JP6843332B1 - Activity meter and walking / running speed calculation method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an activity meter capable of calculating a speed in walking / running with high accuracy and a method for calculating a walking / running speed in a small and portable activity meter.
An activity meter including a personal data input means, an acceleration measuring means, and a data processing means, and the data processing means is a representative of pitch data and a traveling direction acceleration signal, which are cycles of acceleration signals in a time series. A signal processing means that outputs acceleration characteristic data composed of an xy composite value obtained by vector-synthesizing a value and a representative value of an acceleration signal in the direction of gravity, a jerk increase time, a jerk cumulative value, and a jerk evaluation value, and walking / running speed. Based on the regression equation to be calculated, personal data, and acceleration characteristic data, the configuration is provided with a walking / running speed calculating means for calculating the walking / running speed of the user.
[Selection diagram] Fig. 1

Description

The present invention relates to an activity meter having a function of calculating a speed in walking or running (hereinafter, abbreviated as "walking / running") and a method for calculating a walking / running speed.

With aging, motor function declines and speed in walking and running also declines. That is, it is important to grasp the speed in walking / running in grasping the degree and physical strength of the possessed motor function and in setting goals in exercise and rehabilitation.

As one of the conventional methods for measuring walking / running speed, for example, as disclosed in Patent Document 1 below, in a pedometer, the walking speed is calculated from the stride length calculated based on the height and the walking pitch, and the walking pitch. There is a technology to calculate.

Japanese Patent No. 3734429

However, in the technique disclosed in Patent Document 1, the stride length is determined only by the height and the walking pitch, and the acceleration during walking is not taken into consideration. There is a problem that the walking speed is the same even if the walking is performed, and the walking speed calculated by multiplying this stride by the same walking pitch is also the same, and the accuracy of the speed calculation is low.

Therefore, the present invention solves the above-mentioned problems of the prior art, and provides an activity meter capable of calculating a walking / running speed with high accuracy and a walking / running speed calculation method in a small and portable activity meter. Is the purpose.

To elaborate on the configuration of the activity meter and the walking / running speed calculation method according to the present invention for solving the above problems, the activity meter of the present invention is a personal data input means for inputting the personal data of the user. Amount of activity including an acceleration measuring means for outputting a three-dimensional acceleration signal including a traveling direction acceleration signal and a gravitational direction acceleration signal of the user's leg during walking or running of the user, and a data processing means. The data processing means is an xy synthesis formed by vector-synthesizing the pitch data, which is the period of the jerk acceleration signal in the time series, the average value of the jerk acceleration signal, and the average value of the jerk acceleration signal. The value, the jerk signal which is the jerk signal which is the jerk signal of the traveling direction acceleration signal, the jerk increase time which is the time of the section where the jerk signal increases, and the jerk cumulative value which is the cumulative value of the jerk signal in the section where the jerk signal increases. A signal processing means that outputs jerk evaluation value, which is the product of jerk increase time and jerk cumulative value, and a mathematical formula that linearly combines personal data and jerk characteristic data. Alternatively, it is equipped with a regression equation that calculates the speed during running, and is based on the user's personal data input by the personal data input means, the jerk characteristic data output from the signal processing means, and the regression equation. It is an activity meter characterized by being provided with a walking / running speed calculating means for calculating the speed during walking or running.
Further, in the above activity meter, the regression equation provided by the walking / running speed calculation means is a mathematical formula using the height value and the weight value in the personal data, the acceleration characteristic data, and the coefficients a to h, and is "walking / running". Speed = a x height value + b x weight value + c x 1 / (pitch data) + d x (xy composite value) + e x (jerk increase time) + f x (jerk cumulative value) + g x (jerk evaluation value) + h " It is an activity meter characterized by this.
Further, the walking / running speed calculation method of the present invention includes a personal data input step for inputting the user's personal data, a traveling direction acceleration signal and a gravity direction acceleration signal of the user's leg during walking or running of the user. Vector synthesis of the acceleration measurement step that outputs a three-dimensional acceleration signal including, the pitch data that is the period of the traveling direction acceleration signal on the time series, and the average value of the traveling direction acceleration signal and the average value of the gravity direction acceleration signal. The xy composite value, the jerk signal which is the acceleration signal of the traveling direction acceleration signal, the jerk increase time which is the time of the section where the jerk signal increases, and the cumulative value of the jerk signal in the section where the jerk signal increases. A signal processing step that outputs acceleration characteristic data composed of the cumulative jerk value, which is the product of the cumulative jerk value and the cumulative jerk value, and the height value, weight value, and acceleration characteristic in personal data. "Walking / running speed = a x height value + b x weight value + c x 1 /" (walking / running speed = a x height value + b x weight value + c x 1 / Walking / running speed that calculates the walking or running speed of the user based on "pitch data) + d x (xy composite value) + e x (jerk increase time) + f x (jerk cumulative value) + g x (jerk evaluation value) + h" It is a walking / running speed calculation method characterized by executing a calculation step and.

According to the present invention, since the walking / running speed can be easily and easily known by the portable activity meter, it becomes easy to grasp the degree of motor function and physical strength, and an accurate target value in exercise and rehabilitation. Can be set.

It is a block diagram which shows the structural example of the activity meter by this embodiment. It is a side view which shows the attachment state to the leg part of the activity meter by this embodiment. It is the graph which shows the example of the acceleration signal in the activity meter by this embodiment, and the side view which shows the walking posture. It is a graph which shows an example of the measurement result of the activity meter by this embodiment. It is a flowchart which illustrates the operation example of the activity meter by this embodiment, and the walking / running speed calculation method. It is a top view which shows the appearance of the activity meter by this embodiment.

The activity meter of the present embodiment uses an acceleration signal generated at the ankle when the user walks or runs, a jerk signal known as a time-series change amount of the acceleration signal, time information, and use. Based on the personal data of the person, the speed when the person walks / runs is calculated and displayed.

The examples shown below are merely examples, and can be changed within a technically uniform range. Further, the same element number is assigned to the same element, and duplicate description is omitted.

<Structure of activity meter>
Hereinafter, the activity meter according to the present embodiment will be described in detail with reference to FIGS. 1 to 6.

FIG. 1 is a block diagram showing a configuration example of the activity meter 1 according to the present embodiment. As shown in FIG. 1, the activity meter 1 detects the movement of the user's leg and the personal data input means 10 that outputs the personal data S1 including the height value and the weight value of the user set in advance. , Acceleration measuring means 20 that outputs a three-dimensional acceleration signal S2 including a traveling direction acceleration signal and gravity direction acceleration signal, and signal processing means 31 that outputs acceleration characteristic data S3 based on the acceleration signal S2 from the acceleration measuring means 20. A data processing means 30 including a walking / running speed calculation means 32 for calculating motion data S6 from personal data S1 and acceleration characteristic data S3, and a timing means 33 is provided. Further, the activity meter 1 receives motion data S6 from the display means 40, the storage means 50, and the data processing means 30 for displaying the motion data S6 from the data processing means 30, and commands data to the data processing means 30. It has a communication means 60 for outputting S7 and a power supply 70.

In FIG. 1, the communication means 60 of the activity meter 1, the external information terminal 90 for transmitting and receiving data, and the electromagnetic field W for transmitting and receiving are shown by broken lines.

The personal data input means 10 is composed of four contact-type switches, and personal data of the user is set in advance.
The acceleration measuring means 20 is an acceleration sensor that outputs a three-dimensional acceleration signal, is formed by a MEMS (Micro Electro Mechanical Systems) type technology, and is a three-dimensional acceleration signal in the traveling direction, the gravity direction, and the left-right direction of the user. Is output.

The data processing means 30 is composed of a microprocessor, controls the overall operation of the activity meter 1, and includes each element of the data processing means 30 in the form of software.
The display means 40 is composed of a liquid crystal display, and displays walking / running speed, number of steps, stride length, total calorie consumption, and date / time information related to walking / running of the user.
The storage means 50 is a non-volatile memory using a flash memory, and stores motion data S6 and date information.

The communication means 60 is composed of a communication means called NFC (Near Field Communication), and the motion data S6 from the data processing means 30 is transmitted to the external information terminal 90 by the electromagnetic field W, and the control signal is also transmitted from the external information terminal 90. In response to this, the data processing means 30 is controlled by the command data S7. The communication means 60 may be configured by using a communication means called Bluetooth (registered trademark).
The power source 70 uses a button-type primary battery, but may be composed of a secondary battery.

<Detailed description of each element in the data processing means 30>
The data processing means 30 includes a signal processing means 31, a walking / running speed calculating means 32, and a timing means 33.

The signal processing means 31 is a signal processing method known as a moving average, in which three-dimensional acceleration signals S2 of a traveling direction acceleration signal, a gravity direction acceleration signal, and a left-right direction acceleration signal output by the acceleration measuring means 20 are sampled at predetermined time intervals. Filters with, and outputs acceleration characteristic data S3 composed of a plurality of data related to the user's walking / running.

The walking / running speed calculating means 32 includes a regression equation generating means 32a and a walking / running speed returning means 32b, and from the personal data S1 and the acceleration characteristic data S3, the walking / running speed, the number of steps, the stride length, and the total calorie consumption data are obtained. The motion data S6 including the above is output and input to the display means 40 and the communication means 60.

The timing means 33 includes date and time information, outputs the timing signal S4 to the signal processing means 31, manages the operation timing, and supplies the date and time information to the storage means 50.

<Explanation of operation of signal processing means 31>
Hereinafter, the operation of the signal processing means 31 will be described in detail with reference to FIGS. 1 to 3. FIG. 2 is a side view showing a state in which the activity meter 1 is attached to the ankle portion L of the right leg portion of the user. As shown in FIG. 2, the direction shown as X is the traveling direction in walking / running, and the direction shown as Y is the direction of gravity.

FIG. 3A is a schematic diagram showing the posture of the user P during walking / running at arbitrary times T1 to time T4, and FIG. 3B is a traveling direction of the acceleration signal S2 at the times T1 to time T4. It is a time series graph which shows the amplitude change of the acceleration signal S2x, and the time T1 to time T4 shown in each of FIG. 3A and FIG. 3B are the same time.

FIG. 3A shows the walking / running motion of the user P wearing the activity meter 1 on the ankle L, and at the time T1, the user P puts his / her right foot after kicking backward. Just before kicking forward. Further, at time T2, the right foot kicked forward lands and is just before kicking backward. The operation at time T3 is the same as the operation at time T1, and the operation at time T4 is the same as the operation at time T2.

FIG. 3B is a time-series graph showing the time on the horizontal axis and the amplitude (relative value) of the traveling direction acceleration signal S2x on the vertical axis. At time T1 to time T4, the right foot is kicked forward or backward. Since it is immediately before, the amplitude of the traveling direction acceleration signal S2x is 0.
Further, after the time T1 and after the time T3, the traveling direction acceleration signal S2x gradually increases in response to the action of stepping forward on the right foot, reaches a maximum at a time interval jt from the time T1 and the time T3, and then decreases thereafter. .. That is, the section jt is a section from the state where the amplitude of the traveling direction acceleration signal S2x is 0 to the time when the amplitude increases to the maximum.

As shown in FIG. 3B, since the amplitude of the traveling direction acceleration signal S2x is 0 at time T1 to time T3 and the same state in the repetitive operation, the signal processing means 31 has a section pt− of time T1 to time T3. a is calculated as "pitch data" which is a period on the time series of the acceleration signal in the traveling direction.

In FIG. 3B, the signal processing means 31 has a “xy composite value” formed by vector-synthesizing the average value of the traveling direction acceleration signal S2x and the average value of the gravity direction acceleration signal S2y in the section pt-a, and a section. "Jerk" calculated by accumulating the "jerk increase time" which is the time of jt and the jerk signal (hereinafter abbreviated as "jerk signal") which is the jerk signal (hereinafter abbreviated as "jerk signal") of the acceleration signal S2x in the traveling direction in the section jt. The product of the "cumulative value" and the jerk increase time and the jerk cumulative value is calculated as the "jerk evaluation value".

The jerk (jerk) is a physical quantity known as a change amount of the acceleration signal in a time series, and in the present embodiment, a method of calculating the change amount of the acceleration signal within a predetermined time (minute time). Is used. Further, the "jerk cumulative value" of the present embodiment is a value obtained by cumulatively adding a plurality of jerk signals obtained within this section jt, with the section jt as a predetermined interval.

The signal processing means 31 outputs the acceleration characteristic data S3 composed of the “pitch data”, the “xy composite value”, the “jerk increase time”, the “jerk cumulative value”, and the “jerk evaluation value” calculated as described above. Then, it is input to the walking / running speed calculation means 32.

<Explanation of operation of walking / running speed calculation means 32>
As shown in FIG. 1, the walking / running speed calculating means 32 includes a regression equation generating means 32a, a walking / running speed returning means 32b, and means for calculating the number of steps, stride length, and total calorie consumption data (not shown). , The acceleration characteristic data S3 is input from the signal processing means 31, and the user's personal data S1 is input from the personal data input means 10, and the exercise data S6 composed of walking / running speed, number of steps, stride, and total calorie consumption data is output. To do.

The operations of the regression equation generating means 32a and the walking / running speed returning means 32b in the walking / running speed calculating means 32 will be described in detail.
The regression equation generating means 32a collectively collects personal data S1 including height value and weight value, acceleration characteristic data S3 in walking / running of the subject, and actual measurement value of walking / running speed for a plurality of subjects. The regression equation S5 is provided as stored data, and the walking / running speed is calculated from the personal data of an arbitrary user and the acceleration characteristic data in walking / running based on the stored data.

More specifically, the regression equation S5 in the present embodiment is a mathematical expression in which personal data and acceleration characteristic data S3 are linearly combined, and is a regression equation for calculating walking / running speed, in which the height is in m (meters). , Weight is in kg (kilogram), pitch data is in seconds, and relative values are used for "xy composite value", "jerk increase time", "jerk cumulative value", and "jerk evaluation value".
"Walking / running speed = a x height value + b x weight value + c x 1 / (pitch data) + d x (xy composite value) + e x (jerk increase time) + f x (jerk cumulative value) + g x (jerk evaluation value) + H "
Is the formula shown by. Here, the coefficients a to h of the linear combination are a = 0.00879, b = 0.0056, c = 2.34, d = 0.143, e = −0.112, f = in the present embodiment. It is assumed that -0.0624, g = 0.00269, and h = -7.76.

Although not shown, the regression equation generating means 32a is provided with a data updating means, and the measured walking / running speed value of the new subject from the external information terminal 90 and the height value and weight value of the subject. The personal data including the above and the acceleration characteristic data S3 are sequentially input and automatically updated.
Then, the regression equation generating means 32a uses a regression equation S5 that approximates the data distribution including the updated walking / running speed actual measurement value, the personal data including the height value and the weight value, and the acceleration characteristic data S3, for example, the least squares method. Is calculated using.

As a result, the regression equation generating means 32a updates the coefficients a to h of the regression equation S5 to the latest ones. The regression equation S5 may be calculated by an external device or device such as the external information terminal 90, received by the data updating means, and the regression equation S5 itself may be updated directly. That is, the regression equation generating means 32a does not have to include the measured walking / running speed value and the personal data of the subject, and is provided with the regression equation S5 in which the personal data and the acceleration data are linearly combined. All you need is.

The walking / running speed regression means 32b uses the regression equation S5 input from the regression equation generating means 32a, and uses the height value and weight value in the personal data S1 and the acceleration characteristic data S3 in the regression equation S5 to walk / travel speed. Is calculated.
Further, by a means (not shown) in the walking / running speed calculating means 32, the stride is calculated by multiplying the walking / running speed from the walking / running speed returning means 32b by the pitch data. Further, the number of steps and the total calorie consumption data are calculated by a known method from the amplitude change of the xy composite value on the time series and the personal data S1.
As described above, the data processing means 30 outputs the exercise data S6 including the walking / running speed, the number of steps, the stride length, and the total consumption calorie data, and inputs the exercise data S6 to the display means 40 and the communication means 60. The communication means 60 transmits the motion data S6 to the external information terminal 90 by the electromagnetic field W, and the user uses the external information terminal 90 at hand to perform the motion data S6 including walking / running speed, number of steps, stride length, and total calorie consumption data. Can be known.

A measurement example of the activity meter 1 of the present invention will be described with reference to FIG.
FIG. 4 shows a total of 22 subjects, 12 males and 10 females, with the activity meter 1 attached alternately to the left and right ankles, walking and running a predetermined distance at a plurality of speeds, and the required time. The comparison between the measured speed value (km / H) calculated from the above and the calculated speed value (km / H) calculated by the regression equation S5 is shown in the correlation diagram, and the horizontal axis is the measured speed value (km / H). , The vertical axis is the “speed calculation value (km / H)” calculated by the regression equation S5.
The total number of data in the correlation diagram is 2114 points, and the result of linear regression of each point is y = 0.969x + 0.1512 and the correlation coefficient R ^ 2 = 0.969.

That is, based on the above regression equation S5, the height value and weight value of an arbitrary user and the acceleration characteristic data S3 during walking / running are used in the regression equation S5 provided in the regression equation generating means 32a, so that the user can use the regression equation S5. It is possible to calculate the walking / running speed at a high rate with a correlation coefficient of 0.969.

<Explanation of flowchart>
Next, the operation of the activity meter according to the present embodiment will be described with reference to FIG.
FIG. 5 is a flowchart illustrating an operation example of the activity meter and a walking / running speed calculation method according to the present embodiment.

<Q1 step>
The user inputs personal data including height and weight by a personal data input means.

<Q2 step>
The user sets the activity meter 1 on the ankle of the right foot and the display surface of the display means 40 outward so that the direction of the activity meter 1 when not walking or running is the specified direction. Wear it so that it becomes. In addition, the user can see that the output of the acceleration signal from the acceleration measuring means of the activity meter in one direction matches the traveling direction of the user in walking / running, and the output of the acceleration signal in the other direction is in the direction of gravity of the user. Wear it so that it matches the movement.
When the user starts walking / running, the acceleration measuring means of the activity meter outputs a three-dimensional acceleration signal including a traveling direction acceleration signal and a gravity direction acceleration signal.

<Q3 step>
The data processing means is "xy" formed by vector-synthesizing "pitch data", which is a period of the traveling direction acceleration signal on the time series, and the average value of the traveling direction acceleration signal and the average value of the gravity direction acceleration signal in the period. "Composite value", "Jerk increase time" which is the time of the section where the jerk signal increases, "Jerk cumulative value" which is the cumulative value of the jerk signal in the section where the jerk signal increases, and "Jerk increase time" The acceleration characteristic data S3 composed of the “jerk evaluation value” which is the product of the “jerk cumulative value” is calculated.

<Q4 step>
The walking / running speed regression means 32b of the walking / running speed calculating means 32 calculates the walking / running speed by the acceleration characteristic data S3, the personal data S1, and the regression equation S5 stored in the regression equation generating means 32a.

<Q5 step>
The walking / running speed calculation means 32 calculates the number of steps, stride length, and total calorie consumption data based on the acceleration characteristic data S3 and the walking / running speed, and exercises including the walking / running speed, number of steps, stride length, and total calorie consumption data. The data S6 and the date and time information are output to the display means 40 and the communication means 60.

<Q6 step>
The external information terminal 90 receives and displays the motion data S6 and the date and time information by the electromagnetic field W. The user can grasp the walking / running speed, the number of steps, the stride length, the total calorie consumption data, and the date and time information by displaying the display means 40 of the activity meter 1 or the external information terminal 90.

(Explanation of structural example of activity meter 1)
A structural example of the activity meter 1 will be described with reference to FIG. FIG. 6A is an example in which the personal data input means 10 and the display means 40 are provided, and FIG. 6B is an example in which these are not provided.
In FIG. 6A, date and time information is displayed on the date and time display unit 45 of the display means 40, the cumulative number of steps at the date and time on the step number display unit 41, the cumulative total consumption calorie on the total consumption calorie display unit 42, and the stride length display unit 44. The representative value of the stride on the day and the representative value of the walking / running speed on the date and time are displayed on the walking / running speed display unit 43. As a representative value, an average value or an instantaneous value, a maximum value, a minimum value, etc. can be selected.
In FIG. 6B, the activity meter 2 includes a notification means D composed of a light emitting element or a sounding body, and an external information terminal 90 is used for the functions of the personal data input means and the display means.

As described above, according to the present embodiment, the activity meter and the walking / running speed calculation method that easily and easily calculate and display the walking / running speed by the activity meter that is carried on a daily basis in a general household. Realize.

The present invention can be practiced in various other forms without departing from its spirit or key features. Therefore, the above embodiments are merely exemplary in all respects and should not be construed in a limited way. The scope of the present invention is shown by the scope of claims, and is not bound by the text of the specification. Moreover, all modifications, modifications, substitutions and modifications that fall within the equivalent scope of the claims are all within the scope of the present invention.

1, 2: Activity meter 10: Personal data input means 20: Acceleration measuring means 30: Data processing means 31: Signal processing means 32: Walking / running speed calculating means 32a: Regressive expression generating means 32b: Walking / running speed returning means 33: Timing means 40: Display means 41: Step count display unit 42: Total consumption calorie display unit 43: Walking / running speed display unit 44: Stride length display unit 45: Date and time display unit 50: Storage means 60: Communication means 70: Power supply 90 : External information terminal S1: Personal data S2: Acceleration signal S2x: Travel direction acceleration signal S2y: Gravity direction acceleration signal S3: Acceleration characteristic data S4: Timing signal S5: Regressive table data S6: Motion data S7: Command data T1 to T4 : Time pt-a: Section jt: (S2x amplitude increases) Section Q1: Personal data input step Q2: Acceleration measurement step Q3: Signal processing step
Q4: Walking / running speed calculation step Q5: Exercise data calculation step W: Electromagnetic field D: Notification means P: User

Claims (3)

Personal data input means for inputting user's personal data and
An acceleration measuring means that outputs a three-dimensional acceleration signal including a traveling direction acceleration signal and a gravity direction acceleration signal of the user's leg during walking or running of the user, and an acceleration measuring means.
An activity meter equipped with a data processing means
The data processing means
An xy composite value formed by vector-synthesizing pitch data, which is a period of the traveling direction acceleration signal over time, an average value of the traveling direction acceleration signal, and an average value of the gravity direction acceleration signal, and the traveling direction acceleration. The jerk signal which is the jerk signal which is the acceleration signal of the signal, the jerk increase time which is the time of the section where the jerk signal increases, and the jerk cumulative value which is the cumulative value of the jerk signal in the section where the jerk signal increases. A signal processing means for outputting acceleration characteristic data composed of a jerk evaluation value which is a product of the jerk increase time and the jerk cumulative value.
It is a mathematical formula that linearly combines the personal data and the acceleration characteristic data, and includes a regression equation for calculating the speed during walking or running, and the personal data of the user input by the personal data input means. Based on the acceleration characteristic data output from the signal processing means and the regression equation, the walking / running speed calculating means for calculating the walking or running speed of the user, and the walking / running speed calculating means.
An activity meter characterized by being equipped with. The regression equation included in the walking / running speed calculating means is a mathematical expression using the height value and the weight value in the personal data, the acceleration characteristic data, and the coefficients a to h.
"Walking / running speed = a x height value + b x weight value + c x 1 / (pitch data) + d x (xy composite value) + e x (jerk increase time) + f x (jerk cumulative value) + g x (jerk evaluation value) + H "
The activity meter according to claim 1, wherein the activity meter is characterized by the above. Personal data entry step to enter user's personal data,
An acceleration measurement step that outputs a three-dimensional acceleration signal including a traveling direction acceleration signal and a gravity direction acceleration signal of the user's leg during walking or running of the user, and an acceleration measurement step.
An xy composite value formed by vector-synthesizing pitch data, which is a period of the traveling direction acceleration signal over time, an average value of the traveling direction acceleration signal, and an average value of the gravity direction acceleration signal, and the traveling direction acceleration. The jerk signal which is the jerk signal which is the acceleration signal of the signal, the jerk increase time which is the time of the section where the jerk signal increases, and the jerk cumulative value which is the cumulative value of the jerk signal in the section where the jerk signal increases. A signal processing step for outputting acceleration characteristic data composed of a jerk evaluation value which is a product of the jerk increase time and the jerk cumulative value, and a signal processing step.
"Walking / running speed =" is a regression equation using coefficients a to h that linearly combines the height and weight values in the personal data and the acceleration characteristic data to calculate the speed during walking or running. a x height value + b x weight value + c x 1 / (pitch data) + d x (xy composite value) + e x (jerk increase time) + f x (jerk cumulative value) + g x (jerk evaluation value) + h "
Based on the walking / running speed calculation step for calculating the walking or running speed of the user,
A walking / running speed calculation method characterized by executing.