KR20160050304A - Apparatus and method for estimating stride length of user - Google Patents

Abstract

The present invention relates to an apparatus and a method to estimate a stride by reflecting individualized walking characteristics of a user. The apparatus to estimate a stride according to an embodiment of the present invention comprises: an acceleration sensor to measure an acceleration of the apparatus and output the acceleration as an acceleration signal; a gyroscope sensor to measure a motion of the apparatus and output the motion as a gyroscope signal; and a stride estimation unit to estimate a stride of the user by inputting extracted walking characteristics from the acceleration signal or gyroscope signal into a linear equation having different parameters in accordance with the walking characteristics pattern and a motion pattern of the user.

Description

[0001] APPARATUS AND METHOD FOR ESTIMATING STRIDE LENGTH OF USER [0002]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an apparatus and method for estimating a user's step size, and more particularly, to an apparatus and method for estimating a step size reflecting user's individualized step characteristics.

With the increasing use of mobile devices, there is a growing demand for location services that inform users of their current location. Such a positioning service estimates a user's position by using a GPS module or other positioning device built in a mobile device and combines with various application programs such as a navigation program and a map searching program to provide a useful and incredible user experience Contributing.

The technology that the positioning service mainly uses to estimate the user location is GNSS (Global Navigation Satellite System). A representative example of GNSS is GPS (Global Positioning System). However, the positioning technology based on the satellite information can not receive the satellite signals required in the city center or in the rooms with many buildings, so that it is impossible to estimate the accurate position of the user. Since ordinary users are indoors for most of the day, improved positioning techniques are needed to estimate the user's location in the indoor environment.

Recently, research on PDR technology based on an inertial sensor has been conducted as a positioning technique in an indoor environment. Pedestrian Dead Reckoning (PDR) technology is a technique for tracking a user's movement distance and direction through sensors, and tracking a user's expected position in such a manner as to calculate a relative position from a starting point.

However, there are many technical difficulties in applying such PDR technology to mobile devices. A typical example of such technical difficulties is that it is very difficult to accurately estimate the user's stride with conventional PDR technology.

For example, it is difficult for a person to apply a certain step-by-step estimation algorithm to a plurality of persons because the person's walking characteristics (for example, the step width, the step length, the walking direction, or the swing width when walking) Mobile devices can have various motions in use, such as putting them in their pockets, placing them on their hands, waving their mobile devices to shake them, putting them on their ears for calls, and so on. Since the reference axis of the three-axis sensor continuously changes, it is very difficult to estimate an accurate stride corresponding to various motions.

Korean Patent Publication No. 10-2008-0008632 (Jul. 31, 2009) Korean Patent Registration No. 10-0800874 (2008. 01. 28)

The object of the present invention is to provide a pace estimation apparatus and a pace estimation apparatus, which are configured to estimate a pace of a user even in a room where a satellite signal is not received and can accurately estimate a user's pace corresponding to various motions of a user of a mobile device, Method.

It is another object of the present invention to provide a pedometer estimating apparatus which improves accuracy by reflecting the individual walking characteristics of a user and a pedometer estimating method thereof.

It is another object of the present invention to provide an apparatus and method for estimating a user's stride without a separate calibration for estimating a user's stride.

An apparatus for estimating a stride of a user according to embodiments of the present invention includes an acceleration sensor for measuring an acceleration of the stride estimation apparatus and outputting the measured acceleration as an acceleration signal; A gyro sensor for measuring each movement of the stride estimation device and outputting it as a gyro signal; And a step size estimating unit for estimating a step size of the user by substituting the acceleration characteristic signal or the step characteristic value extracted from the gyro signal into a linear equation configured to have different parameters according to the user's type of walking characteristic and the user's motion type, .

The step of estimating a stride of a user according to embodiments of the present invention includes steps of sensing a user's step and measuring characteristic values of the user's step; Determining whether there is previously calculated characteristic information corresponding to the pattern of the measured characteristic values; And reading the previously calculated characteristic information corresponding to the pattern of the measured characteristic values and applying the read characteristic information to a linear equation for estimating a stride, and substituting characteristic values of the user's step into the linear equation And estimating a step width of the user, wherein the characteristic information includes parameter values corresponding to the user's walking characteristic type judged from the pattern and the user's motion type, respectively.

According to the embodiments of the present invention, the apparatus and method for estimating a stride can estimate the stride of a user even in a room where a satellite signal is not received, and generate and apply appropriate characteristic information corresponding to various motions of a user, Even if motion is taken, the user's stride can be accurately estimated.

Further, since such characteristics information reflects the individual walking characteristics of the user, the user's pace can be more accurately estimated in a customized manner.

In addition, the apparatus and method for estimating a pedometer according to the present invention can be achieved only by a software upgrade without using a separate module for estimating the pedometer, and the sensors built in a general mobile device. Therefore, .

Further, the apparatus and method for estimating a stride according to the present invention may further comprise a separate preset operation, such as calibrating a variable related to stride estimation, by walking a predetermined distance (e.g., 10 meters) to accurately estimate the user's stride The user convenience can be improved.

1 is a diagram illustrating various user motions according to usage patterns of a mobile device.
2 is a block diagram exemplarily showing a detailed configuration of a stride estimation apparatus according to an embodiment of the present invention;
Fig. 3 is a diagram exemplarily showing map information used for generating or calculating characteristic information.
4A and 4B are tables illustrating exemplary property information of a stride estimation apparatus according to an embodiment of the present invention.
5 is a flow chart illustrating a method by which a stride estimation apparatus generates characteristic information, according to an embodiment of the present disclosure;
FIG. 6 is a flowchart illustrating a method of estimating a user's step size using a feature information generated according to FIG.

The following detailed description refers to the accompanying drawings, which illustrate, by way of example, specific embodiments in which the invention may be practiced. Embodiments of the detailed description are provided for those of ordinary skill in the art to disclose the detailed description for carrying out the invention described herein.

Each of the embodiments of the present invention can describe different cases, but it does not mean that the embodiments are mutually exclusive. For example, the specific shapes, structures, and characteristics described in connection with one embodiment of the detailed description may be implemented in other embodiments without departing from the spirit and scope of the present invention. It is also to be understood that the location or arrangement of the individual components of the embodiments disclosed herein may be varied without departing from the spirit and scope of the invention.

On the other hand, in various embodiments, the same or similar reference numerals refer to the same or similar components. In the accompanying drawings, the sizes of the respective components may be exaggerated for explanatory purposes and need not be equal to or similar to the actual applied size.

1 is a diagram illustrating various user motions according to usage patterns of a mobile device. Referring to FIG. 1, the user 11 uses the mobile device 12 in various forms during movement or stop. The mobile device 12 incorporates a pedometer estimating apparatus according to the present specification.

1 shows an example of such various user motions as (a) looking at the mobile device 12 in a stationary state, (b) looking at the mobile device 12 while walking, (c) ), Or (d) the mobile device 12 is left in a pocket or waited for a while. It is of course also possible for the user to take a variety of other motions not illustrated in Fig. 1 (e.g., holding the mobile device in his / her hand and shaking).

In the user motions shown in FIG. 1, when the user's motion is changed, the axis of the sensors of the mobile device also changes. For example, when the user looks at the screen of the mobile device as shown in (a) and (b), the length axis of the sensors (i.e., the axis along the longitudinal direction of the mobile device) The width axis of the sensors (i.e., the axis along the lateral direction of the mobile device) faces the front of the user when the mobile device is placed in the user's ear or in a pocket as shown in (d). Therefore, when the user moves forward while looking at the screen of the mobile device as shown in (b), the displacement increases or decreases in the axial direction of the sensors. However, as shown in (c) or (d) When advancing, the displacement increases or decreases in the axial direction of the sensors.

In order to accurately estimate the stride of the user, it is necessary to determine whether a measured value is received from the sensors due to the axis change of the sensors, It is necessary to distinguish whether or not it is caused by the two or more of them.

In addition, users have individual walking characteristics such as different strides, steps, and habits. Since such step characteristics can affect the measured values of the sensor, such step characteristics must be considered for accurate stride estimation. For example, in the case of walking while holding the mobile device while walking, and when walking while swinging the arm holding the mobile device, the measurement values of the acceleration sensor and the gyro sensor may appear different even if they are moved by the same distance with the same step.

Accordingly, the stride estimating apparatus of the present invention minimizes the error probability due to the axis change of the sensors by detecting the motion of the user and reflecting the motion of the user in the stride estimation, and further, by using the map information and the gyro sensor built in the mobile device, Maximize the accuracy of the stride estimation by reflecting the characteristics to the stride estimates.

Hereinafter, a specific configuration and an operation method of the stride estimation apparatus according to the present invention will be described with reference to more detailed embodiments.

2 is a block diagram exemplarily showing a detailed configuration of a stride estimation apparatus according to an embodiment of the present invention; Referring to FIG. 2, the stride estimation apparatus 100 includes an acceleration sensor 110, a gyro sensor 120, a stride estimation unit 130, and a storage unit 140.

The stride estimation apparatus 100 may be included in the mobile device 12 as part of the mobile device 12 (see FIG. 1). On the other hand, the stride estimation apparatus 100 may further include a general configuration of a mobile device not shown in FIG. For example, the stride estimation apparatus 100 may further include a proximity sensor (not shown) that detects whether the user's body is in proximity to the mobile device 12.

The acceleration sensor 110 is a sensor included in the mobile device 12 or the stride estimation device 100 and senses the motion of the mobile device 12 or the stride estimation device 100 as an acceleration signal. The acceleration sensor 110 measures dynamic forces such as acceleration, vibration, and impact of the mobile device 12 or the stride estimation device 100 to generate an acceleration signal. The acceleration sensor 110 may be, for example, an inertial sensor that measures an inertial acceleration based on a stop system. The specific configuration and operation principle of the acceleration sensor 110 are well known in the related art, and a detailed description thereof will be omitted here.

The gyro sensor 120 is a sensor included in the mobile device 12 or the stride estimation device 100 and detects each (rotation) movement of the mobile device 12 or the stride estimation device 100. And generates a gyro signal. The gyro signal may be, for example, an angular velocity signal of the mobile device 12 or the pedometer estimating device 100. [ The gyro sensor 120 may be driven in a variety of ways, for example, the gyro sensor 120 may be an oscillating, mechanical, fluid, or optically driven sensor. The detailed configuration and operation principle of the gyro sensor 120 are well known in the related art, and a detailed description thereof will be omitted here.

As an example, the acceleration sensor 110 and the gyro sensor 120 may be bundled into one sensor unit. At this time, the sensor unit may further include the proximity sensor described above.

The step size estimator 130 applies characteristic information 150 that varies depending on the user's motion type and type of step characteristic to a predetermined linear equation and outputs the acceleration signal 110 received from the acceleration sensor 110 and the gyro sensor 120 And the value of the gyro signal to estimate the current stride of the user.

As an example, the linear equation used for the stride estimation of the stride estimator 130 may be configured to have different parameters, depending on the current walking characteristic type of the user.

The storage unit 140 provides a storage means necessary for operation of the stride estimation apparatus 100. [ The storage unit 140 includes characteristic information 150 indicating different parameters according to a user's motion type and type of walking characteristics, and map information 160 indicating a geographical configuration of a predetermined space. The storage unit 140 may be a temporary or non-temporary storage medium such as a dynamic random access memory (DRAM), a static random access memory (SRAM), a phase change random access memory (PRAM), a magnetic random access memory (MRAM) resistive random access memory, read only memory, erasable programmable read only memory (EPROM), flash memory, solid state drive (SSD), hard disk, floppy disk floppy disk, magnetic tape, or other data storage means.

The detailed operation of the stride estimation apparatus 100 having the above configuration will be described below.

First, an acceleration signal or a gyro signal is received from the acceleration sensor 110 or the gyro sensor 120 in accordance with the movement of the mobile device 12 or the stride estimation device 100. Then, after determining the motion of the user, the stride estimation apparatus 100 refers to the pattern of the acceleration signal or the gyro signal received together with the motion determination result, and determines the motion type of the current user and the type of the walking characteristic.

As an example, the stride estimation apparatus 100 may refer to a pattern of the proximity detection signal received from the proximity sensor (not shown) together with the pattern of the acceleration signal to determine the motion of the user. For example, if the proximity detection signal is continuously output, the user can judge that the mobile device 12 is in a state of talking to his / her ear or motion in a pocket. Alternatively, if the variance or average value of the acceleration signal is small, the user determines that the mobile device 12 is moving with the mobile device 12 still moving, whereas if the dispersion or average value of the acceleration signal is large, It can be judged by the motion that is moving while holding it in the hand or shaking it or putting it in the pocket.

Then, when the user's motion and thus the type of the step characteristic are determined, the device for estimating the pedometer 100 reads the characteristic information 150 corresponding to the determined motion and step characteristic types from the storage unit 150, ≪ / RTI > is determined. At this time, the linear equation can be formed in the form of Equation (1).

Ak represents the acceleration amplitude of the user 11 or the stride estimation apparatus 100 extracted from the input acceleration signal, Fk represents the acceleration amplitude of the user 11 or the stride estimation apparatus 100 extracted from the input acceleration signal, Sk represents the shaking angle of the user 11 or the stride estimation apparatus 100 extracted from the input gyro signal. And,?,?, And? Are coefficients representing weights of the respective terms in Equation (1), and are variables that depend on the user's motion type. And, An, Fn, and Sn are variables indicating the average acceleration amplitude, step frequency, and swing angle according to the type of the user's walking characteristics, respectively. α, β, γ, and An, Fn, and Sn may be values stored as the characteristic information 150 for the stride estimation, as parameters that may vary depending on the motion type of the user and the type of the step characteristic.

In general, an acceleration signal or a gyro signal may have a pattern similar to a sine with amplitude and frequency. Then, depending on the type of the user's walking, the instantaneous acceleration signal and the gyro signal represent different parameters.

For example, if the user is walking faster, the amplitude of the acceleration signal will increase, and vice versa, the amplitude of the acceleration signal will be smaller. Also, if the user walks more often at a faster pace, the pace frequency will be faster, and if you walk slowly with a slow pace, the pace will be slower.

Similarly, when the user shakes the mobile device 12, the swing angle will be larger, and when the user stays still with the mobile device 12, the swing angle will decrease.

As described above, the acceleration amplitude, the step frequency, and the swing angle of the user's step are parameters having correlation with the user's walking characteristics and stride. In this specification, Ak, Fk, and Sk representing the user's current acceleration amplitude, , And An, Fn, and Sn representing the average acceleration amplitude, the average step frequency, and the average swing angle of the user in the past specific time interval are used as the main parameters for the stride estimation. On the other hand,?,?, And? Are weight values weighting each term of the right side of Equation (1) so that Equation (1) can accurately estimate the user's actual stride, and are values experimentally selected or determined by user input .

On the other hand, SLn is an average stride of the user, and its initial value SL0 is a predetermined constant depending on the body dimensions such as the user's key or weight as an initial stride of the user. For example, SL0 may be a constant calculated by multiplying a user's key by a predetermined first value and subtracting a predetermined second value from the result.

In Equation (1),?,?, And? Are parameters that reflect the motion type of the user, and An, Fn Sn, and SLn are parameters that reflect the user's step characteristic type As a parameter, when the user's motion type and the step characteristic type are determined, the stride estimation apparatus 100 refers to the characteristic information 150 to determine values corresponding to the motion type and values corresponding to the step characteristic type as α, β, γ, And the linear equation (see Equation 1) held as An, Fn, and Sn. Then, the current acceleration amplitude, the walking frequency and the shaking angles (Ak, Fk, Sk) extracted from the acceleration signal or the gyro signal are substituted into the determined linear equation to calculate the estimated stride length SLk.

A concrete example of the characteristic information 150 and the map information 160 and a specific calculation method of the characteristic information 150 will be described later with reference to FIG. 3 to FIG.

According to the pedometer estimating apparatus 100 having the above configuration, since the satellite signal is not used, it is possible to estimate the stride of the user even in a room where no satellite signal is received, and appropriate property information is applied corresponding to various motions of the user , It is possible to accurately estimate the user's stride even if the user takes various kinds of motions.

In addition, since the individual characteristic of the user is reflected in the characteristic information together with the acceleration amplitude, the gait frequency, and the shaking angle, the stride estimation apparatus 100 can more accurately estimate the user stride considering the user individual pitch characteristics.

Here, the acceleration sensor, the gyro sensor, and the proximity sensor used for estimating the step size are modules normally provided in a general mobile device. That is, the stride estimation apparatus 100 can estimate the stride of a user using only sensors built in a general mobile device without having to include a separate module, and thus has general versatility that is widely available at low cost.

Further, the stride estimation apparatus 100 does not require a separate preset calibration, so it can provide better user convenience.

Fig. 3 is a diagram exemplarily showing map information used for generating or calculating characteristic information. 3, a concrete method of generating or calculating the characteristic information of the user will be described with reference to the map information 160 shown in FIG.

Referring to FIG. 3, the map information 160 includes two or more minutiae points 161, 162, 163, 164, 165, 166 and information indicating the distance between them.

The feature points 161, 162, 163, 164, 165 and 166 of the map information 160 are points that can be sensed through the output signal (gyro signal) of the gyro sensor 120 Corner, intersection, or intersection.

The map information 160 includes feature points 161, 162, 163, 164, and 164, such as global coordinates of the feature points 161, 162, 163, 164, 165 and 166, local coordinates, relative coordinates therebetween, , 165, and 166).

A method for generating or calculating the characteristic information of the user with reference to the map information 160 by the stride estimation apparatus 100 (see FIG. 2) is as follows.

First, the stride estimation apparatus 100 determines whether the user (i.e., holding the stride estimation apparatus 100) has a point on the map information 160 at any one of the minutiae points 161, 162, 163, 164, 165, It judges whether it passes. Whether or not the user passes through the minutiae points 161, 162, 163, 164, 165, and 166 can be determined based on the gyro signal output from the gyro sensor 120 of the stride estimation apparatus 100. The feature points 161, 162, 163, 164, 165 and 166 are points including corners such as corners, intersections, and three-way intersections, and the user can turn or turn And so on. Turning or turning of the user is accompanied by angular movement of the stride estimation apparatus 100 and each of these movements can be detected from the gyro signal of the gyro sensor 120. Thus, by sensing and analyzing the output gyro signal, the stride estimation apparatus 100 can determine which one of the minutiae points 161, 162, 163, 164, 165, 166 the user is crossing.

The step size estimating apparatus 100 estimates the acceleration amplitude Ak of the user's step, the step frequency Fk, the shaking angle Sk, and the number of steps of the user, if it is judged that the user passes through any feature point (for example, 163) . As an embodiment, the number of steps of the user can be calculated or measured from the result of analyzing the acceleration amplitude (Ak) or the step frequency (Fk) or from the result of analyzing the acceleration signal of the acceleration sensor 110 (see FIG. 2) .

The stride estimation apparatus 100 measures the acceleration amplitude Ak, the walking frequency Fk, the shaking angle Sk and the number of steps until the user passes the next feature point (for example, 164) Continuously measure. If it is determined that the user passes through the next feature point 164, the stride estimation apparatus 100 calculates the feature points 164 based on the acceleration amplitude Ak, the walking frequency Fk, the shake angle Sk, (C) the average acceleration amplitude Ac, the average walking frequency Fc, the average swing angle Sc and the average stride length SLc of the section between the time periods 163 and 163 are calculated.

The average acceleration amplitude Ac is calculated as an average value of the acceleration amplitudes Ak measured during the interval c and the average walking frequency Fc is calculated as the average value of the walking frequency Fk measured during the interval c , And the average swing angle Sc is calculated as an average value of the swing angle Sk measured during the interval c.

As an example, the average stride SLc may be computed via equation (2) based on the distance between known feature points 163 and 164 and the number of steps measured during that interval c through the map information 160 do.

The calculated average acceleration amplitude Ac, the average step frequency Fc, the average swing angle Sc and the average stride SLc are the characteristic information 150 corresponding to the parameters An, Fn and Sn in Equation 1, respectively .

Meanwhile, the gyro sensor 120 used above is a sensor module generally provided in a mobile device (for example, a smart phone). The stride estimation device 100 does not need to have separate hardware, ) And the gyro sensor 120 to generate or generate the characteristic information 150 of the user.

Therefore, the stride estimation apparatus 100 according to the present invention can be implemented using a hardware configuration of a general mobile device as it is, and thus can have a wide versatility.

Furthermore, since the stride estimation apparatus 100 according to the present invention does not require a separate preset calibration for calculating the characteristic information 150, user convenience can also be improved.

On the other hand, the parameters (i.e.,?,?,?) Related to the user's motion type in Equation (1) are determined as predetermined values that depend on the user's motion type. These parameters may be empirically determined or appropriately derived from other information.

The motion type of the user is classified according to the pattern or value of the signal output from the proximity sensor (not shown) and the acceleration sensor 110 of the stride estimation apparatus 100. For example, if the user is walking while shaking the stride estimation device 100 in hand, the proximity sensor will output an off (i.e., not close state) value, and the acceleration sensor 110 will periodically (Since the pedometer 100 is repeatedly swung back and forth). At this time, the output values and patterns of such proximity sensors and acceleration sensors can be determined to correspond to the first motion type. On the other hand, if the user is walking with the stride estimation device 100 on his / her ears, the proximity sensor will output a value on (that is, close state), and the acceleration sensor 110 will output a pattern The shake will not be large). At this time, the output values and patterns of such proximity sensors and acceleration sensors may be determined to correspond to the second motion type, which is distinguished from the first motion type. Further, by appropriately setting the appropriate values of the first, second and third motion types, respectively, the values of α, β, and γ applied when the user motion is changed are different.

As an embodiment, the values of [alpha], [beta], and [gamma] according to the user's motion type may be empirically determined or may be appropriately derived values through analysis data. The α, β, and γ values determined experimentally or inferred may be values previously input by the creator, the developer, or the third data provider of the stride estimation apparatus 100, or may be input by a user or a third party later And changed values.

On the other hand, here, only the first motion type and the second motion type are illustrated based on the user motion type, but this is merely an example, and the scope of the present specification is not limited thereto. For example, the stride estimation apparatus 100 may be configured to have three or more sets of alpha, beta, and gamma, respectively, corresponding to three or more motion types.

4A and 4B are tables illustrating exemplary property information of a stride estimation apparatus according to an embodiment of the present invention. 4A shows information (An, Fn, Sn, SLn) related to the step characteristic type 151 of the characteristic information 150 and FIG. 4B shows information alpha, beta, gamma). 4A and 4B, the stride estimation apparatus 100 stores characteristic information 150 composed of different values according to a plurality of step characteristic types 151 and a plurality of motion types 154. 4A, the characteristic information 150 of the stride estimation apparatus 100 includes a plurality of pieces of information 152, 153 corresponding to a plurality of step characteristic types 151, respectively.

Characteristic information 150 includes at least some of average acceleration amplitude, step frequency and swing angle An, Fn, Sn, and average stride SLn for each type. The significance and role of the average acceleration amplitude, step frequency and shake angles (An, Fn, Sn) and average stride (SLn) are described above, and the customized average acceleration amplitudes The average acceleration amplitude (An), the step frequency (Fn), the swing angle (Sn), and the average stride width (Sn) of the average step frequency Ac, the average step frequency Fc, the mean swing angle Sc, (SLn) are the same as those described above.

Characteristic information 150 is a pattern or average of acceleration amplitudes, step frequency and shake angles (Ak, Fk, Sk) measured at specific intervals (for example, a, b, c, The acceleration amplitude, the average step frequency and the average swing angle Ac, Fc, Sc, and set the sensed pattern at that time as one of the step characteristic types corresponding to the user's specific step characteristics.

For example, assume that the user walks while viewing the mobile device 12 in section b of FIG. 3, while walking while talking on the section c, and runs the mobile device 12 in the pocket during section d. In this case, since the output waveforms of the acceleration signal and the gyro signal will be different in each of the sections b, c, and d, the measured acceleration amplitudes, step frequency, swing angle, or stride The patterns of the average acceleration amplitude, the average step frequency, the average swing angle and the average stride (Ac, Fc, Sc, SLc) patterns of the patterns A, Fk, Sk and SLk will also be different.

In this case, the stride estimating apparatus 100 estimates a pattern of the acceleration amplitudes, the walking frequencies, the shaking angles or the strides (Ak, Fk, Sk, SLk) or average acceleration amplitudes of the respective sections b, The average swing angle or the average stride pattern (Ac, Fc, Sc, and SLc) patterns are respectively associated with different step characteristic types (I, II, and III), and the average acceleration amplitudes Fn, Sn, and SLn) of the corresponding step characteristic types (I, II, and III), the average step frequency, the average swing angles (Ac, Fc, Sc) do.

4B, the property information 150 of the stride estimation apparatus 100 includes a plurality of pieces of information 155 corresponding to a plurality of motion types 154, respectively.

In this case, the characteristic information 150 includes weight values?,?, And? Set in advance for each of the motion types A, B and C. The significance and role of predetermined weights ([alpha], [beta], [gamma]) according to the motion type have already been described above and the values of [alpha], [beta], and [gamma] according to each motion type are empirically determined to be appropriate for the corresponding motion type, The fact that the values can be appropriately deduced from the analysis data has already been explained above with reference to FIG.

As an embodiment, the motion types A, B, C, 154 on the characteristic information 150 may be of a type determined by reference to the proximity sensing signal of the proximity sensor (not shown) and the output signal of the acceleration sensor 110. For example, if any one of the output signal of the proximity sensor or the signal pattern of the acceleration sensor is different, it can be determined that it corresponds to a different motion type.

Based on the characteristic information 150 described in Figs. 4A and 4B, the stride estimation apparatus 100 estimates the user's stride by calculating the acceleration amplitude, the gait frequency, the shake angles (Ak, Fk, Sk) After measuring and analyzing the signals, it is determined whether their values or patterns correspond to which step characteristic type 151 and motion type 154, respectively. Then, the characteristic information (An, Fn, Sn, SLn) of the corresponding step characteristic type 151 and the characteristic information (?,?,?) Of the corresponding motion type 154 are read out to obtain the linear equations After applying, estimate the user's stride by substituting the measured acceleration amplitude, step frequency and shake angle (Ak, Fk, Sk) into the linear equation.

According to the above configuration, since different characteristic information 152, 153 or 155 is applied according to the user's walking characteristic type 151 and the motion type 154, even if the user's motion or walking characteristic is changed, can do.

5 is a flow chart illustrating a method by which a stride estimation apparatus generates characteristic information, according to an embodiment of the present disclosure; Referring to FIG. 5, a method of generating characteristic information includes steps S110 to S140.

In step S110, the stride estimation apparatus 100 (see FIG. 2) detects whether the user passes a first feature point on the map information. For example, the stride estimation apparatus 100 can use the signal of the gyro sensor to sense whether the user passes the first feature point in the manner described in Fig.

In step S120, the stride estimation apparatus 100 measures the step characteristic values (e.g., Ak, Fk, Sk, or the number of steps, etc.) for generating characteristic information from the time when the user passes the first feature point . The measurement of the step characteristic values continues until the user passes the second feature point, which will be described later.

In step S130, the stride estimation apparatus 100 detects whether the user passes a second feature point on the map information. As in the case of the first feature point, the stride estimation apparatus 100 can use the signal of the gyro sensor to sense whether the user passes the second feature point in the manner described in FIG.

In step S140, the stride estimation apparatus 100 calculates an average value of the step characteristic values (for example, Ak, Fk, Sk, or the number of steps) measured between the first feature point and the second feature point, For example, An, Fn, Sn, or SLn.

According to the above configuration, the characteristic information of the user can be calculated and generated with only the map information and the gyro sensor.

Furthermore, since no separate presetting calibration is required for characteristic information calculation, user convenience can be improved.

FIG. 6 is a flowchart illustrating a method of estimating a user's step size using a feature information generated according to FIG. Referring to FIG. 6, a method of estimating a user's stride includes steps S210 to S240.

In step S210, the stride estimation apparatus 100 detects the user's step. Then, after measuring the step characteristic values (e.g., Ak, Fk or Sk) or the output values of the proximity sensor, their patterns are analyzed.

In step S220, the stride estimation apparatus 100 determines whether there is a pattern of the analyzed step characteristic values (for example, Ak, Fk, or Sk) or pre-calculated characteristic information corresponding to the output value of the proximity sensor. If there is characteristic information calculated in advance, the stride estimation method proceeds to step S230. Otherwise, the stride estimation method proceeds to step S240.

In step S230, the stride estimation apparatus 100 estimates the stride of the user using the pattern of the analyzed step characteristic values (for example, Ak, Fk, or Sk) or the characteristic information corresponding to the output value of the proximity sensor do. Specifically, if there is corresponding characteristic information in step S220, the characteristic information is read and then applied to a linear equation (for example, Equation 1) for stride estimation. Then, the step characteristic values (for example, Ak, Fk or Sk) measured in the linear equation are substituted and the result is calculated or outputted as the estimated stride width SLk of the user.

On the other hand, if it is determined in step S220 that there is no characteristic information corresponding to the pattern of the step characteristic values, the process proceeds to step S240.

In step S240, the stride estimation apparatus 100 applies a predetermined initial value of the characteristic information to a linear equation (e.g., equation (1)) for stride estimation. Then, the step characteristic values (for example, Ak, Fk or Sk) measured in the linear equation are substituted and the result is calculated or outputted as the estimated stride width SLk of the user. As an embodiment, the predetermined initial value may be an initial value of the property information specified by the user, set by the creator of the pedometer estimating apparatus 100, or previously distributed by a third party.

According to the above configuration, the stride estimation method can estimate the stride of a user even in a room where a satellite signal is not received, and generates and applies appropriate property information corresponding to various motions of a user. Therefore, Can be accurately estimated.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments.

In addition, although specific terms are used herein, they are used for the purpose of describing the present invention only and are not used to limit the scope of the present invention described in the claims or the claims. Therefore, the scope of the present invention should not be limited to the above-described embodiments, but should be determined as set forth in the appended claims.

11: user 12: mobile device
100: stride estimation device 110: acceleration sensor
120: Gyro sensor 130:
140: storage unit 150: characteristic information
160: Map Information

Claims (11)

1. A stride estimation apparatus for estimating a stride of a user,
An acceleration sensor for measuring the acceleration of the stride estimation device and outputting the measured acceleration as an acceleration signal;
A gyro sensor for measuring each movement of the stride estimation device and outputting it as a gyro signal; And
A stride estimating unit for estimating stride of the user by substituting the acceleration characteristic signal or the step characteristic value extracted from the gyro signal into a linear equation configured to have different parameters depending on the type of the user's walking characteristic and the motion type of the user Gt;
The method according to claim 1,
Wherein the step characteristic value includes an acceleration amplitude, a stepping frequency, or a shaking angle of the user or the stride estimation apparatus.
3. The method of claim 2,
Wherein the step characteristic type corresponds to a predetermined step characteristic of the user,
Wherein the motion type is preset to correspond to a predetermined motion of the user.
The method of claim 3,
Wherein the step characteristic type is determined according to a pattern of the acceleration signal and a pattern of the gyro signal.
The method of claim 3,
Wherein the motion type is determined according to a pattern of the acceleration signal and an output signal of a proximity sensing signal indicating whether the user and the stride estimation device are close to each other.
The method of claim 3,
Wherein the different parameters are calculated and stored in advance as characteristic information corresponding to the walking characteristic type and the motion type, and the average acceleration amplitude, the average step frequency, the average swing angle, the average A stride or a weight that varies depending on the motion type.
The method according to claim 6,
Wherein the different parameters are calculated in advance using map information including the predetermined section,
Wherein the predetermined interval is a section between two points on the map information that the stride estimation apparatus discriminates based on an output signal of the gyro sensor.
The method according to claim 6,
The linear equation is expressed by the following equation,

,
Here, Ak, Fk and Sk are the acceleration amplitude, the step frequency and the shaking angle, respectively,
An, Fn, Sn, and SLn are average acceleration amplitudes, step frequency, shaking angle, and mean stride corresponding to the step characteristic types, respectively,
and?,?,? are the weights corresponding to the motion type.
Detecting a user's step and measuring characteristic values of the user's step;
Determining whether there is previously calculated characteristic information corresponding to the pattern of the measured characteristic values; And
Calculating preliminarily calculated characteristic information corresponding to the pattern of the measured characteristic values and applying the precharacterized characteristic information to a linear equation for estimating a stride width, substituting the characteristic values of the user's equations into the linear equation, Estimating a user's stride,
Wherein the characteristic information includes parameter values corresponding to the user's walking characteristic type and the user's motion type determined from the pattern, respectively.
10. The method of claim 9,
Further comprising estimating a stride of the user by applying a predetermined initial value to the linear equation and substituting characteristic values of the user's step into the linear equation according to a result of the determination, Method of estimating stride.
10. The method of claim 9,
The pre-calculated characteristic information may include:
A first feature point sensing step of sensing whether the user passes a first feature point on map information;
A measurement step of measuring the step characteristic values for generating the previously calculated step characteristic information in response to the first feature point sensing;
A second feature point sensing step of sensing whether the user passes a second feature point on the map information different from the first feature point; And
Wherein the step of estimating the stride of the user is calculated by determining an average value of the step characteristic values measured in the measuring step as at least a part of the previously calculated characteristic information in response to the detection of the second characteristic point.

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