KR20190015851A - Motion sensing method and apparatus - Google Patents

Abstract

A method of motion recognition and a device therefor are disclosed. The device comprises: an acceleration sensor unit for measuring acceleration values of three axes including up and down, left and right, and front and rear directions; an angular velocity sensor unit for measuring angular velocity values of three axes including up and down, left and right, and front and rear directions; a processing unit for generating a first motion state value based on the three-axis direction acceleration values and the three-axis direction angular velocity values; and a user interface unit for controlling a sleep mode or an alive mode of the processing unit. According to the present invention, by using a characteristic analysis algorithm, the walking motion can be effectively and precisely recognized, detected, and analyzed.

Description

[0001] The present invention relates to a motion sensing method and apparatus,

The present invention relates to a motion recognition method and apparatus, and more particularly, to a motion recognition method and apparatus for collecting and analyzing user's walking and running motion data.

In general, it has been pointed out that the amount of exercise in daily life of modern people is insufficient to maintain appropriate physical health. Thus, there is a growing interest in a systematic exercise method that effectively promotes health. Specifically, there are various exercises such as exercise for systematically and efficiently speeding up the body, correction for posture for promoting health in the long-term perspective, and exercise suitable for the elderly with decreased physical ability as the human life is prolonged There is a growing interest in exercise. As one of the exercise methods that meet these various demands, there is a walking exercise that anyone can easily do.

Most people are walking unconsciously in a familiar position because anyone who has no physical problems can walk. However, because the human body is not perfectly symmetrical, in most cases the walking is performed in an unbalanced and incorrect position. Such a persistent gait can lead to skewing of the muscles and skeletal muscles, leading to various systemic pain. In the case of the general public, such a false walking posture causes a problem of deteriorating the physical health. Especially, the elderly who are growing up or suffering from physical abilities are more likely to suffer from body strain and health problems. On the other hand, professionals such as athletes and dancers who need more advanced physical abilities have problems such as limitations on improving their physical abilities.

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

According to the related art, a pressure sensor mounted on a shoe or a footrest is used in detecting a walk. However, according to the related art, if the user intends to perceive and analyze the walking posture, the pressure sensor may be damaged in the process of continuously receiving pressure for a long period of time. As a result, time and cost inconvenience . In addition, since the foot size of a person is largely varied depending on an individual, shoes having a pressure sensor are required to be produced in various sizes, resulting in a problem of a significant decrease in production efficiency and economy. In addition, there is a problem in that the family can not share a single pedometer, and each family member has to purchase the pedometer in accordance with the size of the pug so that the economic burden increases.

There is a growing demand for techniques for recognizing, detecting, and analyzing gait to effectively and precisely recognize, detect, and analyze gait in a manner other than a pressure sensor.

It is an object of the present invention to substantially obviate the various problems caused by the limitations and disadvantages of the related art, The present invention also provides a computer-readable recording medium storing a program for executing the method.

According to an embodiment of the present invention, there is provided a method of recognizing motion of a first device, comprising: measuring acceleration values in three axes including up and down, right and left, and back and forth by an acceleration sensor; Measuring three-axis angular velocity values including up and down, left and right, and front and rear by the angular velocity sensor unit; Generating a first motion state value based on the 3-axis direction acceleration value and the 3-axis direction angular velocity value by a processing unit; And controlling the sleep mode or the alive mode of the processing unit by the user interface unit.

According to an embodiment of the present invention, the method further comprises transmitting the first motion state value to a second device.

According to an embodiment of the present invention, the step of transmitting the first motion state value to the second device includes transmitting at least one of Bluetooth, Wi-Fi, and NFC.

According to an embodiment of the present invention, the method further comprises transmitting the first motion state value to the server.

According to an embodiment of the present invention, the method further comprises the step of measuring the user position value.

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

According to an embodiment of the present invention, the method further comprises transmitting at least one of the first motion state value and the second motion state value to the server.

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

According to an embodiment of the present invention, the method further comprises generating at least one of the first motion state value and the second motion state value and each predetermined reference value to generate the posture correction information.

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

According to an embodiment of the present invention, the step of measuring the three-axis direction acceleration value and the step of measuring the three-axis direction acceleration value may include the step of, when the processing unit is changed to the alive mode by the user interface unit, And generates the three-axis direction acceleration value and the three-axis direction angular velocity value based on a command from the second device or the user interface unit, respectively.

According to an embodiment of the present invention, the acceleration sensor unit and the angular velocity sensor unit store the three-axis direction acceleration value and the three-axis direction 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 an alive mode if the storage space of the first-in first-out queue is a predetermined threshold value or more.

According to an embodiment of the present invention, the first motion state value includes a motion time, an exercise step count, a minute compensation, an interpolation, a compensating angle, a head angle, a ground support time, an air floating time, Vertical force, average vertical force load rate, maximum vertical force load rate, left-right balance, and left-right uniformity.

According to an embodiment of the present invention, the first device may be a band worn on the head and the waist, a clip-on-the-head attachment to the head and the waist, a shape provided on the hat, A form attached to clothes, and a form worn as clothes.

According to an embodiment of the present invention, the glasses form one of an augmented reality glass, a spectacle frame, and a sunglass, and the shape attached to the ear is one of a hands-free earpiece, a headphone and an earphone, And a harness.

According to another embodiment of the present invention, there is provided a method of recognizing a motion of a second device, comprising: receiving from a first device a first motion state value generated based on a triaxial direction acceleration value and a triaxial direction angular velocity value; Measuring a user position value; And generating a second motion state value based on at least one value of the first motion state value, the user position value, and the user profile.

According to an embodiment of the present invention, the method further comprises transmitting at least one of the first motion state value and the second motion state value to the server.

According to an embodiment of the present invention, the first motion state value includes a motion time, an exercise step count, a minute compensation, an interpolation, a compensating angle, a head angle, a ground support time, an air floating time, Vertical force, average vertical force load rate, maximum vertical force load rate, left-right balance, and left-right uniformity.

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

According to an embodiment of the present invention, the method further comprises generating at least one of the first motion state value and the second motion state value and each predetermined reference value to generate the posture correction information.

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

According to an embodiment of the present invention, the step of receiving the first motion state value from the first device is received by at least one of Bluetooth, WiFi, and NFC.

According to another aspect of the present invention, there is provided a computer-readable recording medium having recorded thereon a program for performing the method.

According to an embodiment of the present invention, the motion recognition first apparatus includes an acceleration sensor unit for measuring three-axis acceleration values including up and down, left and right, and front and rear; An angular velocity sensor unit for measuring three-axis angular velocity values including up and down, left and right, and front and rear; A processing unit for generating a first motion state value based on the 3-axis direction acceleration value and the 3-axis direction angular velocity value; And a user interface unit for controlling the sleep mode or the alive mode of the processing unit.

According to another aspect of the present invention, there is provided a motion recognition apparatus including a first communication unit for receiving a first motion state value generated based on a 3-axis acceleration value and a 3-axis angular velocity value from a first device; A position sensor unit for measuring a user position value; And a processing unit for generating a second motion state value based on at least one value of the first motion 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 the acceleration, the position and the like at the user's body (e.g., the head side or the waist side) and converting it into the mass center motion state value, It can 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 body of the user, converting it into the mass center motion state value and estimating the pressure center path therefrom, Pedestrian recognition, detection and analysis can be performed accurately and precisely. Particularly, the present invention uses acceleration measured at a position most similar to the center-of-mass motion of the user's body (specifically, the acceleration in the left-right direction is measured at the head side and the acceleration and the position in the front- And the acceleration in the vertical direction is measured at the head side or the waist side), it is possible to measure the acceleration and the position more accurately.

In addition, according to the present invention, there is an advantage that only a sensor for measuring a user's dynamic physical quantity such as an acceleration sensor, a position sensor, etc. can be used in terms of device configuration. In other words, there have been various problems such as a problem of device durability and life span degradation by using a pressure sensor that recognizes walking by pressing by a user's foot, and problems of production and use of separate devices according to user's body dimensions. However, in the case of the present invention, since the technique of disposing the pressure sensor, which is the cause of such a problem, in the foot portion is completely excluded, various problems as described above are eliminated. From this, it is also possible to obtain effects such as improvement of user convenience and improvement of economic efficiency in each user or producer.

FIG. 1 shows the use states of the exercise posture deriving device according to an embodiment of the present invention.
FIG. 2 shows a schematic diagram of a motion posture deriving device according to an embodiment of the present invention.
FIG. 3 shows a flowchart of a method of deriving a movement attitude according to an embodiment of the present invention.
4 shows a relationship diagram between a center of mass and a center of pressure according to an embodiment of the present invention.
5 illustrates a pressure center direction determination and position analogy according to an embodiment of the present invention.
Figure 6 illustrates an example of an estimated pressure center path in accordance with one embodiment of the present invention.
FIG. 7 illustrates an example of a vertical acceleration graph with respect to time during walking and running according to an embodiment of the present invention.
FIG. 8 illustrates an example of acceleration signal measurement results according to an embodiment of the present invention.
9 shows a flowchart of a motion recognition method according to another embodiment of the present invention.
Figure 10 shows a detailed flowchart of the data acquisition and motion recognition steps according to another embodiment of the present invention.
FIG. 11 shows an example of an acceleration signal measurement result according to another embodiment of the present invention.
12 shows a detailed flowchart of deriving an acceleration-based motion state value according to another embodiment of the present invention.
FIG. 13 shows the use states of the exercise posture deriving device according to another embodiment of the present invention.
Fig. 14 shows a schematic view of the exercise posture deriving device according to another embodiment of the present invention.
15 shows a schematic diagram of an injury risk quantification device according to another embodiment of the present invention.
Figure 16 shows a flowchart of a method for quantifying an injury risk according to another embodiment of the present invention.
FIG. 17 is a graph showing a vertical acceleration graph in a running according to another embodiment of the present invention.
FIG. 18 shows a slope in a vertical acceleration graph when driving according to another embodiment of the present invention.
FIG. 19 shows the amount of impact in the vertical acceleration graph when driving according to another embodiment of the present invention.
20 shows a first embodiment of a motion recognition apparatus according to the present invention.
FIG. 21 shows a second embodiment of a motion recognition apparatus according to the present invention.
22 shows a flowchart of a motion recognition method according to another embodiment of the present invention.

Hereinafter, preferred 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 of explanation.

FIG. 1 shows the use states of the exercise posture deriving device according to an embodiment of the present invention.

The exercise posture derivation apparatus 100 according to an embodiment of the present invention is worn on a user's head as shown in Fig. As shown in FIG. 1, the exercise posture-deriving apparatus 100 according to the present embodiment may be provided in a band or a cap worn on the head, or may be formed into a shape in which the ear- , But it can be made in other forms.

FIG. 2 shows a schematic diagram of a motion posture deriving device according to an embodiment of the present invention.

The exercise posture derivation apparatus 100 according to the present embodiment includes a sensor signal collection unit 110 and a exercise posture derivation unit 121 as shown in FIG. The exercise posture derivation apparatus 100 according to the present embodiment further includes a motion correction generation unit 122 and a calibration information output unit 130. [

The sensor signal collecting unit 110 includes a three-axis direction acceleration sensor 111 including up and down, left and right, front and rear, and a position measuring sensor 112 for measuring a user position. The three-axis directional acceleration sensor 111 may be selected from among sensors used for measuring acceleration in three axial directions, such as a shape in which a gyroscope is built in. The position measurement sensor 112 is for measuring the absolute position of the user. For example, the position measurement sensor 112 may measure the position of the user using a GPS signal, or an ultra-precise GPS navigation technique with higher accuracy than the recent GPS has been developed A sensor to which such a technique is applied may be used. In addition, the sensor signal collecting unit 110 may further include a three-axis direction angular velocity sensor 113 as shown in FIG. 2 in order to increase the accuracy in the motion recognition and analysis process, which will be described in detail below.

The sensor signal collecting unit 110 according to this 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. Previously, a pressure sensor was used for shoes, footsteps, etc., which are pressed directly on foot for gait monitoring. As a result, the damage of the sensor occurs quickly and the durability and life of the device are shortened. In addition, there are problems such as poor pedestrian recognition and accuracy of analysis due to device damage during use, convenience and economical efficiency due to frequent device replacement. In addition, when such a device is provided in the shoe, a separate device is required for each user depending on the size of the user's foot, thereby decreasing convenience and economical efficiency of the user. In addition, And the like.

However, in the present embodiment, the concept of using the pressure pressed by the foot in the walking recognition is completely avoided and the dynamic physical quantity of the user such as the acceleration, the speed and the position measured at the user's head is measured as shown in Fig. Detection, and analysis of gait by applying the characteristic analysis algorithm of the present invention to be described. As described above, the present embodiment is different from the conventional art in the measurement position and the measurement physical quantity. At this time, the root cause of the various problems pointed out in the prior art comes from the technical arrangement that 'the pressure sensor is disposed at the foot part', and according to the present invention, the above- .

The motion posture deriving unit 121 receives a signal from the sensor signal collecting unit 110 and calculates a walking or running motion state value including an acceleration, a velocity, and a position of the center of the user's mass using the three-axis direction acceleration and the position signal And derives a walking or running posture by analyzing the walking or running motion state value. Specifically, the exercise posture deriving unit 121 estimates a pressure center path from the walking or running motion state value, and analyzes the pressure center path to derive a walking or running posture. The analysis algorithm of the present invention used in the exercise posture deriving unit 121 will be described later in detail, and a description thereof will be omitted.

Meanwhile, the exercise posture deriving unit 121 may be formed in the form of an integrated circuit capable of performing various calculations, and may be formed on the sensor signal collecting unit 110 and one substrate, or may be in the form of a separate computer or the like . 2, for signal transmission between the sensor signal collecting unit 110 and the exercise posture deriving unit 121, when the exercise posture deriving unit 121 is formed separately from the sensor signal collecting unit 110, The communication unit 114 may be provided. The communication unit 114 may be a wired or wireless communication. Wireless communication may utilize Bluetooth, Wi-Fi and NFC technology, but it is apparent to those skilled in the art that other wireless communication technologies may be used.

The exercise correction generating unit 122 compares the walking posture derived by the exercise posture deriving unit 121 with the reference posture to generate posture correcting information. The exercise posture deriving unit 121 derives the walking or running posture of the user on the basis of the signals collected by the sensor signal collecting unit 110 as described above. For example, From this, it is possible to obtain the stride, which is one of the elements of the walking postures. In this case, the exercise correction generating unit 122 incorporates the optimum key-stride relationship data for each walking and driving speed, and compares the walking posture information of the user with the key, It is possible to easily calculate the correction amount of the stride which should be reduced or increased when the deviation is out of the optimum range.

The calibration information output unit 130 converts the posture correction information generated by the motion correction generation unit 122 as information that can be recognized by the user including the sound, the picture, and the image, and outputs the information. For example, when it is necessary to reduce the stride width by calculating the stride correction amount, a sound such as "reduce stride length" is outputted through a speaker provided on the exercise posture deriving apparatus 100, It is possible to induce the user to change the walking posture, recognizing that the user is not the optimum stride. Or may be connected to a smart phone, a computer, or a dedicated display or the like so that accurate calibration information can be output from a picture or an image.

In addition, the exercise attitude deriving apparatus 100 may be configured to transmit the walking attitude derived by the exercise attitude deriving unit 121 to an external database 140 and cumulatively store the same. A user who needs such a walking or running motion analysis may be a general person who performs daily walking or jogging to promote health or an expert who is trained to improve physical ability. It is preferable that the change is made so as to be able to see the change. In addition, when a large amount of accumulated motion analysis data is accumulated, such data can be utilized as various types of statistical data or analysis, and thus can be used in various ways.

The method of deriving the exercise attitude according to an embodiment of the present invention performs an analysis such as detecting the user's exercise using the exercise attitude deriving apparatus 100 and determining whether the user is walking or running. As described above, the analysis algorithm used in the present invention uses the dynamic physical quantity measured at the user's head, and the motion posture deriving apparatus 100 calculates the three-axis direction acceleration including at least the up and down, left and right, A sensor 111 and a position measurement sensor 112 for measuring a user's position, and the analysis algorithm described below is performed in the exercise posture deriving unit 121. [ It will be appreciated that the exercise posture derivation apparatus 100 may further include the various additional components described above in order to improve the functions of the apparatus.

FIG. 3 shows a flowchart of a method of deriving a movement attitude according to an embodiment of the present invention. The method of deriving a motion posture according to the present embodiment includes a pressure center path estimation step, a motion type determination step, and a motion posture derivation step as shown in the figure. Each step will be described in more detail below.

In the pressure center path estimating step, using the motion state values of the user's mass center calculated using the three-axis direction acceleration (a _x , a _y , a _z ) collected by the motion posture deriving apparatus 100, Projection is performed on the ground in the direction of acceleration vector at the center position to estimate the pressure center path.

The three-axis direction acceleration sensor 111 can be used to collect accelerations in three axes in the left, right, front, rear, and upper directions in the exercise posture derivation apparatus 100 and can be integrated using the three-axis direction acceleration sensor 111, The speed, position, etc. can be obtained by using the position information. In general, when analyzing the motion of an object, the motion analyzer 100 analyzes the motion of the object based on the motion of the center of mass of the object. Since the motion-posture derivation apparatus 100 is provided in the head of the user, It is preferable to convert it into a motion state value and analyze it. The conversion of the measured value at the user's head position into the value at the center of the user's mass can be easily derived by appropriately multiplying the previously obtained gain value using the body information such as the user key information.

Thus, by deriving the motion state values of the center of mass (acceleration per hour / velocity / position, frequency analysis for each direction, etc.), the pressure center path can be estimated from this. The human body behaves by using the reaction pressure applied to the supporting foot during walking or running. 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 force generated at this time has a characteristic of being directed to the center of mass (COM) from the pressure center.

4 shows a relationship diagram between a center of mass and a center of pressure according to an embodiment of the present invention.

In the present invention, this biomechanical property is reversely used to project the center of the pressure on the ground in the vector direction of the force measured at the center of mass.

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

The pressure center direction is the direction from the center of mass to the pressure center. The pressure center path estimating step may be performed to first determine the direction of the pressure center, and project it in this direction to infer the pressure center position. 5, the ratio of the vertical acceleration a _z and the gravity acceleration g to the sum of the lateral acceleration a _x and the vertical acceleration? the direction of the pressure center is determined by the ratio of the forward and backward acceleration (a _y ) to the sum of the acceleration (a _z ) and the gravity acceleration (g). If the direction of the pressure center is determined as described above, then, in the pressure center position analogy step, it is assumed that the center of mass is located at a height determined by multiplying pre-measured user key information by a predetermined constant for analogy, And the pressure center position is inferred by projecting on the ground in a direction determined in the direction determination step. Here, the constant for inference is the height of the center of mass according to the user's key. It is well known that the mass center of a child is generally higher in proportion to the mass center of an adult, and that the center of mass of a man is proportionately higher than the center of mass of a woman. For example, it is known that the center of mass of an adult male is located at an average of 55.27% of the height. In this case, the analogy constant is 0.5527. Thus, for example, by inputting the child / adult and male / female classification information when inputting the user key information, a suitable constellation for inference 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 in which the pressure center position inferred in the pressure center position inference step is corrected to a value obtained by multiplying a predetermined constant for forward and backward and lateral correction . Here, the constants for the back and forth and left and right directions are constants that can statistically match the pressure center position obtained by the above projection method with the actual front and rear and left and right pressure centers.

In the motion type determination step, it is determined whether the vehicle is walking or running from the pattern of the vertical acceleration (a _z ) graph.

6 is an example of a landing pattern obtained as an estimated pressure center path. As shown in the figure, the left and right feet are alternately supported while supporting the ground surface.

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

Fig. 7 shows an example of a vertical acceleration graph with respect to time during walking and running. In the case of the graph at the time of walking shown in FIG. 7 (A), it can be seen that peaks occur when both feet touch the ground. In the case of the graph at the time of running shown in FIG. 7 (B) It can be confirmed that there is a constant value interval in which the vertical acceleration (a _z ) is the minimum value. In this way, it is possible to judge whether the motion of the current user is a walking or running by using the fact that the patterns of the graph of the vertical acceleration (a _z ) are different from each other at the time of walking and running.

In the exercise attitude deriving step, attitude information including stride, interpolation, angle of repose, and asymmetry are derived based on the pressure center path estimated value and the three-axis direction acceleration (a _x , a _y , a _z ). The pressure center path example of Fig. 6, and the vertical acceleration example of the walking or running of Fig. 7 will be described in more detail.

First of all, it is explained above that the user's movements appear to be slightly different when walking or driving. As described above, in the case of walking, one foot or both feet always touch the ground. In the case of driving, one foot or both feet always float from the ground. That is, there is a section that is supported only by one foot when walking and traveling. In consideration of these points, in the step of deriving the exercise attitude, an intermediate supporting time determining step of determining an intermediate supporting time point and an interval classification determining step of determining a sole supporting period, a pair of supporting period, The basic information for deriving the posture is formed while distinguishing the walking and the running.

First, let 's describe the walking movement as follows. First, at the moment when the heel of one foot touches the ground, the toe of the other foot starts with the state where the foot is not dropped from the ground. In this state, the one foot supports the ground surface while the other foot comes off the ground, and the other foot moves forward as the foot sweeps through the air, and the human body also moves forward. And at the moment when the heel of this other foot touches the ground, the foot of one foot does not fall off from the ground, that is, the condition that the feet are supported is restored, and one step is performed. In this process, when the human body is moved forward while being supported only by one foot, the head of the person is not largely shaken up and down (a local minimum is formed at the vertical acceleration (a _z )), (The peak value is formed at the vertical acceleration (a _z )).

In other words, the walking movement can be divided into a section in which both feet are in a state of standing on the ground, and a section in which one foot is in a state of standing on the ground, and the fluctuation in the upward and downward directions is least when one foot is on the ground. As this is the movement pattern is also well shown in 7 (A) are shown in this example, the intermediate support point determination step, if the user's movement gait one of the vertical direction acceleration measurements in the time domain (a _z) locally on The minimum is defined as the intermediate support point. Also, in the section classification decision step, when the user's motion is a gait, the period in which the peak value is formed in the up-down acceleration (a _z ) measured in the time domain is determined as the bipod support period and the remaining period is determined as the support period.

Next, let 's describe the driving movement as follows. First, one foot from the front begins with the moment when the floor spurs (the other foot is floating in the air). In this state, one foot rises from the floor, and both feet float in the air, and the body moves forward. In addition, the feet swing through the air and the front and rear are changed, and the other foot comes forward. The other side of the foot comes into contact with the ground, and at the same time the moment of spurting the ground is done again, and a stepping motion is made. In this process, the human head is swung most vertically at the moment when the ground is sprung from one foot (the local maxima are formed at the vertical acceleration (a _z )), while in the state of floating in the air, (A constant value is formed at the vertical acceleration (a _z )).

In other words, the running exercise can be divided into a section in which both feet float in the air, and a section in which one foot is standing on the ground, and the fluctuation in the up and down directions is least when both feet are floating in the air. 7 (B). As shown in this example, in the intermediate support timing determination step, when the user's motion is traveling, the vertical acceleration (a _z ) Maxima is defined as the intermediate support point. Also, in the section classification decision step, when the user's motion is traveling, the section which is represented by a constant value in the vertical acceleration (a _z ) measured in the time domain is determined as a floating section, and the remaining section is determined as a pair support section. Here, the constant value appearing in the air floating period is a predetermined value of the signal level level when the accelerometer has no external force other than gravity, and can be appropriately determined to be a value close to approximately zero. In other words, the constant value is a reference value that makes it possible to determine the current stance. In this sense, it can be called a stance phase constant. In summary, when the up-down acceleration is smaller than the stance determination constant during running, And if it is large, it is discriminated as a pair of support sections.

When the basic information for the attitude derivation is derived as described above, it becomes possible to obtain the walking or posture of the stride such as the stride, the interpolation, the angle of inclination, the asymmetry, or the like.

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

Interpolation: The interpolation in the left and right direction can be calculated using the pressure center position value corresponding to the intermediate support point. That is, the time value corresponding to the intermediate support time shown in FIG. 7 (A) or (B) is applied to the pressure center position value shown in FIG. 6 and if the pressure center position corresponding to this time value is found, Position and the position where the right foot is positioned on the ground are extracted, and the interpolation of the user can be accurately calculated by taking the interval between them.

The angle of inclination can be calculated using the pressure center position value corresponding to the starting point of the pair of support sections and the pressure center position value corresponding to the end point of the pair of support sections. To explain the problem, the heel is placed on the ground at the start of a pair of support segments, and the toe of the foot is considered at the end of the pair of support segments.

That is, as described above, the angle between the pressure center positions is obtained by obtaining the angle formed by the heel position and the toe position at the moment when the ground surface is tilted, that is, by this method, the user's bending angle can be accurately calculated .

Left asymmetry: First, the foot supporting the left and right acceleration (a _x ) measured in the time domain is identified. Next, the peak value, the bone value, and the difference between the two values of the vertical acceleration (a _z ) measured in the time domain are compared. That is, by comparing the peak value, the bone value, and the like when the left foot is supported and when the right foot is supported, it is possible to accurately calculate the left-right asymmetry of the user. Also, the repeatability of walking or running can be calculated in the same manner.

FIG. 8 shows an example of an acceleration signal measurement result. In the bottom graph of FIG. 8, the left-right asymmetry of the vertical acceleration (a _z ) is strongly shown.

It is possible to monitor in real time whether the user is walking or running in the correct posture by deriving the walking or running posture such as stride, interpolation, angle of inclination, asymmetry, or the like in the manner as described above. Of course, it is possible to store the stride, interpolation, angle of inclination, and asymmetry value corresponding to the optimum posture at this time, and calculate the correction amount by comparing with the current posture values currently being monitored. By informing the user in real time or by storing it in advance and checking it later, the user can correct his / her posture by walking or running in a more correct posture.

FIG. 9 shows a flowchart of a motion recognition method for walking and running monitoring according to another embodiment of the present invention.

The motion recognition method according to the present embodiment is roughly divided into two steps: a data acquisition and a motion recognition step of collecting and analyzing the three-axis direction acceleration (a _x , a _y , a _z ) , And an acceleration-based motion state value derivation step of calculating the motion state values of the user mass center using the collected three-axis direction acceleration (a _x , a _y , a _z ). The detailed steps of each step will be described in more detail below.

Figure 10 shows a detailed flowchart of the data acquisition and motion recognition steps according to another embodiment of the present invention.

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 three-axis direction acceleration collection step, a Fourier transform step, and a motion shape determination step. The data collection and motion recognition step recognizes whether motion is occurring in the user, and if motion occurs, whether the motion is a walking or running.

As shown in FIG. 10, initially, the data variables to be collected are initialized and ready to perform motion recognition.

In the vertical acceleration collecting step, the three-axis direction acceleration (a _x , a _y , a _z ) are not all collected but the up-down direction acceleration (a _z ) is once collected. Although the collected vertical acceleration (a _z ) may be used as it is, it is more preferable to pass through a predetermined band-pass filter to carry out a noise removing step of removing noise. At this time, the band-pass filter may be formed at a frequency of 0.1 to 5 Hz corresponding to, for example, a walking or running frequency of a general person, but it is apparent to those skilled in the art that the range may be appropriately changed.

The peak detection step detects the peak of the vertical acceleration (a _z ) thus collected. In the motion detection step, it is determined whether or not the peak value of the vertical acceleration (a _z ) is equal to or greater than a predetermined threshold value, It is judged whether or not it has occurred. If it is determined that the motion has not occurred in the motion detection step, the process returns to the initial preparation step to initialize the variable.

In other words, when performing the analysis in the motion recognition apparatus 100, when the three-axis direction acceleration (a _x , a _y , a _z ) is always collected, unnecessary calculation load occurs when the motion is not performed. , Heat generation and the like may occur. On the other hand, in the case where the user moves as much as the user sits down or turns his or her body, and when the user moves to the extent of walking or running, the largest difference is the degree that the user swings up and down, that is, the vertical acceleration (a _z ). Accordingly, the up-down acceleration (a _z ) of the user who wears the motion recognition apparatus 100 is collected first, and when the value exceeds a certain threshold value, the user is determined to walk or run, and then the full- , It is possible to prevent the unnecessary calculation load problem described above.

In the three-axis direction acceleration collecting step, the three-axis direction acceleration (a _x , a _y , a _z ) is collected when the up-down acceleration (a _z ) peak value is equal to or greater than a predetermined threshold value as described above. Likewise, the collected three-axis directional acceleration (a _x , a _y , a _z ) may be used as it is, but it is more preferable to pass through a predetermined band-pass filter to carry out a noise removing step of removing noise. The band-pass filter at this time may be formed in the same manner as the band-pass filter used for removing the up-down acceleration (a _z ) noise, or may be appropriately changed or set.

In the Fourier transform step, the three-axis direction acceleration (a _x , a _y , a _z ) is Fourier-transformed to derive a frequency response graph. In the motion shape determination step, It is judged whether or not it is the walking and running movement or the other exercise. If it is determined that the exercise corresponding to the walking and driving movements has not occurred in the exercise type determination step, the variable is initialized again to the initial preparation stage. If it is determined that the walking and driving movements have occurred, Step is performed.

As described above, in the exercise type determination step, it is determined whether the user's exercise is in the walking and running state.

11 shows an example of an acceleration signal measurement result at the time of walking according to another embodiment of the present invention. 11, there is shown a time-domain acceleration graph in which the abscissa is the three-axis direction acceleration (a _x , a _y , a _z ) of time and the vertical axis are the left and right (x), the front and back (y) And the right side shows the frequency response graph derived through the Fourier transform step in which the horizontal axis indicates the frequency and the vertical axis indicates the size as described above.

During the walking or running, of course, periodically oscillating in the up-and-down direction, the back-and-forth direction, and the left and right direction, that is, a periodic signal is generated as shown on the left side of FIG. In this case, since the walking or running is performed while the left and right feet are alternately taken, the frequencies of the periodic signals in the left and right directions are 1/2 of the frequencies of the periodic signals in the up and down direction and the back and forth direction, It can be easily seen from the graph on the right. On the other hand, one foot or both feet always touch the ground at the time of walking, and one or both feet float from the ground at the time of driving. That is, when walking or running, a large fluctuation in the head necessarily occurs periodically.

In consideration of the above-described points, the exercise form determination step is performed to determine the user's exercise as the walking and running state if the following formula is satisfied, or otherwise. That is, the following relational expression can be easily solved. If the degree of shaking periodically in the up-and-down direction and the left-right direction becomes larger than a certain level, it is judged that the vehicle is walking or running.

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

(From here,

a _z : vertical acceleration,

f _p : frequency having the maximum magnitude in the Fourier transform result of the vertical acceleration a _z ,

M _{z, p} : An energy sum of frequency components belonging to a vertical reference band having a bandwidth of less than 1 Hz with f _p as a center frequency in the Fourier transform result of the vertical acceleration a _z ,

M _{z, other} : A total sum of the remaining frequency components excluding the up-and-down direction reference band from the Fourier transform result of the vertical acceleration a _z ,

c _z : predetermined vertical reference threshold value,

a _x : lateral acceleration,

M _{x, p} : energy sum of frequency components belonging to the left and right direction reference bands having f _p / 2 as the center frequency and having a bandwidth of less than 1 Hz in the Fourier transform result of the lateral acceleration a _x ,

M _{x, other} : A total sum of the remaining frequency components excluding the left and right direction reference bands from the Fourier transform results of the lateral acceleration a _x ,

c _x : predetermined lateral reference threshold)

When the above equation is satisfied, it is judged that the user's motion is a walking or running state, and it is now determined whether the exercise is a walking or running. At this time, as described above, one foot or both feet always touch the ground at the time of walking, and one or both feet float from the ground at the time of traveling. Here, at the time when both feet are floating from the ground during traveling, since both feet stir the air, there is no external force applied to the user in the vertical direction. Therefore, in this case, the vertical acceleration (a _z ) .

In consideration of the above-described points, the motion type determination step determines that the user's motion is in a running state if a section satisfying the following expression exists in the Fourier transform result of the vertical acceleration (a _z ) State.

a _z <k

(From here,

k: stance discrimination constant)

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

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

12 shows a detailed flowchart of deriving an acceleration-based motion state value according to another embodiment of the present invention.

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

In the center-of-gravity acceleration deriving step, the acceleration of the center of the user mass is derived by multiplying the respective values of the three-axis direction acceleration a _x , a _y , and 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 of the variable values used in the analysis are measured at the user's head, Conversion. These gain values may be represented by a constant vector gamma, and can be obtained in advance using body information such as user key information.

In the deriving of the center-of-mass velocity and the position, velocity and position of the center of the user's mass are derived using previously measured user key information, user position information, and center-of-mass acceleration. In other words, the velocity and position of the mass center (obtained by adding the integral constant) can be obtained by integrating the center-of-mass acceleration obtained as described above, or by using the position information measured by the position- , The position can be obtained. There is an error between the two calculated values as much as the integral constant value, and the velocity and position value of the accurate mass center can be obtained by appropriately comparing them.

As described above, according to the present invention, it is possible to accurately determine whether the user is walking or running using the acceleration, the position, and the like measured at the user's head. Also, You can get an accurate picture of how you are doing (ie how the acceleration, velocity, and position of the center of mass appears). Therefore, based on this, various elements of the walking or running posture can be derived and used for the correction of the posture.

FIG. 13 shows the use states of the exercise posture deriving device according to another embodiment of the present invention.

The exercise posture derivation apparatus 1300 according to the present embodiment is configured to be worn on the user's body, more specifically, the head and the waist as shown in Fig. 13, the head side sensor signal collecting unit 1310H, which is worn on the head side, is configured to be plugged into the ear like an earphone, and the waist side sensor signal collecting unit 1310H, Side sensor signal collecting unit 1310W, which is worn on the side of the waist-side sensor signal collecting unit 1310W, However, the present invention is not limited thereto. For example, the head-side sensor signal collecting unit 1310H may be modified in various forms such as a hair band type, a glasses type, a separate cap attached to a cap, Of course.

Fig. 14 shows a schematic view of the exercise posture deriving device according to another embodiment of the present invention.

14, the exercise posture derivation apparatus 1300 according to another embodiment of the present invention includes a head side sensor signal collecting unit 1310H, a waist side sensor signal collecting unit 1310W, (1421). The exercise posture derivation apparatus 1300 further includes a motion correction generation unit 1422 and a calibration information output unit 1430. [

The head side sensor signal collecting unit 1310H includes a head side three axis direction acceleration sensor 1411H including up and down, left and right and front and rear. The waist side sensor signal collecting unit 1310W includes up, Side three-axis directional acceleration sensor 1411W including front and rear, and a position measurement sensor 1412W for measuring a user position.

The head-side and waist-side three-axis direction acceleration sensors 1411H and 1411W can be selected from among sensors used for measuring accelerations in three axial directions, such as a shape in which a gyroscope is built in. The position measuring sensor 1412W is for measuring the absolute position of the user and may be configured to measure the position of the user using, for example, a GPS signal, or a high precision satellite navigation technique with higher accuracy than recent GPS has been developed A sensor to which such a technique is applied may be used. In addition, the head-side and waist-side sensor signal collectors 1310H and 1310H collect signals from the three-axis direction angular velocity sensor 1412H (1312H), as shown in FIG. 14, so as to increase accuracy in the motion recognition and analysis process, ). &Lt; / RTI &gt;

13, the head side and waist side sensor signal collecting units 1310H, 1310W in the exercise posture deriving apparatus 1300 according to the present embodiment are worn on the head and waist of the user to measure acceleration, And the like. Particularly, in this embodiment, when measuring the dynamic physical quantity of the user for attitude determination, the measured value is used at the position most similar to the motion of the center of mass of the user's body. For example, in the present invention, the acceleration in the left-right direction is measured at the head side, the acceleration and the position in the front-rear direction 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 for the acceleration in the up-and-down direction, the acceleration in the up-and-down direction is fairly accurate when measured from either the head or waist side, so that the measured values at either the head or waist side are selectively used Or an average value of the values measured on both sides may be used, or the like.

In other words, in general, the acceleration sensor is configured to measure both acceleration in the vertical direction, in the left-right direction, in the front-rear direction and in the three-axis direction. Therefore, the acceleration sensor can collect both acceleration signals in the head sensor signal collector 1310H alone or in the waist sensor signal collector 1310W It will be appreciated that various calculations to be described later may be performed using the up, down, left, right, and backward accelerations. However, when walking and running are performed, the left and right movements in the head relatively and the left and right movements in the center of the mass of the user are more similar, and the relative movement between the back and forth in the waist and the back and forth movement of the center of mass of the user body are more similar . In addition, up and down movements are similar in both head, waist, and mass centers. On the other hand, in the motion recognition method of the present invention to be described later, ultimately, motion recognition or attitude derivation is performed using dynamic physical quantities at the center of mass of the user's body. Taking all these into consideration, it is possible to measure acceleration in the lateral direction from the head side, measure the longitudinal acceleration in the waist side, measure the vertical acceleration by appropriately selecting the head side or the waist side, And calculating the vertical acceleration as a value obtained by subtracting the average value, the final motion recognition and posture derivation can be obtained with much more accuracy.

The exercise posture deriving unit 1421 receives signals from the head and waist side sensor signal collecting units 1310H and 1310W and uses the three axis direction acceleration and position signals to calculate the acceleration, And derives a walking or running state value by analyzing the walking or running state value. Specifically, the exercise posture deriving unit 1421 estimates a pressure center path from the walking or running motion state value, and analyzes the pressure center path to derive a walking or running posture. Since the analysis algorithm of the present invention used in the exercise posture deriving unit 1421 will be described in more detail later, a description thereof will be omitted here.

On the other hand, the exercise posture deriving unit 1421 may be formed on an integrated circuit with the waist side sensor signal collecting unit 1310W in the form of an integrated circuit capable of performing various calculations, or may be formed as a separate computer . In addition, the head side communication unit 1413H and the waist side communication unit 1413W may be provided to the head side and the waist side sensor signal collectors 1310H, 1310W for signal transmission to the exercise posture deriving unit 1421, respectively . When the waist side sensor signal collecting unit 1310W is integrally formed with the exercise posture deriving unit 1421, the waist side communication unit 1310W can be directly connected to the exercise posture deriving unit 1421 to transmit signals, or The waist side communication unit 1413W may receive a signal transmitted from the head side communication unit 1413H and transmit the signal to the exercise posture deriving unit 1421. [ The head and waist side communication units 1413H and 1413W may be wired or may be configured to transmit signals using at least one wireless communication selected from among Bluetooth, Wi-Fi, and NFC to further enhance user convenience .

The motion correction generating unit 1422 compares the walking posture derived by the exercise posture deriving unit 1421 with the reference posture to generate posture correcting information. The exercise posture deriving unit 1421 derives the walking or running posture of the user on the basis of the signals collected by the head and waist side sensor signal collecting units 1310H and 1310W as described above. It is possible to derive the traveling direction and the speed of the user at the time of driving, thereby obtaining the stride, which is one of the elements of the walking posture. In this case, the exercise correction generating unit 1422 embeds the optimal key-stride relationship data for each walking and driving speed, compares the walking posture information of the user with the key, It is possible to easily calculate the correction amount of the stride that should be reduced or increased when the deviation from the optimum range is determined.

The calibration information output unit 1430 converts the posture correction information generated by the motion correction generation unit 1422 as information that can be recognized by the user including the sound, the picture, and the image, and outputs the information. For example, when it is necessary to reduce the stride width by calculating the stride correction amount, a sound such as "reduce stride length" is output through a speaker provided on the exercise posture deriving apparatus 1300, It is possible to induce the user to change the walking posture, recognizing that the user is not the optimum stride. In particular, in such a configuration, the calibration information output unit 1430 is integrally formed with the head-side sensor signal collecting unit 1310H, and is preferably arranged so as to be disposed close to the user's information collecting institution, that is, Do. More specifically, if the head sensor signal collecting unit 1310H has an earphone-type earphone as shown in the example of FIG. 13 and the output information type is voice or acoustic signal, the efficiency of transmitting the calibration information is maximized . Or may be connected to a smart phone, a computer, or a dedicated display or the like so that accurate calibration information can be output from a picture or an image.

In addition, the exercise posture derivation apparatus 1300 may be configured to transmit the walking posture derived by the exercise posture derivation unit 1421 to an external database 1440 so as to accumulatively store the posture. A user who needs such a walking or running motion analysis may be a general person who performs daily walking or jogging to promote health or an expert who is trained to improve physical ability. It is preferable that the change is made so as to be able to see the change. In addition, when a large amount of accumulated motion analysis data is accumulated, such data can be utilized as various types of statistical data or analysis, and thus can be used in various ways.

Hereinafter, specific and comprehensive examples of the exercise attitude deriving apparatus 1300 according to the present embodiment, which is performed as described above, include the steps of recognizing the motion, deriving the motion attitude, and outputting the calibration information. First, as described above, the acceleration in the left and right direction is collected from the head side so as to resemble the motion in the center of mass of the user's body. The back and forth acceleration and position are collected at the waist side and the vertical acceleration is collected at the head side or waist side .

The waist side sensor signal collecting unit 1310W may be integrated with the exercise posture deriving unit 1421 so that the physical quantities collected by the head side sensor signal collecting unit 1310H may be derived from the exercise attitude through the head side communication unit 1413H And is transmitted to the part 1421 side. In this case, it is more efficient that the waist side communication unit 1413W provided in the waist side sensor signal collecting unit 1310W receives the signal and transmits it to the exercise posture deriving unit 1421. [

Using the physical quantities such as acceleration and position thus collected, the exercise posture deriving unit 1421 derives the user's walking and running posture. The motion correction generating unit 1422 compares the actual prize thus obtained with an ideal reference posture to generate posture correction information. The exercise posture deriving unit 1421 and the exercise correction generating unit 1422 can also be integrally formed, that is, they are integrated with the waist side sensor signal collecting unit 1310W.

In order for the generated posture correction information to be effectively transmitted to the user, it is preferable that the information is transmitted from the head side close to the eyes, ears, or the like, which is the information collecting institution of the user. When the head side sensor signal collecting unit 1310H and the calibration information output unit 1430 are integrally formed as described above, the generated posture correcting information is transmitted to the waist side communication unit 1413W and the head side communication unit 1413H sequentially And transmitted to the calibration information output unit 1430, so that the calibration information is effectively transmitted in the form of transmitting a voice message to the user's ear or the like.

The method for deriving the exercise attitude according to another embodiment of the present invention performs an analysis such as detecting the user's motion using the exercise attitude deriving device 1300 and determining whether the user is walking or running. As described above, the analysis algorithm used in the present invention uses dynamic physical quantities measured at the head and waist of the user, and the exercise posture deriving device 1300 includes at least the head including the up and down, left and right, Side three-axis directional acceleration sensor 1411H, a waist-side three-axis directional acceleration sensor 1411W including up and down, left and right, front and rear, and a position measurement sensor 1412W for measuring a user's position, An analysis algorithm to be described below may be performed in the derivation unit 1421. [ In addition, the exercise posture derivation apparatus 1300 may further include various additional configurations described above for improving the function of the apparatus.

15 shows a schematic diagram of an injury risk quantification device according to another embodiment of the present invention.

The injury risk quantification device 1500 according to the present embodiment is a device that notifies a user of the risk of injury that may occur during walking or running. More specifically, it is as follows. It is well known that when walking or running, there are various reasons such as a wrong posture, a hard floor, and the like, which can lead to ankle, knee, and back and lead to injury. In order to prevent such danger, conventionally, there is no such thing as wearing a functional sneaker such as a shock absorber, and there is no accurate index to know how much injury risk actually occurs. In the present embodiment, such an injury risk is quantified as a judgment index, and when the injury risk increases to a certain level or more, the user is informed of the danger level as an alarm. This allows the user to take appropriate precautions such as stopping the walking or running, calibrating the posture, replacing the sneakers, changing the walking or running course, etc., before the injury occurs, and ultimately occurring during walking or running It is possible to greatly reduce the risk of injury.

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

The sensor signal collecting unit 1510 includes an acceleration sensor 1511 and is worn on the upper body except the user's arm. The sensor signal collecting unit 1510 may be single or plural. The sensor signal collecting unit 1510 may be formed of two pieces and may be worn on each of the user's head and waist. In this case, the sensor signal collecting unit worn on the user's head side is referred to as a head side sensor signal collecting unit 1510H, The sensor signal collecting unit worn on the side of the waist side sensor signal collecting unit 1510W. As a specific example of the wearing state, a head side sensor signal collecting part 1510H worn on the head side is formed into a shape like an earphone, and a waist side sensor signal collecting part 1510W worn on the waist side is attached to a belt . &Lt; / RTI &gt; However, the present invention is not limited thereto. For example, the head-side sensor signal collecting unit 1510H may be modified in various forms such as a hair band shape, a glasses shape, Of course. Although not shown in the drawing, the sensor signal collecting unit 1510 can be worn anywhere on the upper body except the user's arm. For example, when the sensor signal collecting unit 1510 is worn on the chest, A form to be worn using a separate vest or harness, or the like.

The sensor signal collection unit 1510 includes the acceleration sensor 1511 as described above. The acceleration sensor 1511 may be selected from among sensors used for measuring acceleration in three axial directions, such as a shape in which a gyroscope is embedded. Meanwhile, the controller 1520 may be provided directly to the sensor signal collecting unit 1510 to perform calculation and control using the acceleration data signal collected by the acceleration sensor 1511. Alternatively, the control unit 1520 can be implemented in a form of an app on an existing smart phone, and the like. That is, when the control unit 1520 is implemented as a separate device from the sensor signal collecting unit 1510, the acceleration data signal collected by the acceleration sensor 1511 can be transmitted smoothly to the controller 1520, The collecting unit 1510 may further include a communication unit 1512. Such signal transmission may be performed by wire communication through wiring, wireless communication such as Bluetooth, Wi-Fi, NFC, or the like, and may be appropriately selected depending on the required conditions and required performance .

As will be described later in detail with reference to the description of the flood hazard quantification method according to the present embodiment, in this embodiment, vertical acceleration is used in determining the risk of injury.

In this embodiment, the vertical acceleration is used to quantify the risk of injury. In general, when the vehicle is running, the left and right movements in the head relatively and the left and right movements in the center of the mass of the user are more similar, and the back and forth movements in the waist and the back and forth movements in the center of mass of the user body are more similar appear. In addition, the up and down movements are similar in both upper body and mass center including head to waist. In addition, the arms are excluded from the upper body because the arm moves separately in the fore and aft direction in addition to the motion of the center of mass. Considering this point, the acceleration in the up-and-down direction may be measured at any of the upper bodies except the arms. In other words, the acceleration in the up-and-down direction appears fairly accurately even when measured on the upper body except for the arm, so that the measured values may be selectively used on either the head side or the waist side, The average value may be used.

The control unit 1520 receives the signal from the sensor signal collecting unit 1510 and derives at least one flotation risk judgment index calculated on the basis of the vertical acceleration a _z and uses the flotation risk judgment index And to judge and control whether an alarm is generated or not. 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) Thereby quantifying the risk of injury and determining the degree of risk. The flood risk determination index calculation performed by the controller 1520 will be described in more detail with reference to the flood risk quantification method according to the present embodiment.

Actual implementations of the controller 1520 may be variously configured according to needs or purposes. That is, the controller 1520 may be formed as an integrated circuit capable of performing various calculations and formed integrally with the sensor signal collector 1510 on one substrate, or may be formed as a separate dedicated device (i.e., Or a separate computer, or may be implemented in the form of an app on a smart phone that has been used as described above. As described above, when the control unit 1520 is formed integrally with the sensor signal collecting unit 1510, the signal may be directly received from the acceleration sensor 1511. On the other hand, when the control unit 1520 is formed independently of the sensor signal collecting unit 1510 such as a separate device or a smartphone app, the signal is received from the acceleration sensor 1511 by wire or wireless communication .

The alarm unit 1530 receives an alarm generation control signal from the controller 1520 and alerts the user of the risk of injury. The control unit 1520 derives at least one flushing hazard judgment index calculated on the basis of the vertical acceleration a _z and determines whether or not an alarm is generated. If it is determined that the flushing hazard is greater than a predetermined reference, the alarm unit 1530 So that the user is informed of the danger.

The alarm unit 1530 outputs alarm signals as user-recognizable information including sounds, diagrams, and images. For example, when the alarm unit 1530 is configured as a speaker for outputting sound, a warning sound may be generated when the risk of injury is higher than the reference level. Or when the apparatus according to the present embodiment is applied to the augmented reality glasses such as Google Glass, the warning unit 1530 outputs a red warning figure or an image in which such a figure blinks on the augmented reality glasses, "And so on. Or the alarm unit 1530 is implemented as a thermoelectric element and is in direct or indirect contact with the user's skin. If the risk of injury is higher than the criterion, the user may be alarmed by being cold or hot. As another example, the alarm unit 1530 may be configured to be recognized as a changeable braille type by a tactile sense for the case where the user is a blind person. As such, the alarm unit may be configured in any form as long as it can output an alarm signal as information recognizable by a user.

In addition, the injury risk quantification device 1500 may be configured to transmit the injury risk data including the time of occurrence of the injury risk alert and the injury risk determination index value at the time point to the external database 1540 and accumulatively store the injury risk data. A user who needs such a walking or running motion analysis may be a general person who performs daily walking or jogging to promote health or an expert who is trained to improve physical ability. It is preferable that the change is made so as to be able to see the change. In addition, when a large amount of accumulated motion analysis data is accumulated, such data can be utilized as various types of statistical data or analysis, and thus can be used in various ways.

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

The flood risk quantification method according to the present embodiment includes the acceleration sensor 1511 as described above, and the at least one sensor signal collection unit 1510, which is worn on the upper body excluding the user's arm, The directional acceleration (a _z ) is used to quantify the risk of injury by deriving an indicator of injury risk. 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, a noise removal step is further included to increase the accuracy of the derivation of the injury risk judgment index. Each step shown in FIG. 16 will be described in more detail as follows.

In the data collecting step, the vertical acceleration a _z measured by the sensor signal collecting unit 1510 is collected. Although the collected vertical acceleration (a _z ) may be used as it is, it is more preferable to pass through a predetermined band-pass filter to carry out a noise removing step of removing noise. At this time, the band-pass filter may be formed at a frequency of 0.1 to 5 Hz corresponding to, for example, a walking or running frequency of a general person. However, the range may be appropriately changed.

In the determination indicator derivation step, at least one of the injury risk determination indexes 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 index will be described in detail later.

In the flood risk determination step, it is determined whether the flood risk determination index is greater than a predetermined criterion. In this case, the above-mentioned injury risk determination index may be plural as described above. An alarm may be generated when any one of the plurality of determination indices is more than the reference, an alarm may be generated when all of the plurality of determination indices are above the reference, It is also possible to generate an alarm step by step. In the flood risk determination step, if the flood risk determination index is smaller than the predetermined criterion, the alarm data is not generated and the data collection step is returned to the data collection step. In addition, it is preferable that, in the above-mentioned flood risk determination step, determination is made using the above-mentioned flood risk determination index calculated by collecting data of at least two periods with respect to up-down acceleration (a _z ) data appearing as a periodic signal .

If the at least one of the in fl at risk indicators is greater than a predetermined criterion, the user is warned of the risk of injury. As described above, the warning type of injury risk can be various forms such as sound, illustration, and image, and the user can take actions to reduce the risk of injury by actively receiving such an alert (end of exercise, correction of posture, Etc.) can greatly reduce the risk of injury.

Hereinafter, various examples of the injury risk determination indexes used in the present invention and the process of deriving the indexes will be described in more detail.

FIG. 17 is a graph showing a vertical acceleration graph in a running according to another embodiment of the present invention.

As shown, the vertical acceleration (a _z ) appears periodically with respect to time (this is natural because the walking or running itself is a periodic exercise). Here is a description of the driving movement. First, one foot from the front begins with the moment when the floor spurs (the other foot is floating in the air). In this state, as one foot rises from the ground, the human body moves forward while both feet float in the air, and both feet swing through the air, and the other foot comes forward. The other side of the foot comes into contact with the ground, and at the same time the moment of spurting the ground is done again, and a stepping motion is made. In this process, the human head is swung most vertically at the moment when the ground is sprung from one foot (the local maxima are formed at the vertical acceleration (a _z )), while in the state of floating in the air, (A constant value is formed at the vertical acceleration (a _z )).

This shock is applied to the joint at the moment when the foot is sprung, and the shock appears as the first peak in the vertical acceleration graph as shown in FIG. The risk of injury varies depending on the degree of impact at this time. In the present invention, this is used as a basis for 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).

FIG. 18 shows a slope in a vertical acceleration graph when driving according to another embodiment of the present invention. The process of deriving the average slope and the maximum slope of the vertical acceleration (a _z ) will be described below.

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

(Here, a _z: vertical acceleration, mean: the average value calculating function, i: index number, t _i: i-th time, t _{i -1: i-1} th time, t _c: Start impact time, t _m : Impact end time)

The impact start time is actually the moment when the foot lands on the ground. This can be determined as a time point when the vertical acceleration (a _z ) exceeds a predetermined reference value (for example, 0.3 m / s ² ) near 0 at a value of 0 or less. Here, the concrete value of the reference value for determining the impact start time can 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 time at which the first peak value appears and can be easily confirmed 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 an average value of n slope values obtained at each interval when n equally divided from the impact start time to the impact end time. FIG. 18 shows a graph of a vertical acceleration (a _z ) in one cycle. In this one cycle, the average slope value as described above can be obtained. On the other hand, as shown in FIG. 17, during running, the graph of FIG. 18 is continuously repeated, and the average slope value as described above can be obtained for each cycle (that is, for each step). At this time, in the determination index deriving step, the average vertical loading rate calculated as the product of the user mass m and the average slope can be further calculated.

On the other hand, when the above-mentioned injury risk judgment index is selected as the maximum slope value of the vertical acceleration (a _z ), the above-mentioned injury risk judgment index is calculated using the following equation.

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

_{t 0 = t c, t n} = t m

(Here, a _z: vertical acceleration, max: maximum value calculating function, i: index number, t _i: i-th time, t _{i -1: i-1} th time, t _c: Start impact time, t _m : Impact end time)

The maximum slope is the maximum value among the n slope values obtained from the impact start time to the impact end time in one cycle (one step) as described in the description of the average slope. At this time, in the determination index deriving step, the maximum vertical load rate calculated as the product of the user mass m and the maximum slope may be further calculated.

FIG. 19 shows the amount of impact in the vertical acceleration graph when driving according to another embodiment of the present invention. Hereinafter, a process of deriving the maximum impact force and the impact amount will be described.

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, a _z: vertical acceleration, m: mass of the user, t _m: impact end time)

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 appears is the impact end time. In Fig. 19, the first peak of the vertical acceleration (a _z ) is displayed, and the value obtained by multiplying the user mass (m) by this is the maximum impact force value.

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

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

In FIG. 19, the vertical acceleration (a _z ) graph area between the impact start time and the impact end time is displayed. The value obtained by multiplying the area by the user mass (m) is the impact value.

20 shows a first embodiment of a motion recognition apparatus according to the present invention.

The motion recognition first apparatus 2000 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 embodiment analyzes a user's motion state such as walking and running by measuring the dynamic physical quantity of the user such as acceleration and angular velocity, 1, the first device 2000 may include a band worn on the head and waist, a clip-on form on the head and the waist, a form on the hat, a form on the belt, an eyeglass form, a helmet form A shape attached to an ear, a shape attached to clothes, or a shape worn using a separate vest or harness. Specifically, the shape of the glasses may be in the form of an Augmented Reality (AR) glass, an eyeglass frame, sunglasses, or the like. The form attached to the ear can be in the form of a hands-free earpiece, a headphone and an earphone. It will be apparent to those skilled in the art that the first device 2000 may be modified in various ways. 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 acceleration values in three axial directions including up and down, left and right, and back and forth.

The angular velocity sensor 2020 measures angular velocity values in three axial directions including up and down, right and left, and front and rear.

The processing unit 2040 generates a first motion state value based on the three-axis direction acceleration value and the three-axis direction angular velocity value. The first motion state value includes at least one of an exercise time, a number of exercise steps, a minute maintenance, an interpolation, a brace angle, a head angle, a ground support time, an air suspension time, The maximum vertical force load ratio, the left-right balance degree, and the left-right uniformity. The first device 2000 may determine the user's motion state through the first motion state value. The mean values of the first exercise status values are the number of steps per minute, the interpolation interval average of the legs, the average angle of the legs, the average angle of the upper and lower hair, the support time of the ground, The average lifting time is the average of the time when all the legs are not touching the ground, the maximum vertical force is the maximum value of the ground reaction force, the vertical force load ratio average vertical force load rate is the average of the initial slope of the support section of the left and right ground reaction force, The load factor means the maximum of the initial slope of the support zone of the left and right ground reaction forces. The left-right balance indicates the coefficient of variation (CV) of the left-right unbalance. The larger the standard deviation of the coefficient of variation in the left-right balance chart, the larger the left-right imbalance.

The right and left uniformity (Stability) means that the state of motion is consistently maintained in the legs of the left foot and the right foot in time, power, etc., and is represented by% using the coefficient of variation (CV) .

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

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

The values that can be used for the index index include the vertical force maximum value, the vertical acceleration maximum value, the support section impact amount, the support time, the suspension time, the average vertical force load rate and the maximum vertical force load rate. Means the mean value of the coefficient of variation of the leg.

The left-right balance (Balance) represents the left-right unbalance (%) and is obtained by the following equation.

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

The user interface unit 2050 controls the sleep mode or the alive mode of the processing unit 2040. The user interface unit 2050 may be implemented in software or hardware. For example, the user interface unit 2050 may be implemented as a push button that is in the form of software or hardware. A flow chart of the motion recognition method initiated from the user input of the user interface unit 2050 will be described later in detail in FIG.

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 motion state value to the second device 2100. The first communication unit 2070 can transmit the first motion state value to the second device 2100 at predetermined intervals. It is apparent to those skilled in the art that the first communication unit 2070 can be implemented by 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, a clock, and the like.

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 motion 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. It is apparent to those skilled in the art that the position sensor unit 2030 measures the position value of the user based on GPS or ultra-precise GPS navigation technology, but can use other techniques.

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

In addition, the processing unit 2040 may further generate at least one of the first motion state value and the second motion state value and each predetermined reference value to generate posture correction information. For example, the processing unit 2040 stores optimal key-stride relationship data for each exercise speed, and determines whether the stride is too wide or narrow relative to the user's key based on the stride of the second exercise state values . When the stride is out of the optimum range, the processing unit 2040 generates a stride correction amount to be reduced or increased as the 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 the posture correction information into at least one of sound, illustration, image, and vibration, and outputs the converted information as user-recognizable information. For example, if the stride correction amount is calculated and the stride needs to be reduced, the user may be asked to output a voice such as "reduce stride" through the speaker, or to sound a warning tone, . Or the first device 2000 may be connected to an external device such as a mobile terminal, a watch, a computer, and a dedicated display so that the calibration information is output to at least one of sound, illustration, image, and vibration.

The first device 2000 may further include a third communication unit for transmitting the second motion state value to the server 2200 when the first device 2000 further includes the position sensor unit 2030. [ The server 2200 accumulates and stores the second motion state value in the database. The server 2200 provides statistical data based on the second motion 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 motion state values for a predetermined motion duration. A user who needs a motion analysis may receive the statistical data through the server 2200 and utilize his or her exercise habits in various ways. Users who need exercise analysis may have an ordinary person walking or jogging daily to promote health, or an expert trained to improve physical fitness. In addition, the server 2200 stores the second motion state value for each user, and provides a big data service that relationally and statistically analyzes the second motion state value between users.

The first communication unit 2070, the second communication unit 2080, and the third communication unit may include 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 Are obvious 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.

FIG. 21 shows a second embodiment of a motion recognition apparatus according to the present invention.

The motion recognition second device 2100 (hereinafter referred to as a second device) according to the present 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, a clock, and the like.

The first communication unit 2110 receives from the first device 2000 a first motion state value generated based on the three-axis direction acceleration value and the three-axis direction angular velocity value.

The position sensor unit 2170 measures the user position value. It is apparent to those skilled in the art that the position sensor unit 2030 measures the position value of the user based on GPS or ultra-precise GPS navigation technology, but can use other techniques.

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

Alternatively, the processing unit 2150 according to the present embodiment may selectively generate at least one of the first motion state value and the second motion state value and each predetermined reference value to generate motion posture correction information . For example, the processing unit 2150 stores optimal key-stride relationship data for each exercise speed, and determines whether the stride is not excessively wide or narrow relative to the user's key based on the stride of the second exercise state values . When the stride is out of the optimum range, the processing unit 2150 generates the stride correction amount to be reduced or increased as the 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 the posture correction information into at least one of sound, illustration, image, and vibration, and outputs the information as user-recognizable information. For example, if the stride correction amount is calculated and the stride needs to be reduced, the user may be asked to output a voice such as "reduce stride" through the speaker, or to sound a warning tone, .

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 motion state value to the server 2200. The server 2200 accumulates and stores the second motion state value in the database. The server 2200 provides statistical data based on the second motion 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 motion state values for a predetermined motion duration. A user who needs a motion analysis may receive the statistical data through the server 2200 and utilize his or her exercise habits in various ways. In addition, the server 2200 stores the second motion state value for each user, and provides a big data service that relationally and statistically analyzes the second motion state value between users.

The first communication unit 2110 and the second communication unit 2130 are configured to include at least one of wireless communication including Bluetooth, Wi-Fi and NFC, and wired communication through wiring. However, it should be understood by those skilled in the art that other wired / Do. The first communication unit 2110 and the second communication unit 2130 may be physically configured as a single interface or a plurality of interfaces.

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

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

In step 2220, the first device 2000 establishes a connection with the second device 2100 via the first communication unit 2070. [

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

In step 2240, the first device 2000 measures the three-axis direction acceleration value and the three-axis direction angular velocity value through the acceleration sensor unit 2010 and the angular velocity sensor unit 2020, respectively. According to an embodiment of the present invention, the acceleration sensor unit 2010 and the angular velocity sensor unit 2020 store the 3-axis direction acceleration value and the 3-axis direction angular velocity value in a first-in first-out (FIFO) 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 the predetermined threshold, and when the storage space of the first-in first-out queue is the predetermined threshold value or more, Mode, the device can be driven with low power.

In step 2250, the first device 2000 And generates a first motion state value based on the three-axis direction acceleration value and the three-axis direction angular velocity value. The first motion state value includes a motion time, an exercise step number, a payout per minute, an interpolation, a brace angle, a head angle, a ground support time, an air suspension time, The maximum force load ratio, the left-right balance, and the left-right uniformity.

In step 2260, the first device 2000 transmits the first motion state value to the second device 2100.

In step 2270, the second device 2100 measures the user position value.

In step 2280, the second device 2100 generates a second motion state value based on the value of at least one of the first motion state value, the user position value, and the user profile. The second motion state value is at least one of distance, velocity, calorie consumption, altitude, and stride. The user profile includes personal information such as a user's key, weight, and the like.

The second device 2100 may optionally generate at least one of the first motion state value and the second motion state value and each predetermined reference value to generate motion posture correction information. For example, the second device 2100 stores optimal key-stride relationship data for each exercise speed, and if the stride is too wide or narrow compared to the user's key based on the stride of the second exercise state values . When the stride is out of the optimal range, the second device 2100 generates a stride correction amount to be reduced or increased as posture correction information. The second device 2100 converts the posture correction information into at least one of sound, illustration, image, and vibration, and outputs it as information recognizable by the user. For example, if the stride correction amount is calculated and the stride needs to be reduced, the user may be asked to output a voice such as "reduce stride" through the speaker, or to sound a warning tone, .

In step 2290, the second device 2100 transmits the second motion state value to the server 2200. The server 2200 accumulates and stores the second motion state value in the database. The server 2200 provides statistical data based on the second motion 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 motion state values for a predetermined motion duration. A user who needs a motion analysis may receive the statistical data through the server 2200 and utilize his or her exercise habits in various ways. In addition, the server 2200 stores the second motion state value for each user, and provides a big data service that relationally and statistically analyzes the second motion state value between users.

Although the preferred embodiments of the present invention have been described in detail, the scope of the present invention is not limited thereto, and various modifications and other equivalent embodiments are possible. Accordingly, the true scope of the present invention should be determined by the appended claims.

For example, an apparatus according to an exemplary embodiment of the present invention may include a bus coupled to each unit of the apparatus as shown, at least one processor coupled to the bus, A memory coupled to the bus for storing a message or a generated message, and coupled to the at least one processor for performing the 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. A computer-readable recording medium includes all kinds of recording apparatuses in which data that can be read by a computer system is stored. The computer-readable recording medium includes a magnetic storage medium (e.g., ROM, floppy disk, hard disk, etc.) and an optical reading medium (e.g., CD-ROM, DVD, etc.). The computer-readable recording medium may also be distributed over a networked computer system so that computer readable code can be stored and executed in a distributed manner.

Claims (45)

An acceleration sensor unit for measuring three-axis acceleration values including up and down, left and right, and front and rear;
An angular velocity sensor unit for measuring three-axis angular velocity values including up and down, left and right, and front and rear;
A processing unit for generating a first motion state value based on the 3-axis direction acceleration value and the 3-axis direction angular velocity value;
And a user interface unit for controlling the sleep mode or the alive mode of the processing unit. The method according to claim 1,
And a first communication unit for transmitting the first motion state value to the second device. 3. The method of claim 2,
Wherein the first communication unit comprises at least one of Bluetooth, Wi-Fi, and NFC. The method according to claim 1,
And a second communication unit for transmitting the first motion state value to the server. The method according to claim 1,
And a position sensor section for measuring a user position value. 6. The method of claim 5,
Wherein the processing unit generates a second motion state value based on at least one value of the first motion state value, the user position value, and the user profile. The method according to claim 6,
And a third communication unit for transmitting at least one of the first motion state value and the second motion state value to a server. The method according to claim 6,
Wherein the second motion state value is at least one of distance, speed, calorie consumption, altitude, and stride. The method according to claim 6,
Wherein the processing unit compares at least one of the first motion state value and the second motion state value with each predetermined reference value to generate the posture correction information. 10. The method of claim 9,
Further comprising an output unit for outputting the posture correction information as at least one of sound, illustration, image, and vibration. 3. 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,
Wherein the acceleration sensor unit and the angular velocity sensor unit generate the three-axis direction acceleration value and the three-axis direction angular velocity value, respectively, based on a command from the second device or the user interface unit. The method according to claim 1,
Wherein the acceleration sensor unit and the angular velocity sensor unit store the three-axis direction acceleration value and the three-axis direction angular velocity value in a first-in first-out queue;
Wherein 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 an alive mode if the storage space of the first-in first-out queue is equal to or greater than a predetermined threshold. The method according to claim 1,
The first motion state value includes at least one of an exercise time, a number of exercise steps, a minute maintenance, an interpolation, a brace angle, a head angle, a ground support time, an air suspension time, A maximum vertical load ratio, a left-right balance degree, and a left-right uniformity. The method according to claim 1,
The first device may be a band worn on the head and waist, a clip-on-the-head attachment to the head and the waist, a form provided on the hat, a form on the belt, a form of a spectacle, a form of a helmet, And a form to be worn as a garment. The method according to claim 1,
The eyeglass shape may be one of an augmented reality glass, an eyeglass frame, and sunglasses,
The form attached to the ear comprises one of a hands-free earpiece, a headphone and an earphone,
Wherein the wearable garment comprises one of a vest and a harness. A first communication unit for receiving a first motion state value generated based on a three-axis direction acceleration value and a three-axis direction angular velocity value from a first device;
A position sensor unit for measuring a user position value;
And a processing unit for generating a second motion state value based on at least one value of the first motion state value, the user position value, and the user profile. 17. The method of claim 16,
And a second communication unit for transmitting at least one of the first motion state value and the second motion state value to a server. 17. The method of claim 16,
The first motion state value includes at least one of an exercise time, a number of exercise steps, a minute maintenance, an interpolation, a brace angle, a head angle, a ground support time, an air suspension time, A maximum vertical force load ratio, a left-right balance degree, and a left-right uniformity. 17. The method of claim 16,
Wherein the second motion state value is at least one of distance, speed, calorie consumption, altitude, and stride. 17. The method of claim 16,
Wherein the processor compares at least one of the first motion state value and the second motion state value with each predetermined reference value to generate the posture correction information. 21. The method of claim 20,
Further comprising an output unit for outputting the posture correction information as at least one of sound, illustration, image, and vibration. 17. The method of claim 16,
Wherein the first communication unit comprises at least one of Bluetooth, Wi-Fi, and NFC. Measuring acceleration values in three axial directions including up and down, right and left, and back and forth by the acceleration sensor unit;
Measuring three-axis angular velocity values including up and down, left and right, and front and rear by the angular velocity sensor unit;
Generating a first motion state value based on the 3-axis direction acceleration value and the 3-axis direction angular velocity value by a processing unit; And
And controlling the sleep mode or the alive mode of the processing unit by the user interface unit. 24. The method of claim 23,
And transmitting the first motion state value to a second device. &Lt; Desc / Clms Page number 21 &gt; 25. The method of claim 24,
Wherein the step of transmitting the first motion state value to the second device comprises transmitting at least one of Bluetooth, Wi-Fi, and NFC. 24. The method of claim 23,
And transmitting the first motion state value to the server. 24. The method of claim 23,
Further comprising the step of measuring a user position value. 28. The method of claim 27,
Further comprising generating a second motion state value based on at least one value of the first motion state value, the user position value, and the user profile. 29. The method of claim 28,
And transmitting at least one of the first motion state value and the second motion state value to a server. 29. The method of claim 28,
Wherein the second motion state value is at least one of distance, speed, calorie consumption, altitude, and stride. 29. The method of claim 28,
Further comprising the step of generating posture correction information by comparing at least one of the first motion state value and the second motion state value with each predetermined reference value. 32. The method of claim 31,
Further comprising the step of outputting the posture correction information to at least one of sound, illustration, image, and vibration. 25. The method of claim 24,
Wherein the step of measuring the three-axis direction acceleration value and the step of measuring the three-
And establishing a connection with the second device when the processing unit is changed to the alive mode by the user interface unit,
And the three-axis direction acceleration value and the three-axis direction angular velocity value are generated based on commands from the second device or the user interface unit, respectively. 24. The method of claim 23,
Wherein the acceleration sensor unit and the angular velocity sensor unit store the three-axis direction acceleration value and the three-axis direction angular velocity value in a first-in first-out queue;
Wherein 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 an alive mode when the storage space of the first- . 24. The method of claim 23,
The first motion state value includes at least one of an exercise time, a number of exercise steps, a minute maintenance, an interpolation, a brace angle, a head angle, a ground support time, an air suspension time, A maximum vertical force load ratio, a left-right balance degree, and a left-right uniformity. 24. The method of claim 23,
The first device may be a band worn on the head and waist, a clip-on-the-head attachment to the head and the waist, a form provided on the hat, a form on the belt, a form of a spectacle, a form of a helmet, And a form to be worn as a garment. 24. The method of claim 23,
The eyeglass shape may be one of an augmented reality glass, an eyeglass frame, and sunglasses,
The form attached to the ear comprises one of a hands-free earpiece, a headphone and an earphone,
Wherein the wearable garment comprises one of a vest and a harness. Receiving from the first device a first motion state value generated based on the three axis direction acceleration value and the three axis direction angular velocity value;
Measuring a user position value; And
And generating a second motion state value based on at least one value of the first motion state value, the user position value, and the user profile. 39. The method of claim 38,
And transmitting at least one of the first motion state value and the second motion state value to a server. 39. The method of claim 38,
The first motion state value includes at least one of an exercise time, a number of exercise steps, a minute maintenance, an interpolation, a brace angle, a head angle, a ground support time, an air suspension time, The maximum vertical force load ratio, the left-right balance, and the left-right uniformity. 39. The method of claim 38,
Wherein the second motion state value is at least one of distance, velocity, calorie consumption, altitude, and stride. 39. The method of claim 38,
Further comprising the step of generating posture correction information by comparing at least one of the first motion state value and the second motion state value with each predetermined reference value. 39. The method of claim 38,
Further comprising the step of outputting the posture correction information to at least one of sound, illustration, image, and vibration. 39. The method of claim 38,
Wherein the receiving of the first motion state value from the first device comprises receiving at least one of Bluetooth, WiFi, and NFC. 44. A computer-readable recording medium on which a program for performing the method according to any one of claims 23 to 44 is recorded.

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