JP2010216987A - Method and device for estimating stride - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a new method of more accurately estimating a stride. <P>SOLUTION: A pedometer 1 functions as a stride estimation device and estimates a stride of a user by using a correlation between a predetermined acceleration and a stride and a synthesis acceleration calculated from detection results by an acceleration sensor 40. More specifically, walking timing is estimated by detecting a peak value of the synthesis acceleration above a predetermined threshold acceleration, and based on the estimated walking timing, a time interval for one stride is calculated. Using the obtained peak value of the synthesis acceleration and the time interval of one stride, a stride is estimated according to a multiple regression equation using the synthesis acceleration and the time interval for one stride as explanatory variables and using the stride as a response variable. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a stride estimation method and a stride estimation apparatus.

  2. Description of the Related Art As a stride estimation apparatus, an apparatus that includes an acceleration sensor and estimates a stride by referring to a table from a change in acceleration detected by the acceleration sensor is known (for example, Patent Document 1).

JP-A-9-152355

  Since the stride is indispensable information for calculating the walking distance, it is necessary to estimate it as accurately as possible. However, the stride varies from person to person depending on the shape of the user who is a pedestrian, and also changes sequentially depending on the user's walking situation (for example, how to walk). For this reason, the presence of these uncertainties has become a major barrier in estimating the stride.

  The present invention has been made in view of the above-described problems, and an object thereof is to propose a new method for estimating the stride more accurately.

  The first mode for solving the above-described problem is to detect the user's acceleration, use the correlation between the predetermined acceleration and the stride, and the user's acceleration to calculate the user's stride. A stride estimation method including estimating.

  Further, as another form, a stride provided with a detection unit that detects acceleration, a correlation between a predetermined acceleration and a stride, and an estimation unit that estimates a stride using the detected acceleration An estimation device may be configured.

  According to the first aspect and the like, the user's stride is estimated using the correlation between the predetermined acceleration and the stride and the detected user acceleration. By determining the correlation between the user's acceleration and the stride and estimating the stride based on the correlation, the stride can be estimated more accurately without being influenced by uncertain factors such as the user's body shape. It becomes like this.

  Further, as a second form, the step estimation method according to the first form, wherein the correlation is expressed by a multiple regression equation in which the magnitude of acceleration and the time interval of one step are explanatory variables and the step is an objective variable. And estimating the walking timing based on the acceleration of the user and calculating a time interval of one step from the walking timing, and estimating the stride is the walking timing. A step estimation method including estimating a step according to the multiple regression equation using the acceleration of the user and the calculated time interval may be configured.

  According to the second embodiment, the walking timing is estimated based on the acceleration of the user, and the time interval of one step is calculated from the estimated walking timing. Then, using the user's acceleration at the estimated walking timing and the calculated time interval, the step size is estimated according to a multiple regression equation with the magnitude of acceleration and the time interval of one step as explanatory variables and the step length as an objective variable. Since simple processing such as calculating the stride according to the multiple regression equation at each walking timing is sufficient, the responsiveness of one step is better than the conventional method, and the calculation amount is reduced.

  Further, as a third mode, the step estimation method according to the second mode, wherein the user's acceleration at the walking timing and the time interval of one step are stored and stored as sampling data, and the stored and stored sampling data The step estimation method may further comprise: performing a multiple regression analysis process using to obtain the multiple regression equation.

  According to the third aspect, the acceleration of the user at the walking timing and the time interval of one step are accumulated and stored as sampling data. Then, a multiple regression analysis process is performed using the stored and stored sampling data to obtain a multiple regression equation for estimating the stride. For each user, the multiple regression equation is obtained using the acceleration obtained by the user actually walking and the sampling data of the time interval of one step, and the stride is estimated using the optimal multiple regression equation for each user. It becomes possible to do.

  Further, as a fourth aspect, in the step estimation method according to the second or third aspect, the estimation of the walking timing is a peak value when the time change of the acceleration satisfies a predetermined high threshold condition You may comprise the stride estimation method including estimating the time corresponding to to walk timing.

  According to this 4th form, the time corresponding to the peak value when the time change of acceleration satisfy | fills predetermined high threshold conditions is estimated as a walk timing. At the walking timing, a peak value is observed such that the magnitude of the resultant acceleration is greater than a certain value. Therefore, it is possible to accurately detect the walking timing by specifying the time corresponding to this peak value. .

  Further, as a fifth form, the stride estimation method according to any one of the first to fourth forms, wherein a plurality of correlations according to the walking situation are predetermined, and the user's walking situation is determined. And estimating the stride may constitute a stride estimation method including estimating using a correlation corresponding to the walking situation of the user and the acceleration of the user.

  According to the fifth embodiment, the user's walking situation is determined. Then, the user's stride is estimated using the correlation corresponding to the determined walking situation of the user and the user's acceleration. Predetermining multiple types of correlation according to the walking situation, selecting the correlation corresponding to the user's walking situation and estimating the stride, without being influenced by uncertain factors such as how the user walks, It is possible to perform step length estimation with high accuracy step by step.

The schematic block diagram of a pedometer. The block diagram which shows the function structure of a pedometer. The figure which shows an example of the data stored in ROM. The figure which shows an example of the data stored in flash ROM. The figure which shows an example of the data stored in RAM. The figure which shows an example of the data structure of sampling data. The figure which shows an example of the data structure of sensor data. The figure which shows an example of a data structure of synthetic | combination acceleration data. The flowchart which shows the flow of a main process. The flowchart which shows the flow of a multiple regression analysis process. The figure which shows an example of the data stored in ROM in the modification. The figure which shows an example of the data stored in flash ROM in a modification. The flowchart which shows the flow of a 2nd main process.

  Hereinafter, with reference to the drawings, an embodiment in which the present invention is applied to a pedometer which is a kind of electronic apparatus provided with a stride estimation device will be described. However, embodiments to which the present invention can be applied are not limited to the embodiments described below.

1. Schematic Configuration FIG. 1 is a diagram for explaining a state in which a pedestrian 1 wears a pedometer 1 in the present embodiment and a schematic configuration of the pedometer 1. The pedometer 1 is an electronic device including a step number detection device and a step length estimation device that calculate the number of steps and the walking distance in accordance with the walking of a user who is a pedestrian according to an instruction operation from the operation unit 20, and is shown in FIG. For example, it is used by being worn on the right waist of the user.

  The pedometer 1 includes an acceleration sensor 40 and detects the number of steps of the user based on the detection result of the acceleration sensor 40. Also, the pedometer 1 estimates the stride according to a calculation formula for estimating a stride that is obtained in advance as an initial setting, and calculates the walking distance of the user by accumulating the estimated stride. The detected and calculated number of steps and walking distance are displayed on the display unit 30.

  The acceleration sensor 40 is a sensor that detects acceleration in three orthogonal axes, and may be either a strain gauge type or a piezoelectric type, or a MEMS (Micro Electro Mechanical Systems) sensor. In the present embodiment, the detection axis of the acceleration sensor 40 is mounted with the pedometer 1 in the orientation as shown in FIG. 1, and the front-rear direction as viewed from the user is the “X axis”, and the vertical direction is the “Y axis”. The left and right direction will be described as the “Z axis”.

  In order to make the explanation easy to understand, the axial direction of the detection axis of the acceleration sensor 40 is described as being the same as the axial directions of the X axis, the Y axis, and the Z axis, but may actually be different. Since the detection axis of the acceleration sensor 40 and the axial directions of the X-axis, Y-axis, and Z-axis are used by being fixed to the user in a relative relationship, X, X, Each value of Y and Z can be calculated.

2. Principle The pedometer 1 is a composite acceleration obtained by instructing the user to perform a test walk in a state (initial state) used for the first time by the user and synthesizing the three-axis acceleration detected by the acceleration sensor 40. Collect data of size. The magnitude of the combined acceleration is calculated as the square root of the sum of squares of the output values of the acceleration sensor 40 on the X-axis, Y-axis, and Z-axis. We decided to use the combined acceleration because if we try to detect the number of steps using the output values of the X-axis, Y-axis and Z-axis individually, the output value will change due to the inclination of the acceleration sensor 40, This is because the number of steps cannot be detected correctly.

  The pedometer 1 determines the walking timing based on the temporal change in the magnitude of the combined acceleration. The walking timing is detected as the timing at which the peak value (maximum value) of the combined acceleration exceeds a predetermined threshold acceleration. In addition, the pedometer 1 acquires a time interval of one step at each walking timing by calculating a time difference between the walking timings. Then, the peak value of the combined acceleration and the time interval of one step are associated and stored as sampling data.

  If a sufficient number of sampling data can be acquired, the pedometer 1 performs a multiple regression analysis process to calculate / estimate the user's stride (hereinafter referred to as “multiple regression formula for stride estimation”). "). In the multiple regression analysis process, the peak value of the combined acceleration and the time interval of one step are used as independent variables (explanatory variables), and the distance of one step, which is the step length, is used as a dependent variable (target variable). Obtain the multiple regression equation for stride estimation. The multiple regression equation for stride estimation is a kind of a formula representing the correlation between acceleration and stride.

The multiple regression analysis is a kind of multivariate analysis, in which multiple independent variables are used for regression analysis. In the present embodiment, a multiple regression equation for stride estimation represented by the following equation (1) is obtained.
y = a ₀ + a ₁ x ₁ + a ₂ x ₂ (1)
However, “y” is a step length (distance of one step) as a dependent variable, “x ₁ ” is a time interval of one step as an independent variable, and “x ₂ ” is a peak value of a composite acceleration as an independent variable.

More specifically, the constant term “a ₀ ” and the independent variable “x ₁ ” included in the equation (1) according to the least square method using the time interval of one step that is sampling data and the peak value of the resultant acceleration, and The weights “a ₁ ” and “a ₂ ” of “x ₂ ” are respectively determined. Thereby, for example, a multiple regression equation as shown in the following equation (2) can be obtained.
y = −0.1553 + 0.0062x ₁ + 0.0526x ₂ (2)

  If the multiple regression equation for estimating the stride can be obtained, this multiple regression equation for estimating the stride is stored and used for estimating the user's stride in the subsequent walking detection process. Specifically, similarly to the acquisition of the sampling data described above, the walking timing is detected by detecting the peak value of the composite acceleration, the number of steps is counted, and the time interval of one step that is the time interval of each walking timing is determined. calculate.

  Then, the step “y” is calculated and estimated by substituting the calculated peak value of the combined acceleration and the time interval of one step into the multiple regression equation for step estimation. Then, the walking distance is updated by adding the estimated stride to the latest walking distance, and is displayed on the display unit 30 together with the number of steps.

3. Functional Configuration FIG. 2 is a block diagram showing a functional configuration of the pedometer 1. The pedometer 1 includes a CPU (Central Processing Unit) 10, an operation unit 20, a display unit 30, an acceleration sensor 40, a ROM (Read Only Memory) 50, a flash ROM 60, and a RAM (Random Access Memory) 70. And each part is connected by a bus 80.

  The CPU 10 is a processor that comprehensively controls each unit of the pedometer 1 according to various programs such as system programs stored in the ROM 50 and the flash ROM 60 and various data. The CPU 10 performs a main process, measures the number of steps and the walking distance of the user, and performs a process of displaying on the display unit 30.

  The operation unit 20 is an input device configured by, for example, a touch panel or a button switch, and outputs a pressed key or button signal to the CPU 10. By the operation of the operation unit 20, various instruction operation inputs such as a step start instruction operation for detecting the number of steps and the walking distance, a reset instruction operation for the number of steps and the walking distance, and a power-off instruction operation are performed.

  The display unit 30 is configured by an LCD (Liquid Crystal Display) or the like, and is a display device that performs various displays based on a display signal input from the CPU 10. The display unit 30 displays information such as the number of steps and the walking distance.

  The acceleration sensor 40 is a sensor that detects the triaxial acceleration of the pedometer 1 and outputs the detection result to the CPU 10.

  The ROM 50 is a read-only nonvolatile storage device, and stores a system program for the CPU 10 to control the pedometer 1, various programs and data for realizing a step detection function and a step estimation function, and the like. .

  The flash ROM 60 is a readable and writable nonvolatile storage device, and stores various programs and various data necessary for the CPU 10 to control the pedometer 1, as with the ROM 50.

  The RAM 70 is a readable / writable volatile storage device, and forms a work area for temporarily storing a system program executed by the CPU 10, various processing programs, data being processed in various processing, processing results, and the like.

4). Data Configuration FIG. 3 is a diagram illustrating an example of data stored in the ROM 50. The ROM 50 stores a main program 501 that is read by the CPU 10 and executed as main processing (see FIG. 9). The main program 501 includes a multiple regression analysis program 5011 executed as a multiple regression analysis process (see FIG. 10) as a subroutine.

  The main process is a step estimation weight obtained by the CPU 10 detecting the number of steps based on the temporal change in the magnitude of the resultant acceleration calculated from the detection result of the acceleration sensor 40 and performing the multiple regression analysis process. This is a process for calculating the walking distance by estimating the stride according to the regression equation and displaying the number of steps and the walking distance on the display unit 30.

  The multiple regression analysis process is a process in which the CPU 10 obtains a multiple regression equation for estimating a stride by performing a known multiple regression analysis using sampling data acquired by causing the user to perform a test walk. These processes will be described later in detail using a flowchart.

  FIG. 4 is a diagram illustrating an example of data stored in the flash ROM 60. The flash ROM 60 stores sampling data 601 and multiple regression equation data 603 for stride estimation.

  FIG. 6 is a diagram illustrating an example of the data configuration of the sampling data 601. In the sampling data 601, the peak value 6011 of the resultant acceleration obtained by causing the user to perform a test walk and the time interval 6013 of one step are accumulated and stored in association with each other. For example, the peak value of the latest combined acceleration is “A5”, and the time interval of one step is “Δt1”.

  Multiple regression equation data 603 for stride estimation is data of a multiple regression equation for stride estimation obtained by performing multiple regression analysis processing using sampling data 601.

  FIG. 5 is a diagram illustrating an example of data stored in the RAM 70. The RAM 70 stores sensor data 701, combined acceleration data 702, stride length estimation data 703, stride length data 704, step count data 705, and walking distance data 706.

  FIG. 7 is a diagram illustrating an example of the data configuration of the sensor data 701. The sensor data 701 stores a triaxial acceleration 7013 detected by the acceleration sensor 40 in association with a detection time 7011 (for example, milliseconds). For example, the acceleration detected at the detection time “t1” is “(Ax1, Ay1, Az1)”.

  FIG. 8 is a diagram illustrating an example of a data configuration of the combined acceleration data 702. As illustrated in FIG. The combined acceleration data 702 stores a combined acceleration 7023 having a magnitude obtained by combining three-axis acceleration in association with the detection time 7021 of the acceleration sensor 40. For example, the combined acceleration at the detection time “t1” is “A1”.

  The stride estimation data 703 is data used to estimate the stride. Similar to the sampling data 601 in FIG. 6, the peak value of the resultant acceleration obtained by the user walking and the time interval of one step are calculated. Correspondingly stored.

  The stride data 704 is the latest stride data calculated using the stride estimation multiple regression equation data 603 and the stride estimation data 703.

  The step count data 705 is step count data that is sequentially counted each time a peak value of the resultant acceleration that exceeds a predetermined threshold acceleration is detected.

  The walking distance data 706 is data about the latest walking distance, and is data obtained by accumulating the stride sequentially estimated according to the multiple regression equation for stride estimation.

5). Process Flow FIG. 9 is a flowchart showing the flow of the main process executed in the pedometer 1 when the main program 501 stored in the ROM 50 is read and executed by the CPU 10. The main process is a process of starting execution when the CPU 10 detects that a power-on instruction operation has been performed by the user via the operation unit 20.

  First, the CPU 10 determines the state of the pedometer 1 (step A1). If it is determined that the pedometer 1 is in the initial state (step A1; initial state), the multiple regression analysis program 5011 stored in the ROM 50 is read and executed to perform multiple regression analysis processing. (Step A3).

FIG. 10 is a flowchart showing the flow of the multiple regression analysis process.
First, the CPU 10 outputs a test walking instruction (step B1). Specifically, for example, a message for instructing the user to perform a test walk is displayed on the display unit 30. Note that a sound output unit may be provided in the pedometer 1, and voice guidance for instructing the user to perform a test walk may be output from the sound output unit.

  Next, the CPU 10 captures the detection result from the acceleration sensor 40 and starts storing the sensor data 701 in the RAM 70 (step B3). Then, the CPU 10 calculates a combined acceleration 7023 that is the magnitude of the acceleration from the latest three-axis acceleration 7011 stored in the sensor data 701, and stores it in the combined acceleration data 702 of the RAM 70 (step B5).

  Thereafter, the CPU 10 detects the peak value of the resultant acceleration exceeding a predetermined threshold acceleration (step B7), and based on the detection time associated with the detected peak value of the synthesized acceleration, the time interval of one step. Is calculated (step B9). Then, the CPU 10 accumulates and stores the peak value 6011 of the resultant acceleration and the data of the one-step time interval 6013 as sampling data 601 in the flash ROM 60 (step B11).

  Next, the CPU 10 determines whether or not a sufficient number of data has been accumulated and stored as the sampling data 601 (step B13), and if it is determined that a sufficient number of data has not yet been accumulated and stored (step B13; No), it returns to step B5.

  If it is determined that a sufficient number of data has been accumulated and stored (step B13; Yes), the CPU 10 uses the stride as an objective variable, the accumulated acceleration peak value 6011 and the time interval 6013 of one step as explanatory variables. Multiple regression analysis is performed to obtain a multiple regression equation for stride estimation (step B15). The CPU 10 stores the obtained step regression multiple regression equation in the flash ROM 60 as the step regression multiple regression equation data 603 (step B17), and ends the multiple regression analysis process.

  After returning to the main process of FIG. 9 and performing the multiple regression analysis process, the CPU 10 determines whether or not to continue to detect walking (step A5), and if it determines not to start walking detection ( Step A5; No), the main process is terminated. Moreover, when it determines with starting a walk detection continuously (step A5; Yes), a process is transfered to step A9.

  On the other hand, when it is determined in step A1 that the state of the pedometer 1 is “detectable state” (step A1: detectable state), the CPU 10 captures the detection result from the acceleration sensor 40, and sensor data 701 in the RAM 70. The memory is started (step A7). Then, the CPU 10 calculates a combined acceleration 7023 that is the magnitude of the acceleration from the latest three-axis acceleration 7011 stored in the sensor data 701, and stores it in the combined acceleration data 702 of the RAM 70 (step A9).

  Thereafter, the CPU 10 detects the peak value of the combined acceleration exceeding the predetermined threshold acceleration (step A11), and based on the detection time associated with the detected peak value of the combined acceleration, the time interval of one step. Is calculated (step A13). Then, the CPU 10 associates the peak value of the combined acceleration with the data of the time interval of one step and stores it in the stride length estimation data 703 of the RAM 70 (step A15).

  Next, the CPU 10 uses the latest peak value of the synthesized acceleration stored in the stride estimation data 703 and the time interval of one step, for stride estimation stored in the stride estimation multiple regression equation data 603 of the flash ROM 60. The stride is calculated / estimated according to the multiple regression equation and stored in the stride data 704 of the RAM 70 (step A17).

  Thereafter, the CPU 10 updates the step count stored in the step count data 705 of the RAM 70 (step A19). Further, the walking distance is newly calculated by adding the stride calculated in step A17 to the latest walking distance stored in the walking distance data 706 of the RAM 70, and the walking distance data 706 of the RAM 70 is updated (step A21). ).

  Then, the CPU 10 updates the display of the number of steps and the walking distance on the display unit 30 (step A23). Thereafter, the CPU 10 determines whether or not a reset instruction operation has been performed by the user via the operation unit 20 (step A25). When it is determined that the user has not performed a reset instruction operation (step A25; No), the process proceeds to step A29. To do.

  When it is determined that a reset instruction operation has been performed (step A25; Yes), the CPU 10 resets the number of steps and the walking distance stored in the step number data 705 and the walking distance data 706 of the RAM 70 (step A27).

  Then, the CPU 10 determines whether or not a power-off instruction operation has been performed by the user via the operation unit 20 (step A29), and when determining that it has not been performed (step A29; No), the CPU 10 returns to step A9. If it is determined that the power-off instruction operation has been performed (step A29; Yes), the main process is terminated.

6). Action Effect The pedometer 1 also functions as a stride estimation device, and uses the correlation between a predetermined acceleration and stride and the magnitude of the composite acceleration calculated from the detection result of the acceleration sensor 40 to determine the stride of the user. presume. More specifically, the walking timing is estimated by detecting the peak value of the combined acceleration exceeding a predetermined threshold acceleration, and the time interval of one step is calculated from the estimated walking timing. Then, using the obtained peak value of the combined acceleration and the time interval of one step, the step length is estimated according to a multiple regression equation using the combined acceleration and the time interval of one step as explanatory variables and the step length as an objective variable.

  Also, the pedometer 1 causes the user to perform a test walk in the initial state, and accumulates and stores the combined acceleration obtained by the test walk and the data of the time interval of one step as sampling data. Then, a multiple regression analysis is performed using these sampling data to obtain a multiple regression equation for estimating the stride, and the stride is estimated according to the obtained multiple regression equation.

  Since multiple regression equations for estimating the stride are obtained using sampling data obtained by actually walking the user, appropriate multiple regression can be performed for each user regardless of uncertain factors such as the user's body shape. The stride can be estimated more accurately using the formula. In this case, the accuracy of the walking distance calculation is improved by appropriately estimating the stride. Further, since simple processing such as calculating the stride according to the multiple regression equation for estimating the stride for each walking timing is sufficient, the one-step response is better than the conventional method, and the calculation amount is reduced.

7). Modification 7-1. Electronic Device The present invention is applicable to any electronic device other than a pedometer as long as it is an electronic device including a stride estimation device. For example, the present invention can be similarly applied to a wristwatch or the like.

7-2. Step length estimation according to walking situation Multiple types of multiple regression equations for step estimation according to walking situation are obtained in advance, and multiple regression equations for step estimation corresponding to the walking situation of the user's walking are selected for step estimation. You may decide to use it.

  FIG. 11 is a diagram showing an example of the data configuration of the ROM 52 provided in the pedometer 1 in this case. The ROM 52 stores a second main program 521 that is executed as the second main process (see FIG. 13). The second main program includes a slow walking multiple regression analysis program 5211 executed as a slow walking multiple regression analysis process and a normal walking multiple regression analysis program 5213 executed as a normal walking multiple regression analysis process. And a rapid walking multiple regression analysis program 5215 executed as a rapid walking multiple regression analysis process are included as subroutines.

  FIG. 12 is a diagram showing an example of the data configuration of the flash ROM 62 provided in the pedometer 1 in this case. The flash ROM 62 stores multiple regression analysis data 601 and stride estimation multiple regression equation data 623. In addition, the multiple regression equation data 623 for estimating the stride includes a multiple regression equation 6231 for slow walking, a multiple regression equation 6233 for normal walking, and a multiple regression equation 6235 for fast walking.

  FIG. 13 is a flowchart showing the flow of the second main process executed in the pedometer 1 when the second main program 521 stored in the ROM 52 is read and executed by the CPU 10. Note that the same steps as those in the main process of FIG. 9 are denoted by the same reference numerals and description thereof will be omitted, and description will be made focusing on portions different from the main process.

  In the second main process, when the CPU 10 determines in step A1 that the state of the pedometer 1 is the “initial state” (step A1; initial state), the second main program 521 stored in the ROM 52. Are read out and executed, a slow-walking multiple regression analysis process, a normal-walking multiple regression analysis process, and a fast-walking multiple regression analysis process are performed (steps C2 to C4).

  In these processes, as in the multiple regression analysis process of FIG. 10, the CPU 10 instructs the user to perform a test walk, and uses the sampling data obtained by the test walk to obtain a multiple regression equation for stride estimation. At this time, the multiple regression analysis process for slow walking instructs the user to walk slowly, the multiple regression analysis process for normal walking instructs the user to walk normally, and the multiple regression analysis process for fast walking , Instruct the user to walk fast. As a result, three types of multiple regression equations for estimating the stride that are different from the multiple regression equation for slow walking 6231, the multiple regression equation for normal walking 6233, and the multiple regression equation for fast walking 6235 are acquired, and the flash ROM 62 is used for estimating the stride. It is stored as multiple regression equation data 623.

  Then, after step A15, the CPU 10 determines the user's walking situation based on the one-step time interval obtained in step A13 (step C16). For example, if the time interval for one step is longer than a predetermined threshold for determining slow walking, the walking situation is determined to be “slow walking”, and if the time interval for one step is shorter than a predetermined threshold for determining fast walking. It can be determined that the walking situation is “fast walk”. If none of the conditions is applicable, it is determined that the walking situation is “ordinary walking”. Note that the walking situation may be determined using the magnitude of the combined acceleration.

  Thereafter, the CPU 10 reads out the step regression multiple regression equation corresponding to the walking situation determined in step C16 from the multiple regression equation data 623 in the flash ROM 62 (step C17). Then, the CPU 10 calculates and estimates the stride using the peak value of the combined acceleration and the time interval of one step according to the read multiple regression equation for estimating the stride, and stores it in the stride data 704 of the RAM 70 (step C18). Then, the CPU 10 shifts the processing to step A19.

  In this way, multiple types of multiple regression equations for estimating the stride are obtained in advance, and by selecting the multiple regression equations for estimating the stride corresponding to the user's current walking situation and estimating the stride, the user's Regardless of uncertain factors such as how to walk, step length estimation can be performed step by step with high accuracy. Further, the accuracy of the walking distance calculation is further improved, and the accuracy of the walking distance calculation is further improved.

7-3. Correlation In the embodiment described above, the relationship represented by the multiple regression equation obtained by performing multiple regression analysis is described as an example of the correlation between acceleration and stride, but the correlation is limited to this. Do not mean. It is sufficient that the stride is obtained as an output value by giving the acceleration as an input value. For example, the correlation may be obtained by using a multivariate analysis method other than the multiple regression analysis.

  Even when multiple regression analysis is used, stride estimation considering various states of the user can be realized by appropriately adding / changing explanatory variables. For example, by performing a multiple regression analysis by adding the user's heart rate at each walking timing to an explanatory variable, it is possible to realize stride estimation in consideration of the user's physical condition.

7-4. In the embodiment described above, the peak value is detected using the “raw” value of the synthesized acceleration. However, the synthesized acceleration is smoothed by performing a predetermined smoothing process. The peak value may be detected using the magnitude of the combined acceleration.

1 pedometer, 10 CPU, 20 operation unit, 30 display unit,
40 acceleration sensor, 50 ROM, 60 flash ROM,
70 RAM, 80 buses

Claims (6)

Detecting user acceleration,
Estimating the user's stride using a predetermined correlation between acceleration and stride and the user's acceleration;
A stride estimation method including The correlation is a relationship represented by a multiple regression equation with the magnitude of acceleration and the time interval of one step as explanatory variables and the step length as an objective variable,
Estimating walking timing based on the acceleration of the user;
Calculating a time interval of one step from the walking timing;
Further including
Estimating the stride includes estimating the stride according to the multiple regression equation using the acceleration of the user at the walking timing and the calculated time interval.
The stride estimation method according to claim 1. Accumulating and storing the user's acceleration at the walking timing and the time interval of one step as sampling data;
Performing multiple regression analysis using the accumulated stored sampling data to obtain the multiple regression equation;
The stride estimation method according to claim 2, further comprising: Estimating the walking timing includes estimating a time corresponding to a peak value when the time change of the acceleration satisfies a predetermined high threshold condition as a walking timing,
The stride estimation method according to claim 2 or 3. Multiple correlations according to the walking situation are predetermined,
Further comprising determining a user's walking situation;
Estimating the stride includes estimating using a correlation corresponding to the user's walking situation and the acceleration of the user.
The stride estimation method according to any one of claims 1 to 4. A detection unit for detecting acceleration;
An estimation unit for estimating a stride using a correlation between a predetermined acceleration and a stride and the detected acceleration;
A stride estimation apparatus.

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