Estimation device, information presentation system, estimation method, and program

Abstract

In order to estimate care-related information corresponding to the physical condition of a user on the basis of the gait of the user, this estimation device comprises: an acquiring unit for acquiring sensor data measured in accordance with walking of the user, and physical data of the user; a storage unit storing an estimation model and the physical data, the estimation model outputting care-related information in accordance with the input of a feature quantity extracted from the sensor data and the physical data; an estimation unit that inputs the feature quantity extracted from the sensor data of the user and the physical data into the estimation model to estimate the care-related information of the user; and an output unit for outputting the estimated care-related information of the user.

Description

[Technical Field]

[0001] The present disclosure relates to an estimation device and the like that estimates care-related information based on gait. [Background technology]

[0002] In response to growing interest in healthcare, services that provide users with information based on characteristics contained in walking patterns (also called gait) are gaining attention. For example, technology has been developed that analyzes a user's gait using sensor data measured by sensors mounted on footwear such as shoes. If physical condition can be estimated based on information about gait, appropriate measures can be taken according to signs appearing in gait. For example, if the progression of frailty, which indicates physical and mental decline due to aging, or the risk of falling can be estimated based on information about gait, it may be possible to prevent elderly people from becoming dependent on care due to unexpected falls, etc. Non-Patent Documents 1 to 5 disclose several examples of the relationship between frailty, fall risk, and gait.

[0003] Non-Patent Document 1 discloses the early symptoms and phenotypes of frailty. Non-Patent Document 1 also discloses a diagram relating to the cycle of frailty (Figure 1 of the non-patent document). Non-Patent Document 1 shows that frailty progresses as factors such as body weight, activity level, walking speed, muscle strength, and balance interact with each other. According to Non-Patent Document 1, as muscle strength declines, walking speed declines and balance disorders become more likely to occur.

[0004] Non-Patent Document 2 discloses differences in characteristics that appear in walking speed depending on whether or not a symptom of frailty is present (Table 4 in Non-Patent Document 2). According to Non-Patent Document 2, subjects who exhibit symptoms of frailty tend to have a reduced walking speed and a wider distribution of walking speeds.

[0005] Non-Patent Document 3 discloses the prevalence of frailty according to age group (Table 1 in Non-Patent Document 3). According to Non-Patent Document 3, the prevalence of frailty is less than 10 percent (%) in the age group between 65 and 74 years old, but reaches 35% in the age group 80 years old and older.

[0006] Non-Patent Document 4 discloses characteristics that appear in the gaits of people who fall (prone fallers) and people who do not fall (non-fallers) during a specific verification period (Figure 1 in Non-Patent Document 4). According to Non-Patent Document 4, prone fallers have greater variability in stride time compared to non-fallers.

[0007] Non-Patent Document 5 discloses the fall rates by age group and gender (Figure 3 in Non-Patent Document 5). According to Non-Patent Document 5, the fall rate for men was slightly over 10% for those aged 65 to 69, but exceeded 30% for those aged 85 and over. Additionally, the fall rate for men was slightly over 10% for those aged 65 to 69, but reached 50% for those aged 85 and over.

[0008] Patent Document 1 discloses an information processing device that extracts features used for personal identification using foot movement information of a user. The device in Patent Document 1 extracts features using foot movement information measured by a movement measurement device attached to the user's feet.

[0009] Patent Document 2 discloses a motor ability evaluation system that evaluates the motor ability of a subject during physical movements in daily life for the purpose of nursing care, nursing care prevention, etc. The system of Patent Document 2 measures the positions and angles of body parts of the subject during physical movements using a motion capture device or the like. The system of Patent Document 2 calculates the motor ability of the subject's body parts in a time series based on the measurement results. The system of Patent Document 2 calculates a specific value of the motor ability of the body parts based on the motor ability of the subject's body parts calculated in a time series. The system of Patent Document 2 evaluates the calculated specific value of the motor ability of the body parts as the motor ability during physical movements in daily life of the subject.

[0010] Patent Document 3 discloses a user assistance system that generates user assistance data based on measurement data measured by a measurement device such as a wearable device attached to a user's wrist or arm. The system in Patent Document 3 generates user assistance data to indicate the user's frailty level. [Prior art documents] [Patent documents]

[0011] [Patent Document 1] International Publication No. 2020 / 240751 [Patent Document 2] Japanese Patent Application Laid-Open No. 2019-154489 [Patent Document 3] Patent No. 6815055 [Non-patent literature]

[0012] [Non-Patent Document 1] Q. Xue. et al., “Initial Manifestations of Frailty Criteria and the Development of Frailty Phenotype in the Women's Health and Aging Study II”, Journal of Gerontology: MEDICAL SCIENCES, 2008, 63A(9), pp.984-990. [Non-patent document 2] M. Schwenk, et al., “Wearable Sensor-Based In-Home Assessment of Gait, Balance, and Physical Activity for Discrimination of Frailty Status: Baseline Results of the Arizona Frailty Cohort Study”, Gerontology, 2015, 61(3), pp.258-67. [Non-patent document 3] H. Shimada, et al., “Combined Prevalence of Frailty and Mild Cognitive Impairment in a Population of Elderly Japanese People”, Journal of the American Medical Directors Association, 2013, pp.1-7. [Non-patent document 4] J. Hausdorff, et al., “Gait Variability and Fall Risk in Community-Living Older Adults: A 1-Year Prospective Study”, Arch Phys Med Rehabil, Vol 82, August 2001, pp.1050-1056. [Non-patent document 5] C. Pearson, et al., “Understanding seniors' risk of falling and their perception of risk”, Statistics Canada, Catalog no.82-624-X, Health at a Glance, October 2014, pp.1-11. Summary of the Invention [Problem to be solved by the invention]

[0013] According to the technique of Patent Document 1, feature amounts used for personal identification can be extracted using information on the user's foot movement. However, the technique of Patent Document 1 cannot estimate symptoms of frailty.

[0014] The method of Patent Document 2 can classify elderly people who require immediate rehabilitation intervention and elderly people who may require rehabilitation intervention in the near future based on specific values of the upper body, left lower body, and right lower body. However, the method of Patent Document 2 requires measuring the positions and angles of body parts during physical movements using a motion capture device or the like. Therefore, the method of Patent Document 2 cannot estimate the progression of a user's frailty or the risk of falling in general daily life.

[0015] According to the technique of Patent Document 3, the probability of risk occurrence can be reduced by providing the user with notifications for preventing frailty, etc., based on user assistance data. The technique of Patent Document 3 generates user assistance data indicating a frailty level or symptom level associated with a comparison result obtained by comparing a measurement value with a reference value. However, the technique of Patent Document 3 does not disclose specific procedures for estimating a frailty level or symptom level.

[0016] That is, the methods of Patent Documents 1 to 3 were unable to estimate care-related information according to the user's physical condition based on the user's gait.

[0017] An object of the present disclosure is to provide an estimation device etc. that can estimate care-related information according to the physical condition of a user based on the user's gait. [Means for solving the problem]

[0018] An estimation device according to one aspect of the present disclosure includes an acquisition unit that acquires sensor data measured in response to a user's walking and the user's physical data, an estimation model that outputs care-related information in response to input of features extracted from the sensor data and the physical data, a memory unit that stores the physical data, an estimation unit that inputs the features extracted from the user's sensor data and the physical data into the estimation model to estimate the user's care-related information, and an output unit that outputs the estimated care-related information of the user.

[0019] In an estimation method according to one aspect of the present disclosure, sensor data measured in response to a user's walking and physical data of the user are acquired, features extracted from the user's sensor data and physical data are input into an estimation model that outputs care-related information in response to the input of the features extracted from the sensor data and physical data, thereby estimating the user's care-related information and outputting the estimated care-related information of the user.

[0020] A program according to one aspect of the present disclosure causes a computer to execute the following processes: a process of acquiring sensor data measured in response to a user's walking and the user's physical data; a process of inputting features extracted from the user's sensor data and the physical data into an estimation model that outputs care-related information in response to the input of the features extracted from the sensor data and the physical data, thereby estimating the user's care-related information; and a process of outputting the estimated care-related information of the user. [Effects of the Invention]

[0021] According to the present disclosure, it is possible to provide an estimation device or the like that can estimate care-related information according to the physical condition of a user based on the user's gait. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a block diagram showing an example of a configuration of an information presentation system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of the arrangement of measuring devices included in the information presentation system according to the first embodiment. [Figure 3] FIG. 2 is a conceptual diagram for explaining an example of a walking cycle used in the information presentation system according to the first embodiment. [Figure 4] FIG. 2 is a conceptual diagram for explaining an example of a frailty cycle related to the information presentation system according to the first embodiment. [Figure 5] FIG. 2 is a conceptual diagram for explaining an example of a walking speed distribution related to the information presentation system according to the first embodiment. [Figure 6]FIG. 2 is a conceptual diagram for explaining an example of a frailty prevalence rate related to the information presentation system according to the first embodiment. [Figure 7] 10 is a graph for explaining an example of deriving a constant for a curve relating to the prevalence of frailty used by the information presentation system according to the first embodiment. [Figure 8] 4 is a graph showing an example of a function used to estimate the prevalence of frailty by the information presentation system according to the first embodiment. [Figure 9] 1 is a block diagram showing an example of the configuration of a measurement device included in the information presentation system according to the first embodiment. [Figure 10] 1 is a block diagram showing an example of the configuration of an estimation device included in an information presentation system according to a first embodiment. [Figure 11] FIG. 2 is a conceptual diagram for explaining a stride length calculated by an estimation device included in the information presentation system according to the first embodiment. [Figure 12] 4 is a graph for explaining an example of a walking event detected by the estimation device included in the information presentation system according to the first embodiment. [Figure 13] 10 is a flowchart for explaining an example of estimation of care-related information by the estimation device included in the information presentation system according to the first embodiment. [Figure 14] 6 is a flowchart illustrating an example of a walking parameter calculation process performed by the estimation device included in the information presentation system according to the first embodiment. [Figure 15] FIG. 2 is a conceptual diagram for explaining a first application example of the information presentation system according to the first embodiment. [Figure 16] FIG. 10 is a block diagram showing an example of the configuration of an information presentation system according to a second embodiment. [Figure 17] FIG. 10 is a conceptual diagram for explaining an example of a walking speed distribution related to the information presentation system according to the second embodiment. [Figure 18] FIG. 10 is a conceptual diagram for explaining an example of stride time variation related to the information presentation system according to the second embodiment. [Figure 19] FIG. 10 is a conceptual diagram for explaining an example of tipping tendency in the information presentation system according to the second embodiment. [Figure 20] 10 is a graph for explaining an example of deriving constants of a curve relating to a fall rate used by the information presentation system according to the second embodiment. [Figure 21] 10 is a graph showing an example of a function used for estimating a fall rate by the information presentation system according to the second embodiment. [Figure 22] FIG. 10 is a block diagram showing an example of the configuration of an estimation device included in an information presentation system according to a second embodiment. [Figure 23] 10 is a flowchart illustrating an example of estimation of care-related information by an estimation device included in an information presentation system according to a second embodiment. [Figure 24] 10 is a flowchart illustrating an example of a walking parameter calculation process performed by an estimation device included in an information presentation system according to a second embodiment. [Figure 25] FIG. 10 is a conceptual diagram for explaining a second application example of the information presentation system according to the second embodiment. [Figure 26] FIG. 10 is a block diagram showing an example of the configuration of an estimation device according to a third embodiment. [Figure 27] FIG. 2 is a block diagram showing an example of a hardware configuration for executing control and processing in each embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0023] Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings. However, the embodiments described below are limited in a manner that is technically preferable for carrying out the present invention, but the scope of the invention is not limited to the following. In all drawings used to describe the following embodiments, the same reference numerals are used for similar parts unless otherwise specified. Furthermore, in the following embodiments, repeated explanations of similar configurations and operations may be omitted.

[0024] (First embodiment) An information presentation system according to a first embodiment will be described with reference to the drawings. The information presentation system of this embodiment measures features (also called gait) included in a user's walking pattern. The information presentation system of this embodiment estimates care-related information of the user by analyzing the measured gait. In this embodiment, an example of estimating care-related information based on sensor data related to foot movement will be described. In this embodiment, a system in which the right foot is the reference foot and the left foot is the contralateral foot will be described. The method of this embodiment can also be applied to a system in which the left foot is the reference foot and the right foot is the contralateral foot.

[0025] (composition) FIG. 1 is a block diagram showing the configuration of an information presentation system 1 according to this embodiment. The information presentation system 1 includes a measurement device 11 and an estimation device 12. The measurement device 11 and the estimation device 12 may be connected by wire or wirelessly. The measurement device 11 and the estimation device 12 may also be configured as a single device. Alternatively, the measurement device 11 may be excluded from the configuration of the information presentation system 1, and the information presentation system 1 may be configured with only the estimation device 12.

[0026] The measurement device 11 is installed on the foot. For example, the measurement device 11 is installed in footwear such as shoes. In this embodiment, an example in which the measurement device 11 is installed at a position on the back of the arch of the foot will be described. The measurement device 11 includes an acceleration sensor and an angular velocity sensor. The measurement device 11 measures physical quantities related to the movement of the foot of a user wearing the footwear, such as acceleration (also referred to as spatial acceleration) measured by an acceleration sensor and angular velocity (also referred to as spatial angular velocity) measured by an angular velocity sensor. The physical quantities related to the movement of the foot measured by the measurement device 11 also include velocity, angle, and position (trajectory) calculated by integrating the acceleration and angular velocity. The measurement device 11 converts the measured physical quantities into digital data (also referred to as sensor data). The measurement device 11 transmits the converted sensor data to the estimation device 12. For example, the measurement device 11 is connected to the estimation device 12 via a mobile terminal (not shown) carried by the user.

[0027] The mobile terminal (not shown) is a communication device that can be carried by a user. For example, the mobile terminal is a portable communication device with a communication function, such as a smartphone, a smart watch, or a mobile phone. The mobile terminal receives sensor data related to the user's foot movements from the measurement device 11. The mobile terminal transmits the received sensor data to a server or cloud on which the estimation device 12 is implemented. Note that the function of the estimation device 12 may be realized by application software or the like installed on the mobile terminal. In this case, the mobile terminal processes the received sensor data using the application software or the like installed on the mobile terminal.

[0028] The measurement device 11 is realized by, for example, an inertial measurement device including an acceleration sensor and an angular velocity sensor. An example of an inertial measurement device is an IMU (Inertial Measurement Unit). The IMU includes an acceleration sensor that measures acceleration in three axial directions and an angular velocity sensor that measures angular velocity around three axes. The measurement device 11 may also be realized by an inertial measurement device such as a VG (Vertical Gyro) or an AHRS (Attitude Heading). The measurement device 11 may also be realized by a GPS / INS (Global Positioning System / Inertial Navigation System). Note that the measurement device 11 is not limited to an inertial measurement device as long as it can measure physical quantities related to foot movement.

[0029] FIG. 2 is a conceptual diagram showing an example of the placement of the measuring device 11 inside the shoe 100. In the example of FIG. 2, the measuring device 11 is placed at a position that touches the back of the arch of the foot. For example, the measuring device 11 is placed in an insole inserted into the shoe 100. For example, the measuring device 11 is placed on the bottom of the shoe 100. For example, the measuring device 11 is embedded in the body of the shoe 100. The measuring device 11 may be detachable from the shoe 100 or may not be detachable from the shoe 100. Note that the measuring device 11 may be placed at a position other than the back of the arch of the foot as long as it can acquire sensor data related to foot movement. The measuring device 11 may also be placed in socks worn by the user or in an accessory such as an anklet worn by the user. The measuring device 11 may also be attached directly to the foot or embedded in the foot. FIG. 2 shows an example in which the measuring device 11 is placed in the shoe 100 on the right foot, but the measuring device 11 may also be placed in the shoe 100 on the left foot. Furthermore, the measuring device 11 may be installed in the shoes 100 for both feet. If the measuring device 11 is installed in the shoes 100 for both feet, the physical condition can be estimated based on the movements of both feet.

[0030] FIG. 3 is a conceptual diagram illustrating a step cycle based on the right foot. The horizontal axis in FIG. 3 represents a normalized gait cycle, starting from the point when the heel of the right foot hits the ground and ending with the point when the heel of the right foot hits the ground, with one right-foot step cycle normalized to 100 percent (%). A single step cycle is broadly divided into a stance phase, in which at least a portion of the sole of the foot is in contact with the ground, and a swing phase, in which the sole of the foot is off the ground. In this embodiment, the step cycle is normalized so that the stance phase accounts for 60% and the swing phase accounts for 40%. The stance phase is further divided into an early stance phase T1, a mid-stance phase T2, a late stance phase T3, and an early swing phase T4. The swing phase is further divided into an early swing phase T5, a mid-swing phase T6, and a late swing phase T7. Note that the gait waveform for one step cycle does not necessarily have to start from the point when the heel hits the ground.

[0031] Figure 3(1) shows the event of the heel of the right foot touching the ground (heel strike) (HS: Heel Strike). Figure 3(2) shows the event of the toe of the left foot lifting off the ground (opposite toe off) while the sole of the right foot is still in contact with the ground (OTO: Opposite Toe Off). Figure 3(3) shows the event of the heel of the right foot lifting off the ground (heel rise) while the sole of the right foot is still in contact with the ground (HR: Heel Rise). Figure 3(4) shows the event of the heel of the left foot touching the ground (opposite heel strike) (OHS: Opposite Heel Strike). Figure 3(5) shows the event of the toe of the right foot lifting off the ground (toe off) while the sole of the left foot is still in contact with the ground (TO: Toe Off). Figure 3 (6) shows the event where the left and right feet cross (Foot Crossing) with the sole of the left foot touching the ground (FA: Foot Adjacent). Figure 3 (7) shows the event where the tibia of the right foot is almost perpendicular to the ground (Tibia Vertical) with the sole of the left foot touching the ground (TV: Tibia Vertical). Figure 3 (8) shows the event where the heel of the right foot touches the ground (Heel Strike) (HS: Heel Strike). Figure 3 (8) corresponds to the end of the gait cycle that began with Figure 3 (1) and also corresponds to the starting point of the next gait cycle.

[0032] The estimation device 12 acquires sensor data corresponding to the walking of the subject from a measurement device 11 worn by the subject. The estimation device 12 also acquires physical data such as the subject's age input via an input device or the like (not shown). For example, physical data such as age, gender, and height are input to the estimation device 12. For example, the functions of the estimation device 12 are installed in a mobile terminal (not shown) such as a smartphone or tablet carried by the user. For example, the functions of the estimation device 12 may be implemented on a server or cloud.

[0033] The estimation device 12 estimates the probability that a user is frail (also called frailty probability) based on sensor data acquired from the measurement device 11 worn by the user and the user's physical data. Frailty refers to physical and mental decline due to aging. In other words, the estimation device 12 estimates the frailty probability, which is one piece of care-related information, using sensor data (gait data) related to foot movement.

[0034] Figure 4 is a conceptual diagram showing an example of a frailty cycle (also called a frailty cycle). The frailty cycle in Figure 4 is based on Figure 1 in Non-Patent Document 1 (Non-Patent Document 1: Q. Xue et al., "Initial Manifestations of Frailty Criteria and the Development of Frailty Phenotype in the Women's Health and Aging Study II," Journal of Gerontology: MEDICAL SCIENCES, 2008, 63A(9), pp.984-990.).

[0035] As shown in Figure 4, frailty can progress as a result of the interplay of many factors. Decreased energy expenditure can lead to chronic malnutrition and weight loss. Weight loss reduces skeletal muscle mass, which can lead to declines in basal metabolism, maximal oxygen intake, and muscle strength. Decreased maximal oxygen intake can lead to a decrease in walking speed. Decreased walking speed can lead to a decrease in activity level and physical functional impairment. Decreased activity level leads to a decrease in energy expenditure. Decreased muscle strength can lead to a decrease in walking speed and balance impairment. Balance impairment can lead to falls / fractures. When falls / fractures make mobility difficult, physical functional impairments are more likely to occur. As physical functional impairment progresses, the likelihood of requiring nursing care increases. As such, many factors are intertwined in physical condition. Therefore, it is necessary to prevent or delay the need for nursing care by detecting signs of declining physical condition.

[0036] In this embodiment, attention is paid to walking speed in order to prevent the occurrence of physical functional disorders and the need for nursing care. Walking speed can be an indicator of muscle strength and cardiopulmonary function. By improving / maintaining muscle strength in the legs and hips, frailty can be prevented and its progression can be suppressed. In particular, in this embodiment, attention is paid to walking speed to estimate the probability of frailty.

[0037] FIG. 5 is a graph illustrating the effect of frailty on walking speed. The graph in FIG. 5 is based on the values in Table 4 of Non-Patent Document 2 (Non-Patent Document 2: M. Schwenk, et al., “Wearable Sensor-Based In-Home Assessment of Gait, Balance, and Physical Activity for Discrimination of Frailty Status: Baseline Results of the Arizona Frailty Cohort Study,” Gerontology, 2015, 61(3), pp. 258-67.). FIG. 5 shows the walking speed distribution according to the walking speed (standard deviation) values for single-task walking in Table 4 of Non-Patent Document 2. For normal subjects, the mean walking speed was 1.17 meters per second (m / s), with a standard deviation of 0.15 m / s. For subjects with frailty symptoms, the mean walking speed was 0.71 meters per second (m / s), with a standard deviation of 0.36 m / s. In Figure 5, the walking speed of normal subjects is shown by a solid line, and the walking speed of subjects with frailty symptoms is shown by a dashed line. As shown in Figure 5, compared to normal subjects, subjects with frailty symptoms tend to have slower walking speeds and a wider distribution of walking speeds.

[0038] Figure 6 shows a frequency distribution showing the prevalence of frailty by age. The frequency distribution in Figure 6 is based on the values in Table 1 of Non-Patent Document 3 (Non-Patent Document 3: H. Shimada, et al., "Combined Prevalence of Frailty and Mild Cognitive Impairment in a Population of Elderly Japanese People," Journal of the American Medical Directors Association, 2013, pp. 1-7.). Figure 6 also shows a curve (dashed line) smoothly connecting the prevalence frequencies at the median for each age group. The dashed curve shows the correlation between age and frailty prevalence.

[0039] Here, an example will be described in which the estimation device 12 estimates the probability that a user is frail (also referred to as frailty probability) based on walking speed. In the following, it is assumed that the frequency of occurrence of frailty depends on age and that frailty affects walking speed. The following equation 1-1 represents a probability model assuming that the frequency of occurrence of frailty f depends on age y and that frailty f affects walking speed v. Frailty f is 1 if the user is frail and 0 if the user is not frail. TIFF0007726283000001.tif6150

[0040] The first term on the right-hand side of the above equation 1-1, p(v|f), is a term related to the walking speed distribution of frail / non-frail individuals. The second term on the right-hand side, p(f|y), is a term related to the prevalence of frailty. The third term, p(y), is a term related to age.

[0041] The following formula 1-2 is an estimation formula for the frailty probability p(f|y, v) according to age y and walking speed v, based on the above formula 1-1. The frailty probability p(f|y, v) is an example of care-related information. TIFF0007726283000002.tif10150

[0042] The first term p(v|f) in the numerator on the right side of the above equation 1-2 is a term relating to the frailty dependency of the walking speed distribution. The second term p(f|y) in the numerator is a term relating to the age dependency of the incidence rate of frailty. The denominator p(v|y) is a term relating to the age dependency of walking speed. The estimation device 12 estimates frailty f according to age y and walking speed v based on equation 1-2.

[0043] The following formula 1-3 is a formula that embodies the first term p(v|f) in the numerator on the right side of the above formula 1-2. TIFF0007726283000003.tif13150

[0044] In the above formulas 1-3, i indicates either non-frailty or frailty. i is the average walking speed for i. σ i is the standard deviation of the walking speed for i. Referring to Table 4 in Non-Patent Document 2, if i is not frail, μ i is 1.17 m / s, and σ i is 0.15 m / s. Similarly, referring to Table 4 in Non-Patent Document 2, when i is frail, μ i is 0.71 m / s, and σ i is 0.36 m / s.

[0045] The second term p(f|y) in the numerator on the right side of Equation 1-2 can be applied to the numerical value related to the prevalence of frailty in Non-Patent Document 3. Equation 1-4 below is an approximation of the curve (dashed line) related to the prevalence of frailty in Figure 6 using a sigmoid function. TIFF0007726283000004.tif9150

[0046] In the above equations 1-4, the exponent of the second term in the denominator is set to "-(ay+b)" to include the effect of age y (a and b are real numbers).

[0047] By transforming the above formula 1-4, we obtain the following formula 1-5. TIFF0007726283000005.tif11150By substituting age y and prevalence rate p(f|y) into the above equations 1-5, a and b can be derived. In Table 1 of Non-Patent Document 2, if the median age for each age group is y, then p(f|67.5) is 0.056, p(f|72.5) is 0.072, p(f|77.5) is 0.16, and p(f|85.0) is 0.349.

[0048] Figure 7 is a graph plotting Z(y) against age y after substituting the above values into equation 1-4. Linear regression of the plotted points using the least squares method yields a as 0.1315 and b as -11.86. Substituting the derived values of a and b into equation 1-4 yields equation 1-6 below. TIFF0007726283000006.tif9150

[0049] Figure 8 is a graph related to Equation 1-6 derived by the above procedure. Using the relationship (Equation 1-6) in the graph of Figure 8, the frailty prevalence rate p(f|y) according to age y can be estimated. For example, physical data such as age y is input via an input device (not shown).

[0050] The estimation device 12 calculates the walking speed v, which corresponds to the stride length per unit time, based on the time-series data of the sensor data measured by the measurement device 11. A specific method for calculating the walking speed v will be described later. The estimation device 12 calculates the average value μ and standard deviation σ of the walking speed v for a predetermined walking cycle. The estimation device 12 calculates the denominator p(v|y) of Equation 1-2 based on the user's age y and the calculated average value μ and standard deviation σ.

[0051] The estimation device 12 estimates the probability that the user is frail (frailty probability) by applying the user's age y, the calculated walking speed v, and the like to the above formula 1-2. For example, the estimation device 12 stores an estimation model that outputs a frailty probability in response to input of a numerical value related to the walking speed v and the age y. For example, the estimation model can be generated by learning using the age y, walking speed v, and presence or absence of frailty for multiple subjects as training data.

[0052] The estimation device 12 outputs information about the estimated frailty probability. There are no particular limitations on the destination to which the information about frailty probability is output. For example, the estimation device 12 outputs information about the user's frailty probability to an external system or device (not shown). For example, the estimation device 12 outputs information about the user's frailty probability to a display device (not shown).

[0053] The estimation device 12 may also calculate a frailty age as care-related information. The frailty age is an age according to a scale of frailty estimated using walking parameters and physical data. The estimation device 12 calculates the frailty age Y f Calculate. TIFF0007726283000007.tif6150

[0054] The left side of the above equation 1-7, p(f|y, v), is the probability of frailty according to age y and walking speed v. The right side of the equation 1-7, p(f|Y f ) is the frailty age Y f The probability of frailty depends on the right side of Equation 1-7, p(f|Y f ) is a function of the curve relating age and frailty prevalence shown by the dashed line in the frequency distribution in Figure 6.

[0055] For example, the estimation device 12 substitutes the value obtained by the above equation 1-2 into the left side of equation 1-7, p(f|y, v). The estimation device 12 calculates the frailty age Y that is the same as the value on the left side of the curve shown by the dashed line in FIG. f In other words, the estimation device 12 estimates the frailty age of the user based on the estimated frailty probability and the correlation between age and frailty prevalence. For example, the estimation device 12 outputs information about the calculated frailty age as care-related information.

[0056] [Measuring equipment] Next, details of the measuring device 11 will be described with reference to the drawings. Fig. 9 is a block diagram showing an example of the detailed configuration of the measuring device 11. The measuring device 11 has an acceleration sensor 111, an angular velocity sensor 112, a control unit 113, and a transmission unit 115. The measuring device 11 also includes a power supply (not shown).

[0057] Acceleration sensor 111 is a sensor that measures acceleration in three axial directions (also called spatial acceleration). Acceleration sensor 111 outputs the measured acceleration to control unit 113. For example, a piezoelectric, piezo-resistive, or capacitance type sensor can be used for acceleration sensor 111. Note that the sensor used for acceleration sensor 111 is not limited to a specific measurement type as long as it can measure acceleration.

[0058] Angular velocity sensor 112 is a sensor that measures angular velocity around three axes (also called spatial angular velocity). Angular velocity sensor 112 outputs the measured angular velocity to control unit 113. For example, a vibration type, a capacitance type, or other type of sensor can be used for angular velocity sensor 112. Note that the measurement method of the sensor used for angular velocity sensor 112 is not limited as long as it can measure angular velocity.

[0059] The control unit 113 acquires actual measured values of acceleration in three axial directions from the acceleration sensor 111. The control unit 113 acquires actual measured values of angular velocity around an axis from the angular velocity sensor 112. The control unit 113 converts the acquired actual measured values of acceleration and angular velocity into digital data (also referred to as sensor data). The control unit 113 outputs the converted digital data to the transmission unit 115. The sensor data includes at least acceleration data (including acceleration vectors in three axial directions) and angular velocity data (including angular velocity vectors around three axes) converted into digital data. The sensor data includes the acquisition time of the actual measured values that are the basis of the acceleration data and angular velocity data. The control unit 113 may also be configured to output sensor data that has been corrected for mounting error, temperature, linearity, etc., to the acquired acceleration data and angular velocity data. The control unit 113 may also convert the coordinate system of the sensor data from a local coordinate system to a world coordinate system. The control unit 113 may also generate angle data around three axes (also referred to as plantar angle) using the acquired acceleration data and angular velocity data.

[0060] For example, the control unit 113 is a microcomputer or microcontroller that performs overall control of the measuring device 11 and performs data processing. For example, the control unit 113 includes a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), flash memory, etc. The control unit 113 controls the acceleration sensor 111 and the angular velocity sensor 112 to measure angular velocity and acceleration. For example, the control unit 113 performs analog-to-digital conversion (AD conversion) on physical quantities (analog data) such as the measured angular velocity and acceleration, and stores the converted digital data in a flash memory. Note that the physical quantities (analog data) measured by the acceleration sensor 111 and the angular velocity sensor 112 may be converted into digital data by the acceleration sensor 111 and the angular velocity sensor 112, respectively. The digital data stored in the flash memory is output to the transmission unit 115 at a predetermined timing.

[0061] The transmitting unit 115 acquires sensor data from the control unit 113. The transmitting unit 115 transmits the acquired sensor data to the measuring device 15. For example, the transmitting unit 115 transmits the sensor data to the measuring device 15 via a wired connection such as a cable. For example, the transmitting unit 115 transmits the sensor data to the measuring device 15 via wireless communication. For example, the transmitting unit 115 is configured to transmit the sensor data to the measuring device 15 via a wireless communication function (not shown) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the transmitting unit 115 may be conforming to a standard other than Bluetooth (registered trademark) or WiFi (registered trademark).

[0062] [Estimation device] Next, details of the estimation device 12 will be described with reference to the drawings. Fig. 10 is a block diagram showing an example of a detailed configuration of the estimation device 12. The estimation device 12 has an acquisition unit 121, a storage unit 123, an estimation unit 125, and an output unit 127.

[0063] Acquisition unit 121 acquires data (also referred to as physical data) such as the user's age input via an input device (not shown). Acquisition unit 121 stores the acquired physical data in storage unit 123. For example, acquisition unit 121 receives the physical data from the input device via a wired connection such as a cable. For example, acquisition unit 121 receives the physical data from the input device via wireless communication.

[0064] Furthermore, the acquisition unit 121 receives sensor data from the measurement device 11. The acquisition unit 121 outputs the received sensor data to the estimation unit 125. For example, the acquisition unit 121 receives the sensor data from the measurement device 11 via a wired connection such as a cable. For example, the acquisition unit 121 receives the sensor data from the measurement device 11 via wireless communication.

[0065] For example, the acquisition unit 121 is connected to the measuring device 11 and the input device (not shown) via a wireless communication function (not shown) conforming to standards such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the acquisition unit 121 may conform to standards other than Bluetooth (registered trademark) or WiFi (registered trademark).

[0066] The storage unit 123 stores an estimation model for estimating the subject's frailty probability based on the subject's age and walking speed distribution. The estimation model is constructed based on past knowledge. For example, the estimation model is a model in which Equation 1-3 or Equation 1-6 is applied to Equation 1-2 described above. The estimation model outputs the frailty probability in response to input of a numerical value related to the walking speed distribution calculated using sensor data measured according to the user's walking and the user's age. The storage unit 123 also stores the user's physical data. The physical data includes at least the subject's age. The physical data may also include data such as the user's gender, height, and weight.

[0067] For example, the storage unit 123 may store an estimation model for estimating a user's frailty age based on the user's age and walking speed distribution. For example, the estimation model is a model to which the above-mentioned formula 1-7 is applied. The estimation model outputs a frailty age in response to input of a numerical value related to the walking speed distribution calculated using sensor data measured according to the walking of the subject and the age of the subject.

[0068] The estimation unit 125 acquires physical data related to the subject from the storage unit 123. The estimation unit 125 also acquires sensor data measured by the measurement device 11 attached to the footwear worn by the subject from the acquisition unit 121. The estimation unit 125 generates time-series data of the sensor data. The estimation unit 125 extracts walking waveform data for at least one step from the generated time-series data. The estimation unit 125 converts the coordinate system of the acquired sensor data from the local coordinate system to the world coordinate system. When the user is standing upright, the local coordinate system (x-axis, y-axis, z-axis) and the world coordinate system (x-axis, y-axis, z-axis) coincide. While the user is walking, the spatial orientation of the measurement device 11 changes, so the local coordinate system (x-axis, y-axis, z-axis) and the world coordinate system (x-axis, y-axis, z-axis) do not coincide. Therefore, the estimation unit 125 converts the sensor data acquired by the measurement device 11 from the local coordinate system (x-axis, y-axis, z-axis) of the measurement device 11 to a world coordinate system (X-axis, Y-axis, Z-axis). The estimation unit 125 generates time-series data (also called a walking waveform) of the sensor data converted into the world coordinate system.

[0069] For example, the estimation unit 125 generates time series data such as spatial acceleration and spatial angular velocity. For example, the estimation unit 125 integrates the spatial acceleration and spatial angular velocity to generate time series data of spatial velocity and spatial angle (sole angle). For example, the estimation unit 125 performs a second-order integration of the spatial acceleration and spatial angular velocity to generate time series data such as a spatial trajectory. The estimation unit 125 generates the time series data at predetermined timings or time intervals set in accordance with a general walking cycle or a walking cycle specific to the user. The timing at which the estimation unit 125 generates the time series data can be set arbitrarily. For example, the estimation unit 125 is configured to continue generating the time series data for a period during which the user continues walking. Furthermore, the estimation unit 125 may be configured to generate the time series data at a specific time.

[0070] The estimation unit 125 detects walking events from the generated walking waveform. For example, the estimation unit 125 extracts features specific to walking events from the walking waveform. For example, the estimation unit 125 detects the timing at which the features specific to the extracted walking event are extracted as the timing of the walking event. For example, the estimation unit 125 detects toe-off and heel-strike as walking events. For example, the estimation unit 125 detects foot crossing as walking events. Taking the right foot as a reference, foot crossing corresponds to the timing at which the toe of the right foot passes the midpoint between the toe and heel of the left foot. For example, the estimation unit 125 may detect tibia vertical, opposite toe-off, and opposite toe-heel-strike as walking events.

[0071] The estimation unit 125 calculates the stride length of the subject based on the detected walking events. For example, the estimation unit 125 calculates the walking speed of the subject by dividing the stride length by the time required for one stride.

[0072] FIG. 11 is a conceptual diagram for explaining walking parameters such as step length and stride length. In FIG. 11, for a walking subject, the right direction is X, the forward direction is Y, and the vertical upward direction is X. In FIG. 11, the right foot step length S R , left foot step length S L , and stride length T. Right foot step length S R is the difference in Y coordinate between the heel of the right foot and the heel of the left foot when the state transitions from the state where the sole of the left foot is on the ground to the state where the heel of the right foot, which is swung in the direction of travel, lands on the ground. Left foot step length S L is the difference in Y coordinate between the heel of the left foot and the heel of the right foot when the sole of the right foot is on the ground and the heel of the left foot is on the ground, swinging forward in the direction of travel. The stride length T is the difference between the right foot step length S R and left foot step length S L where the right foot step length S R and left foot step length S L may have different values depending on the walking events used to calculate them.

[0073] FIG. 12 is an example of a gait waveform measured by the measurement device 11. FIG. 12 is an example of a gait waveform of Y-direction acceleration for one gait cycle, starting from the middle timing of the stance phase (the start of the final stance phase). The gait waveform of Y-direction acceleration for one gait cycle shows two main peaks (the first peak and the second peak). The first peak appears around 20% to 40% of the gait cycle. The first peak includes two maximum peaks and one minimum peak. The timing of the minimum peak included in the first peak corresponds to the timing of toe-off. The second peak appears around 50% to 70% of the gait cycle. The second peak includes a minimum peak located around 60% of the gait cycle and a maximum peak located around 70% of the gait cycle. The timing of the midpoint between the minimum peak and the maximum peak included in the second peak corresponds to the timing of heel-contact. The timing of the maximum of the gentle peak between the first and second peaks corresponds to the timing of foot-crossing.

[0074] For example, the estimation unit 125 calculates the stride length T using a walking waveform (not shown) of the Y-direction trajectory for one walking cycle. The Y-direction trajectory is obtained by second-order integration of the Y-direction acceleration for one walking cycle. The estimation unit 125 calculates the difference between the spatial position in the Y-direction at the timing of heel-strike and the spatial position in the Y-direction at the timing of toe-off as the stride length T. The estimation unit 125 also calculates the time from the timing of toe-off to the timing of heel-strike as the time t of one stride. The estimation unit 125 calculates the walking speed v by dividing the stride length T by the time t of one stride. For example, the estimation unit 125 can calculate the distance traveled from toe-off to foot crossing or the distance traveled from foot crossing to heel-strike as the step length.

[0075] The estimation unit 125 calculates the average value μ and standard deviation σ of the walking speed v for a predetermined walking cycle. For example, the estimation unit 125 calculates the average value μ and standard deviation σ of the walking speed v using sensor data for 3 to 10 walking cycles. The calculated average value μ and standard deviation σ by the estimation unit 125 are numerical values related to the walking speed distribution.

[0076] The estimation unit 125 inputs the subject's physical data (age) and a numerical value related to the walking speed distribution calculated based on the walking of the subject to an estimation model for estimating the frailty probability stored in the storage unit 123. In response to the input of the numerical value related to the walking speed distribution and the physical data, the estimation unit 125 outputs the frailty probability output from the estimation model to the output unit 127 as care-related information.

[0077] For example, the estimation unit 125 may estimate the frailty age of the subject based on the age and walking speed distribution of the subject. The estimation unit 125 inputs the user's physical data (age) and a numerical value related to the walking speed distribution calculated based on the user's walking into an estimation model for estimating the frailty age stored in the storage unit 123. The estimation unit 125 outputs the frailty age output from the estimation model in response to the input of the numerical value related to the walking speed distribution and the physical data to the output unit 127 as care-related information.

[0078] The output unit 127 outputs the care-related information estimated by the estimation unit 125. There is no particular limitation on the output destination (not shown) of the care-related information. For example, the output unit 127 outputs the care-related information to the output destination via a wired connection such as a cable. For example, the acquisition unit 121 outputs the care-related information to the output destination via wireless communication. For example, the output unit 127 is connected to the output destination via a wireless communication function (not shown) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the output unit 127 may conform to a standard other than Bluetooth (registered trademark) or WiFi (registered trademark).

[0079] For example, the output unit 127 outputs the user's care-related information to an external system or device (not shown). For example, the output unit 127 outputs the care-related information to a display device (not shown). For example, the output unit 127 outputs the care-related information to the user's mobile device (not shown). For example, the output unit 127 outputs the user's care-related information to a terminal device (not shown) that can be viewed by a doctor or the like who is examining the user's physical condition. The output care-related information can be used for any purpose.

[0080] (operation) Next, the operation of the information presentation system 1 will be described with reference to the drawings. In the following, the operation of the measurement device 11 will be omitted, and the operation of the estimation device 12 will be described. Fig. 13 is a flowchart for explaining an example of the operation of the estimation device 12. In the explanation following the flowchart of Fig. 13, the estimation device 12 will be described as the subject of the operation.

[0081] 13, first, the estimation device 12 acquires sensor data related to foot movement (step S11). The estimation device 12 acquires the sensor data related to foot movement from the measurement device 11. The estimation device 12 may acquire the sensor data measured by the measurement device 11 for one walking cycle at a time, or may acquire the sensor data for multiple walking cycles at a time.

[0082] Next, the estimation device 12 generates time series data of sensor data (step S12). The estimation device 12 generates time series data of sensor data related to acceleration and trajectory in three axial directions, angular velocity around three axes, etc. In this embodiment, the estimation device 12 generates time series data of sensor data related to acceleration in the Y direction and trajectory.

[0083] Next, the estimation device 12 executes a walking parameter calculation process (step S13). In the walking parameter calculation process, the estimation device 12 calculates a walking speed based on time-series data (walking waveform) of the sensor data for one walking cycle. The walking parameter calculation process will be described in detail later.

[0084] When the calculation for the predetermined walking cycle is completed (Yes in step S14), the estimation device 12 derives values related to the walking speed distribution using the walking speeds for the predetermined walking cycle (step S15). The values related to the walking speed distribution include the average value and standard deviation of the walking speeds for walking for the predetermined walking cycle. If the calculation for the predetermined walking cycle is not completed (No in step S14), the process returns to step S13.

[0085] The estimation device 12 inputs the numerical values related to the walking speed distribution and the physical data into the estimation model to estimate the care-related information of the user (step S16). For example, the estimation device 12 estimates the frailty probability and the frailty age as the care-related information.

[0086] The estimation device 12 outputs the care-related information of the user (step S17). For example, the estimation device 12 outputs the frailty probability and the frailty age as the care-related information. There are no particular limitations on the output destination or use of the care-related information of the user.

[0087] [Walking parameter calculation process] Next, the gait parameter calculation process by the estimation device 12 will be described with reference to the drawings. Fig. 14 is a flowchart for explaining an example of the gait parameter calculation process. In the explanation following the flowchart of Fig. 14, the estimating device 12 will be described as the subject of the operation.

[0088] In FIG. 13, first, the estimation device 12 detects the first and second peaks in the walking waveform of the Y-direction acceleration (advancement direction acceleration) for one walking cycle (step S111).

[0089] Next, the estimation device 12 detects the timing of toe lift-off from the first peak (step S112).

[0090] Next, the estimation device 12 detects the timing of heel strike from the second peak (step S113).

[0091] Next, the estimation device 12 calculates the stride length based on the difference in spatial position between the timing of heel-contact and toe-off in the walking waveform of the Y-direction trajectory (traveling direction trajectory) for one walking cycle (step S114).

[0092] Next, the estimation device 12 calculates the time from the toe lift-off to the heel contact as the time for one stride (step S115).

[0093] Next, the estimation device 12 calculates the walking speed by dividing the stride length by the time for one stride (step S116).

[0094] [Application example 1] Next, a first application example of the information presentation system 1 of this embodiment will be described with reference to the drawings. In this application example, information related to the frailty probability estimated by the estimation device 12 is displayed on the screen of the mobile terminal 110. In this application example, a measurement device 11 is installed in a user's shoe 100, and sensor data based on physical quantities related to foot movement measured by the measurement device 11 is transmitted to the mobile terminal 110 carried by the user. The sensor data transmitted to the mobile terminal 110 is processed by a program (estimation device 12) installed in the mobile terminal 110.

[0095] FIG. 15 shows an example in which information about the physical condition of a user wearing shoes 100 equipped with a measuring device 11 is displayed on the screen of the mobile terminal 110 of the user. In the example of FIG. 15, the frailty probability and frailty age estimated based on the user's walking are displayed as care-related information on the screen of the mobile terminal 110 of the user. A user who views the care-related information displayed on the screen of the mobile terminal 110 can take action according to the information. For example, a pedestrian who views the care-related information displayed on the screen of the mobile terminal 110 can consult a medical institution, a care-related facility, or the like according to the information.

[0096] For example, the information presentation system 1 may display, on the screen of the mobile terminal 110, recommendation information that recommends consulting an appropriate medical institution, care-related facility, etc., according to the estimated care-related information. For example, the information presentation system 1 may display, on the screen of the mobile terminal 110, recommendation information including telephone numbers, email addresses, etc. of appropriate medical institutions, care-related facilities, etc., according to the care-related information. For example, the information presentation system 1 may present recommendation information that encourages appropriate exercise according to the estimated care-related information. For example, the information presentation system 1 may present estimated information regarding body balance and muscle strength according to the estimated care-related information. For example, the information presentation system 1 may present, in a graph, changes over time in accumulated past care-related information.

[0097] As described above, the information presentation system of this embodiment includes a measurement device and an estimation device. The measurement device is placed on a user's footwear. The measurement device measures spatial acceleration and spatial angular velocity in response to the user's walking. The measurement device generates sensor data based on the measured spatial acceleration and spatial angular velocity. The measurement device outputs the generated sensor data to the estimation device, which includes an acquisition unit, a storage unit, an estimation unit, and an output unit. The acquisition unit acquires sensor data measured in response to the user's walking and the user's physical data. The storage unit stores the physical data and an estimation model that outputs care-related information in response to input of features extracted from the sensor data and the physical data. The estimation unit inputs the features extracted from the user's sensor data and the physical data into the estimation model to estimate the user's care-related information. The output unit outputs the estimated care-related information of the user.

[0098] The information presentation system of this embodiment uses an estimation model that outputs care-related information in response to input of features extracted from sensor data and physical data, and therefore, the information presentation system of this embodiment can estimate care-related information according to the physical condition of the user based on the user's gait.

[0099] In one aspect of the present embodiment, the storage unit stores an estimation model that outputs care-related information in response to input of a numerical value related to a distribution of walking speeds and physical data. The estimation unit generates a walking waveform for a predetermined walking cycle using time-series data of sensor data for the user's predetermined walking cycle. The estimation unit calculates predetermined walking parameters for each step cycle based on walking events detected from the walking waveform for the predetermined walking cycle. The estimation unit calculates the user's walking speed using the calculated predetermined walking parameters. The estimation unit calculates a numerical value related to the distribution of walking speeds measured in response to the user's walking for the predetermined walking cycle. The estimation unit inputs the calculated numerical value related to the distribution of walking speeds and the physical data into the estimation model. The estimation unit estimates care-related information of the user. According to this aspect, by inputting a numerical value related to the distribution of walking speeds and the physical data into the estimation model, care-related information corresponding to the user's physical condition can be estimated.

[0100] In one aspect of this embodiment, the estimation unit generates a walking waveform of forward acceleration and a walking waveform of a forward trajectory using sensor data for a predetermined walking cycle of the user. The estimation unit detects toe-off and heel-strike as walking events from the walking waveform of forward acceleration. The estimation unit extracts spatial positions at the timings of toe-off and heel-strike in the walking waveform of the forward trajectory. The estimation unit calculates the difference in spatial positions at the extracted timings of toe-off and heel-strike as the stride length of the user. The estimation unit calculates the time from toe-off to heel-strike as the time for one stride. The estimation unit calculates the walking speed by dividing the stride length by the time for one stride. According to this aspect, the walking speed can be calculated based on the timings of toe-off and heel-strike detected from the walking waveform of forward acceleration.

[0101] In one aspect of this embodiment, the acquisition unit acquires the user's age as physical data. The storage unit stores an estimation model that outputs a frailty probability as care-related information in response to input of a numerical value related to the distribution of walking speeds and the user's age. The estimation unit inputs the numerical value related to the distribution of walking speeds and the user's age into the estimation model to estimate the user's frailty probability. According to this aspect, the user's frailty probability can be estimated based on the numerical value related to the distribution of walking speeds and the user's age.

[0102] In one aspect of the present embodiment, the estimation unit estimates the frailty age of the user based on the estimated frailty probability and the correlation between age and frailty prevalence. According to this aspect, the frailty age of the user can be estimated based on the frailty probability of the user.

[0103] In one aspect of the present embodiment, the estimation unit outputs the care-related information to a terminal device having a screen and causes the terminal device to display the care-related information on the screen. According to this aspect, the care-related information estimated based on the user's gait can be displayed on the screen of the terminal device.

[0104] The information presentation system of this embodiment may also be configured to present information about sarcopenia. Sarcopenia is a decline in muscle strength throughout the body, including grip strength, lower limb muscles, and trunk muscles, due to a decrease in muscle mass caused by aging or disease. Sarcopenia also includes declines in physical function, such as a decrease in walking speed, caused by a decline in muscle strength throughout the body. For example, an estimation model is generated in advance to estimate the probability of sarcopenia (also referred to as sarcopenia probability) based on input of physical data and numerical values related to walking speed distribution. Using such an estimation model, sarcopenia probability can be estimated in the same way as frailty. For example, the information presentation system of this embodiment may be configured to estimate sarcopenia age in the same way as frailty age. Sarcopenia age is an age according to a scale of sarcopenia, estimated using walking parameters and physical data.

[0105] Furthermore, the care-related information output by the information presentation system of this embodiment may be used not only for estimating frailty and sarcopenia, but also as criteria for determining the level of care required and the level of support required. For example, if an estimation model for estimating the level of care required and the level of support required is generated in accordance with input of physical data and numerical values related to walking speed distribution, the level of care required and the level of support required can be estimated. For example, if an estimation model for estimating a reference time for care required certification, etc., is generated in accordance with input of physical data and numerical values related to walking speed distribution, the level of care required and the level of support required can be estimated in accordance with the reference time for care required certification, etc.

[0106] (Second embodiment) Next, an information presentation system according to a second embodiment will be described with reference to the drawings. The information presentation system of this embodiment estimates fall susceptibility, which indicates how likely a person is to fall, by focusing on gait variations related to balance in addition to walking speed.

[0107] (composition) FIG. 16 is a block diagram showing the configuration of an information presentation system 2 of this embodiment. The information presentation system 2 includes a measurement device 21 and an estimation device 22. The measurement device 21 and the estimation device 22 may be connected by wire or wirelessly. The measurement device 21 and the estimation device 22 may be configured as a single device. The measurement device 21 may be removed from the configuration of the information presentation system 2, and the information presentation system 2 may consist of only the estimation device 22. The measurement device 21 has the same configuration as the measurement device 11 of the first embodiment. Therefore, in this embodiment, detailed description of the measurement device 21 will be omitted.

[0108] In this embodiment, attention is paid to walking speed and balance in order to prevent the occurrence of physical functional disorders and the need for nursing care. Walking speed can be an indicator of muscle strength and cardiopulmonary function. Maintaining balance by improving / maintaining leg and hip muscle strength can prevent frailty and slow its progression. In particular, this embodiment focuses on walking speed and gait variability to estimate the likelihood of falling (fall susceptibility).

[0109] FIG. 17 is a graph illustrating the walking speeds of subjects who had experienced falls (fallers) and subjects who had not experienced falls (non-fallers) during a specific verification period. The graph in FIG. 17 is based on the values in Table 3 of Non-Patent Document 4 (Non-Patent Document 4: J. Hausdorff, et al., “Gait Variability and Fall Risk in Community-Living Older Adults: A 1-Year Prospective Study,” Arch Phys Med Rehabil, Vol. 82, August 2001, pp. 1050-1056.). FIG. 17 shows the walking speed distribution according to the average walking speed (standard deviation) values in Table 3 of Non-Patent Document 4. For non-fallers, the average walking speed is 0.91 meters per second (m / s), and the standard deviation is 0.24 m / s. For fallers, the average walking speed is 0.71 meters per second (m / s), and the standard deviation is 0.33 m / s. In Figure 17, the walking speed of people who did not fall is shown by a solid line, and the walking speed of people who fell is shown by a dashed line. As shown in Figure 17, people who fell tend to have slower walking speeds and a wider distribution of walking speeds than people who did not fall.

[0110] FIG. 18 is a graph illustrating stride time variation in subjects who have experienced falls (fallers) and subjects who have not experienced falls (non-fallers) within a specific period. The graph in FIG. 18 is based on FIG. 1 in Non-Patent Document 4. Stride time is the time of the stance phase in a gait cycle. Stride time variation corresponds to variation in stride time. In this embodiment, stride time is defined as the time from heel-contact to toe-off. For example, stride time can be calculated by subtracting the time for one stride from the time for one gait cycle. As shown in FIG. 18, fallers tend to have larger stride time variation and a wider distribution of stride time variation compared to non-fallers.

[0111] Figure 19 shows a frequency distribution of fall rates by age. The frequency distribution in Figure 19 is based on Figure 3 in Non-Patent Document 5 (C. Pearson, et al., "Understanding seniors' risk of falling and their perception of risk," Statistics Canada, Catalogue no. 82-624-X, Health at a Glance, October 2014, pp. 1-11.). Figure 19 shows the fall rates by gender. Figure 19 shows a smooth curve connecting the fall rate frequencies at the median for each age group. The dashed curve shows the correlation between age and fall rate for men. The dash-dotted curve shows the correlation between age and fall rate for men.

[0112] Here, an example will be described in which the estimation device 22 estimates the user's likelihood of falling (also called fall susceptibility) based on walking speed and gait variability. In the following, it is assumed that fall susceptibility depends on age and gender. It is also assumed that fall susceptibility affects walking speed and gait variability. The following formula 2-1 is the fall susceptibility f a depends on age and gender, and the risk of falling a represents the probability of falling when it is assumed that affects walking speed v and gait variability w. TIFF0007726283000008.tif10150

[0113] The first term p(v|f a ) is a term related to the walking speed distribution of fallers / non-fallers. The second term in the numerator, p(w|f a ) is a term related to the gait variability distribution of fallers / non-fallers. The third term in the numerator, p(f a |y, s) is a term relating to the fall rate according to age y and gender s. The denominator p(v, w|y, s) is a term relating to the walking speed distribution and gait variability distribution according to age y and gender s. For example, physical data such as age y and gender s may be input via an input device (not shown). The estimation device 22 calculates the fall susceptibility f according to age y, gender s, walking speed v, and gait variability w based on Equation 2-1.a Estimate.

[0114] The following equation 2-2 is the first term p(v|f a ) is an expression that instantiates TIFF0007726283000009.tif13150

[0115] In the above formula 2-2, j represents either a non-faller or a faller. 1j is the average walking speed for j. σ 1j is the standard deviation of the walking speed for j. Based on Table 3 in Non-Patent Document 4, if j is a non-faller, μ 1j is 0.91 m / s, and σ 1j is 0.24 m / s. Based on Table 3 in Non-Patent Document 4, if j is a faller, μ 1j is 0.71 m / s, and σ 1j is 0.33 m / s.

[0116] The following equation 2-3 is the second term p(w|f a ) is an expression that instantiates TIFF0007726283000010.tif13150

[0117] In the above equations 2-3, j indicates either a non-faller or a faller. The stride time variability graph in Figure 18 (Figure 1 in Non-Patent Document 4) allows us to read the distribution of gait variability between non-fallers and fallers. Based on Figure 18, the stride time variability of non-fallers has an average value of 50.3 milliseconds (ms) and a standard deviation of 4.2 ms. Based on Figure 18, the stride time variability of fallers has an average value of 105.9 ms and a standard deviation of 30.1 ms.

[0118] The third term p(f a For |y, s), the values related to the fall rate by age / gender in Figure 3 of Non-Patent Document 5 can be applied. The following equations 2-4 are equations that approximate the curve (dashed line) related to the fall rate in Figure 19 using a sigmoid function. TIFF0007726283000011.tif9150

[0119] In the above equations 1-4, the exponent in the second term of the denominator is "-(a s y+b s )" (a s , b s is a real number). s and b s is a constant depending on gender.

[0120] By transforming the above equation 2-4, we obtain the following equation 2-5. TIFF0007726283000012.tif11150The above equation 2-5 is used to calculate the age y and the fall rate p(f a |y, s) and then a s and b s With reference to Figure 3 in Non-Patent Document 5, if the median age of each age group is y, then for men, p(f a |67.5) is 0.122, p(f a |72.5) is 0.131, p(f a |77.5) is 0.171, p(f a |82.5) is 0.261, p(f a |90) is 0.372. Similarly, for women, p(f a |67.5) is 0.129, p(f a |72.5) is 0.190, p(f a |77.5) is 0.260, p(f a |82.5) is 0.340, p(f a |90) is 0.500.

[0121] Figure 20 shows the results of substituting the above values for each gender into Equation 2-5 and calculating Z for age y. s The graph shows plots of (y). Diamonds (◇) are plots for men. The straight line obtained by linear regression of the plots for men is shown as a dashed line. Circles (○) are plots for women. The straight line obtained by linear regression of the plots for women is shown as a dashed line. When the points plotted for men are linearly regressed using the least squares method, a sis derived as 0.0687, and b s is derived as -6.76. A linear regression of the plotted points for women using the least squares method yields a s is derived as 0.0838, and b s is derived as -7.55. For each gender, the derived a s and b s Substituting the values of into equation 2-4, we obtain the following equations 2-6 and 2-7. TIFF0007726283000013.tif9150

[0122] TIFF0007726283000014.tif9150

[0123] The above equation 2-6 is the fall rate p(f a |y, m). The above equation 2-7 shows the fall rate p(f a |y, f) is shown.

[0124] Figure 21 is a graph related to Equations 2-6 and 2-7 derived using the above procedure. In Figure 21, the curve for men is shown by a dashed line (Equation 2-6), and the curve for women is shown by a dashed line (Equation 2-7). Using the relationship in the graph of Figure 21 (Equations 2-6 and 2-7), the fall rate p(f a For example, physical data such as age y and gender s are input via an input device (not shown).

[0125] The estimation device 22 calculates the walking speed v, which corresponds to the stride length per unit time, based on the time-series data (gait waveform) of the sensor data measured by the measurement device 21. The estimation device 22 also calculates the stride time, which corresponds to the time of the stance phase in a walking cycle. The estimation device 22 calculates the average value and standard deviation of the walking speed v and gait variation w for several steps. The estimation device 22 calculates the denominator p(v, w|y, s) of Equation 2-1 based on the user's age y and gender, and the average value and standard deviation of the calculated walking speed v and gait variation w.

[0126] The estimation device 22 applies the user's age y, sex s, the calculated walking speed v, walking variation w, etc. to the above formula 2-1 to calculate the user's fall tendency f a The estimation device 22 outputs information related to the estimated fall tendency. There are no particular limitations on the destination to which the information related to fall tendency is output. For example, the estimation device 22 outputs information related to the user's fall tendency to an external system or device (not shown). For example, the estimation device 22 outputs information related to the user's fall tendency to a display device (not shown).

[0127] The estimation device 22 may also calculate a fall-prone age as care-related information. The fall-prone age is an age corresponding to a fall-proneness scale estimated using walking parameters and physical data. The estimation device 22 calculates the fall-prone age Y e Calculate. TIFF0007726283000015.tif6150

[0128] The left side of the above equation 2-8, p(f a |y, s, v, w) is the fall susceptibility according to age y, gender s, walking speed v, and walking variability w. a |Y e ) is the fall-prone age Y e The right side of Equation 2-8, p(f a |Y e ) is a function of age and stride time variation in the frequency distribution of Figure 19.

[0129] The estimation device 22 calculates the fall-prone age Y that satisfies the relationship of the above formula 2-8. e The estimation device 22 calculates the value obtained by the above equation 2-1 as the left side p(f a 19. The estimation device 22 finds the fall-prone age Y that is the same as the value on the left side of the function relating to age and stride time variation in the frequency distribution of FIG. e For example, the estimation device 22 outputs information relating to the calculated fall-prone age as care-related information.

[0130] [Estimation device] Next, details of the estimation device 22 will be described with reference to the drawings. Fig. 22 is a block diagram showing an example of a detailed configuration of the estimation device 22. The estimation device 22 has an acquisition unit 221, a storage unit 223, an estimation unit 225, and an output unit 227.

[0131] The acquisition unit 221 has the same configuration as the acquisition unit 121 of the first embodiment. The acquisition unit 221 acquires data (also referred to as physical data) such as the age and sex of the user input via an input device (not shown). The acquisition unit 221 stores the acquired physical data in the storage unit 223. The acquisition unit 221 also receives sensor data from the measurement device 21. The acquisition unit 221 outputs the received sensor data to the estimation unit 225.

[0132] The storage unit 223 stores an estimation model for estimating fall susceptibility based on age, gender, walking speed distribution, and gait variability distribution. The estimation model is constructed based on past knowledge. The estimation model outputs fall susceptibility in response to input of walking speed distribution and gait variability distribution calculated using sensor data measured according to the user's walking, as well as the user's age and gender. The storage unit 223 also stores physical data of the user. The physical data includes the subject's age and gender. The physical data may also include data such as the user's height and weight.

[0133] The estimation unit 225 has the same configuration as the estimation unit 125 of the first embodiment. The estimation unit 225 acquires physical data related to the user from the storage unit 223. Furthermore, the estimation unit 225 acquires sensor data measured by a measuring device 21 attached to footwear worn by the user from the acquisition unit 221. The estimation unit 225 generates time-series data of the sensor data. The estimation unit 225 extracts gait waveform data for at least one walking cycle from the generated time-series data. The estimation unit 225 converts the coordinate system of the acquired sensor data from the local coordinate system to a world coordinate system, and generates time-series data of the sensor data converted into the world coordinate system (also referred to as gait waveform).

[0134] The estimation unit 225 detects walking events from the generated walking waveform. The estimation unit 225 calculates the user's stride length based on the detected walking events. The estimation unit 225 calculates the user's walking speed by dividing the stride length by the time for one stride. The method of calculating the walking speed is the same as in the first embodiment. The estimation unit 225 also calculates the stride time, which corresponds to the time of the stance phase in a walking cycle. The stride time corresponds to the time from heel-contact to toe-off.

[0135] The estimation unit 225 calculates the average value and standard deviation of the walking speed v and walking fluctuation w for several steps. For example, the estimation unit 225 calculates the average value and standard deviation of the walking speed v and walking fluctuation w using sensor data for 3 to 10 steps.

[0136] Estimation unit 225 inputs the user's physical data (age, sex) and numerical values related to the walking speed distribution and gait variability distribution calculated based on the user's walking to an estimation model for estimating the likelihood of falling stored in storage unit 223. Estimation unit 225 outputs the likelihood of falling output from the estimation model to output unit 227 in response to the input of the physical data, walking speed distribution, and gait variability distribution.

[0137] For example, the estimation unit 225 may estimate the fall prone age of the user based on the user's age and walking speed distribution / gait variability distribution. The estimation unit 225 inputs the user's physical data (age, sex) and numerical values related to the walking speed distribution / gait variability distribution calculated based on the user's walking to an estimation model for estimating the fall prone age stored in the storage unit 223. The estimation unit 225 outputs the fall prone age output from the estimation model in response to the input numerical values related to the walking speed distribution / gait variability distribution and the physical data to the output unit 227 as care-related information.

[0138] The output unit 227 has the same configuration as the output unit 127 of the first embodiment. The output unit 227 outputs the estimated care-related information. There are no particular limitations on the output destination (not shown) of the care-related information. The output care-related information can be used for any purpose.

[0139] (operation) Next, the operation of the information presentation system 2 will be described with reference to the drawings. In the following, the operation of the measurement device 21 will be omitted, and the operation of the estimation device 22 will be described. Fig. 23 is a flowchart for explaining an example of the operation of the estimation device 22. In the explanation following the flowchart of Fig. 23, the estimation device 22 will be described as the subject of the operation.

[0140] 23, first, the estimation device 22 acquires sensor data related to foot movement (step S21). The estimation device 22 acquires the sensor data related to foot movement from the measurement device 21. The estimation device 22 may acquire the sensor data measured by the measurement device 21 for one walking cycle at a time, or may acquire the sensor data for multiple walking cycles at a time.

[0141] Next, the estimation device 22 generates time series data of sensor data (step S22). The estimation device 22 generates time series data of sensor data related to acceleration and trajectory in three axial directions, angular velocity around three axes, etc. In this embodiment, the estimation device 22 generates time series data of sensor data related to acceleration in the Y direction and trajectory.

[0142] Next, the estimation device 22 executes a gait parameter calculation process (step S23). In the gait parameter calculation process, the estimation device 22 calculates a walking speed and a stride time based on time-series data (gait waveform) for one walking cycle. The gait parameter calculation process will be described in detail later.

[0143] When the calculations for the predetermined walking cycle are completed (Yes in step S24), the estimation device 22 derives numerical values related to the walking speed distribution / gait variability distribution using the walking parameters for the predetermined walking cycle (step S25). The numerical values related to the walking speed distribution / gait variability distribution include the average value and standard deviation of the walking speed / gait variability for walking for the predetermined walking cycle. If the calculations for the predetermined walking cycle are not completed (No in step S24), the process returns to step S23.

[0144] The estimation device 22 inputs the values related to the walking speed distribution / gait variation distribution and the physical data into the estimation model to estimate the care-related information of the user (step S26). For example, the estimation device 22 estimates the fall-proneness and fall-proneness age as the care-related information.

[0145] The estimation device 22 outputs the care-related information of the user (step S27). For example, the estimation device 22 outputs the fall-proneness and fall-prone age as the care-related information. There are no particular limitations on the output destination or use of the care-related information of the user.

[0146] [Walking parameter calculation process] Next, the gait parameter calculation process by the estimation device 22 will be described with reference to the drawings. Fig. 24 is a flowchart for explaining an example of the gait parameter calculation process. In the explanation following the flowchart of Fig. 24, the estimating device 22 will be described as the subject of the operation.

[0147] In FIG. 24, first, the estimation device 22 detects the first and second peaks in the walking waveform of the Y-direction acceleration (advancement direction acceleration) for one walking cycle (step S211).

[0148] Next, the estimation device 22 detects the timing of toe lift-off from the first peak (step S212).

[0149] Next, the estimation device 22 detects the timing of heel strike from the second peak (step S213).

[0150] Next, the estimation device 22 calculates the stride length based on the difference in spatial position between the timing of heel-contact and toe-off in the walking waveform of the Y-direction trajectory (traveling direction trajectory) for one walking cycle (step S214).

[0151] Next, the estimation device 22 calculates the time from the toe lift-off to the heel contact as the time for one stride (step S215).

[0152] Next, the estimation device 22 calculates the walking speed by dividing the stride length by the time for one stride (step S216).

[0153] Next, the estimation device 22 calculates the time from heel contact to toe lift as the stride time (step S217). Step S217 may be performed before steps S214 to S216, or may be performed in parallel with steps S214 to S216.

[0154] [Application example 2] Next, a second application example of the information presentation system 2 of this embodiment will be described with reference to the drawings. In this application example, information related to the tendency to fall estimated by the estimation device 22 is displayed on the screen of the mobile terminal 210. In this application example, a measuring device 21 is installed in a user's shoe 200, and sensor data based on physical quantities related to foot movement measured by the measuring device 21 is transmitted to the mobile terminal 210 carried by the user. The sensor data transmitted to the mobile terminal 210 is processed by a program (estimation device 22) installed in the mobile terminal 210.

[0155] FIG. 25 shows an example in which information about the physical condition of a user wearing shoes 200 equipped with a measuring device 21 is displayed on the screen of the mobile terminal 210 of the user. In the example of FIG. 25, the fall susceptibility and fall susceptibility age estimated based on the user's gait are displayed as care-related information on the screen of the mobile terminal 210 of the user. A user who views the care-related information displayed on the screen of the mobile terminal 210 can take action in accordance with the information. For example, a pedestrian who views information about fall susceptibility displayed on the screen of the mobile terminal 210 can consult a medical institution, a care-related facility, or the like in accordance with the information.

[0156] As described above, the information presentation system of this embodiment includes a measurement device and an estimation device. The measurement device is placed on a user's footwear. The measurement device measures spatial acceleration and spatial angular velocity in response to the user's walking. The measurement device generates sensor data based on the measured spatial acceleration and spatial angular velocity. The measurement device outputs the generated sensor data to the estimation device, which includes an acquisition unit, a storage unit, an estimation unit, and an output unit. The acquisition unit acquires sensor data measured in response to the user's walking and physical data of the user. The storage unit stores an estimation model that outputs nursing care-related information in response to input of numerical values related to the distribution of walking speed and gait fluctuations and the physical data. The estimation unit generates a gait waveform for a predetermined walking cycle using time-series data of the sensor data for a predetermined walking cycle of the user. The estimation unit calculates predetermined gait parameters for each step cycle based on walking events detected from the gait waveform for the predetermined walking cycle. The estimation unit calculates the user's walking speed using the calculated predetermined gait parameters. The estimation unit calculates a numerical value related to the distribution of walking speed measured in response to the user's walking for the predetermined walking cycle. The estimation unit calculates the user's gait variation in the stance phase using the calculated predetermined gait parameters. The estimation unit calculates a numerical value related to the distribution of gait variation measured in accordance with the user's walking for a predetermined walking cycle. The estimation unit inputs the calculated numerical value related to the distribution of gait variation, the numerical value related to the distribution of walking speed, and the physical data into an estimation model. The estimation unit estimates the user's care-related information. The output unit outputs the estimated user's care-related information.

[0157] The information presentation system of this embodiment uses an estimation model that outputs care-related information in response to input of values related to the distribution of walking speed and gait variation and physical data. Therefore, the information presentation system of this embodiment can estimate care-related information according to the user's physical condition based on the values related to the distribution of walking speed and gait variation and physical data.

[0158] In one aspect of this embodiment, the estimation unit generates a walking waveform of forward acceleration and a walking waveform of a forward trajectory using sensor data for a predetermined walking cycle of the user. The estimation unit detects heel strike and toe lift as walking events from the walking waveform of forward acceleration. The estimation unit calculates the time from heel strike to toe lift as the stride time. According to this aspect, the stride time can be calculated based on the time from heel strike to toe lift detected from the walking waveform of forward acceleration.

[0159] In one aspect of this embodiment, the acquisition unit acquires the user's age and gender as physical data. The storage unit stores an estimation model that outputs a fall likelihood as care-related information in response to input of values related to the distribution of walking speed and gait variability and the user's age and attributes. The estimation unit inputs the values related to the distribution of walking speed and gait variability and the user's age and attributes into the estimation model to estimate the user's fall likelihood. According to this aspect, the user's fall likelihood can be estimated based on the values related to the distribution of the user's walking speed and gait variability and the user's age and gender.

[0160] In one aspect of the present embodiment, the estimation unit estimates the user's frailty age based on the estimated fall susceptibility and the correlation between age and fall susceptibility. According to this aspect, the fall susceptibility age of the user can be estimated based on the user's fall susceptibility.

[0161] (Third embodiment) Next, an estimation device according to a third embodiment will be described with reference to the drawings. The estimation device of this embodiment has a simplified configuration of the estimation device included in the information presentation systems of the first and second embodiments. FIG. 26 is a block diagram showing the configuration of an estimation device 32 of this embodiment. The estimation device 32 includes an acquisition unit 321, a storage unit 323, an estimation unit 325, and an output unit 327.

[0162] The acquisition unit 321 acquires sensor data measured in response to the user's walking and the user's physical data. The storage unit 323 stores the physical data and an estimation model that outputs care-related information in response to input of feature amounts extracted from the sensor data and the physical data. The estimation unit 325 inputs the feature amounts extracted from the user's sensor data and the physical data into the estimation model to estimate the user's care-related information. The output unit 327 outputs the estimated care-related information of the user.

[0163] As described above, the estimation device of this embodiment can estimate care-related information corresponding to the physical condition of a user based on the user's gait by using an estimation model that outputs care-related information in response to input features extracted from sensor data and physical data.

[0164] (Hardware) Here, a hardware configuration for executing control and processing according to each embodiment of the present disclosure will be described using an information processing device 90 in Fig. 27 as an example. Note that the information processing device 90 in Fig. 27 is an example configuration for executing control and processing according to each embodiment, and does not limit the scope of the present disclosure.

[0165] As shown in Fig. 27, an information processing device 90 includes a processor 91, a main storage device 92, an auxiliary storage device 93, an input / output interface 95, and a communication interface 96. In Fig. 27, interface is abbreviated as I / F (Interface). The processor 91, the main storage device 92, the auxiliary storage device 93, the input / output interface 95, and the communication interface 96 are connected to each other via a bus 98 so as to be able to communicate data with each other. The processor 91, the main storage device 92, the auxiliary storage device 93, and the input / output interface 95 are also connected to a network such as the Internet or an intranet via the communication interface 96.

[0166] The processor 91 loads a program stored in an auxiliary storage device 93 or the like into a main storage device 92. The processor 91 executes the program loaded into the main storage device 92. In this embodiment, a software program installed in the information processing device 90 may be used. The processor 91 executes control and processing according to each embodiment.

[0167] The main memory device 92 has an area in which programs are loaded. Programs stored in the auxiliary memory device 93 or the like are loaded into the main memory device 92 by the processor 91. The main memory device 92 is realized by a volatile memory such as a DRAM (Dynamic Random Access Memory). Furthermore, a non-volatile memory such as an MRAM (Magnetoresistive Random Access Memory) may be configured / added to the main memory device 92.

[0168] The auxiliary storage device 93 stores various data such as programs. The auxiliary storage device 93 is realized by a local disk such as a hard disk or flash memory. Note that it is also possible to configure the main storage device 92 to store various data, thereby omitting the auxiliary storage device 93.

[0169] The input / output interface 95 is an interface for connecting the information processing device 90 to peripheral devices based on standards and specifications. The communication interface 96 is an interface for connecting to external systems and devices via a network such as the Internet or an intranet based on standards and specifications. The input / output interface 95 and the communication interface 96 may be a common interface for connecting to external devices.

[0170] Input devices such as a keyboard, mouse, and touch panel may be connected to the information processing device 90 as needed. These input devices are used to input information and settings. When a touch panel is used as the input device, the display screen of the display device may also serve as the interface for the input device. Data communication between the processor 91 and the input devices may be mediated by an input / output interface 95.

[0171] The information processing device 90 may also be equipped with a display device for displaying information. When a display device is equipped, the information processing device 90 preferably includes a display control device (not shown) for controlling the display of the display device. The display device may be connected to the information processing device 90 via the input / output interface 95.

[0172] The information processing device 90 may also be equipped with a drive device. The drive device acts as an intermediary between the processor 91 and a recording medium (program recording medium) for reading data and programs from the recording medium, writing the processing results of the information processing device 90 to the recording medium, etc. The drive device may be connected to the information processing device 90 via an input / output interface 95.

[0173] The above is an example of a hardware configuration for enabling control and processing according to each embodiment of the present invention. Note that the hardware configuration in FIG. 27 is an example of a hardware configuration for executing control and processing according to each embodiment and does not limit the scope of the present invention. Furthermore, a program that causes a computer to execute control and processing according to each embodiment is also within the scope of the present invention. Furthermore, a program recording medium on which a program according to each embodiment is recorded is also within the scope of the present invention. The recording medium can be realized, for example, as an optical recording medium such as a CD (Compact Disc) or a DVD (Digital Versatile Disc). The recording medium may also be realized as a semiconductor recording medium such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) card. The recording medium may also be realized as a magnetic recording medium such as a flexible disk or other recording medium. When a program executed by a processor is recorded on a recording medium, the recording medium corresponds to a program recording medium.

[0174] The components of each embodiment may be combined in any manner, and may be realized by software or by a circuit.

[0175] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

[0176] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes. (Appendix 1) an acquisition unit that acquires sensor data measured in response to a user's walking and physical data of the user; a storage unit that stores an estimation model that outputs care-related information in response to input of features extracted from the sensor data and the physical data; and the physical data; an estimation unit that inputs features extracted from the sensor data of the user and the physical data of the user into the estimation model to estimate the care-related information of the user; An estimation device comprising: an output unit that outputs the care-related information of the estimated user. (Appendix 2) The storage unit storing the estimation model that outputs the care-related information in response to input of a value relating to the distribution of walking speeds and the physical data; The estimation unit generating a walking waveform for a predetermined walking cycle using time series data of the sensor data for the predetermined walking cycle of the user; calculating predetermined gait parameters for each stride cycle based on gait events detected from the gait waveform for the predetermined gait cycle; calculating a walking speed of the user using the calculated predetermined walking parameters; calculating a value relating to the distribution of the walking speed measured in accordance with the user's walking for the predetermined walking cycle; inputting the calculated values relating to the distribution of walking speeds and the physical data into the estimation model; 2. The estimation device according to claim 1, which estimates the care-related information of the user. (Appendix 3) The estimation unit generating the walking waveform of a forward acceleration and the walking waveform of a forward trajectory using the sensor data for the predetermined walking cycle of the user; detecting a toe-off and a heel-contact as the walking events from the walking waveform of the forward acceleration; extracting spatial positions at the timings of the toe-off and the heel-contact in the walking waveform of the travel direction trajectory; calculating a difference in spatial position between the extracted timings of the toe-off and the heel-contact as a stride length of the user; The time from the toe-off to the heel-contact is calculated as the time for one stride. 3. The estimation device according to claim 2, wherein the walking speed is calculated by dividing the time for one stride by the stride length. (Appendix 4) The acquisition unit acquiring the user's age as the physical data; The storage unit storing the estimation model that outputs a frailty probability as the care-related information in response to input of a value related to the distribution of the walking speed and an age of the user; The estimation unit 4. The estimation device according to claim 2, wherein a numerical value relating to the distribution of walking speeds and the age of the user are input into the estimation model to estimate the frailty probability of the user. (Appendix 5) The estimation unit 5. The estimation device according to claim 4, which estimates the frailty age of the user based on the estimated frailty probability and the correlation between age and frailty prevalence. (Appendix 6) The storage unit storing the estimation model that outputs the care-related information in response to input of values relating to the distribution of walking speed and gait variation and the physical data; The estimation unit generating a walking waveform for a predetermined walking cycle using time series data of the sensor data for the predetermined walking cycle of the user; calculating predetermined gait parameters for each stride cycle based on gait events detected from the gait waveform for the predetermined gait cycle; calculating a walking speed of the user using the calculated predetermined walking parameters; calculating a value relating to the distribution of the walking speed measured in accordance with the user's walking for the predetermined walking cycle; calculating gait variation in a stance phase of the user using the calculated predetermined gait parameters; calculating a value relating to the distribution of the gait variation measured in accordance with the user's walking for the predetermined walking cycle; inputting the calculated values relating to the distribution of gait variation, the values relating to the distribution of walking speed, and the physical data into the estimation model; 2. The estimation device according to claim 1, which estimates the care-related information of the user. (Appendix 7) The estimation unit generating the walking waveform of a forward acceleration and the walking waveform of a forward trajectory using the sensor data for the predetermined walking cycle of the user; detecting a heel strike and a toe lift from the walking waveform of the forward acceleration as the walking events; 7. The estimation device according to claim 6, wherein the time from the heel contact to the toe off is calculated as a stride time. (Appendix 8) The acquisition unit acquiring the user's age and sex as the physical data; The storage unit storing the estimation model that outputs a fall likelihood as the care-related information in response to input of values relating to the distribution of the walking speed and the gait variation and the age and attributes of the user; The estimation unit 8. The estimation device according to claim 6, wherein numerical values relating to the distribution of the walking speed and the gait variation, as well as the age and attributes of the user, are input into the estimation model to estimate the fall susceptibility of the user. (Appendix 9) The estimation unit 9. The estimation device according to claim 8, which estimates the frailty age of the user based on the estimated fall susceptibility and a correlation between age and the fall susceptibility. (Appendix 10) The estimation unit outputting the care-related information to a terminal device having a screen; 10. The estimation device according to any one of appendices 1 to 9, wherein the care-related information is displayed on the screen of the terminal device. (Appendix 11) An estimation device according to any one of Supplementary Notes 1 to 10; a measuring device that is placed on a user's footwear, measures spatial acceleration and spatial angular velocity in accordance with the user's walking, generates sensor data based on the measured spatial acceleration and spatial angular velocity, and outputs the generated sensor data to the estimation device. (Appendix 12) The computer Acquire sensor data measured in response to the user's walking and physical data of the user; inputting the feature amounts extracted from the sensor data and the physical data of the user into an estimation model that outputs care-related information in response to input of the feature amounts extracted from the sensor data and the physical data, and estimating the care-related information of the user; An estimation method for outputting the care-related information of the estimated user. (Appendix 13) A process of acquiring sensor data measured in response to a user's walking and physical data of the user; a process of inputting the feature amounts extracted from the sensor data and the physical data of the user into an estimation model that outputs care-related information in response to input of the feature amounts extracted from the sensor data and the physical data, thereby estimating the care-related information of the user; and outputting the care-related information of the estimated user. [Explanation of symbols]

[0177] 1, 2 Information presentation system 11, 21 Measurement equipment 12, 22, 32 Estimator 100, 200 shoes 110, 210 mobile devices 111 Acceleration Sensor 112 Angular rate sensor 113 Control Unit 115 Transmitter 121, 221, 321 Acquisition Department 123, 223, 323 storage section 125, 225, 325 Estimation part 127, 227, 327 output section

Claims

1. an acquisition means for acquiring sensor data measured in response to a user's walking and physical data of the user; a storage means for storing an estimation model that outputs care-related information in response to input of features extracted from the sensor data and the physical data; and the physical data; an estimation means for estimating the care-related information of the user by inputting the feature extracted from the sensor data of the user and the physical data into the estimation model; an output means for outputting the estimated care-related information of the user; The storage means storing the estimation model that outputs the care-related information in response to input of a value relating to the distribution of walking speeds and the physical data; The estimation means generating a walking waveform for a predetermined walking cycle using time series data of the sensor data for the predetermined walking cycle of the user; calculating predetermined gait parameters for each stride cycle based on gait events detected from the gait waveform for the predetermined gait cycle; calculating a walking speed of the user using the calculated predetermined walking parameters; calculating a value relating to the distribution of the walking speed measured in accordance with the user's walking for the predetermined walking cycle; inputting the calculated values relating to the distribution of walking speeds and the physical data into the estimation model; An estimation device that estimates the care-related information of the user.

2. The estimation means generating the walking waveform of a forward acceleration and the walking waveform of a forward trajectory using the sensor data for the predetermined walking cycle of the user; detecting a toe-off and a heel-contact as the walking events from the walking waveform of the forward acceleration; extracting spatial positions at the timings of the toe-off and the heel-contact in the walking waveform of the travel direction trajectory; calculating a difference in spatial position between the extracted timings of the toe-off and the heel-contact as a stride length of the user; The time from the toe-off to the heel-contact is calculated as the time for one stride. The estimation device according to claim 1 , wherein the walking speed is calculated by dividing the stride length by the time required for one stride.

3. The acquisition means acquiring the user's age as the physical data; The storage means storing the estimation model that outputs a frailty probability as the care-related information in response to input of a value related to the distribution of the walking speed and an age of the user; The estimation means The estimation device according to claim 1 or 2, wherein the estimation model estimates the frailty probability of the user by inputting a numerical value relating to the distribution of walking speeds and the age of the user.

4. an acquisition means for acquiring sensor data measured in response to a user's walking and physical data of the user; a storage means for storing an estimation model that outputs care-related information in response to input of features extracted from the sensor data and the physical data; and the physical data; an estimation means for estimating the care-related information of the user by inputting the feature extracted from the sensor data of the user and the physical data into the estimation model; an output means for outputting the estimated care-related information of the user; The storage means storing the estimation model that outputs the care-related information in response to input of values relating to the distribution of walking speed and gait variation and the physical data; The estimation means generating a walking waveform for a predetermined walking cycle using time series data of the sensor data for the predetermined walking cycle of the user; calculating predetermined gait parameters for each stride cycle based on gait events detected from the gait waveform for the predetermined gait cycle; calculating a walking speed of the user using the calculated predetermined walking parameters; calculating a value relating to the distribution of the walking speed measured in accordance with the user's walking for the predetermined walking cycle; Calculating the gait variation during a stance phase of the user using the calculated predetermined gait parameters; calculating a value relating to the distribution of the gait variation measured in accordance with the user's walking for the predetermined walking cycle; inputting the calculated values relating to the distribution of gait variation, the values relating to the distribution of walking speed, and the physical data into the estimation model; An estimation device that estimates the care-related information of the user.

5. The acquisition means acquiring the user's age and sex as the physical data; The storage means storing the estimation model that outputs a fall likelihood as the care-related information in response to input of values relating to the distribution of the walking speed and the gait variation and the age and sex of the user; The estimation means The estimation device according to claim 4 , wherein the fall tendency of the user is estimated by inputting values relating to the walking speed and the distribution of the gait variation, and the age and sex of the user into the estimation model.

6. The estimation means outputting the care-related information to a terminal device having a screen; The estimation device according to claim 1 , wherein the care-related information is displayed on the screen of the terminal device.

7. An estimation device according to any one of claims 1 to 6; a measuring device that is placed on a user's footwear, measures spatial acceleration and spatial angular velocity in accordance with the user's walking, generates sensor data based on the measured spatial acceleration and spatial angular velocity, and outputs the generated sensor data to the estimation device.

8. The computer Acquire sensor data measured in response to the user's walking and physical data of the user; inputting the feature amounts extracted from the sensor data and the physical data of the user into an estimation model that outputs care-related information in response to input of the feature amounts extracted from the sensor data and the physical data, and estimating the care-related information of the user; outputting the estimated care-related information of the user; In the above estimation, generating a walking waveform for a predetermined walking cycle using time series data of the sensor data for the predetermined walking cycle of the user; calculating predetermined gait parameters for each stride cycle based on gait events detected from the gait waveform for the predetermined gait cycle; calculating a walking speed of the user using the calculated predetermined walking parameters; calculating a value relating to the distribution of the walking speed measured in accordance with the user's walking for the predetermined walking cycle; inputting the calculated value relating to the distribution of walking speeds and the physical data into an estimation model that outputs the care-related information in response to input of the value relating to the distribution of walking speeds and the physical data; An estimation method for estimating the care-related information of the user.

9. A process of acquiring sensor data measured in response to a user's walking and physical data of the user; a process of inputting the feature amounts extracted from the sensor data and the physical data of the user into an estimation model that outputs care-related information in response to input of the feature amounts extracted from the sensor data and the physical data, thereby estimating the care-related information of the user; a process of outputting the estimated care-related information of the user; In the estimating process, generating a walking waveform for a predetermined walking cycle using time series data of the sensor data for the predetermined walking cycle of the user; A process of calculating predetermined gait parameters for each step cycle based on gait events detected from the gait waveform for the predetermined gait cycle; calculating a walking speed of the user using the calculated predetermined walking parameters; A process of calculating a numerical value related to the distribution of the walking speed measured in accordance with the user's walking for the predetermined walking cycle; inputting the calculated value relating to the distribution of walking speeds and the physical data into an estimation model that outputs the care-related information in response to input of the value relating to the distribution of walking speeds and the physical data; and a process of estimating the care-related information of the user.

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