JP2019528105A - Motion recognition method and apparatus - Google Patents

Abstract

Acceleration sensor unit for measuring triaxial acceleration values including up / down, left / right and front / rear, angular velocity sensor unit for measuring triaxial direction angular velocity values including up / down, left / right and front / rear, the triaxial acceleration value and the triaxial angular velocity Disclosed is a motion recognition method and apparatus including a processing unit that generates a first motion state value based on a value and a user interface unit that controls a sleep mode or an alive mode of the processing unit. [Selection] Figure 2

Description

  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 walking and running motion data.

  In general, it has been continually pointed out that the amount of exercise in modern people's daily life is considerably insufficient to maintain proper physical health. Therefore, there is a growing interest in systematic exercise methods that effectively promote health. Specifically, exercises that enable systematic and efficient rapid body training, exercises such as posture correction to promote health from a long-term perspective, and human life expectancy At present, interest in various types of exercise is increasing, such as exercise suitable for elderly people whose physical ability declines as they extend. One of the exercise methods suitable for such various requirements is walking exercise that anyone can easily do.

  Anyone who has no physical problems can walk, so most people walk unconsciously. However, because the human body is not perfectly symmetrical, most people walk in an imbalanced and incorrect posture. Such persistent walking in the wrong posture distorts muscles and skeleton, and may cause various systemic pains. In the case of ordinary people, such an incorrect walking posture causes a problem of lowering the health of the body. Particularly, in the case of a child or an elderly person whose physical ability is lowered, the problem of the distortion of the body shape and the deterioration of the health are more serious. On the other hand, specialists who require physical abilities that are further improved than ordinary people, such as athletes and dancers, have problems such as creating limits to physical abilities.

  Thus, the correct walking posture is important for everyone from ordinary people to experts. Therefore, various studies have been conducted on how to effectively correct the walking posture.

  According to the prior art, in detecting walking, a pressure sensor attached to footwear or a footrest is mainly used. However, when trying to recognize and analyze the walking posture according to the prior art, the pressure sensor may be damaged in the process of receiving pressure continuously for a long time. Cause inconvenience. In addition, the size of a person's foot varies greatly depending on the individual, and therefore, footwear fitted with a pressure sensor must be produced in various sizes, which means that production efficiency and economy are considerably reduced. There's a problem. In addition, there is a problem that an economic burden increases because the family cannot share a single walking posture correction device and must purchase it separately according to the size of the foot.

  As described above, in the technology for recognizing, detecting and analyzing walking in order to correct the walking posture, it is possible to recognize, detect and analyze walking effectively and accurately by other methods in addition to the pressure sensor. The demand for technology is increasing.

  The present invention has been derived to address the above-described technical problems, and the object of the present invention is to provide a user who can substantially supplement various problems caused by limitations and disadvantages in the prior art. It is an object to provide a motion recognition method and apparatus for collecting and analyzing walking and running motion data, and a computer-readable recording medium on which a program for executing the method is recorded.

  According to an embodiment of the present invention, the motion recognition method of the first device includes a step of measuring acceleration values in three axes including vertical and horizontal and front and rear by the acceleration sensor unit, and vertical and horizontal and front and rear by the angular velocity sensor unit. Including a step of measuring a triaxial angular velocity value, a step of generating a first motion state value based on the triaxial acceleration value and the triaxial angular velocity value by a processing unit, and a sleep of the processing unit by a user interface unit Controlling the mode or alive mode.

  According to an embodiment of the present invention, the method further includes transferring the first motion state value to the second device.

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

  According to an embodiment of the present invention, the method further includes the step of transferring the first motion state value to a server.

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

  The method may further include generating a second exercise state value based on at least one of the first exercise state value, the user position value, and the user profile.

  The method may further include transferring at least one of the first motion state value and the second motion state value to a server.

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

  The method may further include 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.

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

  According to an embodiment of the present invention, the step of measuring the triaxial acceleration value and the step of measuring the triaxial acceleration value may be performed when the processing unit is changed to the alive mode by the user interface unit. A connection with the second device is set, and the three-axis direction acceleration value and the three-axis direction angular velocity value are respectively generated based on a command from the second device or the user interface unit.

  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, and a storage space of the first-in first-out queue is predetermined. When it is less than the threshold, the processing unit is in a sleep mode, and when the storage space of the first-in first-out queue is equal to or greater than a predetermined threshold, the processing unit is in an alive mode.

  According to one embodiment of the present invention, the first motion state value includes: exercise time, number of steps taken, number of steps per minute, step length, step angle, head angle, ground support time, air suspension time, air suspension time. Is at least one of the ratio of the ground support time to the maximum vertical force, the average vertical force load factor, the maximum vertical force load factor, the left-right balance, and the left-right uniformity.

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

  According to an embodiment of the present invention, the glasses form is one of augmented reality glasses, glasses frames, and sunglasses, and the form attached to the ear is one of hands-free earpieces, headphones, and earphones. The form to be worn as the garment is one of a waistcoat and a harness.

  Meanwhile, according to the embodiment of the present invention, the motion recognition method of the second device receives the first motion state value generated based on the triaxial acceleration value and the triaxial angular velocity value from the first device. Measuring a user position value; and generating a second movement state value based on at least one of the first movement state value, the user position value, and a user profile.

  The method may further include transferring at least one of the first motion state value and the second motion state value to a server.

  According to one embodiment of the present invention, the first motion state value includes: exercise time, number of steps taken, number of steps per minute, step length, step angle, head angle, ground support time, air suspension time, air suspension time. Is at least one of the ratio of the ground support time to the maximum vertical force, the average vertical force load factor, the maximum vertical force load factor, the left-right balance, and the left-right uniformity.

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

  The method may further include 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.

  According to an embodiment of the present invention, the method further includes the step of outputting the posture correction information as 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 via at least one of Bluetooth, Wi-Fi, and NFC.

In addition, according to an embodiment of the present invention, a computer-readable recording medium on which a program for executing the method is recorded is included.
Further, according to one embodiment of the present invention, the first apparatus for motion recognition includes an acceleration sensor unit that measures triaxial acceleration values including vertical, horizontal, and longitudinal directions, and triaxial angular velocity that includes vertical, horizontal, and longitudinal directions. An angular velocity sensor unit for measuring a value, a processing unit for generating a first motion state value based on the triaxial acceleration value and the triaxial angular velocity value, and a user interface for controlling a sleep mode or an alive mode of the processing unit Part.

  According to an embodiment of the present invention, the second device for motion recognition receives the first motion state value generated from the first device based on the triaxial acceleration value and the triaxial angular velocity value. 1 communication unit, a position sensor unit that measures the position value of the user, and a processing unit that generates a second movement state value based on at least one of the first movement state value, the user position value, and the user profile including.

  According to the present invention, the characteristic analysis algorithm of the present invention, such as measuring acceleration, position, etc. on the user's body (for example, the head side or the waist side) and converting it to a motion state value centered on the mass, etc. By using, walking can be recognized, detected and analyzed effectively and accurately.

  In addition, according to the present invention, it is characteristic of the present invention to measure acceleration, position, etc. in the user's body, convert it into a mass-centered motion state value, and thereby estimate a pressure center path, etc. By using an analysis algorithm, walking can be recognized, detected and analyzed effectively and accurately. In particular, the present invention uses acceleration measured at the position most similar to the motion of the center of mass of the user's body (specifically, the lateral acceleration is measured on the head side and the longitudinal By measuring the acceleration and position on the lumbar side and the acceleration in the vertical direction on the head side or the lumbar side), more accurate acceleration and position measurement is possible.

  In addition, according to the present invention, there is an advantage that only a sensor that measures a user's dynamic physical quantity, such as an acceleration sensor and a position sensor, can be used from the aspect of device configuration. That is, conventionally, by using a pressure sensor that recognizes walking by being pushed by the user's foot, problems such as device durability and a decrease in lifespan, production and use problems of a separate device according to the user's body dimensions, etc. There were various problems. However, in the case of the present invention, the technical configuration itself of disposing the pressure sensor that is the cause of such a problem is completely eliminated, so that the various problems as described above are fundamentally eliminated. It is. Further, it is possible to obtain effects such as improvement of user convenience and improvement of economy in each user or producer.

The use condition of the movement posture derivation device by one embodiment of the present invention is shown. 1 shows a schematic diagram of a motion posture deriving device according to an embodiment of the present invention. FIG. 2 shows a flowchart of an exercise posture deriving method according to an embodiment of the present invention. FIG. 6 shows a relationship diagram between a center of mass and a center of pressure according to an embodiment of the present invention. The determination of the pressure center direction and the position analogy according to an embodiment of the present invention will be described. FIG. 6 illustrates an example of an estimated pressure center path according to an embodiment of the present invention. FIG. The illustration of the up-down direction acceleration graph with respect to the time at the time of the walk and driving | running | working by one Embodiment of this invention is shown. 3 illustrates an example of an acceleration signal measurement result according to an embodiment of the present invention. 6 shows a flowchart of a motion recognition method according to another embodiment of the present invention. 6 shows a detailed flowchart of data collection and motion recognition steps according to another embodiment of the present invention. 6 illustrates an example of an acceleration signal measurement result according to another embodiment of the present invention. 6 shows a detailed flowchart of a step of deriving a motion state value based on acceleration according to another embodiment of the present invention. The use condition of the movement posture derivation device by other embodiments of the present invention is shown. The schematic diagram of the movement posture derivation device by other embodiments of the present invention is shown. FIG. 3 shows a schematic diagram of an injury risk quantification apparatus according to another embodiment of the present invention. 6 shows a flowchart of an injury risk quantification method according to another embodiment of the present invention. The vertical direction acceleration graph at the time of driving | running | working by other embodiment of this invention is shown. An inclination is displayed on the vertical acceleration graph during traveling according to another embodiment of the present invention. The impact amount is displayed on the vertical acceleration graph during traveling according to another embodiment of the present invention. 6 shows a first device for motion recognition according to another embodiment of the present invention. 6 shows a second device for motion recognition according to another embodiment of the present invention. 6 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. In the drawings, the same reference numerals indicate the same components, and the size of each component may be exaggerated for clarity of explanation.

  FIG. 1 shows a use state of a motion posture deriving device according to an embodiment of the present invention.

  The exercise posture deriving device 100 according to an embodiment of the present invention is worn on the head side of a user as shown in FIG. The exercise posture deriving device 100 according to the present embodiment may be provided in a band or a hat worn on the head side as shown in FIG. 1, and from a form that is further miniaturized and put on an ear like an earphone. It will be apparent to those skilled in the art that other forms may be used.

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

  The exercise posture deriving device 100 according to the present embodiment includes a sensor signal collecting unit 110 and an exercise posture deriving unit 121 as shown in FIG. The exercise posture deriving device 100 according to the present embodiment further includes an exercise correction generation unit 122 and a correction information output unit 130.

  The sensor signal collecting unit 110 includes a triaxial acceleration sensor 111 including up and down, left and right, and front and rear, and a position measurement sensor 112 that measures the position of the user. As the triaxial acceleration sensor 111, a suitable sensor can be selected and used from among sensors generally used for measuring the triaxial acceleration, such as a form incorporating a gyroscope. 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 by using a GPS signal. Where precision satellite navigation technology is being developed, sensors to which such technology is applied may be used. Further, the sensor signal collection unit 110 may further include a triaxial angular velocity sensor 113 as shown in FIG. 2 to increase accuracy in the motion recognition and analysis process described in more detail below.

  The sensor signal collection unit 110 according to the present embodiment is worn on the user's head as shown in FIG. 1 and measures the user's dynamic physical quantities such as acceleration, speed, and position. Conventionally, a pressure sensor provided on footwear, a footstool or the like, which is a portion directly pressed by a foot for walking monitoring, has been used. For this reason, there has been a problem that the damage of the sensor progresses quickly and the durability and life of the apparatus are shortened. In addition, there are problems such as a decrease in walking recognition and analysis accuracy due to damage to a device in use, and a decrease in convenience and economy due to frequent replacement of the device. In addition, when such a device is provided in footwear, a separate device is required for each user according to the size of the user's foot, and a decrease in user convenience and economy is weighted. There was a problem that it had to be produced separately according to size and caused an economic burden.

  However, this embodiment completely departs from the concept of using the pressure pressed by the foot in walking recognition, such as acceleration, velocity, position, etc. measured on the user's head as shown in FIG. The user's dynamic physical quantity is measured, and the characteristic analysis algorithm of the present invention described below is applied to realize walking recognition, detection and analysis. As described above, this embodiment is different from the related art in the measurement position and the measurement physical quantity. At this time, the root cause of various problems pointed out in the prior art comes from the technical configuration of “placement of the pressure sensor on the foot portion”. According to the present invention, only the configuration described above is used. Such various problems can be fundamentally eliminated.

  The motion posture deriving unit 121 receives a signal from the sensor signal collecting unit 110 and derives a walking or running motion state value including the acceleration, speed, and position of the user mass center using the triaxial acceleration and position signals. The walking or running motion state value is analyzed to derive a walking or running posture. Specifically, the motion 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. Since the analysis algorithm of the present invention used in the motion posture deriving unit 121 will be described in detail later, the description thereof is omitted here.

  On the other hand, the motion posture deriving unit 121 may be formed on the same substrate as the sensor signal collecting unit 110 in the form of an integrated circuit capable of executing various calculations, or may be formed in a form such as a separate computer. May be. At this time, when the exercise posture deriving unit 121 is formed separately from the sensor signal collecting unit 110, the communication unit 114 is used for signal transmission between the sensor signal collecting unit 110 and the exercise posture deriving unit 121 as shown in FIG. May be provided. The communication unit 114 can be composed of wired or wireless communication. As wireless communication, Bluetooth, Wi-Fi, and NFC technologies may be used, but it is obvious to those skilled in the art that other wireless communication technologies may be used.

  The motion correction generation unit 122 serves to generate posture correction information by comparing the walking posture derived by the motion posture deriving unit 121 with the reference posture. The exercise posture deriving unit 121 derives the user's walking or running posture based on the signals collected by the sensor signal collecting unit 110 as described above. For example, the user's progress during walking or running is given. The direction, speed, and the like can be derived, whereby the stride that is one of the elements of the walking posture can be obtained. In this case, the motion correction generation unit 122 has built-in optimal height-stride relationship data for each walking and running speed, and the walking posture information of the user is compared with that, and the stride is excessive compared to the height of the user. It is possible to easily calculate the amount of stride correction that must be reduced or increased when it is determined that it is not wide or narrow, and when it is out of the optimum range.

  The correction information output unit 130 converts the posture correction information generated by the movement correction generation unit 122 in this way as information that can be recognized by the user, including sound, illustration, and image, and outputs the information. For example, when it is necessary to reduce the stride when the stride correction amount is calculated, is it possible to output a sound such as “Please reduce the stride” via a speaker provided on the exercise posture deriving device 100? Alternatively, it is possible to guide the user to change the walking posture by recognizing that the stride is not the optimum stride by sounding a warning sound. Alternatively, it can be connected to a smartphone, a computer, a dedicated display, or the like, and correct correction information can be output as an illustration or an image.

  Furthermore, the exercise posture deriving device 100 can transfer the walking posture derived by the exercise posture deriving unit 121 to the external database 140 and store it cumulatively. Users who need such walking or running motion analysis are ordinary people who walk or jog every day for health promotion, experts who train to improve physical ability, etc., and such motion analysis data accumulates Of course, it is preferable to be able to see changes over time. In addition, when a large amount of motion analysis data is accumulated and stored in this way, such data can be used as big data and used for various statistics and analyses.

  The motion posture deriving method according to an embodiment of the present invention uses the motion posture deriving device 100 to detect the user's motion and perform analysis such as determining whether the user is walking or running. At this time, as described above, the analysis algorithm used in the present invention uses a dynamic physical quantity measured on the user's head side. The three-axis direction acceleration sensor 111 and the position measuring sensor 112 for measuring the position of the user may be included, and the motion / attitude derivation unit 121 may execute an analysis algorithm described below. In addition, the motion posture deriving device 100 may further include the various additional configurations described above in order to improve the function of the device.

  FIG. 3 shows a flowchart of a method for deriving a motion posture according to an embodiment of the present invention. The exercise posture deriving method according to the present embodiment includes a pressure center path estimating step, an exercise type determining step, and an exercise posture deriving step, as shown in the figure. Hereinafter, each step will be described in more detail.

Wherein in the pressure center path estimation step, using the three-axis direction acceleration collected by the motion and orientation derivation device _{100 (a x, a y,} a z) of the motion state value of the user centroid calculated using the mass The pressure center path is estimated by projecting on the ground in the direction of the acceleration vector from the center position.

  In the movement posture deriving device 100, the three-axis acceleration sensor 111 can be used to collect the left, right, front, back, and top and bottom three-axis accelerations, and can be integrated or collected using the position measurement sensor 112 per time. All the speed, position, etc. can be obtained using the obtained position information. On the other hand, in general, when analyzing the motion of an object, the analysis is performed based on the motion of the center of mass of the object, and the motion posture deriving device 100 is provided on the user's head side. It is preferable to analyze by converting the value into a motion state value at the center of mass. In this way, converting the value measured at the user's head position into the value of the center of mass of the user appropriately applies a gain value obtained in advance using physical information such as the height information of the user, etc. Can be easily derived.

  Thus, by deriving the motion state value of the center of mass (acceleration / velocity / position per time for each direction, frequency analysis, etc.), it is possible to estimate the pressure center path. When walking or running, the human body behaves using reaction pressure applied to the supporting foot. The sum of the reaction pressures is referred to as a ground reaction force (GRF), and the center of the pressure is referred to as a center of pressure (COP). It has been clarified that the ground reaction force generated at this time has a characteristic from the center of pressure to the center of mass of the human body (center of mass; COM).

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

  In the present invention, such a biomechanical characteristic is immediately utilized in reverse, and the pressure center is estimated by projecting onto the ground in the vector direction of the force measured at the center of mass.

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

The pressure center direction refers to a direction from the center of mass toward the pressure center. In the pressure center path estimating step, the pressure center direction is determined first, and the pressure center position is estimated by projecting in the direction. In more detail, first, in the pressure center direction determining step, as shown in FIG. 5, the ratio of the lateral direction acceleration a _x to the sum of the vertical acceleration a _z, and the gravitational acceleration g and the vertical acceleration a _z and gravity The direction of the pressure center is determined by the ratio of the longitudinal acceleration _ay to the sum of acceleration g. When the direction of the pressure center is determined as described above, in the pressure center position analogizing step, the mass center is determined by a value obtained by multiplying the height information of the user measured in advance by a predetermined analogy constant. Assuming that it is located at a height, the pressure center position is estimated by projecting onto the ground in the direction determined in the pressure center direction determining step. Here, the analogy constant refers to the height of the center of mass corresponding to the height of the user. In general, it is well known that a child's center of mass appears higher in proportion than an adult's center of mass, and a male's center of mass appears higher in proportion than a woman's center of mass. Of course, the ratio is also known. As a specific example, it is known that the center of mass of an adult male is on average 55.27% of the height, and in this case, the analogy constant is 0.5527. Therefore, for example, when inputting the height information of the user, by inputting the child / adult and male / female classification information together, a suitable analogy constant can be selected and used for the calculation.

  In order to further improve the accuracy of the pressure center position thus obtained, the pressure center position estimated in the pressure center position analogy step is corrected by a value obtained by multiplying a predetermined longitudinal correction constant. The pressure center position correcting step may be further executed. Here, the front-rear and left-right direction correction constants are constants that statistically match the pressure center position obtained by the above-described projection method with the actual front-rear and left-right direction pressure centers.

In the exercise type determination step, it is determined whether the vehicle is walking or running from the pattern of the vertical acceleration _az graph.

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

  On the other hand, the distinction between walking and running is that one or both feet are always in contact with the ground in walking, while one or both feet are always floating from the ground in running.

FIG. 7 shows an example of a vertical acceleration graph with respect to time during walking and running. In the case of the walking graph shown in FIG. 7 (A), it can be confirmed that a peak is generated at the moment when both feet are in contact with the ground. The graph of the running time shown in FIG. In this case, it can be confirmed that there is a constant value interval in which the vertical acceleration _az is the minimum value at the moment when both feet are floating from the ground. Thus, in each case of walking and running, it is determined whether the user's currently exercised is walking or running using the vertical acceleration _az graph patterns appearing differently. be able to.

Wherein in the motion and orientation derivation step, the step length based on the pressure center path estimated value and the three-axis direction acceleration _{(a x, a y, a} z), between step, step angle, derives the posture information including asymmetrical. This will be described in more detail with reference to the example of the pressure center path in FIG. 6 and the example of the vertical acceleration during walking or running in FIG.

  First, it is described above that the appearance appears to be slightly different when the user's movement is walking and when running, and of course there are also matters that appear in common. As described above, one or both feet are always in contact with the ground during walking, and one or both feet are always floating from the ground during running. That is, there is a section that is supported by only one foot in common during walking and running. In consideration of the above, in the exercise posture derivation step, first, an intermediate support time determination step for determining an intermediate support time point, and a section classification determination step for determining both foot support sections, one leg support sections, and air suspension sections The basic information for deriving the posture while distinguishing walking and running is formed.

Now, if you walk and describe the walking movement, it is as follows. First, it starts from a state in which the toes of the other foot are not yet separated from the ground at the moment when the heel of one foot steps on the ground, that is, a state where both feet are supported. In this state, while supporting the ground with only one foot, the other foot is separated from the ground, and the human torso moves forward while moving forward while swinging the other foot in the air. Then, at the moment when the heel of the other foot steps on the ground, the state in which the toes of one foot are not yet separated from the ground, that is, the state in which both feet are supported, is made again to make a one-step walk. In this process, the human head does not shake greatly in the vertical direction at the moment when the human torso moves forward while being supported by only one foot (on the other hand, a local minimum is formed at the vertical acceleration _az ). At the moment of stepping on the foot, the largest swing is made in the vertical direction (a peak value is formed in the vertical acceleration _az ).

In other words, walking movement can be divided into a section where both feet are stepping on the ground and a section where only one foot is stepping on the ground. There is the least amount of shaking. Such an aspect of movement is well shown in FIG. 7A, and as shown in such an example, in the intermediate support time point determination step, when the user's movement is walking, time domain The local minimum is defined as the intermediate support point in the vertical acceleration _az measured in (1). Further, in the section classification determining step, when the user's movement is walking, a section where a peak value is formed in the vertical acceleration _az measured in the time domain is determined as a both foot support section, and the remaining section is set as one leg. Decide on support section.

Next, the running motion is described as follows. First, it starts from the moment when one of the legs that is moving forward kicks the ground (the other leg is floating in the air at this moment). In this state, one of the legs kicks off the ground and floats while all the legs are floating in the air, while the human torso moves forward, and with that, the front and rear changes while swinging both legs in the air. Comes out ahead. The moment when the other leg that comes out forward touches the ground and kicks the ground again is made again, and it is a one-step run. In this process, at the moment of kicking the ground with one foot, the person's head shakes the most in the vertical direction (local maximum is formed at the vertical acceleration _az ), but in the vertical direction when moving in the air (A constant value is formed at the vertical acceleration _az ).

That is, the running motion can be divided into a section in which both feet are floating in the air and a section in which only one foot is stepping on the ground. There is the least amount of shaking. Such an aspect of movement is well shown in FIG. 7B, and as shown in such an example, in the intermediate support time determination step, when the user's movement is running, the time domain The local maximum is defined as the intermediate support point in the vertical acceleration _az measured in step (1). In the section classification determining step, when the user's movement is running, a section represented by a constant value in the vertical acceleration _az measured in the time domain is determined as an air floating section, and the remaining section is _defined as a one-foot support section. To decide. Here, the constant value appearing in the air floating section is an already set value of the signal level when the external force other than gravity is not applied to the accelerometer, and can be appropriately determined to be a value close to about zero. That is, the constant value is a reference value that enables the current stance to be determined. In this sense, the constant value is referred to as a stance determination constant. In summary, the vertical acceleration during driving is a stance determination constant. If it is smaller, it is determined as an airborne section, and if it is larger, it is determined as a one-leg support section.

  Thus, when basic information for posture derivation is derived, it is possible to derive a walking or running posture such as a stride, a step distance, a step angle, and a left-right asymmetry for the first time.

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

  Step length: The step length in the left-right direction can be calculated using the pressure center position value corresponding to the intermediate support time point. That is, by applying the time value corresponding to the intermediate support time shown in FIG. 7 (A) or (B) to the pressure center position value shown in FIG. 6 and searching for the pressure center position corresponding to the time value, The position where the left foot stepped on the ground and the position where the right foot stepped on the ground come out, and by measuring these intervals, the user's step distance can be accurately calculated.

  Walking angle: The walking angle can be calculated using the pressure center position value corresponding to the start time of the one foot support section and the pressure center position value corresponding to the end time of the one foot support section. If it crushes and demonstrates, a toe will step on the ground at the start time of a one leg support area, and a toe will step on the ground at the end time of a one leg support area.

  That is, as described above, obtaining the angle between the pressure center positions means obtaining the angle formed by the position of the heel and the position of the toe at the moment of stepping on the ground, that is, the walking angle. Thus, the user's walking angle can be accurately calculated.

Asymmetrical: First, grasp the legs supporting the sign of the lateral direction acceleration a _x measured in the time domain to the reference. Next, the peak value and valley value of the vertical acceleration _az measured in the time domain and the difference value between the two are compared. That is, the user's left-right asymmetry can be accurately calculated by comparing peak values and valley values when the left foot is supporting and when the right foot is supporting. Further, the repeatability of walking or running can be calculated in the same manner.

FIG. 8 shows that the left-right asymmetry of the vertical acceleration _az appears strongly in the bottom graph of FIG. 8 as an example of the acceleration signal measurement result.

  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, step, step angle, left / right asymmetry by the method described above. . Of course, at this time, the stride length, step length, step angle, and left / right asymmetry value corresponding to the optimal posture can be stored in advance, and the correction amount can be calculated by comparing with the current posture values currently being monitored. . By letting the user know or store it in real time so that it can be confirmed later, the user can effectively correct his or her posture for walking or running to 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.

Motion recognition method according to the present embodiment consists of two large steps, i.e., three-axis direction acceleration _{(a x, a y, a} z) and collection and analysis and data collection and movement recognition step of determining the walking and running presence in the And a motion state value deriving step based on acceleration for calculating a motion state value of the user mass center using the collected triaxial accelerations (a _x , a _y , a _z ). Hereinafter, detailed steps of each step will be described in more detail.

  FIG. 10 shows a detailed flowchart of data collection 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 triaxial acceleration collection step, a Fourier transform step, and a motion form determination step. The data collection and exercise recognition step recognizes whether or not the user has exercise, and if exercise has occurred, recognizes that the exercise corresponds to walking or running.

  As shown in FIG. 10, first, the collected data variables are initialized and prepared for performing motion recognition.

Wherein in the vertical acceleration collection step, the three-axis direction acceleration _{(a x, a y, a} z) instead of collecting all the temporarily collects vertical acceleration _{a z.} The collected vertical acceleration _az may be used as it is, but it is more preferable to pass a noise removal step of removing noise by passing through a predetermined bandpass filter. At this time, the band pass filter may be formed at 0.1 to 5 Hz corresponding to a general human walking or running frequency, for example, but it is obvious to those skilled in the art that this range can be appropriately changed. It is.

In the peak detection step, the peak of the vertical acceleration _az collected in this way is detected. In the motion detection step, whether or not the peak value of the vertical acceleration _az is greater than or equal to a predetermined threshold value. It is judged whether exercise | movement generate | occur | produced by judging. If it is determined in the motion detection step that no motion has occurred, the process returns to the initial preparation step and variable initialization is performed.

To describe in more detail, in performing the analysis in the motion recognition apparatus 100, always three-axis direction acceleration _{(a x, a y, a} z) may become to collect, when without movement, unnecessary calculations When a load is generated, problems such as power consumption and heat generation may occur. On the other hand, the biggest difference between when the user moves quietly or sits and moves while walking or running is the largest difference between the user shaking up and down, that is, vertical acceleration. _az . Accordingly, the vertical acceleration _az of the user wearing the motion recognition device 100 is collected first, and if the value exceeds a certain threshold value, it is determined that the user is walking or running, and from that time, full-fledged By starting the motion detection, the above-described problem of unnecessary calculation load can be prevented.

In the three-axis direction acceleration acquisition step, if the vertical acceleration a _z threshold above which the peak value has been determined in advance as described above, the three-axis direction acceleration _{(a x, a y, a} z) to collect . Again, the collected triaxial direction acceleration _{(a x, a y, a} z) but may be used as it is, the noise removal step of removing noise is passed through a bandpass filter with a predetermined More preferably, it passes. The bandpass filter at this time may be formed in the same manner as the bandpass filter used for noise removal in the vertical acceleration _az described above, or may be appropriately changed and set.

Wherein in the Fourier transform step, the three-axis direction acceleration _{(a x, a y, a} z) by Fourier transform to derive a frequency response graph, the first time a predetermined frequency response frequency response graph in motion form determining step It is determined whether the movement is a walking and running movement or other movement compared with the general shape or size criterion. If it is determined in the exercise form determination step that no movement corresponding to walking and running movement has occurred, the process returns to the first preparation step again to initialize the variables, and instead it is determined that walking and running movement has occurred. Then, the motion state value deriving step based on the acceleration is executed.

  Thus, in the said exercise | movement form judgment step, it is judged whether a user's exercise | movement is a walking and running state.

FIG. 11 shows an example of an acceleration signal measurement result during walking according to another embodiment of the present invention. On the left side of FIG. 11, the horizontal axis represents time / vertical axis right (x), longitudinal (y), vertical (z) each of the three axial acceleration _{(a x, a y, a} z) time domain acceleration is A graph is shown, and on the right side, as described above, a frequency response graph derived through the Fourier transform step in which the horizontal axis is frequency and the vertical axis is magnitude is shown.

  Naturally, when walking or running, it periodically sways in the vertical direction, the front-rear direction, and the left-right direction, that is, a periodic signal is generated as shown on the left side of FIG. At this time, the left and right feet are alternately stepped on, and walking or running is performed. Therefore, the frequency of the periodic signal in the left-right direction is half the frequency of the periodic signal in the up-down direction and the front-rear direction. This can be easily confirmed from the graph on the right side of FIG. On the other hand, one or both feet are always in contact with the ground during walking, and one or both feet are floating from the ground during running. That is, when walking or running, a large shaking on the head side always occurs periodically.

In consideration of the above, the motion form determination step determines that the user's motion is the walking and running state if the following equation is satisfied, and otherwise determines that the motion is other motion. In other words, if the following relational expression is easily broken and described, it will be determined that walking or running has occurred if it appears larger than a certain level that swings periodically in the vertical and horizontal directions.
_{M z, p / M z,} other> c z and M x, p / M x, other> c x
(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:} vertical in direction acceleration a _z Fourier transform result of the center frequency f _p, the energy sum of the frequency components belonging to the vertical direction reference band having a bandwidth of less than 1 Hz,
M _{z, other} : the sum of the energy of the remaining frequency components excluding the vertical direction reference band in the Fourier transform result of the vertical direction acceleration a _z ,
c _z : a predetermined vertical reference threshold,
a _x : lateral acceleration,
M _{x, p:} the Fourier transform result of the lateral direction acceleration a _x, and a center frequency f p _{/ 2,} total energy of the frequency components belonging to the left-right direction reference band having a bandwidth of less than 1 Hz,
M _{x, other} : In the Fourier transform result of the lateral acceleration a _x , the total energy of the remaining frequency components excluding the lateral reference band,
c _x : predetermined left-right direction reference threshold)

When it is determined that the user's motion is in the walking and running state by satisfying the above formula, it is then necessary to determine whether the motion is walking or running. At this time, as described above, one or both feet are always in contact with the ground during walking, and one or both feet are floating from the ground during running. Here, when both feet are floating from the ground during running, both feet are swung in the air, so there is no external force further applied to the user in the vertical direction. Therefore, at this time, the vertical acceleration a _z Is formed as a minimum constant value.

In consideration of the above, the motion form determination step determines that the user's motion is in a running state if there is a section satisfying the following formula in the Fourier transform result of the vertical acceleration _az : Otherwise, it is determined that the user is walking.
a _z <k
(here,
k: Stance discrimination constant)

  Here, the stance discriminant constant (stance phase constant) is an already set value of the signal level when the external force other than gravity is not applied to the accelerometer, and can be appropriately determined to be a value close to about zero.

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

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

  As illustrated, the motion state value deriving step based on acceleration includes a mass center acceleration deriving step, and a mass center velocity and position deriving step.

In the mass center acceleration deriving step, the three-axis direction acceleration deriving the acceleration of the user centroid over _{(a x, a y, a} z) predetermined gain value to each value. In general, when analyzing the motion of an object, the analysis is based on the motion of the center of mass of the object, and all the variable values previously used for the analysis are measured on the user's head side. , And convert it into a mass-centered motion state value. Such a gain value is indicated by a constant vector (γ) and can be obtained in advance using physical information such as the height information of the user.

  In the center-of-mass velocity and position deriving step, the velocity and position of the user center of mass are derived using previously measured user height information, user position information, and mass center acceleration. That is, the speed and position of the center of mass (with the integral constant value added) can be obtained by integrating the center-of-mass acceleration obtained as described above, or the user measured in time by the position measurement sensor The velocity and position of the center of mass can be obtained using the position information of Where there is an error corresponding to the integral constant value between these two calculated values, it is possible to obtain an accurate velocity and position value of the center of mass 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, position, etc. measured on the head side of the user, It is possible to accurately grasp how the user's center of mass moves during traveling (that is, how the acceleration, speed, and position of the center of mass are expressed). Therefore, various elements of the walking or running posture can be derived based on this and used for posture correction.

  FIG. 13 shows a use state of a motion posture deriving device according to another embodiment of the present invention.

  As shown in FIG. 13, the exercise posture deriving device 1300 according to this embodiment is worn on the user's body, more specifically, on the head side and the waist side. That is, in the exercise posture deriving device 1300 according to the present embodiment, as shown in the schematic diagram of FIG. 13, the head side sensor signal collecting unit 1310H worn on the head side is configured to be put on the ear like an earphone. The waist-side sensor signal collecting unit 1310W worn on the belt is configured to be put on a belt. Of course, the present invention is not limited to this, and for example, the head side sensor signal collecting unit 1310H can be variously modified such as a hair band form, glasses form, a form attached to a separate hat, and a helmet form. Of course.

  FIG. 14 is a schematic diagram of a motion posture deriving device according to another embodiment of the present invention.

  As shown in FIG. 14, an exercise posture deriving device 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, and an exercise posture deriving unit 1421. The exercise posture deriving device 1300 further includes an exercise correction generation unit 1422 and a correction information output unit 1430.

  The head-side sensor signal collecting unit 1310H includes a head-side triaxial acceleration sensor 1411H including up / down, left / right, and front / back. The waist-side sensor signal collecting unit 1310W includes a waist-side triaxial direction including up / down, left / right, and front / back. An acceleration sensor 1411W and a position measurement sensor 1412W for measuring the position of the user are included.

  For the head side and waist side triaxial acceleration sensors 1411H and 1411W, a suitable sensor is generally selected from among sensors generally used for measuring triaxial acceleration, such as a form incorporating a gyroscope. can do. The position measurement sensor 1412W is for measuring the absolute position of the user. For example, the position measurement sensor 1412W may measure the position of the user by using a GPS signal, or recently, the position measurement sensor 1412W is more highly accurate than GPS. Where precision satellite navigation technology is being developed, sensors to which such technology is applied may be used. Further, the head-side and waist-side sensor signal collecting units 1310H and 1310W are configured to provide a triaxial angular velocity sensor 1412H as shown in FIG. 14 so as to improve accuracy in the motion recognition and analysis process described in more detail below. Further can be included.

  In the exercise posture deriving device 1300 according to the present embodiment, the head side and waist side sensor signal collection units 1310H and 1310W are worn on the head side and the waist side of the user as shown in FIG. The user's dynamic physical quantity is measured. In particular, in this embodiment, when measuring a user's dynamic physical quantity for posture derivation, a value measured at a position that appears most similar to the motion of the center of mass of the user's body is used. For example, in the present invention, lateral acceleration is measured on the head side, longitudinal acceleration and position are measured on the waist side, and vertical acceleration is measured on the head side or waist side. Like that. To explain in more detail about the case of vertical acceleration, the vertical acceleration is accurately measured regardless of whether it is measured on the head side or the waist side, so it is measured on one of the head side and waist side. The selected value may be used selectively, or an average value of the values measured on both sides may be used.

  Of course, in general, the acceleration sensor can measure all the accelerations in the vertical, left and right, front and rear directions, that is, in the three-axis directions. Therefore, the acceleration sensor signal collecting unit 1310H alone or the lumbar sensor signal collecting unit 1310W alone can measure. Of course, various calculations described later may be executed using the collected vertical, horizontal, and longitudinal accelerations. However, when walking and running, the left and right movements at the head side and the left and right movements at the center of mass of the user's body appear more similar, and the back and forth movements at the waist side and the user are relatively similar. The back and forth movement of the center of mass of the body appears more similar. Furthermore, the up and down movement appears in the head side, the waist side, and the center of mass in a similar manner. On the other hand, in the motion recognition method of the present invention described later, ultimately, motion recognition, posture derivation, and the like are performed using a dynamic physical quantity at the center of mass of the user's body. When looking at all of these things, measure the lateral acceleration on the head side, measure the longitudinal acceleration on the waist side, and select either the head side or the waist side as desired and select the vertical direction. By measuring the acceleration in the vertical direction as the average value obtained by measuring the acceleration or both, it is possible to obtain the effect that the final motion recognition and posture derivation are performed much more accurately. .

  The motion posture deriving unit 1421 receives signals from the head side and waist side sensor signal collection units 1310H and 1310W, and walks or runs including the acceleration, speed, and position of the user mass center using triaxial acceleration and position signals. It plays a role of deriving a walking or running posture by deriving a motion state value and analyzing the walking or running motion state value. Specifically, the motion posture deriving unit 1421 estimates a pressure center path from the walking or running motion state value, analyzes the pressure center path, and derives a walking or running posture. Since the analysis algorithm of the present invention used in the motion posture deriving unit 1421 will be described in more detail later, the description thereof is omitted here.

  On the other hand, the exercise posture deriving unit 1421 is formed of an integrated circuit that can execute various calculations, and may be formed on a single substrate integrally with the waist sensor signal collecting unit 1310W, or may be a separate computer or the like. It may consist of such forms. Further, the head-side and waist-side sensor signal collecting units 1310H and 1310W may be provided with a head-side communication unit 1413H and a waist-side communication unit 1413W, respectively, for signal transmission with the exercise posture deriving unit 1421. . When the lumbar sensor signal collecting unit 1310W is integrated with the exercise posture deriving unit 1421, the lumbar side communication unit 1310W may be directly connected to the exercise posture deriving unit 1421 to transmit signals, or the lumbar side communication unit 1413W may be The signal transmitted from the head-side communication unit 1413H may be received and transmitted to the exercise posture deriving unit 1421. The head-side and waist-side communication units 1413H and 1413W may be wired, or signals may be transmitted using at least one wireless communication selected from Bluetooth, Wi-Fi, and NFC to further enhance user convenience. May be communicated.

  The motion correction generation unit 1422 serves to generate posture correction information by comparing the walking posture derived by the motion posture deriving unit 1421 with the reference posture. In the exercise posture deriving unit 1421, as described above, the user's walking or running posture is derived based on the signals collected by the head side and waist side sensor signal collecting units 1310H and 1310W. It is possible to derive the user's traveling direction, speed, etc. during walking or running, thereby obtaining the stride that is one of the elements of the walking posture. In this case, the motion correction generation unit 1422 incorporates optimum height-step ratio relation data for each walking and running speed, compares the user's walking posture information with it, and the step length is greater than the user's height. It is possible to easily calculate the amount of striation correction that must be reduced or increased when it is determined that the width is not excessively wide or narrow, and is out of the optimum range.

  The correction information output unit 1430 converts the posture correction information generated by the movement correction generation unit 1422 in this way as information that can be recognized by the user including sound, illustration, and image, and outputs the information. For example, when it is necessary to reduce the step length by calculating the step correction amount, is it possible to output a voice such as “Please reduce the step length” via a speaker provided on the exercise posture deriving device 1300? Alternatively, it is possible to guide the user to change the walking posture by recognizing that the stride is not the optimum stride by sounding a warning sound. In particular, in the case of such a configuration, the correction information output unit 1430 is integrated with the head side sensor signal collection unit 1310H, so that the correction information output unit 1430 is disposed close to the user's information collection organ, that is, the eyes, ears, and the like. It is preferable to do so. That is, as a specific example, when the head side sensor signal collecting unit 1310H is in the form of an earphone that is pointed to the ear as illustrated in FIG. 13 and the output information form is a voice or an acoustic signal, correction is performed. The efficiency of information transmission is maximized. Alternatively, it can be connected to a smartphone, a computer, a dedicated display, or the like, and correct correction information can be output as an illustration or an image.

  Furthermore, the exercise posture deriving device 1300 can transfer the walking posture derived by the exercise posture deriving unit 1421 to the external database 1440 and store it cumulatively. Users who need such walking or running motion analysis are ordinary people who walk or jog every day for health promotion, experts who train to improve physical ability, etc., and such motion analysis data accumulates Of course, it is preferable to be able to see changes over time. In addition, when a large amount of motion analysis data is accumulated and stored in this way, such data can be used as big data and used for various statistics and analyses.

  In the motion posture deriving device 1300 according to the present embodiment as described above, specific and comprehensive examples from motion recognition through motion posture derivation to correction information output are as follows. First, as described above, the lateral acceleration is collected on the head side, the longitudinal acceleration and the position are collected on the waist side, and the vertical direction on the head side or the waist side, similar to the movement of the center of mass of the user's body as described above. Collect acceleration.

  The lumbar sensor signal collecting unit 1310W can be integrated with the exercise posture deriving unit 1421. Therefore, the physical quantity collected by the head side sensor signal collecting unit 1310H is on the exercise posture deriving unit 1421 side via the head side communication unit 1413H. Is transmitted to. At this time, it is more efficient if 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 collected in this way, the motion posture deriving unit 1421 derives the user's walking and running posture. The motion correction generation unit 1422 generates posture correction information by comparing the actual posture derived in this way with an ideal reference posture. The exercise posture deriving unit 1421 and the exercise correction generating unit 1422 may be integrated, that is, they are integrated with the waist sensor signal collecting unit 1310W.

  In order for the posture correction information generated in this way to be effectively transmitted to the user, it is preferable that the information is transmitted from the head side close to the user's information collecting organ, such as eyes and ears. . As described above, when the head side sensor signal collection unit 1310H and the correction information output unit 1430 are integrated, the generated posture correction information sequentially passes through the waist side communication unit 1413W and the head side communication unit 1413H. 1430, and the corrective information is effectively transmitted in a form such as transmitting a voice message to the user's ear.

  The motion posture deriving method according to another embodiment of the present invention uses the motion posture deriving device 1300 to detect a user's motion and perform analysis such as determining whether the user is walking or running. At this time, as described above, the analysis algorithm used in the present invention uses dynamic physical quantities measured on the head side and the waist side of the user. It is sufficient to include a head side triaxial acceleration sensor 1411H including left and right and front and back, a waist side triaxial acceleration sensor 1411W including top and bottom, left and right, front and rear, and a position measurement sensor 1412W that measures the position of the user. The analysis algorithm described below may be executed in the unit 1421. Of course, the motion posture deriving device 1300 may further include the various additional components described above in order to improve the function of the device.

  FIG. 15 shows a schematic diagram of an injury risk quantification apparatus 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 an injury risk that may occur during walking or running. More specifically, it is as follows. There is a well-known problem that when walking or running, there is a risk of injury due to overloading the ankle, knees, hips, etc. for various reasons such as wrong posture or hard ground. . In order to avoid such a risk, there has been only a measure such as wearing functional sneakers such as shock absorbers in the past, and it is accurate to know how much injury risk actually occurs It is the fact that there was no indicator. In the present embodiment, such a risk of injury is quantified using a judgment index, and if the risk of injury increases to a certain level or higher, the danger level is notified to the user by an alarm. As a result, the user can take appropriate measures such as stopping walking or running appropriately before the injury occurs, correcting posture, changing sneakers, changing walking or running course, etc. Thus, the risk of injury that occurs during walking or running can be greatly reduced.

  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. 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 excluding the user's arm. The sensor signal collection unit 1510 may be single or plural. Two sensor signal collection units 1510 may be formed and worn on each of the user's head side and waist side. In this case, the sensor signal collection unit worn on the user's head side is the head side sensor signal collection unit. In 1510H, the sensor signal collection unit worn on the user's waist can be divided into a waist sensor signal collection unit 1510W. As a specific example of the wearing state, the head side sensor signal collecting unit 1510H worn on the head side is configured to be put on the ear like an earphone, and the waist side sensor signal collecting unit 1510W worn on the waist side is a belt. It consists of a form. Of course, the present invention is not limited to this, and for example, the head side sensor signal collecting unit 1510H can be variously modified such as a hair band form, glasses form, a form attached to a separate hat, and a helmet form. 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 part, the sensor signal collecting unit 1510 is accommodated in the breast pocket of clothes. Various modifications can be made, such as a form to be attached or even attached, a form to be worn using a separate waistcoat or harness, and the like.

  The sensor signal collection unit 1510 includes the acceleration sensor 1511 as described above. As the acceleration sensor 1511, a suitable sensor can be selected and used from among sensors generally used for measuring acceleration in the three-axis directions, such as a form incorporating a gyroscope. On the other hand, the sensor signal collection unit 1510 may be directly provided with a control unit 1520 that performs a role of performing calculation and controlling using the acceleration data signal collected by the acceleration sensor 1511. Alternatively, the controller 1520 can be implemented in various ways, such as being realized in the form of an application on a smartphone used in the past. That is, when the control unit 1520 is realized by a device different from the sensor signal collection unit 1510 as described above, the sensor signal is transmitted so that the acceleration data signal collected by the acceleration sensor 1511 is smoothly transmitted to the control unit 1520. The collection unit 1510 may further include a communication unit 1512. Such signal transmission may be performed by wired communication through wiring, or may be performed by wireless communication such as Bluetooth, Wi-Fi, NFC, etc., depending on necessary conditions and required performance. A suitable form can be selected and adopted.

  This will be described in more detail later in the description of the injury risk quantification method according to this embodiment. In this embodiment, vertical acceleration is used to determine the injury risk.

  In this embodiment, vertical acceleration is used to quantify the risk of injury. In general, when running, the left and right movements on the head side and the left and right movements of the center of mass of the user's body appear more similar, and the back and forth movements on the waist side and the movement of the user's body are relatively similar. The back and forth movement of the center of mass appears more similar. Further, the up and down movement appears in a similar manner in all of the upper body and the center of mass including the head to the waist. However, the arm portion of the upper body is excluded because it moves in the front-rear direction in addition to the movement of the center of mass. When this is taken into consideration, the vertical acceleration may be measured anywhere on the upper body excluding the arm. In more detail, since vertical acceleration appears accurately and accurately wherever it is measured on the upper body excluding the arm, the value measured at one of the head side or waist side is selectively used. It may be used, or an average value of values measured on both sides may be used.

The control unit 1520 receives the signal from the sensor signal collecting unit 1510, derives at least one injury risk determination index calculated based on the vertical acceleration _az , and uses the injury risk determination index to generate an alarm. It plays a role in judging and controlling the occurrence. At least More specifically, the control unit 1520, as the injury risk judgment index, the slope average vertical acceleration a _z, the slope maximum vertical acceleration a _z, selected from among the maximum impact force, the impact weight One is derived, thereby quantifying the risk of injury and determining the degree of danger. The derivation of the injury risk determination index performed by the control unit 1520 will be described in more detail later while describing the injury risk quantification method according to the present embodiment.

  The actual implementation of the controller 1520 can be variously formed according to necessity and purpose. That is, the control unit 1520 may be formed as an integrated circuit that can perform various calculations, and may be formed on a single substrate integrally with the sensor signal collection unit 1510, or may be a separate dedicated device (ie, injury risk). Independent devices made only for quantification purposes), a separate computer, or the like, or may be realized in the form of an application on a conventionally used smartphone as described above. As described above, when the control unit 1520 is integrated with the sensor signal collection unit 1510, a signal can be directly transmitted from the acceleration sensor 1511. On the other hand, when the control unit 1520 is formed independently of the sensor signal collection unit 1510, for example, in the form of another device or a smartphone application, the signal is transmitted from the acceleration sensor 1511 by wired or wireless communication. You can start receiving.

The alarm unit 1530 receives an alarm generation control signal from the control unit 1520 and serves to warn the user of the danger of injury. The control unit 1520 derives at least one injury risk determination index calculated based on the vertical acceleration _az and uses it to determine whether or not an alarm has occurred, so that the risk of injury exceeds a predetermined standard. If it is determined, the alarm unit 1530 notifies the user of the danger by controlling the alarm unit 1530 to generate an alarm.

  The alarm unit 1530 outputs an alarm signal as information that can be recognized by the user including sounds, illustrations, and images. For example, when the alarm unit 1530 is configured as a speaker that outputs sound, if the risk of injury is greater than or equal to a reference, a warning sound can be generated. Alternatively, when the apparatus according to the present embodiment is applied to augmented reality glasses such as Google Glass, the alarm unit 1530 outputs a red warning graphic or an image in which such a graphic blinks on the augmented reality glasses, Or a message such as “What is the risk of injury?” Can be output. Alternatively, if the alarm unit 1530 is realized by a thermoelectric element and is in direct or indirect contact with the user's skin, and the risk of injury exceeds a standard, the user is warned by being cold or hot. You can also As another example, for a case where the user is a visually impaired person, the alarm unit 1530 may be configured to be recognized by tactile sense as a changeable Braille form. As described above, the alarm unit may have any form as long as it can output an alarm signal as information that can be recognized by the user.

  Further, the injury risk quantification apparatus 1500 transfers injury risk data including an injury risk alarm occurrence time and an injury risk judgment index value at that time to an external database 1540 and cumulatively stores it. be able to. Users who need such walking or running motion analysis are ordinary people who walk or jog every day for health promotion, experts who train to improve physical ability, etc., and such motion analysis data accumulates Of course, it is preferable to be able to see changes over time. In addition, when a large amount of motion analysis data is accumulated and stored in this way, such data can be used as big data and used for various statistics and analyses.

  FIG. 16 shows a flowchart of an injury risk quantification method according to another embodiment of the present invention.

The injury risk quantification method according to the present embodiment includes the acceleration sensor 1511 as described above, and is measured using at least one sensor signal collection unit 1510 worn on the upper body excluding the user's arm. An injury risk determination index is derived using the vertical acceleration _az to quantify the injury risk. For this purpose, 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. Furthermore, in order to improve the accuracy of deriving an injury risk determination index, a noise removal step is further included. The steps shown in FIG. 16 will be described in more detail as follows.

In the data collecting step, the sensor signal collecting unit 1510 collects the measured vertical acceleration _az . The collected vertical acceleration _az may be used as it is, but it is more preferable to pass a noise removal step of removing noise by passing through a predetermined bandpass filter. At this time, for example, the bandpass filter may be formed at 0.1 to 5 Hz corresponding to a general human walking or running frequency, but this range may be appropriately changed and determined. It is.

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

In the injury risk determination step, it is determined whether or not the injury risk determination index is larger than a predetermined reference. At this time, the injury risk determination index may be plural as described above, and an alarm may be generated when only one of the various determination indexes is above the reference, and all are above the reference. An alarm may be generated at a certain time, or an alarm may be generated in stages with appropriate priorities. In the injury risk determination step, if the injury risk determination index is smaller than a predetermined reference, no alarm is generated and the process returns to the data collection step again. Further, in the injury risk determination step, determination is made using the injury risk determination index calculated by collecting data of at least two cycles with respect to the vertical acceleration _az data represented by a periodic signal. It is preferable to do so.

  In the injury risk warning step, if at least one of the injury risk determination indexes is greater than a predetermined criterion, the user is warned of the injury risk. The warning form of injury risk may be various forms such as sound, illustration, and image as described above, and the user can actively reduce the risk of injury by receiving the warning in this way. By coping (exercise termination, posture correction, footwear replacement, course change, etc.), the risk of injury can be greatly reduced.

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

  FIG. 17 shows a vertical acceleration graph during traveling according to another embodiment of the present invention.

As shown in the figure, the vertical acceleration _az appears in a periodic form with respect to time (this is natural since walking or running itself is a periodic movement). The running motion can be described as follows. First, it starts from the moment when one of the legs that is moving forward kicks the ground (the other leg is floating in the air at this moment). In this state, one of the legs kicks off the ground and floats while all the legs are floating in the air, while the human torso moves forward, and with that, the front and rear changes while swinging both legs in the air. Comes out ahead. The moment when the other leg that comes out forward touches the ground and kicks the ground again is made again, and it is a one-step run. In this process, at the moment of kicking the ground with one foot, the person's head shakes the most in the vertical direction (local maximum is formed at the vertical acceleration _az ), but in the vertical direction when moving in the air (A constant value is formed at the vertical acceleration _az ).

Immediately in this way, the most impact is applied to the joint at the moment when the foot kicks off the ground, and such impact appears in the form of 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, and in the present invention, it is used as a basis for judgment quantified by indexing it. Such determination index, in the present invention, as described above, the slope average vertical acceleration a _z, the slope maximum vertical acceleration a _z, maximum impact force, the impact amount used.

FIG. 18 shows an inclination in a vertical acceleration graph during traveling according to another embodiment of the present invention. A process for deriving the average inclination and the maximum inclination of the vertical acceleration _az will be described.

First, when the average inclination value of the vertical acceleration _az is selected as the injury risk determination index, the injury risk determination index is calculated using the following equation.
(Where, a _z : vertical acceleration, mean: average value calculation function, i: index number, t _i : i-th time, t _i-1 : i-1th time, t _c : impact start time, t _m : impact end time)

The impact start time actually means the moment when the foot lands on the ground. This can be determined when the vertical acceleration _az breaks upward from a predetermined reference value (for example, 0.3 m / s ² ) close to 0 from a value of 0 or less. Here, the specific value of the reference value for determining the impact start time can be appropriately determined from values 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 a time index obtained by digitizing the time from the impact start time to the impact end time divided by n, and n may be appropriately determined as necessary.

The average slope value is an average value of n slope values obtained at each interval when the time from the impact start time to the impact end time is equally divided into n. FIG. 18 shows a vertical acceleration _az graph in one cycle, and the average slope value as described above can be obtained in such one cycle. On the other hand, as shown in FIG. 17, the graph of the form as shown in FIG. 18 is continuously repeated during traveling, and the average slope value as described above can be obtained for each period (that is, for each step). it can. At this time, in the determination index deriving step, an average vertical loading rate calculated as a product of the user mass m and the average inclination can be further calculated.

On the other hand, when the maximum inclination value of the vertical acceleration _az is selected as the injury risk determination index, the injury risk determination index is calculated using the following equation.
i = 1, 2,..., n,
t ₀ = t _c , t _n = t _m
(Where, a _z : vertical acceleration, max: maximum value calculation function, i: index number, t _i : i-th time, t _i-1 : i-1th time, t _c : impact start time, t _m : impact end time)

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

  FIG. 19 shows the amount of impact in a vertical acceleration graph during travel according to another embodiment of the present invention. The process of deriving the maximum impact force and impact amount through this will be described.

First, when the maximum impact force value is selected as the injury risk determination index, the injury risk determination index is calculated using the following equation.
(Here, _{a z:} vertical acceleration, m: user mass, _{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 naturally the impact end time. In FIG. 19, the first peak (1st peak) of the vertical acceleration _az is displayed, and a value obtained by multiplying this by the user mass m immediately becomes the maximum impact force value.

On the other hand, when an impact amount value is selected as the injury risk determination index, the injury risk determination index is calculated using the following equation.
(Here, _{a z:} vertical acceleration, m: user mass, _{t c:} shock start time, _{t m:} impact end time)

FIG. 19 shows the vertical acceleration _az graph area between the impact start time and the impact end time, and the value obtained by multiplying this area by the user mass m immediately becomes the impact amount value.

  FIG. 20 illustrates a first apparatus for motion recognition according to another embodiment of the present invention.

  A first device 2000 for motion recognition according to the present embodiment (hereinafter referred to as a first device) includes an acceleration sensor unit 2010, an angular velocity sensor unit 2020, a processing unit 2040, and a user interface unit 2050. The first apparatus 2000 according to the present embodiment analyzes a user's motion state such as walking and running by measuring the user's dynamic physical quantities such as acceleration and angular velocity when worn on the user's body. As in the example shown in FIG. 1, the first device 2000 includes a band worn on the head side and the waist side, a form attached to the head side and the waist side in the form of a clip, a form provided in a hat, a belt It consists of a form to wear, a form of glasses, a form of helmet, a form attached to ears, a form attached to clothes, and a form worn with a separate waistcoat or harness. Specifically, the glasses form includes augmented reality (AR) glasses, glasses frames, sunglasses, and the like. The form attached to the ear includes forms such as hands-free earpieces, headphones, and earphones. It will be apparent to those skilled in the art that the first device 2000 may have various modifications. The first device 2000 may be formed on a single substrate in the form of an integrated circuit capable of performing various calculations.

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

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

  The processing unit 2040 generates a first motion state value based on the triaxial acceleration value and the triaxial angular velocity value. The first motion state value includes exercise time, number of steps taken, number of steps taken per minute, step distance, step angle, head angle, ground support time, air suspension time, ratio of ground support time to air suspension time, maximum vertical force , Average normal force load factor, maximum normal force load factor, left-right balance, left-right uniformity. The first device 2000 can determine the user's exercise state based on the first exercise state value. Looking at the meaning of each of the first exercise state values, the number of steps per minute is the number of steps per minute, the distance between steps is the average distance between legs, the step angle is the average leg angle, and the head angle is the upper and lower head The average angle, the ground support time is the support time in contact with the land, the air suspension time is the average time when all legs are not in contact with the land, the maximum normal force is the maximum value of the ground reaction force, the average normal force load factor is The average and maximum vertical force load factors of the left and right ground reaction force support sections mean the maximum initial inclination of the left and right ground reaction force support sections.

The left / right uniformity (Stability) means whether the state of movement of the left leg and the right leg is maintained consistently in time, force, etc., and the coefficient of variation (CV: Coefficient) of each leg is used. And obtained by the following formula.
Stability (Left) = 1-std (Left indications) / mean (Left indications)
Stability (Right) = 1-std (Right indicators) / mean (Right indicators)

  The value used as an index that is an evaluation index includes a vertical force maximum value, a vertical acceleration maximum value, a support section impact amount, a support time, a floating time, an average normal force load factor, and a maximum normal force load factor.

The left-right balance (Balance) indicates the left-right imbalance (%), 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 alive mode of the processing unit 2040. The user interface unit 2050 may be realized in the form of software or hardware. For example, the user interface unit 2050 may be realized by a push button that is in the form of software or hardware. A flowchart of the motion recognition method started from user input of the user interface unit 2050 will be described later in detail with reference to FIG.

  Meanwhile, the first device 2000 according to the present embodiment may further include a first communication unit 2070. The first communication unit 2070 transfers the first motion state value to the second device 2100. The first communication unit 2070 can transfer the first motion state value to the second device 2100 in a predetermined cycle, but it is obvious to those skilled in the art that the first communication unit 2070 may be realized by various transfer methods. The second device 2100 according to the present embodiment may be various types of devices such as a computer, a mobile terminal, and a clock.

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

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

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

  When the first apparatus 2000 further includes a position sensor unit 2030, the processing unit 2040 generates a second exercise state value based on at least one of the first exercise state value, the user position value, and the user profile. To do. The second exercise state value is at least one of exercise distance, exercise speed, calorie consumption, altitude, and stride. Looking at the meaning of each of the second motion state values, the altitude means the vertical height moved during exercise, and the stride means the distance moved forward between the ground support section and the air suspension section. The user profile includes personal information such as the height and weight of the user.

  In addition, the processing unit 2040 may selectively generate 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. For example, the processing unit 2040 stores optimal height-step length relationship data for each exercise speed, and the step length is excessively wider or narrower than the user's height based on the step length among the second exercise state values. Judge whether there is. The processing unit 2040 generates, as posture correction information, the amount of striation correction that must be reduced or increased when the stride departs from the optimum range.

  Meanwhile, the first device 2000 according to the present embodiment may further include an output unit 2060. The output unit 2060 converts and outputs the posture correction information as information that can be recognized by the user and is at least one of sound, illustration, image, and vibration. For example, when the stride correction amount is calculated and the stride needs to be reduced, the user outputs a voice such as “Please reduce the stride” through the speaker or sounds a warning sound. Recognizing that is not the optimal stride, it can be guided to change the walking posture. Alternatively, the first device 2000 can be connected to an external device such as a mobile terminal, a clock, a computer, and a dedicated display so that correction information is output as at least one of sound, illustration, image, and vibration. .

  When the first device 2000 further includes the position sensor unit 2030, the first device 2000 may further include a third communication unit that transfers the second motion state value to the server 2200. The server 2200 accumulates and stores the second motion state value in a 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, an average value, and the like for each of the second motion state values for a predetermined motion section. A user who needs exercise analysis can receive the statistical data through the server 2200 and use it for various purposes such as improving his exercise habits. The users who need exercise analysis are ordinary people who take a walk or jog every day for health promotion, and experts who train to improve physical ability. Also, the server 2200 stores the second exercise state value for each user and provides a big data service in which the second exercise state value is analyzed relationally and statistically between users.

  The first communication unit 2070, the second communication unit 2080, and the third communication unit are configured by at least one of wireless communication including Bluetooth, Wi-Fi, and NFC and wired communication via wiring. It will be apparent to those skilled in the art that wireless communication techniques may be utilized. The first communication unit 2070, the second communication unit 2080, and the third communication unit may be physically configured with a single interface or may be configured with a plurality of interfaces.

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

  A second apparatus 2100 for motion recognition (hereinafter referred to as a second apparatus) 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, and a clock.

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

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

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

  Meanwhile, the processing unit 2150 according to the present embodiment selectively generates at least one of the first movement state value and the second movement state value and each predetermined reference value to further generate movement posture correction information. can do. For example, the processing unit 2150 stores optimal height-step length relation data for each exercise speed, and the step length is excessively wide or narrow compared to the user's height based on the step length among the second exercise state values. Judge whether there is. The processing unit 2150 generates, as posture correction information, the amount of striation correction that must be reduced or increased when the stride departs from the optimum range.

  Meanwhile, the second device 2100 according to the present embodiment may further include an output unit 2190. The output unit 2190 converts and outputs the posture correction information as information that can be recognized by the user, which is at least one of sound, illustration, image, and vibration. For example, when the stride correction amount is calculated and the stride needs to be reduced, the user outputs a voice such as “Please reduce the stride” through the speaker or sounds a warning sound. Recognizing that is not the optimal stride, it can be guided to change the walking posture.

  Meanwhile, the second device 2100 according to the present embodiment may further include a second communication unit 2150. The second communication unit 2150 transfers the second exercise state value to the server 2200. The server 2200 accumulates and stores the second motion state value in a 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, an average value, and the like for each of the second motion state values for a predetermined motion section. A user who needs exercise analysis can receive the statistical data through the server 2200 and use it for various purposes such as improving his exercise habits. Also, the server 2200 stores the second exercise state value for each user and provides a big data service in which the second exercise state value is analyzed relationally and statistically between users.

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

  FIG. 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 sets up a connection with the second device 2100 via the first communication unit 2070.

  If the connection with the second device 2100 is set, in step 2230, the first device 2000 receives an instruction from the user interface unit 2050 or the second device 2100.

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

  In step 2250, the first apparatus 2000 generates a first motion state value based on the triaxial acceleration value and the triaxial angular velocity value. The first motion state value includes exercise time, number of steps taken, number of steps taken per minute, step distance, step angle, head angle, ground support time, air suspension time, ratio of ground support time to air suspension time, maximum vertical force , Average normal force load factor, maximum normal force load factor, left-right balance, left-right uniformity.

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

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

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

  The second device 2100 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 second apparatus 2100 stores optimal height-step length relationship data for each exercise speed, and the step length is excessively wide or narrow compared to the user's height based on the step length among the second exercise state values. Judge whether there is. The second device 2100 generates, as posture correction information, the amount of striation correction that must be reduced or increased when the stride departs from the optimum range. The second device 2100 converts the posture correction information as information that can be recognized by the user, which is at least one of sound, illustration, image, and vibration, and outputs the information. For example, when the stride correction amount is calculated and the stride needs to be reduced, the user outputs a voice such as “Please reduce the stride” through the speaker or sounds a warning sound. Recognizing that is not the optimal stride, it can be guided to change the walking posture.

  In step 2290, the second device 2100 transfers the second motion state value to the server 2200. The server 2200 accumulates and stores the second motion state value in a 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, an average value, and the like for each of the second motion state values for a predetermined motion section. A user who needs exercise analysis can receive the statistical data through the server 2200 and use it for various purposes such as improving his exercise habits. Also, the server 2200 stores the second exercise state value for each user and provides a big data service in which the second exercise state value is analyzed relationally and statistically between users.

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

  For example, a device according to an exemplary embodiment of the present invention can include a bus coupled to a unit of each device as illustrated, at least one processor coupled to the bus, command, receive Or a message coupled to the bus for storing generated messages or generated messages and coupled to at least one processor for executing commands as described above.

  The system according to the present invention can be realized as a computer readable code on a computer readable recording medium. Computer-readable recording media include all recording devices that store data that can be read by a computer system. The computer-readable recording medium includes a magnetic storage medium (for example, ROM, floppy disk, hard disk, etc.) and an optical reading medium (for example, CD-ROM, DVD, etc.). The computer-readable recording medium can be distributed to computer systems connected via a network, and can store and execute computer-readable codes in a distributed manner.

Claims (45)

An acceleration sensor unit that measures triaxial acceleration values including up and down, left and right, and front and rear;
Angular velocity sensor unit that measures angular velocity values in three axial directions including up and down, left and right, and front and rear,
A processing unit that generates a first motion state value based on the triaxial acceleration value and the triaxial angular velocity value, and a user interface unit that controls a sleep mode or an alive mode of the processing unit. First device.   The first device of claim 1, further comprising a first communication unit that transfers the first motion state value to the second device.   The first apparatus according to claim 2, wherein the first communication unit includes at least one of Bluetooth, Wi-Fi, and NFC.   The first apparatus according to claim 1, further comprising a second communication unit that transfers the first motion state value to a server.   The first apparatus according to claim 1, further comprising a position sensor unit that measures a position value of the user.   The processing unit according to claim 5, wherein the processing unit generates a second motion state value based on at least one of the first motion state value, the user position value, and a user profile. 1 device.   The first apparatus of claim 6, further comprising a third communication unit that transfers at least one of the first motion state value and the second motion state value to a server.   The first apparatus according to claim 6, wherein the second exercise state value is at least one of distance, speed, calorie consumption, altitude, and stride.   The processing unit according to claim 6, wherein the processing unit generates 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. The first device.   The first apparatus according to claim 9, further comprising an output unit configured to output the posture correction information as at least one of sound, illustration, image, and vibration. When the processing unit is changed to the alive mode by the user interface unit,
The processing unit sets a connection with the second device via the first communication unit,
Based on a command from the second device or the user interface unit, the acceleration sensor unit and the angular velocity sensor unit generate the triaxial acceleration value and the triaxial angular velocity value, respectively. The first device according to claim 2. The acceleration sensor unit and the angular velocity sensor unit store the triaxial acceleration value and the triaxial angular velocity value in a first-in first-out queue,
When the storage space of the first-in first-out queue is less than a predetermined threshold, the processing unit is in a sleep mode, and when the storage space of the first-in first-out queue is greater than or equal to a predetermined threshold, the processing unit is in an alive mode. The first device according to claim 1.   The first motion state value includes exercise time, number of steps taken, number of steps taken per minute, step distance, step angle, head angle, ground support time, air suspension time, ratio of ground support time to air suspension time, maximum vertical force The first apparatus according to claim 1, wherein the first apparatus is at least one of an average normal force load factor, a maximum normal force load factor, a left-right balance, and a left-right uniformity.   The first device includes a band worn on the head side and the waist side, a form attached to the head side and the waist side in the form of a clip, a form provided in a hat, a form worn on a belt, a form of glasses, a form of helmet, and an ear. The first apparatus according to claim 1, wherein the first apparatus is one of a form to be attached, a form to be attached to clothes, and a form to be worn as clothes. The glasses form consists of one of augmented reality glasses, glasses frames and sunglasses,
The form attached to the ear consists of one of hands-free earpieces, headphones and earphones,
The first apparatus according to claim 14, wherein the form worn as the clothes is one of a waistcoat and a harness. A first communication unit that receives a first motion state value generated based on a triaxial acceleration value and a triaxial angular velocity value from the first device;
A position sensor unit that measures the position value of the user, and a processing unit that generates a second movement state value based on at least one of the first movement state value, the user position value, and the user profile. A featured second device.   The second apparatus according to claim 16, further comprising a second communication unit that transfers at least one of the first motion state value and the second motion state value to a server.   The first motion state value includes exercise time, number of steps taken, number of steps taken per minute, step distance, step angle, head angle, ground support time, air suspension time, ratio of ground support time to air suspension time, maximum vertical force The second device according to claim 16, wherein the second device is at least one of average vertical force load factor, maximum normal force load factor, left-right balance, left-right uniformity.   The second apparatus according to claim 16, wherein the second motion state value is at least one of distance, speed, calorie consumption, altitude, and stride.   The processing unit according to claim 16, wherein the processing unit generates 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. The second device.   The second apparatus according to claim 20, further comprising an output unit that outputs the posture correction information as at least one of sound, illustration, image, and vibration.   The second apparatus according to claim 16, wherein the first communication unit includes at least one of Bluetooth, Wi-Fi, and NFC. Measuring the acceleration values in the three-axis directions including up and down, left and right, and front and rear by the acceleration sensor unit;
Measuring angular velocity values in three axial directions including up and down, left and right, and front and rear by an angular velocity sensor unit;
Generating a first motion state value based on the triaxial acceleration value and the triaxial angular velocity value by a processing unit; and controlling a sleep mode or an alive mode of the processing unit by a user interface unit. A motion recognition method for the first device, characterized by:   The method of claim 23, further comprising transferring the first motion state value to the second device.   The motion of the first device according to claim 24, wherein the step of transferring the first motion state value to the second device is transferring via at least one of Bluetooth, Wi-Fi, and NFC. Recognition method.   The method of claim 23, further comprising transferring the first motion state value to a server.   The method of claim 23, further comprising measuring a position value of the user.   28. The method of claim 27, further comprising generating a second exercise state value based on at least one of the first exercise state value, the user position value, and a user profile. Device motion recognition method.   The method of claim 28, further comprising transferring at least one of the first motion state value and the second motion state value to a server.   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 according to claim 28, further comprising: 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. A motion recognition method of the first device.   32. The motion recognition method of the first device according to claim 31, further comprising the step of outputting the posture correction information as at least one of sound, illustration, image and vibration. Measuring the triaxial acceleration value and measuring the triaxial acceleration value;
When the processing unit is changed to the alive mode by the user interface unit, setting a connection with the second device,
The motion recognition of the first device according to claim 24, wherein the three-axis direction acceleration value and the three-axis direction angular velocity value are respectively generated based on a command from the second device or the user interface unit. Method. The acceleration sensor unit and the angular velocity sensor unit store the triaxial acceleration value and the triaxial angular velocity value in a first-in first-out queue,
When the storage space of the first-in first-out queue is less than a predetermined threshold, the processing unit is in a sleep mode, and when the storage space of the first-in first-out queue is greater than or equal to a predetermined threshold, the processing unit is in an alive mode. The motion recognition method for the first device according to claim 23.   The first motion state value includes exercise time, number of steps taken, number of steps taken per minute, step distance, step angle, head angle, ground support time, air suspension time, ratio of ground support time to air suspension time, maximum vertical force 24. The motion recognition method of the first apparatus according to claim 23, wherein the motion recognition method is at least one of average vertical force load factor, maximum vertical force load factor, left-right balance, left-right uniformity.   The first device includes a band worn on the head side and the waist side, a form attached to the head side and the waist side in the form of a clip, a form provided in a hat, a form worn on a belt, a form of glasses, a form of helmet, and an ear. The motion recognition method of the first device according to claim 23, comprising one of a form to be attached, a form to be attached to clothes, and a form to be worn as clothes. The glasses form consists of one of augmented reality glasses, glasses frames and sunglasses,
The form attached to the ear consists of one of hands-free earpieces, headphones and earphones,
The motion recognition method of the first device according to claim 23, wherein the form to be worn as clothing is one of a waistcoat and a harness. Receiving a first motion state value generated based on the triaxial acceleration value and the triaxial angular velocity value from the first device;
Measuring a user's position value; and generating a second movement state value based on at least one of the first movement state value, the user position value, and a user profile. A motion recognition method of the second device.   The method of claim 38, further comprising transferring at least one of the first motion state value and the second motion state value to a server.   The first motion state value includes exercise time, number of steps taken, number of steps taken per minute, step distance, step angle, head angle, ground support time, air suspension time, ratio of ground support time to air suspension time, maximum vertical force 40. The motion recognition method of the second device according to claim 38, wherein at least one of average vertical force load factor, maximum vertical force load factor, left-right balance, and left-right uniformity.   The method of claim 38, wherein the second motion state value is at least one of distance, speed, calorie consumption, altitude, and stride.   39. The method according to claim 38, further comprising: 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. A motion recognition method of the second device.   40. The motion recognition method of the second device according to claim 38, further comprising outputting the posture correction information as at least one of sound, illustration, image, and vibration.   39. The motion of the second device according to claim 38, wherein the step of receiving the first motion state value from the first device is received via at least one of Bluetooth, Wi-Fi, and NFC. Recognition method.   The computer-readable recording medium with which the program for performing the method of any one of Claims 23-44 was recorded.