state value to a second device.
The method of claim 2,

The first device, characterized in that the first communication unit is composed of at least one of Bluetooth, Wi-Fi and NFC.
The method of claim 1,

And a second communication unit configured to transmit the first exercise state value to a server.
The method of claim 1,

The first device further comprises a position sensor for measuring a user position value.
The method of claim 5,

The processing unit generates a second exercise state value based on at least one of the first exercise state value, the user position value, and the user profile.
The method of claim 6,

And a third communication unit configured to transmit at least one of the first exercise state value and the second exercise state value to a server.
The method of claim 6,

And the second exercise state value is at least one of distance, speed, calorie consumption, altitude, and stride length.
The method of claim 6,

And the processing unit generates posture correction information by comparing at least one of the first exercise state value and the second exercise state value with each predetermined reference value.
The method of claim 9,

The apparatus of claim 1, further comprising an output unit configured to output the posture correction information to at least one of sound, illustration, image, and vibration.
The method of claim 2,

When the processing unit is changed to the alive mode by the user interface unit,

The processing unit establishes a connection with the second device through the first communication unit,

And the acceleration sensor unit and the angular velocity sensor unit generate the triaxial acceleration value and the triaxial angular velocity value, respectively, based on a command from the second device or the user interface unit.
The method of claim 1,

The acceleration sensor unit and the angular velocity sensor unit store the triaxial acceleration value and the triaxial angular velocity value in a first-in first-out queue;

And the processing unit is in a sleep mode when the storage space of the first in, first out queue is less than a predetermined threshold, and the processing unit is in the alive mode when the storage space of the first in, first out queue is more than a predetermined threshold.
The method of claim 1,

The first exercise state value is exercise time, exercise step, minute per minute, interpolation, interpolation, head angle, ground support time, air float time, air support time to ground support time ratio, maximum vertical force, average vertical force load ratio, The first device, characterized in that at least one of the maximum vertical load ratio, left and right balance, left and right uniformity.
The method of claim 1,

The first device is a band worn on the head and waist, a form attached to the head and waist in the form of a clip, a form provided in the hat, a belt attached form, glasses form, a helmet form, a form attached to the ear, a form attached to the garment And a form to be worn as a garment.
The method of claim 1,

The glasses form is made of one of augmented reality glasses, glasses frames and sunglasses,

The form attached to the ear is made of one of a hands-free earpiece, headphones and earphones,

The first device, characterized in that the form worn with the garment consists of one of a vest and a harness.
A first communication unit configured to receive from the first device a first motion state value generated based on the 3-axis acceleration value and the 3-axis angular velocity value;

A position sensor unit measuring a user position value;

And a processing unit to generate a second exercise state value based on at least one of the first exercise state value, the user position value, and the user profile.
The method of claim 16,

And a second communication unit configured to transmit at least one of the first exercise state value and the second exercise state value to a server.
The method of claim 16,

The first exercise state value is exercise time, exercise step, minute per minute, interpolation, interpolation, head angle, ground support time, air float time, air support time to ground support time ratio, maximum vertical force, average vertical force load ratio, The second device, characterized in that at least one of the maximum vertical force load ratio, left and right balance, left and right uniformity.
The method of claim 16,

And the second exercise state value is at least one of distance, speed, calorie consumption, altitude, and stride length.
The method of claim 16,

And the processing unit generates posture correction information by comparing at least one of the first exercise state value and the second exercise state value with each predetermined reference value.
The method of claim 20,

And an output unit configured to output the posture correction information to at least one of sound, illustration, image, and vibration.
The method of claim 16,

The second communication unit is characterized in that the first communication unit is composed of at least one of Bluetooth, Wi-Fi and NFC.
Measuring a three-axis acceleration value including up, down, left and right, and front and rear by an acceleration sensor unit;

Measuring a three-axis angular velocity value including up, down, left, and right directions by the angular velocity sensor unit;

Generating a first motion state value based on the triaxial acceleration value and the triaxial angular velocity value by a processing unit; And

Controlling a sleep mode or an alive mode of the processing unit by a user interface unit.
The method of claim 23, wherein

And transmitting the first exercise state value to a second device.
The method of claim 24,

The transmitting of the first exercise state value to the second device may include transmitting at least one of Bluetooth, Wi-Fi, and NFC.
The method of claim 23, wherein

And transmitting the first exercise state value to a server.
The method of claim 23, wherein

The method of claim 1, further comprising measuring a user position value.
The method of claim 27,

And generating a second exercise state value based on at least one of the first exercise state value, the user position value, and a user profile.
The method of claim 28,

And transmitting at least one of the first exercise state value and the second exercise state value to a server.
The method of claim 28,

And the second exercise state value is at least one of distance, speed, calorie consumption, altitude, and stride length.
The method of claim 28,

And comparing the at least one of the first exercise state value and the second exercise state value with each predetermined reference value to generate posture correction information.
The method of claim 31, wherein

And outputting the posture correction information to at least one of a sound, a diagram, an image, and a vibration.
The method of claim 24,

Measuring the three-axis acceleration value and measuring the three-axis acceleration value,

Establish a connection with the second device when the processing unit is changed to the alive mode by the user interface unit,

And generating the three-axis acceleration value and the three-axis angular velocity value, respectively, based on a command from the second device or the user interface unit.
The method of claim 23, wherein

The acceleration sensor unit and the angular velocity sensor unit store the triaxial acceleration value and the triaxial angular velocity value in a first-in first-out queue;

The processing unit is in a sleep mode when the storage space of the first-in, first-out queue is less than a predetermined threshold, and the processing unit is in the alive mode when the storage space of the first-in, first-out queue is above a predetermined threshold. .
The method of claim 23, wherein

The first exercise state value is exercise time, exercise step, minute per minute, interpolation, interpolation, head angle, ground support time, air float time, air support time to ground support time ratio, maximum vertical force, average vertical force load ratio, And at least one of a maximum vertical force load ratio, left and right balance, and left and right uniformity.
The method of claim 23, wherein

The first device is a band worn on the head and waist, a form attached to the head and waist in the form of a clip, a form provided in the hat, a belt attached form, glasses form, a helmet form, a form attached to the ear, a form attached to the garment And a form worn in a garment.
The method of claim 23, wherein

The glasses form is made of one of augmented reality glasses, glasses frames and sunglasses,

The form attached to the ear is made of one of a hands-free earpiece, headphones and earphones,

Wearing the garment is a form of the exercise recognition method of the first device, characterized in that made of one of a vest and a harness.
Receiving from the first device a first motion state value generated based on the triaxial acceleration value and the triaxial angular velocity value;

Measuring a user position value; And

And generating a second exercise state value based on at least one of the first exercise state value, the user position value, and the user profile.
The method of claim 38,

And transmitting at least one of the first exercise state value and the second exercise state value to a server.
The method of claim 38,

The first exercise state value is exercise time, exercise step, minute per minute, interpolation, interpolation, head angle, ground support time, air float time, air support time to ground support time ratio, maximum vertical force, average vertical force load ratio, And at least one of a maximum vertical force load ratio, left and right balance, and left and right uniformity.
The method of claim 38,

And the second exercise state value is at least one of distance, speed, calorie consumption, altitude, and stride length.
The method of claim 38,

And generating posture correction information by comparing at least one of the first exercise state value and the second exercise state value with each predetermined reference value.
The method of claim 38,

And outputting the posture correction information to at least one of a sound, a diagram, an image, and a vibration.
The method of claim 38,

The receiving of the first exercise state value from the first device may include receiving at least one of Bluetooth, Wi-Fi, and NFC.
A computer-readable recording medium having recorded thereon a program for performing the method according to any one of claims 23 to 44.