JP7844971B2 - Activity estimation device, wireless communication terminal, activity estimation system, activity estimation method, and activity estimation program - Google Patents

Description

This invention relates to an activity estimation device, etc.

In recent years, the use of wearable devices that collect information related to human activity has become widespread in order to manage a person's exercise status and physical condition. Among these, wearable devices that monitor basic human activities such as walking and running have been proposed.

For example, Reference 1 discloses a ground contact pattern evaluation system. This system incorporates a pressure sensor into footwear to acquire pressure information indicating changes in pressure within the footwear. Based on this pressure information, the system classifies the ground contact pattern of the user's foot. This system identifies the portion of the pressure information that shows periodically large values as the landing timing, and determines the ground contact pattern of a runner wearing the footwear based on the degree of pressure decrease immediately after the landing timing. Here, determining the ground contact pattern means determining whether it is a heel strike (heel-first landing) or a forefoot strike (toe-first landing).

Furthermore, Patent Document 2 discloses a technology for a calorie consumption calculation device using an acceleration sensor for step counting and a barometric pressure sensor. When using the calorie consumption calculation device, the user (the person being measured) secures the device to their waist using a belt or other fastener. First, the calorie consumption calculation device detects the number of steps using the step count sensor. The barometric pressure sensor also detects changes in barometric pressure corresponding to vertical movement. Then, based on the signals from the step count sensor and the barometric pressure signal, a determination means determines the exercise state of the person being measured. By determining the exercise state, a calorie consumption coefficient value corresponding to the person's exercise state can be selected, and the total calorie consumption amount corresponding to the person's exercise can be calculated by multiplying the calorie consumption coefficient value by the number of steps.

Furthermore, related technology is disclosed in Patent Document 3.

Japanese Patent Publication No. 2021-058524 Japanese Patent Application Publication No. 11-347021 Japanese Patent Publication No. 2016-140639

However, the technology described in Patent Document 1 had the problem of only being able to determine the type of ground contact during running, which is a limited activity. Furthermore, the technology described in Patent Document 2 determines the motion state by analyzing the output of an acceleration sensor and a barometric pressure sensor. This resulted in a problem of requiring a large amount of computational processing for the analysis.

This invention was made in view of the above-mentioned problems, and aims to provide an activity estimation device, etc., that can estimate user activity with a small amount of computational processing.

To solve the above problems, the activity estimation device includes a pressure data acquisition means for acquiring pressure data inside the shoes worn by the user, and a movement state estimation means for estimating whether the user is moving or stationary based on the time change of the pressure data.

Furthermore, the wireless communication terminal of the present invention comprises the above-mentioned activity estimation device and wireless communication means for transmitting and receiving information related to the activity estimation device.

Furthermore, the activity estimation system of the present invention comprises the above-described activity estimation device and a pressure measuring device attached to the shoe. The pressure measuring device includes a pressure measuring means for measuring the air pressure inside the shoe as pressure data, and a communication means for transmitting the measured pressure data to the activity estimation device.

Furthermore, the activity estimation method of the present invention involves a computer acquiring atmospheric pressure data from inside the shoes worn by the user, and estimating whether the user is moving or stationary based on the time change of the atmospheric pressure data.

Furthermore, the activity estimation program of the present invention causes a computer to perform the following processes: acquiring atmospheric pressure data inside the shoes worn by the user, and estimating whether the user is moving or stationary based on the time change of the atmospheric pressure data.

The advantage of this invention is that it can provide an activity estimation device, etc., that can estimate user activity with a small amount of computational processing.

This is a block diagram of an activity estimation device according to the first embodiment. This is a flowchart showing the operation of the activity estimation device according to the first embodiment. This is a block diagram showing the activity estimation system of the second embodiment. This is a block diagram showing a specific example of the activity estimation system according to the second embodiment. This is a block diagram showing another specific example of the activity estimation system of the second embodiment. This is a schematic cross-sectional view showing a first specific example of the placement of the pressure measuring device in a shoe according to the second embodiment. This is a schematic perspective view showing a first specific example of the placement of the pressure measuring device in a shoe according to the second embodiment. This is a schematic perspective view showing an example of the arrangement of a pressure sensor in a first specific example of the placement of a pressure measuring device in a shoe according to the second embodiment. This is a schematic cross-sectional view showing the movement of the foot inside the shoe in the second embodiment. This is a flowchart showing the first operation of the activity estimation device according to the second embodiment. This is a flowchart showing the second operation of the activity estimation device according to the second embodiment. This is a flowchart showing the first half of the third operation of the activity estimation device according to the second embodiment. This is a flowchart showing the latter half of the third operation of the activity estimation device according to the second embodiment. This graph shows an example of atmospheric pressure data acquired by the activity estimation device of the second embodiment. This graph shows an example of the correspondence between barometric pressure data acquired by the activity estimation device of the second embodiment and the user's activity. This is an enlarged graph of section G in Figure 15. This is a graph where the slope correction was applied in section G of Figure 15. This is an enlarged graph of section A in Figure 15. This is an enlarged graph of section B in Figure 15. This is a magnified graph of section F in Figure 15. This is an enlarged graph of section G in Figure 15. This is a block diagram showing a modified example of the activity estimation device according to the second embodiment. This table shows an example of a calorie calculation method in the activity estimation device of the second embodiment. This is a schematic cross-sectional view showing a second specific example of the placement of the pressure measuring device in a shoe according to the second embodiment. This is a schematic plan view showing a second specific example of the placement of the pressure measuring device in a shoe according to the second embodiment. This is a schematic cross-sectional view showing a third specific example of the placement of the pressure measuring device in a shoe according to the second embodiment. This is a schematic plan view showing a third specific example of the placement of the pressure measuring device in a shoe according to the second embodiment. This is a schematic cross-sectional view showing a fourth specific example of the placement of the pressure measuring device in a shoe according to the second embodiment. This is a schematic cross-sectional view showing a fifth specific example of the placement of the pressure measuring device in a shoe according to the second embodiment.

The embodiments of the present invention will be described in detail below with reference to the drawings. However, while the embodiments described below include technically preferred limitations for carrying out the present invention, they do not limit the scope of the invention. Similar components in each drawing are given the same number, and their descriptions may be omitted.

(First embodiment)
Figure 1 is a block diagram of the activity estimation device 10 according to the first embodiment. The activity estimation device 10 includes a pressure data acquisition means 1 and a movement state estimation means 2.

The pressure data acquisition means 1 acquires pressure data inside the shoe 90 worn by the user. The pressure data inside the shoe 90 is, for example, pressure data measured by a pressure measuring means 91 located inside the shoe 90. The measured pressure data is transmitted to the activity estimation device 10 by the communication means 92.

The movement state estimation means 2 estimates whether the user is in a moving state or a stationary state based on the time change of atmospheric pressure data.

The pressure measuring means 91 is located inside the shoe 90. The pressure measuring means 91 measures the air pressure inside the shoe 90.

The communication means 92 communicates with the activity estimation device 10. Specifically, the communication means 92 transmits the atmospheric pressure data measured by the atmospheric pressure measurement means 91 to the activity estimation device 10.

Figure 2 is a flowchart illustrating the operation of the activity estimation device 10 in the first embodiment. The activity estimation device 10 first acquires atmospheric pressure data from inside the shoe 90 (S1). The shoe 90 is worn by the user. Next, the activity estimation device 10 estimates whether the user's activity is in a moving state or a stationary state based on the time change of the atmospheric pressure data (S2).

The activity estimation device 10 of this embodiment has been described above.

The activity estimation device 10 of this embodiment includes a pressure data acquisition means 1 and a movement state estimation means 2. The pressure data acquisition means 1 acquires pressure data from inside the shoes 90 worn by the user. The movement state estimation means 2 estimates whether the user is moving or stationary based on the time change of the acquired pressure data. Pressure data changes more rapidly when the user walks, runs, or uses other means of transportation than pressure changes due to climate changes. Examples of means of transportation include elevators and escalators. Therefore, it is possible to estimate the user's movement based on rapid changes in pressure data. The activity estimation device 10 of this embodiment can estimate whether the user is moving or stationary based solely on the time change of pressure data inside the shoes 90. Therefore, compared to cases using both the number of steps measured by an acceleration sensor and pressure, as in Patent Document 2, the user's activity can be estimated with less computation.

(Second embodiment)
Next, we will describe a specific configuration example of an activity estimation device and an activity estimation system using the activity estimation device. Figure 3 is a block diagram of the activity estimation system 1000 of the second embodiment. The activity estimation system 1000 includes an activity estimation device 100 and a barometric pressure measuring device 200. Note that the activity estimation system 1000 of the second embodiment is an example of the activity estimation device 10 of the first embodiment.

The activity estimation device 100 includes a pressure data acquisition unit 110 and a movement state estimation unit 120. The activity estimation device 100 is implemented, for example, on a computer 300. The activity estimation device 100 is an example of the activity estimation device 10 of the first embodiment. Furthermore, the pressure data acquisition unit 110 is an example of the pressure data acquisition means 1, and the movement state estimation unit 120 is an example of the movement state estimation means 2.

The pressure data acquisition unit 110 acquires pressure data from inside the shoe 400 worn by the user. This pressure data is, for example, pressure data measured by a pressure measuring device 200 located inside the shoe 400. The measured pressure data is transmitted to the activity estimation device 100 via the communication module 220.

The movement state estimation unit 120 estimates whether the user is in a moving state or a stationary state based on the time change in atmospheric pressure data.

The pressure measuring device 200 includes a pressure sensor 210 and a communication module 220. The pressure sensor 210 measures the air pressure inside the shoes 400 worn by the user as pressure data. The communication module 220 transmits the pressure data measured by the pressure sensor 210 to the activity estimation device 100. Here, the pressure sensor 210 is an example of the pressure measuring means 91 in the first embodiment, and the communication module 220 is an example of the communication means 92.

The pressure sensor 210 is located inside the shoe 400. The pressure sensor 210 measures the air pressure inside the shoe 400.

The communication module 220 communicates with the activity estimation device 100. Specifically, the communication module 220 transmits the atmospheric pressure data measured by the atmospheric pressure measuring device 200 to the activity estimation device 100.

Next, a specific example of the activity estimation system 1000 will be described. Figure 4 is a block diagram showing a specific example of the activity estimation system 1000 according to the second embodiment. In this example, the activity estimation device 100 is implemented in a wireless communication terminal 310 equipped with wireless communication means 311. The wireless communication means 311 transmits and receives information related to the activity estimation device 100.

Figure 5 is a block diagram showing another specific example of the activity estimation system 1000 according to the second embodiment. In this example, the activity estimation system 1000 includes an activity estimation device 100, a barometric pressure measuring device 200, and a wireless communication terminal 320. The computer 300 and the wireless communication terminal 320 can be connected to a network 500. Barometric pressure data measured by the barometric pressure measuring device 200 is transmitted from the communication module 220 to the wireless communication terminal 320. The barometric pressure data is then transmitted from the wireless communication terminal 320 to the computer 300 via the network 500. For example, if the wireless communication terminal 320 is a smartphone or tablet device owned by the user, the communication module 220 can send the barometric pressure data to the wireless communication terminal 320 using short-range wireless technology such as Bluetooth (registered trademark).

Next, the configuration of the pressure measuring device 200 and its placement in the shoe 400 will be described. Figure 6 is a schematic cross-sectional view showing a first specific example of the placement of the pressure measuring device 200 in the shoe 400 according to the second embodiment. Figure 7 is a schematic perspective view showing a first specific example of the placement of the pressure measuring device 200 in the shoe 400 according to the second embodiment. Figure 8 is a schematic perspective view showing a specific example of the placement of the pressure sensor 210 in the pressure measuring device 200 according to the second embodiment. The pressure sensor 210 is placed in the shoe 400.

As shown in Figure 6, the shoe 400 is worn on the user's foot 900. An insole 410 is placed inside the bottom of the shoe 400. The barometric pressure measuring device 200 is attached to the insole 410. As shown in Figure 7, a recessed portion 411 is provided in the arch of the insole 410. A communication module 220 equipped with a battery 221 is housed in the recessed portion 411. A cover (not shown) is provided above the recessed portion 411 to prevent the intrusion of moisture and dust from the outside. A groove 411a is connected to the recessed portion 411. The groove 411a is formed between the recessed portion 411 and the upright portion at the heel end. In the following description, this upright portion will be referred to as the heel cup 412. The groove 411a also houses the cable 230. As shown in Figure 8, a pressure sensor 210 is mounted on the end of the cable 230 on the heel cup 412 side. A cover (not shown) is provided in the recessed portion 411 and groove 411a. This cover can be made of, for example, a soft-touch fabric. In this case, the fabric is attached to the entire side of the insole 410 that rests on the foot 900. Note that the placement of the pressure sensor 210 is not limited to the above. The placement of the pressure sensor 210 is acceptable as long as air can pass through the gap between the inner space of the shoe 400 and the cover.

The insole 410 is made of an elastic material with elasticity, such as foamed urethane. The pressure sensor 210 can be, for example, a piezoresistive semiconductor pressure sensor. The cable 230 can be, for example, a flexible printed circuit board (FPC).

Next, we will explain how to estimate user activity from atmospheric pressure data using a specific example of atmospheric pressure data. First, we will explain the relationship between the time-dependent changes in atmospheric pressure data and user activity. Figure 9 is a schematic cross-sectional view showing the shoe 400 and foot 900 in the second embodiment. Figure 9 shows the state in which the foot 900 moves inside the shoe 400. For example, when the state changes from one where the entire sole of the foot 900 is touching the bottom of the shoe 400, as shown in Figure 5, to one where the heel of the foot 900 is lifted off the insole 410, as shown in Figure 9, the atmospheric pressure inside the shoe 400 changes. In other words, when the user's foot moves due to walking or running, the atmospheric pressure inside the shoe 400 changes.

Next, the operation of the activity estimation device 100 will be described. Figure 10 is a flowchart showing the first operation of the activity estimation device of the second embodiment. First, the activity estimation device 100's pressure data acquisition unit 110 samples pressure data at a predetermined period (S101). Next, the movement state estimation unit 120 calculates a first difference, which is the difference between a given pressure data and the previous pressure data (S102). Then, if the first difference is greater than or equal to a first threshold (S103_Yes), the movement state estimation unit 120 estimates that the user is in a movement state (S104). The movement state estimation unit 120 then stores the section in which the movement state is continuous as a movement section (S105). At this time, the pressure data from the section in which the movement state is continuous and the data from the section after are added to the movement section. The movement state estimation unit 120 performs this process to estimate the altitude before movement and the altitude after movement. The estimation of altitude will be described later. On the other hand, if the first difference is less than the first threshold (S013_No), the movement state estimation unit 120 estimates that the user is in a stopped state (S106). The movement state estimation unit 120 then stores the section in which the stopped state is continuous as a stopped section (S107). In this way, the acquired atmospheric pressure data is divided into movement sections and stopped sections.

The movement state estimation unit 120 can also estimate altitude changes. Figure 11 is a flowchart showing the second operation of the activity estimation device according to the second embodiment. In the second operation, altitude changes are estimated for each movement section and each stopping section. The movement state estimation unit 120 calculates the difference between the first and last atmospheric pressure data for each movement section and each stopping section as the second difference (S201). Then, based on the second difference, the altitude change of the location of the shoes 400 in each movement section and stopping section is estimated (S202).

Figure 12 is a flowchart showing the first half of the third operation of the activity estimation device according to the second embodiment. Figure 13 is a flowchart showing the second half of the third operation of the activity estimation device according to the second embodiment. In the third operation, the movement state estimation unit 120 estimates whether the user's movement is walking, running, or movement using a means of transport. First, the movement state estimation unit 120 generates a graph plotting the atmospheric pressure data for each movement section against time (S301). Next, it generates a first straight line connecting the first and last atmospheric pressure data points on this graph (S302). Next, it generates corrected atmospheric pressure data by correcting the atmospheric pressure data within the movement section so that the slope of the first straight line is zero (S303). Next, it calculates the standard deviation σ of the corrected atmospheric pressure data (S304). Next, based on the calculated standard deviation σ, the movement state estimation unit 120 estimates whether the movement is walking, running, or movement using a means of transport. As is clear from the intensity of the movement, the standard deviations σ, in descending order, are stopping, walking, and running within the means of transport. Therefore, two thresholds are set for the standard deviation σ. A second threshold _σ1 is set as the threshold for distinguishing between stopping and walking within the means of transport. A third threshold _σ2 is set as the threshold for distinguishing between walking and running. Here, _σ1 < _σ2 . Next, the movement state estimation unit 120 determines whether the standard deviation σ is less than the second threshold _σ1 (S305). If the standard deviation σ is less than the second threshold _σ1 (S305_Yes), the movement state estimation unit 120 estimates that the user is moving using the means of transport (S306). On the other hand, if the standard deviation σ is greater than or equal to the second threshold _σ1 (S305_No), the movement state estimation unit 120 determines whether the standard deviation σ is less than the third threshold _σ2 ( _σ1 ≤ σ < _σ2 ) (S307). If the standard deviation σ is less than the third threshold _σ² (S307_Yes), the movement state estimation unit 120 estimates that the user is walking (S308). On the other hand, if the standard deviation σ is greater than or equal to the third threshold _σ² (S307_No), the movement state estimation unit 120 estimates that the user is running (S309).

As described above, the movement state estimation unit 120 estimates whether the user's movement is walking, running, or using a means of transportation. Furthermore, although not shown in the diagram, if the altitude changes in each movement section and each stopping section have been estimated beforehand, the movement state estimation unit 120 can also estimate whether the walking, running, or movement using a means of transportation was uphill, downhill, or on level ground.

The above explanation used standard deviation as an indicator of variability. However, other indicators of variability may also be used. Other indicators of variability include, for example, variance or the difference between the maximum and minimum values.
(Specific example 1)
Next, using specific atmospheric pressure data obtained in the experiment, we will explain the relationship between user activity and the time change of atmospheric pressure data inside the shoe 400. Figure 14 is a graph showing an example of atmospheric pressure data obtained by the activity estimation device 100 of the second embodiment. The sampling period for atmospheric pressure data is ₁ s. In this specific example, the second threshold σ1 that distinguishes between stopping and walking within the means of movement is 80 Pa. The third threshold _σ2 that distinguishes between walking and running is 300 Pa.

As can be seen from Figure 14, the time variation of atmospheric pressure data has both flat sections and sections where the altitude changes rapidly. As mentioned earlier, if adjacent users of atmospheric pressure data walk or run, changes in atmospheric pressure occur. Therefore, the parts of the atmospheric pressure data that fluctuate in altitude are thought to correspond to the users walking or running. Also, the flat sections of the atmospheric pressure data graph are thought to correspond to the users being stationary. Furthermore, as is obvious from common sense, the absolute value of atmospheric pressure changes with altitude. The relationship between altitude and atmospheric pressure near sea level in a standard atmosphere can be expressed, for example, by the following equation (1).
H = 44330 × {1 - (P/1013)^0.190263}, where the temperature is 15°C ・・・・・(1)
From equation (1), a 1 Pa pressure change corresponds to approximately 8.3 cm near sea level (0 m). Based on this relationship, the change in altitude at the user's location can be estimated.

Next, a specific example will be explained. Figure 15 is a graph showing an example of the correspondence between barometric pressure data acquired by the activity estimation device 100 of the second embodiment and user activity. This barometric pressure data was acquired experimentally. Therefore, the correspondence between user activity and barometric pressure data is known. For this reason, Figure 15 shows the user's activity for each period.

Next, we will explain how to estimate user activity from the time-dependent changes in atmospheric pressure data. First, we will explain the method for slope correction to calculate the standard deviation in each travel interval.

Figure 16 is an enlarged graph of section G in Figure 14. Figure 17 is a graph of section G in Figure 14 after slope correction. The movement state estimation unit 120 performs slope correction on this graph. First, the movement state estimation unit 120 generates a first straight line by connecting the first and last atmospheric pressure data points of this movement section. Next, the movement state estimation unit 120 corrects each atmospheric pressure data point within the movement section so that the slope of the first straight line becomes zero. The movement state estimation unit 120 then stores the generated corrected atmospheric pressure data as corrected atmospheric pressure data. Figure 17 shows the corrected atmospheric pressure data from Figure 16.

Next, we will explain an example of estimating the movement state in each movement segment of Figure 15. Figure 18 is an enlarged graph of section A of Figure 15. In Figure 15, the first threshold for distinguishing between movement and stationary states is set to 30 Pa. The movement segments are indicated by triangle markers. The movement state estimation unit 120 sets the third threshold σ2 of the standard deviation for distinguishing between walking and running to 300 Pa. In the case of Figure 18, the movement segment is from 126s to 152s. First, the movement state estimation unit 120 calculates the second difference, which is the difference between the first and last data points in this movement segment. The second difference is approximately 9 Pa. This second difference corresponds to a descent of approximately 0.7 m in altitude. Furthermore, the standard deviation of the atmospheric pressure data in this movement segment calculated using the regression line described above is 78 Pa. Therefore, the movement state estimation unit 120 estimates the user's movement as walking. This walking is estimated to be a slight downhill. In reality, the user is walking on the first floor, so there is an error in the estimation by the movement state estimation unit 120. However, this error is smaller than the difference between the first and second floors. Therefore, it can be seen that the estimation by the movement state estimation unit 120 provides a rough estimate of the altitude difference.

Figure 19 is an enlarged graph of section B of Figure 15. In Figure 19, the movement intervals are 187s-190s and 195s-202s. The second difference between 187s-190s is almost zero, and the second difference between 195s-202s is approximately -36 Pa. From this, the movement state estimation unit 120 estimates that the user (location of the shoe) has risen by approximately 3.0 m. Furthermore, the standard deviation after slope correction during the period 195s-201s is 184 Pa. Therefore, the movement state estimation unit 120 estimates that the user is walking uphill and has risen by approximately 3 m during this period.

Figure 20 is an enlarged graph of section F in Figure 15. In Figure 20, the movement interval is from 374s to 398s. The second difference in this movement interval is approximately -16 Pa. From this, the movement state estimation unit 120 estimates that the user is rising by approximately 1.2 m. Furthermore, the standard deviation after slope correction during this period is 444 Pa. This is above the third threshold of 300 Pa. Therefore, the movement state estimation unit 120 estimates that the user is in a driving state and moving on relatively flat ground.

Figure 21 is an enlarged graph of section G in Figure 15. In Figure 21, 412s–416s and 420s–428s are estimated to be the movement intervals. The second difference between 412s–416s is almost zero. On the other hand, the second difference between 420s–428s is approximately 126 Pa. From this, the movement state estimation unit 120 estimates that the user is descending approximately 10.4 m. Furthermore, the standard deviation after slope correction during the period 420s–428s is 38 Pa. This standard deviation is less than the second threshold of 80 Pa. Therefore, the movement state estimation unit 120 estimates that the user is moving by means of transportation during this interval. As noted in the addendum to Figure 15, in reality, the user is moving from the 5th floor to the 3rd floor by elevator.

(Variant)
As described above, the movement state estimation unit 120 can estimate whether the user is walking, running, or stopped, and whether they are going uphill or downhill. Since the intensity of exercise differs for each of these activities, the movement state estimation unit 120 can estimate the user's calorie consumption by setting a guideline calorie consumption coefficient for each activity. Figure 22 is a block diagram showing a modified example of the activity estimation device of the second embodiment. The movement state estimation unit 120 of the modified activity estimation device 101 has a calorie consumption coefficient holding unit 121 that holds the calorie consumption coefficient and a calorie consumption calculation unit 122.

The calorie expenditure coefficients mentioned above are set for each activity, such as walking on flat ground, walking uphill, walking downhill, running on flat ground, running uphill, and running downhill. By calculating the duration of the activity and applying these calorie expenditure coefficients, the user's calorie expenditure during that activity can be calculated. Furthermore, by summing the calorie expenditures for each activity, the user's total calorie expenditure can be estimated.

Figure 23 is a table showing an example of setting and calculating the calorie consumption coefficient. As shown in Figure 23, calorie consumption can be calculated for each activity, and the total calorie consumption (Total) can be estimated.

(Specific example 2)
Next, a specific example of how to attach the pressure measuring device 200 to the shoe 400 will be described. The pressure measuring device 200 can have the configuration illustrated in Figure 3-5, but other configurations are also possible. Figure 24 is a schematic cross-sectional view showing a second specific example of the placement of the pressure measuring device 200 of the second embodiment on the shoe 400. Figure 25 is a schematic plan view showing a second specific example of the placement of the pressure measuring device of the second embodiment on the shoe. In the configuration of the second specific example, the communication module 220 is placed in the arch portion of the insole 410, and the pressure sensor 210 is placed under the tongue on the upper side of the shoe 400. The cable 230 is placed along the edge of the insole 410 from the arch portion of the insole 410 to the center of the upper part of the shoe 400. The cable 230 is also placed along the inner surface of the shoe 400 so as to wrap around the upper part of the foot 900 (not shown) from that position. Furthermore, cable 230 extends to near the center of the tongue.

Figure 26 is a schematic cross-sectional view showing a third specific example of the arrangement of the pressure measuring device 200 in the shoe 400 according to the second embodiment. Figure 27 is a schematic plan view showing a third specific example of the arrangement of the pressure measuring device in the shoe according to the second embodiment. In the configuration of the third specific example, the communication module 220 is placed in the arch portion of the insole 410, and the pressure sensor 210 is placed at the toe tip of the insole 410. The cable 230 is placed within the plane of the insole 410. Furthermore, the cable 230 is arranged to meander between the arch and the toe. By making the cable 230 meander in this way, the bending stress applied to the cable 230 at points where bending stress is applied during walking or running can be mitigated.

Figure 28 is a schematic cross-sectional view showing a fourth specific example of the placement of the pressure measuring device 200 in a shoe 400 according to the second embodiment. The communication module 220 of the pressure measuring device 200 is located in the sole portion of the shoe 400. The cable 230 extends to the heel portion inside the shoe 400. The pressure sensor 210 is located in the heel cup 412 portion of the insole 410. With this configuration, the pressure measuring device 200 can be placed in the shoe 400 even when using a standard insole 410.

Figure 29 is a schematic cross-sectional view showing a fifth specific example of the arrangement of the pressure measuring device in a shoe according to the second embodiment. The communication module 220 is placed in the arch portion of the insole 410. The pressure sensors 210 are placed in three locations: on the communication module 220, at the toe portion of the insole 410, and at the heel cup 412 portion of the insole 410. By using multiple pressure sensors 210 placed in multiple locations in this way, it becomes possible to analyze walking habits and measure pressure more accurately. In addition, vents 421, 422, and 423 are provided in the vicinity of where the pressure sensors 210 are placed in the shoe 400. The presence of vents 421, 422, and 423 improves the responsiveness of the pressure sensors 210 when measuring the outside air pressure. Although not shown in the figure, vents 421, 422, and 423 may be covered with water-repellent nonwoven fabric or a breathable waterproof sheet. This cover ensures the waterproofing of the shoe 400.

The activity estimation device 100 and other components of this embodiment have been described above.

The activity estimation device 100 of this embodiment includes a pressure data acquisition unit 110 (pressure data acquisition means) and a movement state estimation unit 120 (movement state estimation means). The pressure data acquisition unit 110 acquires pressure data from inside the shoes 400 worn by the user. The movement state estimation unit 120 estimates whether the user is moving or stationary based on the time change of the acquired pressure data. With this configuration, the activity estimation device 100 of this embodiment can estimate whether the user is moving or stationary based solely on the time change of pressure data inside the shoes 400. Therefore, compared to cases that use both the number of steps measured by an acceleration sensor and pressure, as in Patent Document 2, the user's activity can be estimated with less computation.

In another embodiment, the activity estimation device 100 includes a pressure data acquisition unit 110 (pressure data acquisition means) that samples pressure data at predetermined intervals. Then, a movement state estimation unit 120 (movement state estimation means) calculates a first difference. The first difference is the difference between a given pressure data and the previous pressure data. The movement state estimation unit 120 estimates that the user is in a moving state if the first difference is greater than or equal to a first threshold. Conversely, if the first difference is less than the first threshold, the movement state estimation unit 120 estimates that the user is in a stationary state. By performing these estimation operations, it is possible to estimate whether the user is in a moving state or a stationary state based solely on the time change in pressure inside the shoe 400.

In another embodiment, the activity estimation device 100 has a movement state estimation unit (movement state estimation means) 120 that stores atmospheric pressure data divided into movement sections and stop sections. A movement section is a section containing continuous movement data and one atmospheric pressure data point before and after it. A stop section is a section containing continuous stop data. This configuration allows the movement state estimation unit 120 to divide atmospheric pressure data into movement sections and stop sections.

In another embodiment, the activity estimation device 100 has a movement state estimation unit 120 (movement state estimation means) that calculates a second difference. This second difference is the difference between the initial and final atmospheric pressure data for each movement and stopping section. The movement state estimation unit 120 also estimates the altitude change of the shoe's location in each movement and stopping section based on this second difference. This configuration allows the movement state estimation unit 120 to estimate the altitude change of the shoe's (user's) location.

In another embodiment, the activity estimation device 100 has a movement state estimation unit 120 (movement state estimation means) that generates a graph plotting the atmospheric pressure data for each of the aforementioned movement sections against time. Next, the movement state estimation unit 120 generates a first straight line connecting the first atmospheric pressure data and the last atmospheric pressure data on the graph. Next, the movement state estimation unit 120 generates corrected atmospheric pressure data by correcting the values of the atmospheric pressure data in each movement section so that the slope of the first straight line becomes zero. Next, the movement state estimation unit 120 calculates an index of the variability of the corrected atmospheric pressure data in the movement section using the corrected atmospheric pressure data. If the index of variability is less than a second threshold, the movement state estimation unit 120 estimates that the user's movement state in the movement section is movement by means of transportation that involves altitude changes. Also, if the index of variability is greater than or equal to the second threshold and less than a third threshold greater than the second threshold, the movement state estimation unit 120 estimates that the user's movement state in the movement section is walking. Furthermore, if the variability index exceeds the third threshold, the movement state estimation unit 120 estimates the user's movement state in the movement section as running. In this way, the movement state estimation unit 120 can estimate whether the user's movement is by means of transportation, walking, or running.

In another embodiment, the activity estimation device 100 has a movement state estimation unit 120 (movement state estimation means) that estimates whether the running and walking states were uphill, downhill, or on level ground, based on the altitude changes in each of the aforementioned movement sections. With this configuration, the movement state estimation unit 120 can estimate whether the walking or running state is uphill, downhill, or on level ground. Furthermore, this estimation is based solely on the time change of atmospheric pressure data. In other words, the movement state estimation unit 120 can estimate the details of the user's activity with minimal computation.

Furthermore, the wireless communication terminal 310 of this embodiment includes one of the activity estimation devices 100 described above, and wireless communication means 311 for transmitting and receiving information about the activity estimation device 100. By including the activity estimation device 100 in the wireless communication terminal 310, for example, a user can estimate their calorie consumption based on the air pressure data inside the shoes 400 they are wearing.

Furthermore, the activity estimation system 1000 of this embodiment comprises one of the above-described activity estimation devices 100 and a pressure measuring device 200 attached to the shoe 400. The pressure measuring device 200 includes a pressure sensor 210 (pressure measuring means) that measures the pressure inside the shoe as pressure data, and a communication module 220 (communication means) for transmitting the measured pressure data to the activity estimation device 100. With this configuration, the activity estimation system 1000 can estimate the user's activity based on the pressure inside the shoe 400 worn by the user. Because the activity estimation system 1000 estimates the user's activity based solely on the pressure inside the shoe 400, it can estimate the user's activity with less computational effort compared to methods using both acceleration and pressure, as described in Patent Document 2.

Furthermore, in this embodiment, the activity estimation method involves the computer 300 acquiring atmospheric pressure data from inside the user's shoes 400 and estimating whether the user is moving or stationary based on the time-dependent changes in the atmospheric pressure data. With this configuration, the activity estimation method of this embodiment can estimate whether the user is walking, running, or stationary based solely on the time-dependent changes in the atmospheric pressure data inside the shoes 400. Therefore, compared to methods that use both the number of steps measured by an acceleration sensor and atmospheric pressure, as described in Patent Document 2, this method can estimate the user's activity with less computation.

Furthermore, the activity estimation program of this embodiment causes the computer to perform two processes: acquiring atmospheric pressure data inside the user's shoes 400, and estimating whether the user is moving or stationary based on the time change in atmospheric pressure data. With this configuration, the activity estimation program of this embodiment can estimate whether the user is walking, running, or stationary based solely on the time change in atmospheric pressure data inside the shoes 400. Therefore, compared to cases that use both the number of steps measured by an acceleration sensor and atmospheric pressure, as in Patent Document 2, the user's activity can be estimated with less computation.

The present invention also includes programs that cause a computer to execute the processing described in the first and second embodiments above, and recording media storing such programs. Examples of recording media that can be used include magnetic disks, magnetic tapes, optical disks, magneto-optical disks, semiconductor memory, and the like.

The first and second embodiments described above serve as exemplary examples of the present invention. However, the present invention is not limited to the above embodiments. That is, within the scope of the present invention, various embodiments that can be understood by those skilled in the art can be applied.

Some or all of the above embodiments may also be described as follows, but are not limited to the following:
(Note 1)
A means for acquiring atmospheric pressure data to acquire atmospheric pressure data inside shoes worn by the user,
A movement state estimation means that estimates whether the user is in a moving state or a stationary state based on the time change of the aforementioned atmospheric pressure data,
An activity estimation device characterized by having the following features.
(Note 2)
The aforementioned pressure data acquisition means,
The barometric pressure data is sampled at a predetermined interval.
The aforementioned movement state estimation means
The first difference, which is the difference between the aforementioned pressure data and the previous pressure data, is calculated.
If the first difference is greater than or equal to the first threshold, it is determined that the user is in a moving state.
If the first difference is less than the first threshold, it is determined that the user is in a stopped state.
The activity estimation device according to Appendix 1, characterized in that it is a device that provides activity estimation.
(Note 3)
The aforementioned movement state estimation means
The barometric pressure data is stored as a movement section, which includes the section in which the movement state is continuous and the section before and after it that includes the barometric pressure data, and the section in which the stop state is continuous is stored as a stop section.
The activity estimation device described in Appendix 2, characterized by the features described herein.
(Note 4)
The aforementioned movement state estimation means
The difference between the first and last atmospheric pressure data in each of the aforementioned moving and stopping sections is calculated as the second difference.
The activity estimation device according to Appendix 3, characterized in that it estimates the change in altitude of the location of the shoes in each of the aforementioned movement section and stop section based on the second difference.
(Note 5)
The aforementioned movement state estimation means
A graph is generated by plotting the atmospheric pressure data for each of the aforementioned travel sections against time.
A first straight line is generated connecting the first and last pressure data points of the graph.
Corrected pressure data is generated by correcting the values of the pressure data within each of the aforementioned movement sections so that the slope of the first straight line becomes zero.
Using the corrected pressure data, an index of the variation in the corrected pressure data in the moving section is calculated.
If the aforementioned variability index is less than the second threshold, it is estimated that the user's movement state in the movement section is movement by means of transportation involving altitude changes.
If the variability index is greater than or equal to a second threshold and less than a third threshold greater than the second threshold, the movement state of the user in the movement section is estimated to be walking.
If the aforementioned variability index is greater than or equal to the third threshold, it is estimated that the user's movement state in the movement section is driving.
The activity estimation device according to appendix 3 or 4, characterized by the above.
(Note 6)
The aforementioned movement state estimation means
Based on the altitude changes in each of the aforementioned travel sections, it is estimated whether the running and walking conditions were uphill, downhill, or on level ground.
The activity estimation device according to Appendix 5, characterized by the features described herein.
(Note 7)
The aforementioned movement state estimation means
The activity estimation device according to any one of the appendices 4 to 6, characterized in that when the rate of altitude change in the aforementioned stopping section or walking section is greater than or equal to a third threshold, it is determined that the user is moving using a means of transportation that involves altitude changes.
(Note 8)
An activity estimation device described in any one of the appendices 1 to 7,
Wireless communication means for transmitting and receiving information related to the activity estimation device,
A wireless communication terminal characterized by having the following features.
(Note 9)
An activity estimation device described in any one of the appendices 1 to 7,
A pressure measuring device attached to the aforementioned shoe,
It has,
The aforementioned pressure measuring device is
A pressure measuring means for measuring the air pressure inside the shoe as pressure data,
The system includes a communication means for transmitting the measured atmospheric pressure data to the activity estimation device.
An activity estimation system characterized by the following:
(Note 10)
Computers
It acquires air pressure data from inside the shoes worn by the user.
Based on the time change of the aforementioned atmospheric pressure data, it is estimated whether the user is in a moving state or a stationary state.
A method for estimating activity characterized by the following features.
(Note 11)
A process to acquire air pressure data inside the shoes worn by the user,
A process to estimate whether the user is in a moving state or a stationary state based on the time change of the aforementioned atmospheric pressure data,
An activity estimation program characterized by having a computer execute it.

1. Barometric pressure data acquisition means 2. Movement state estimation means 10, 100, 101. Activity estimation device 90, 400. Shoes 91. Barometric pressure measurement means 92. Communication means 110. Barometric pressure data acquisition unit 120. Movement state estimation unit 200. Barometric pressure measurement device 210. Barometric pressure sensor 220. Communication module 230. Cable 300. Computer 310, 320. Wireless communication terminal 311. Wireless communication means 410. Insole 500. Network 1000. Activity estimation system

Claims (8)

A means for acquiring pressure data to acquire pressure data inside shoes worn by the user,
A movement state estimation means that estimates whether the user is in a moving state or a stationary state based on the time change of the aforementioned atmospheric pressure data,
It has,
The aforementioned pressure data acquisition means,
The barometric pressure data is sampled at a predetermined interval.
The aforementioned movement state estimation means
A first difference is calculated, which is the difference between the aforementioned pressure data and the previous pressure data.
If the first difference is greater than or equal to the first threshold, it is estimated that the user is in a moving state.
If the first difference is less than the first threshold, it is presumed that the user is in a stopped state.
The section in which the movement state of the aforementioned pressure data is continuous, and the sections containing the pressure data before and after it, are stored as movement sections, and the section in which the stop state is continuous is stored as stop sections.
A graph is generated by plotting the atmospheric pressure data for each of the aforementioned travel sections against time.
A first straight line is generated connecting the first and last pressure data points of the graph.
Corrected pressure data is generated by correcting the values of the pressure data within each of the aforementioned movement sections so that the slope of the first straight line becomes zero.
Using the corrected pressure data, an index of the variation in the corrected pressure data in the moving section is calculated.
If the variability index is greater than or equal to a second threshold and less than a third threshold greater than the second threshold, the movement state of the user in the movement section is estimated to be walking.
If the aforementioned variability index is greater than or equal to the third threshold, it is estimated that the user's movement state in the movement section is driving.
An activity estimation device characterized by the following features. The aforementioned movement state estimation means
The difference between the first and last atmospheric pressure data in each of the aforementioned moving and stopping sections is calculated as the second difference.
Based on the second difference described above, the change in altitude of the shoe's position in each of the aforementioned movement and stopping sections is estimated.
The activity estimation device according to claim 1 . The aforementioned movement state estimation means
Based on the altitude changes in each of the aforementioned travel sections, it is estimated whether the running and walking were uphill, downhill, or on level ground.
The activity estimation device according to claim 1 or 2 . If the aforementioned variability index is less than the second threshold, it is estimated that the user's movement state in the movement section is movement by means of transportation.
The activity estimation device according to any one of claims 1 to 3 . An activity estimation device according to any one of claims 1 to 4 ,
Wireless communication means for transmitting and receiving information related to the activity estimation device,
A wireless communication terminal characterized by having the following features. An activity estimation device according to any one of claims 1 to 4 ,
A pressure measuring device attached to the aforementioned shoe,
It has,
The aforementioned pressure measuring device is
A pressure measuring means for measuring the air pressure inside the shoe as pressure data,
The system includes a communication means for transmitting the measured atmospheric pressure data to the activity estimation device.
An activity estimation system characterized by the following: Computers
It acquires air pressure data from inside the shoes worn by the user.
In acquiring the aforementioned pressure data, sampling of the pressure data is performed at a predetermined period.
Based on the time change of the aforementioned pressure data , a first difference is calculated, which is the difference between the current pressure data and the previous pressure data.
If the first difference is greater than or equal to the first threshold, it is estimated that the user is in a moving state.
If the first difference is less than the first threshold, it is presumed that the user is in a stopped state.
The section in which the movement state of the aforementioned pressure data is continuous, and the sections containing the pressure data before and after it, are stored as movement sections, and the section in which the stop state is continuous is stored as stop sections.
A graph is generated by plotting the atmospheric pressure data for each of the aforementioned travel sections against time.
A first straight line is generated connecting the first and last pressure data points of the graph.
Corrected pressure data is generated by correcting the values of the pressure data within each of the aforementioned movement sections so that the slope of the first straight line becomes zero.
Using the corrected pressure data, an index of the variation in the corrected pressure data in the moving section is calculated.
If the variability index is greater than or equal to a second threshold and less than a third threshold greater than the second threshold, the movement state of the user in the movement section is estimated to be walking.
If the aforementioned variability index is greater than or equal to the third threshold, it is estimated that the user's movement state in the movement section is driving.
A method for estimating activity characterized by the following features. A process to acquire air pressure data inside the shoes worn by the user,
A process to estimate whether the user is in a moving state or a stationary state based on the time change of the aforementioned atmospheric pressure data,
Have the computer run it,
In the process of acquiring the aforementioned pressure data,
The barometric pressure data is sampled at a predetermined interval.
In the process of estimating whether the user is in a moving state or a stationary state,
A first difference is calculated, which is the difference between the aforementioned pressure data and the previous pressure data.
If the first difference is greater than or equal to the first threshold, it is estimated that the user is in a moving state.
If the first difference is less than the first threshold, it is presumed that the user is in a stopped state.
The section in which the moving state of the aforementioned pressure data is continuous, and the sections containing the pressure data immediately before and after it, are defined as the moving section, and the section in which the stationary state is continuous is defined as the stationary section.
A graph is generated by plotting the atmospheric pressure data for each of the aforementioned travel sections against time.
A first straight line is generated connecting the first and last pressure data points of the graph.
Corrected pressure data is generated by correcting the values of the pressure data within each of the aforementioned movement sections so that the slope of the first straight line becomes zero.
Using the corrected pressure data, an index of the variation in the corrected pressure data in the moving section is calculated.
If the variability index is greater than or equal to a second threshold and less than a third threshold greater than the second threshold, the movement state of the user in the movement section is estimated to be walking.
If the aforementioned variability index is greater than or equal to the third threshold, it is estimated that the user's movement state in the movement section is driving.
Activity estimation program.