Overturn risk evaluation device, overturn risk evaluation system, and overturn risk evaluation method

Abstract

To provide an overturn risk evaluation device, an overturn risk evaluation system, and an overturn risk evaluation method for evaluating an overturn risk of a subject in a daily life.SOLUTION: A portable terminal 1 is an overturn risk evaluation device for evaluating an overturn risk of a subject P. The portable terminal 1 includes: an input section for receiving walking data of the subject P measured by smart shoes 2 and environment information of the subject P; a calculation section for obtaining an overturn risk based on the walking data received in the input section; and an output section for outputting the overturn risk obtained in the calculation section. The calculation section obtains the overturn risk based on the walking data in consideration of evaluation reference of the overturn risk set for each piece of the environment information.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a fall risk assessment device, a fall risk assessment system, and a fall risk assessment method. [Background technology]

[0002] A fall risk assessment device that assesses the risk of falling from walking data that measures the walking movement of a subject is disclosed in JP 2022-079814 A (Patent Document 1). In this device, motion sensors are attached to the subject at least in two locations, the lower back and the thorax and back, and the risk of falling is assessed from walking data of the lower back and thorax that is measured while the subject is walking. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-079814 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the device disclosed in JP 2022-079814 A (Patent Document 1) evaluates the risk of falling based on walking data measured by a subject wearing a motion sensor walking in a laboratory. Therefore, the device only evaluates the risk of falling under limited conditions in a laboratory, and there is a risk that the device cannot evaluate the risk of falling of a subject during everyday walking.

[0005] The present disclosure has been made to solve such problems, and its purpose is to provide a fall risk assessment device, a fall risk assessment system, and a fall risk assessment method that can assess a subject's risk of falling in everyday life. [Means for solving the problem]

[0006] A fall risk assessment device according to an aspect of the present disclosure is a fall risk assessment device that assesses the risk of a fall of a subject, and includes an input unit that receives walking data of the subject measured by a measurement device and environmental information of the subject, a calculation unit that calculates the fall risk based on the walking data received by the input unit, and an output unit that outputs the fall risk calculated by the calculation unit. The calculation unit calculates the fall risk based on the walking data, taking into account a fall risk evaluation criterion set for each piece of environmental information.

[0007] A fall risk assessment system according to one aspect of the present disclosure includes a measuring device that measures walking data, a storage device that stores fall risk assessment criteria set for each piece of environmental information, and the above-mentioned fall risk assessment device.

[0008] A fall risk assessment method according to one aspect of the present disclosure is a fall risk assessment method for assessing a subject's risk of falling, and includes the steps of receiving the subject's gait data measured by a measuring device and the subject's environmental information, determining the fall risk based on the gait data taking into account fall risk assessment criteria set for each piece of environmental information, and outputting the determined fall risk. Effect of the Invention

[0009] According to the present disclosure, the risk of falling is calculated based on walking data while taking into consideration the assessment criteria for the risk of falling set for each piece of environmental information, so that the subject's risk of falling in everyday life can be evaluated. [Brief description of the drawings]

[0010] [Figure 1] 1 is a schematic diagram showing the configuration of a fall risk assessment system including a fall risk assessment device according to an embodiment. [Diagram 2] 1A and 1B are schematic diagrams for explaining smart shoes according to an embodiment. [Diagram 3] 1 is a block diagram showing the configuration of a fall risk assessment device according to an embodiment. [Figure 4]1 is a diagram for explaining the risk of falling evaluated by a fall risk assessment device in accordance with an embodiment. FIG. [Diagram 5] FIG. 1 is a diagram showing walking data obtained by measuring the foot contact angle for each walking location and time period. [Figure 6] FIG. 6 is a histogram showing the walking data shown in FIG. 5. [Figure 7] FIG. 13 is a diagram for explaining an assessment of a fall risk taking into account the walking location. [Figure 8] 11 is a flowchart for explaining the process of fall risk assessment performed by the fall risk assessment device of the embodiment. [Figure 9] FIG. 13 is a diagram showing an example of a map of fall risk. [Figure 10] FIG. 1 is a diagram for explaining the assessment of fall risk using MTC. [Figure 11] FIG. 13 is a diagram for explaining an evaluation of a fall risk using a slip probability. [Figure 12] FIG. 13 is a diagram for explaining the evaluation of a fall risk using the probability of unsteadiness. [Figure 13] FIG. 13 is a diagram for explaining an evaluation of a risk of falling using a probability of disturbance occurrence. [Figure 14] FIG. 13 is a diagram for explaining evaluation of a fall risk using a compensation failure probability. [Figure 15] 13 is a flowchart illustrating a process for calculating a compensation failure probability. [Figure 16] FIG. 13 is a diagram for explaining a process for obtaining prescription information for improving a fall risk. [Figure 17] FIG. 13 is a diagram for explaining a fall risk assessment system that updates assessment criteria. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] In this disclosure, examples of a fall risk assessment device, a fall risk assessment system, and a fall risk assessment method that can assess the risk of a fall of a subject from walking data of the walking movement (which may include running movement) of the subject obtained from a sensor provided on the subject will be described. In the following examples, a case will be described in which a sensor is provided on a shoe to assess the risk of a fall during walking, but the attachment position of the sensor that acquires the walking data of the subject is not limited to the shoe and may be attached to the leg with a supporter or the like. In addition, there is no limitation on the type or number of sensors as long as the walking movement of the subject can be measured continuously on a daily basis. Furthermore, the fall risk to be assessed is not limited to the walking movement, and may be the risk of a fall during running.

[0012] Hereinafter, a fall risk assessment device, a fall risk assessment system, and a fall risk assessment method according to embodiments will be described with reference to the drawings. In the following description, the same components are given the same reference numerals. Their names and functions are also the same. Therefore, detailed descriptions thereof will not be repeated.

[0013] (Embodiment) [Configuration of fall risk assessment system] 1 is a schematic diagram showing the configuration of a fall risk assessment system 100 including a fall risk assessment device according to an embodiment. The fall risk assessment system 100 includes a mobile terminal 1, smart shoes 2, a server 3, and a computer 4. The mobile terminal 1 executes a fall risk assessment program to function as a fall risk assessment device and evaluates the risk of a fall of the subject P from walking data acquired by the smart shoes 2. The smart shoes 2 include a sensor that measures the movement of the subject P when walking or running. The sensor functions as a measuring device that measures the walking data.

[0014] The server 3 functions as a storage device that stores the fall risk evaluation criteria set for each environmental information. Here, the environmental information includes, for example, at least one of the following information: the time of day when walking, the place where walking, the weather when walking, and the temperature and humidity when walking. The fall risk evaluation criteria includes, for example, information on a threshold value for evaluating the fall risk, and the range of walking data for evaluating the fall risk. Note that the server 3 may execute a fall risk evaluation program to cause the server 3 to function as a fall risk evaluation device. When the server 3 functions as a fall risk evaluation device, the fall risk of the subject P is evaluated from the walking data acquired via the mobile terminal 1.

[0015] The computer 4 functions as a terminal for an administrator, and collectively displays the fall risk evaluation results transmitted from each of the mobile terminals 1 of the subjects to the server 3. For example, the computer 4 can display each evaluation result of the subject P in descending order, or display each evaluation result of the subject P as a daily fluctuation graph. The computer 4 can also set or edit the fall risk evaluation criteria for each piece of environmental information stored in the server 3. For example, the computer 4 sets a threshold value for evaluating the fall risk for each walking time period and each walking location. The computer 4 may execute a fall risk evaluation program to cause the computer 4 to function as a fall risk evaluation device. When the computer 4 functions as a fall risk evaluation device, the computer 4 evaluates the fall risk of each of the subjects from the walking data of the subjects stored in the server.

[0016] The mobile terminal 1 may also evaluate the risk of falling of the subject P from the walking data, taking into consideration the attributes of the subject P (for example, gender, age, body shape, etc.). For example, the mobile terminal 1 may receive attributes input by the subject P, and evaluate the risk of falling of the subject P from the walking data based on the evaluation criteria for the risk of falling set for each of the received attributes. It is assumed that the server 3 stores the evaluation criteria for the risk of falling set for each attribute.

[0017] [Smart shoes] First, a description will be given of smart shoes 2 including a sensor that measures movements when walking or running in the fall risk assessment system 100. Fig. 2 is a schematic diagram for explaining smart shoes 2 according to an embodiment. A sensor module 21 (an example of a sensor) is incorporated in the smart shoes 2 shown in Fig. 2, and the sensor module 21 measures walking data of a subject P.

[0018] Although not shown, the sensor module 21 includes an acceleration sensor, an angular velocity sensor, a calculation circuit for calculating walking parameters from the measured values ​​of these sensors, and a communication circuit for wirelessly transmitting walking data including the walking parameters and measured values ​​calculated by the calculation circuit to the mobile terminal 1. The acceleration sensor can measure, for example, the acceleration of three axes, X, Y, and Z, and the angular velocity sensor can measure, for example, the angular velocity of each of the three axes, X, Y, and Z. Therefore, the sensor module 21 can include, as walking data of the subject P, walking parameters such as stride, pitch, walking speed, time taken for one step, stance phase period, swing phase period, toe angle at the time of landing (landing angle), pronation, maximum foot height, and landing impact (force at the time of landing) based on the measured values ​​of the acceleration sensor and the angular velocity sensor. Here, the landing impact is a force applied to the foot at the time of landing, and is an example of a walking parameter evaluated based on the maximum value of the vertical acceleration at the time of landing. In addition, when subject P is running, the sensor module 21 can include running parameters such as stride, pitch, running speed, time taken for one step, stance phase duration, swing phase duration, toe angle at contact with the ground, pronation, maximum foot height, and landing impact as running data based on the measurement values ​​of the acceleration sensor and the angular velocity sensor.

[0019] Although the transmission of walking data from the sensor module 21 to the mobile terminal 1 has been described as being performed by wireless communication, this is not limited thereto. For example, the transmission of walking data from the sensor module 21 to the mobile terminal 1 may be performed by wired communication or by a recording medium (for example, a memory chip, a USB memory, etc.).

[0020] Also, although it has been described that the sensor module 21 calculates the walking parameters from the measurement values ​​of the acceleration sensor and the measurement values ​​of the angular velocity sensor, this is not limited to this. For example, the sensor module 21 may simply transmit the measurement values ​​of the acceleration sensor (three-axis acceleration values) and the measurement values ​​of the angular velocity sensor (three-axis angular velocity values) to the mobile terminal 1, or the mobile terminal 1 may calculate the walking parameters from the measurement values ​​of the acceleration sensor and the measurement values ​​of the angular velocity sensor acquired by the sensor module 21.

[0021] [Configuration of the fall risk assessment device] In the present disclosure, the mobile terminal 1 executes a fall risk assessment program to function as a fall risk assessment device and assesses the fall risk of the subject P from walking data acquired by the smart shoes 2. FIG. 3 is a block diagram showing the configuration of the mobile terminal 1 according to an embodiment. As shown in FIG. 3, the mobile terminal 1 includes a processor 11, a memory 12, a storage 13, an input interface 14, an output interface 15, a communication interface 16, and a media reader 17. These components are connected via a processor bus 18.

[0022] The processor 11 is a computer that reads out programs (e.g., an OS (Operating System) 130, a fall risk assessment program 131, and a compensation failure probability calculation program 132) stored in the storage 13, and deploys and executes the read out programs in the memory 12. The processor 11 is configured, for example, with a CPU (Central Processing Unit), an FPGA (Field Programmable Gate Array), a GPU (Graphics Processing Unit), or an MPU (Multi Processing Unit). The processor 11 may also be configured with a processing circuitry.

[0023] The memory 12 is composed of a volatile memory such as a dynamic random access memory (DRAM) or a static random access memory (SRAM), or a non-volatile memory such as a read only memory (ROM) or a flash memory.

[0024] The storage 13 is configured by a non-volatile storage device such as a hard disk drive (HDD) or a solid state drive (SSD), etc. The storage 13 stores an OS 130, a fall risk assessment program 131, a compensation failure probability calculation program 132, as well as walking data 133, an evaluation criterion 134, etc.

[0025] The fall risk assessment program 131 is a program for assessing the fall risk of the subject P from the walking data. The compensation failure probability calculation program 132 is a program for calculating the probability that a compensation action for avoiding a fall will fail when the subject P falls.

[0026] The input interface 14 (an example of an input unit) accepts input operations from a keyboard, a mouse, a touch device, etc. Furthermore, the input interface 14 accepts position information from a GPS (Global Positioning System) receiver (not shown) provided in the mobile terminal 1. The output interface 15 (an example of an output unit) outputs the fall risk of the subject P evaluated by the mobile terminal 1 to a display, a speaker, etc.

[0027] The communication interface 16 (an example of an input unit or an output unit) receives the input of walking data from the sensor module 21 and transmits and receives data to and from the server 3 by performing wired or wireless communication. The communication interface 16 may output the assessed fall risk of the subject P to the server 3 by communicating with the server 3.

[0028] The media reading device 17 accepts storage media such as a removable disk, a memory chip, a USB memory, etc., and acquires data stored in the removable disk, the memory chip, the USB memory, etc. The media reading device 17 may read out a program stored in a removable disk, or may store the evaluated fall risk of the subject P in a removable disk, etc., and output it.

[0029] [Fall risk assessment method] Next, the process of the fall risk assessment executed by the fall risk assessment device will be described with reference to the drawings. FIG. 4 is a diagram for explaining the fall risk assessed by the fall risk assessment device according to the embodiment. When the fall risk assessment device (particularly the mobile terminal 1) assesses the fall risk, it is possible to, for example, obtain the probability of the subject P falling from walking data and assess the fall risk. One of the reasons why a person falls is tripping, and one of the causes of tripping is that the foot is not raised sufficiently above the floor surface. As a walking parameter for evaluating the cause, for example, there is the toe angle at the time of landing (landing angle). That is, as shown in FIG. 4(a), if the landing angle a is large, the foot is raised sufficiently above the floor surface and the person does not trip, and conversely, if the landing angle a is small, the foot is not raised sufficiently above the floor surface and the person may trip.

[0030] The contact angle a is not constant during walking movements in a day, but has a distribution B as shown in Figure 4(b). Therefore, when a threshold K is set as an evaluation criterion for the risk of falling, the rate of contact angles a below the threshold K (the number of walking data with contact angles a below the threshold K divided by the total number of walking data) is calculated as the probability of falling (probability of tripping), and the risk of falling can be evaluated.

[0031] Specifically, the following description will be based on the results of measuring the ground contact angle a of subject P. FIG. 5 is a diagram showing walking data obtained by measuring the ground contact angle of the foot for each place and time of walking. FIG. 5 shows the results of measuring the ground contact angle a (black square points) measured by a certain subject P walking in a laboratory, the ground contact angle a (white circle points) measured by walking outdoors (morning) on ​​a daily basis, the ground contact angle a (black circle points) measured by walking outdoors (night) on a daily basis, and the ground contact angle a (black triangle points) measured by walking indoors on a daily basis. The number of times that the ground contact angle a was measured by walking in the laboratory was 6, the number of times that the ground contact angle a was measured by walking outdoors (morning) on ​​a daily basis was 37, the number of times that the ground contact angle a was measured by walking outdoors (night) on a daily basis was 20, and the number of times that the ground contact angle a was measured by walking indoors on a daily basis was 21. In addition, the graph in FIG. 5 shows a value three times the standard deviation SD of the ground contact angle a measured by walking in the laboratory as a dashed line.

[0032] FIG. 6 is a histogram of the walking data shown in FIG. 5. As can be seen from FIG. 6, the distribution B1 of the everyday indoor ground contact angle a is to the left of the other distributions, and there is a difference between the distribution B2 of the everyday outdoor ground contact angle a and the distribution B3 of the everyday outdoor ground contact angle a. Therefore, the ground contact angle a is not constant during walking in a day, and the ground contact angle a tends to be smaller indoors than outdoors, and there is a change in the ground contact angle a between outdoors (morning) and outdoors (night). This also shows that the distribution B0 of the ground contact angle a in the laboratory does not reflect the everyday ground contact angle a, and it is difficult to evaluate the everyday risk of falling from the ground contact angle a measured by walking in the laboratory.

[0033] 6, it can be seen that the probability of falling differs depending on the environment, such as the time of day and the place of walking, and the fall risk to be evaluated also changes. Therefore, the fall risk assessment device (mobile terminal 1) determines the fall risk based on the walking data, taking into account the fall risk assessment criteria set for each piece of environmental information. Specifically, if the morning is selected as the time of walking and the outdoors is selected as the place of walking, the fall risk assessment device selects, for example, walking data with a ground contact angle a outdoors from 6:00 to 10:00 as the range of walking data for evaluating the fall risk, determines the fall probability below the threshold K, and evaluates the fall risk.

[0034] Furthermore, the threshold value K for assessing the risk of falling does not have to be the same indoors and outdoors, and the value may be different depending on the location outdoors. FIG. 7 is a diagram for explaining the assessment of the risk of falling taking into account the location of walking. In FIG. 7, the probability of falling is calculated outdoors when the contact angle a is equal to or less than the threshold value K. However, for example, if the outdoor location has a large step, the risk of falling is evaluated by calculating the probability of falling equal to or less than a threshold value K1 that is larger than the threshold value K. Also, indoors, the step that constitutes a risk of falling is small, so the risk of falling is evaluated by calculating the probability of falling equal to or less than a threshold value K2 that is smaller than the threshold value K.

[0035] The threshold K for evaluating the risk of falling may be stored in advance in the server 3 for each walking location. The mobile terminal 1 identifies the walking location based on the acquired position information, reads out the threshold of the risk of falling set for each walking location from the server 3, and evaluates the risk of falling by determining the probability of falling below the threshold. The threshold for evaluating the risk of falling set for each walking location may be set in advance by an administrator, or may be set based on fall information from multiple subjects as described below. For example, if multiple subjects fall at the same location, the evaluation criteria are updated to increase the threshold value for that location.

[0036] Next, the fall risk assessment by the fall risk assessment device will be described with reference to a flowchart. Fig. 8 is a flowchart for explaining the process of the fall risk assessment executed by the fall risk assessment device according to the embodiment. Each step shown in Fig. 8 is realized by the processor 11 of the mobile terminal 1 executing the fall risk assessment program 131.

[0037] First, the mobile terminal 1 receives walking data from the smart shoes 2 (sensor module 21) (step S101). The mobile terminal 1 continuously receives walking data and cumulatively stores the walking data for a certain period (for example, several days or several months) in the storage 13.

[0038] Furthermore, the mobile terminal 1 receives environmental information from the input interface 14 (step S102). Specifically, the subject P inputs environmental information (for example, the time of day when the subject P will be walking, the place where the subject will be walking, etc.) by operating a touch device or the like. The input interface 14 also receives location information of the subject P identified by the GPS receiver of the mobile terminal 1. Furthermore, based on the location information of the subject P, at least one of information on the weather at the time of walking and the temperature and humidity at the time of walking may be received from the Internet via the communication interface 16.

[0039] The mobile terminal 1 sets an evaluation criterion based on the received environmental information (step S103). Since the server 3 stores the evaluation criterion of the risk of falling set for each piece of environmental information, the mobile terminal 1 reads the evaluation criterion from the server 3 based on the received environmental information and sets the evaluation criterion. Specifically, if the received environmental information includes the time period for walking and the place for walking, the mobile terminal 1 reads from the server 3 the range of walking data corresponding to the time period for walking and the threshold value of the risk of falling corresponding to the place for walking. Note that the mobile terminal 1 may store the evaluation criterion of the risk of falling set for each piece of environmental information in the storage 13, or may read and set the evaluation criterion based on the environmental information received from the storage 13 instead of the server 3.

[0040] The mobile terminal 1 obtains a fall risk of the subject P from the walking data based on the set evaluation criteria (step S104). Specifically, the mobile terminal 1 selects walking data within a set range from the walking data stored in the storage 13, obtains a fall probability equal to or less than a set threshold, and evaluates the fall risk.

[0041] The mobile terminal 1 accepts an operation from the subject P as to whether or not to display the fall risk map (step S105). If an operation from the subject P not to display the fall risk map is accepted (NO in step S105), the mobile terminal 1 displays the calculated fall risk (probability of falling) on ​​the display (step S106). If an operation from the subject P to display the fall risk map is accepted (YES in step S105), the mobile terminal 1 displays a fall risk map indicating areas with high fall risk (probability of falling) on ​​the map selected by the subject P on the display (step S107).

[0042] Fig. 9 is a diagram showing an example of a fall risk map. The map shown in Fig. 9 shows an area M1 where the subject P has a high fall risk (probability of falling). The mobile terminal 1 calculates the fall probability below a set threshold for each location on the map, and creates a fall risk map with the area M1 being the area where the probability (e.g., 30%) set as the high fall risk is or exceeds. This makes it easier to understand which locations on the map where the subject P is walking have a high fall risk (probability of falling).

[0043] Returning to Fig. 8, the mobile terminal 1 determines whether or not an operation to change the environmental information has been accepted from the input interface 14 (step S108). If an operation to change the environmental information has been accepted (YES in step S108), the mobile terminal 1 returns the process to step S102 and evaluates the risk of falling based on the newly accepted environmental information. If an operation to change the environmental information has not been accepted (NO in step S108), the mobile terminal 1 ends the process of evaluating the risk of falling.

[0044] [Probability of falling] In the above, it has been explained that the focus is on falls due to tripping, and whether the foot is sufficiently raised above the floor surface is evaluated using the contact angle a to determine the probability of falling. However, whether the foot is sufficiently raised above the floor surface may be evaluated based on the distance between the toe and the floor surface during the swing phase (MTC: Minimum toe clearance). In other words, the probability of tripping only needs to be calculated based on information regarding the distance between the toe and the floor surface during the swing phase. FIG. 10 is a diagram for explaining the evaluation of the risk of falling using MTC. The distance between the toe and the floor surface during the swing phase (MTC) can be defined as shown in FIG. 10(a). The sensor module 21 estimates the trajectory of the toe from the acceleration and angular velocity of the smart shoes 2 to calculate the MTC, and transmits it to the mobile terminal 1 together with the walking data.

[0045] Specifically, the calculation method of MTC will be described with reference to Fig. 10(b). The sensor module 21 calculates the sagittal plane angle b and the foot height h from the measured acceleration and angular velocity, and estimates the trajectory of the toe based on the distance l between the sensor position and the toe, which is set in advance, the sagittal plane angle b and the foot height h. The sensor module 21 calculates the minimum value of the distance between the toe and the floor surface during the swing phase from the estimated trajectory of the toe as the MTC. The mobile terminal 1 stores the MTC included in the walking data received from the sensor module 21 in the storage 13 for a certain period of time. Note that the MTC may be calculated by the mobile terminal 1 instead of the sensor module 21.

[0046] The mobile terminal 1 obtains the distribution of MTC from walking data for a certain period of time, and calculates the ratio of MTC values ​​below a threshold as the stumbling probability. Specifically, the MTC distribution C shown in FIG. 10(c) is a distribution obtained from walking data for one day, and the ratio of MTC values ​​below a threshold L is the stumbling probability. The threshold L is, for example, the average step height. The MTC distribution C can be divided into different distributions according to the time period of walking. Specifically, the indoor MTC distribution C1 is a distribution with a relatively low MTC value. Furthermore, the outdoor MTC distribution C2 in the morning time period (for example, from 6:00 to 10:00) is a distribution with a relatively high MTC value because it is walking before fatigue, but the outdoor MTC distribution C3 in the evening time period (for example, from 18:00 to 22:00) is a distribution with a lower MTC value than the MTC distribution C2 because it is walking after fatigue.

[0047] Furthermore, the reasons why a person falls include not only tripping but also slipping and unsteadiness, and the probability of falling can be calculated by taking into account a plurality of reasons for falling. In this embodiment, the probability of disturbance occurrence is a combination of the probability of tripping, the probability of slipping, and the probability of unsteadiness. Of course, the probability of disturbance occurrence may be a combination of at least two of the probability of tripping, the probability of slipping, and the probability of unsteadiness, or may be a combination of an additional probability.

[0048] First, slip probability will be explained using a diagram. FIG. 11 is a diagram for explaining the evaluation of fall risk using slip probability. Slipping is one of the reasons why people fall, and a factor of slipping is the frictional force between the floor surface and shoes. A gait parameter for evaluating this factor is, for example, the coefficient of friction with the floor surface. It is sufficient that the slip probability is calculated based on information about the friction with the floor surface.

[0049] 11(a), the sensor module 21 divides the force F when the heel touches the ground into a vertical force F1 and a horizontal force F2, and obtains the ratio of the horizontal force F2 to the vertical force F1 as the friction coefficient. The friction coefficient may be obtained by the mobile terminal 1 based on the force F when the heel touches the ground calculated by the sensor module 21, or may be obtained by the sensor module 21. The mobile terminal 1 stores the obtained friction coefficient in the storage 13 for a certain period of time. Note that if the horizontal force F2 becomes larger than the static friction force FA, the subject P will slip and fall.

[0050] 11(b), the mobile terminal 1 obtains a distribution D of friction coefficients stored for a certain period of time, and calculates the percentage of friction coefficients that are equal to or less than a threshold N as the slip probability. In other words, when the average static friction coefficient in the distribution D of friction coefficients is set to a threshold N, the slip probability is the percentage of values ​​that are equal to or less than the threshold N.

[0051] In the above description, the slip probability is evaluated based on the friction coefficient, but this is not limiting and may be based on the contact angle, for example. As the contact angle increases, the proportion of horizontal force in the floor reaction force at the time of contact increases, making it easier to slip, and conversely, as the contact angle decreases, the proportion of horizontal force in the floor reaction force at the time of contact decreases, making it harder to slip. Therefore, the slip probability may be calculated based on the contact angle as the proportion of contact angles that are equal to or greater than a certain threshold value.

[0052] Next, the probability of swaying will be explained using a diagram. FIG. 12 is a diagram for explaining the evaluation of the risk of falling using the probability of swaying. One of the reasons why people fall is swaying, and a factor of the swaying is related to the length of the period during which the body is supported by a single leg. An example of a gait parameter for evaluating the factor is the period of single leg support. The probability of swaying may be calculated based on information on gait fluctuations (for example, stride period, period of double leg support, stride length, step width, left and right sway of the center of gravity, etc.).

[0053] In a person's walking motion, the period from when the heel of a foot touches the ground to when the heel of the same foot touches the ground again (slide period) includes a period in which the body is supported by either the left or right foot (single leg support period) and a period in which the body is supported by both the left and right feet (double leg support period). As shown in FIG. 12(a), the sensor module 21 calculates the single leg support period from the acceleration and angular velocity, and calculates the coefficient of variation of the single leg support period for about 100 steps. The single leg support period may be determined by the mobile terminal 1 from the acceleration and angular velocity measured by the sensor module 21, or may be determined by the sensor module 21. The mobile terminal 1 stores the determined single leg support period in the storage 13 for a certain period.

[0054] 12(b), the mobile terminal 1 obtains a distribution E of the coefficient of variation of the single leg support period stored for a certain period, and calculates as the wobble probability the proportion of the coefficient of variation of the single leg support period that is equal to or less than a threshold Q. In other words, when the coefficient of variation of the average single leg support period in the distribution E of the coefficient of variation of the single leg support period is set to a threshold Q, the wobble probability is the proportion that is equal to or less than the threshold Q.

[0055] Next, calculation of the disturbance occurrence probability, which is a combination of the tripping probability, the slipping probability, and the wobbling probability, will be described. FIG. 13 is a diagram for explaining the evaluation of the risk of falling using the disturbance occurrence probability. The tripping probability is the proportion of cases that are equal to or less than the threshold L when the average step height is set to a threshold L in the MTC distribution C. The slipping probability is the proportion of cases that are equal to or less than the threshold N when the average static friction coefficient is set to a threshold N in the friction coefficient distribution D. The wobbling probability is the proportion of cases that are equal to or less than the threshold Q when the coefficient of variation of the average single support period is set to a threshold Q in the single support period distribution E.

[0056] The reasons why people fall occur at the rates shown in pie chart G in FIG. 13. The rates of the reasons for falls shown in pie chart G are described in detail in, for example, "Berg WP et al., 1997, Circumstances and consequences of falls in independent community-dwelling older adults" (doi: 10.1093 / ageing / 26.4.261.). Therefore, it is preferable that the mobile terminal 1 calculates the disturbance occurrence probability by taking a weighted average considering the rates of tripping, slipping, and swaying, rather than calculating the disturbance occurrence probability as a simple sum of the tripping probability, slipping probability, and swaying probability. Specifically, the mobile terminal 1 multiplies the tripping probability by a coefficient of (34 / 68), the slipping probability by a coefficient of (25 / 68), and the swaying probability by a coefficient of (9 / 68), and adds them together to obtain the disturbance occurrence probability.

[0057] The disturbance occurrence probability may be calculated taking into consideration environmental information, and the coefficients by which the stumbling probability, the slipping probability, and the wobbling probability are multiplied may be changed depending on the time of day when the person walks, the place where the person walks, the weather when the person walks, etc. In this way, the mobile terminal 1 can obtain the disturbance occurrence probability taking into consideration environmental information by multiplying each of the stumbling probability, the slipping probability, and the wobbling probability by a coefficient changed based on the time of day when the person walks, the place where the person walks, the weather when the person walks, etc., and calculating a weighted average.

[0058] [Probability of compensation failure] To assess the risk of falling with even greater accuracy, the person's compensatory movement is also taken into consideration. It is known that when a person falls, they will take a compensatory movement (for example, taking a step in the direction of the fall) to avoid falling. Therefore, by considering not only the disturbance occurrence probability of tripping, slipping, and wobbling, but also the probability that the compensatory movement to avoid falling will fail (compensation failure probability), the risk of falling can be assessed with even greater accuracy. In other words, it is preferable for the mobile terminal 1 to obtain the probability of falling by multiplying the disturbance occurrence probability and the compensation failure probability.

[0059] Specifically, a method for calculating the compensation failure probability will be described. FIG. 14 is a diagram for explaining the evaluation of the risk of falling using the compensation failure probability. In order to calculate the compensation failure probability, an experiment was conducted on multiple subjects, and from the results, variables (explanatory variables) were obtained that explain the case where subject P performs a compensatory action but falls when a disturbance (stumbling, slipping) occurs (compensation failure) and the case where subject P performs a compensatory action and avoids falling (compensation success). The explanatory variables specifically obtained were the minimum vibration intensity that subject P could feel on the sole of the foot and the reaction time after feeling the vibration, and as shown in FIG. 14, it was possible to separate the distribution of compensation failure from the distribution of compensation success. Furthermore, it was possible to obtain the formula f by a logistic regression model using the minimum vibration intensity and reaction time of each subject P as explanatory variables, and setting the case where compensation fails when a disturbance occurs as "1" and the case where compensation is successful as "0".

[0060] The compensation failure probability can be expressed as a function of the minimum vibration intensity and reaction time of subject P as shown in formula f in FIG. 14. Note that β 0 , β 1 , β 2 is a constant. Therefore, by using the formula f obtained from the experiment, it became possible to calculate the compensation failure probability by simply measuring the minimum vibration strength and reaction time of subject P.

[0061] The process of calculating the compensation failure probability will be described with reference to a flowchart. Fig. 15 is a flowchart for explaining the process of calculating the compensation failure probability. Each step shown in Fig. 15 is realized by the processor 11 of the mobile terminal 1 executing the compensation failure probability calculation program 132.

[0062] First, on the premise that the minimum vibration intensity and reaction time of the subject P can be simply measured, the smart shoes 2 can apply vibrations to the soles of the feet of the subject P using the sensor module 21 or another module, and can be controlled by the mobile terminal 1. The minimum vibration intensity and reaction time of the subject P may be simply measured using a dedicated device instead of the smart shoes 2. Note that the following process will be described assuming that the vibration device is built into the sensor module 21.

[0063] When the portable terminal 1 executes the compensation failure probability calculation program 132, it vibrates the vibration device of the sensor module 21 in the initial setting (step S201). The initial setting is set to a vibration intensity that allows all subjects P to feel the vibration of the vibration device. The portable terminal 1 determines whether or not it has accepted the operation of the vibration detection button that the subject P presses when he or she feels the vibration of the vibration device on the sole of his or her foot (step S202).

[0064] If the operation of the vibration detection button is not accepted (NO in step S202), the mobile terminal 1 returns the process to step S201 and continues vibrating the vibration device with the initial setting. If the operation of the vibration detection button is accepted (YES in step S202), the mobile terminal 1 measures the time from when the vibration device starts vibrating to when the operation of the vibration detection button is accepted as the reaction time (step S203).

[0065] Next, the mobile terminal 1 vibrates the vibration device at a vibration intensity weakened by a predetermined value from the vibration intensity before accepting the operation of the vibration detection button (step S204). If the vibration device can change the vibration intensity in 10 steps, for example, the mobile terminal 1 weakens the vibration intensity by 1 or 2 steps from the predetermined value and vibrates the vibration device. Then, the mobile terminal 1 determines whether or not the operation of the vibration detection button, which is pressed when the subject P feels the vibration of the vibration device on the sole of the foot, has been accepted (step S205).

[0066] If the operation of the vibration detection button is accepted (YES in step S205), the mobile terminal 1 returns the process to step S203 and measures the time from when the vibration device starts to vibrate until the operation of the vibration detection button is accepted as the reaction time. If the operation of the vibration detection button is not accepted (NO in step S205), the mobile terminal 1 determines that the subject P cannot detect the vibration of the vibration device with the soles of his / her feet for a certain period of time, and determines the minimum vibration intensity and reaction time of the subject P (step S206). The minimum vibration intensity of the subject P is determined to be the vibration intensity that the subject P was able to detect before the judgment in step S205 is NO. The reaction time may be the reaction time when the subject P detects the minimum vibration intensity, or the shortest time among the measured reaction times.

[0067] The mobile terminal 1 substitutes the minimum vibration intensity and reaction time values ​​of the subject P determined in step S206 into the formula f shown in Fig. 14 to obtain the compensation failure probability (step S207). The compensation failure probability is the probability that a person fails to make a compensatory action (for example, taking a step in the direction of the fall) to avoid falling when falling over. Therefore, the probability of falling is considered to be the proportion of the compensation action that fails among the possibilities of falling due to the disturbance occurrence probability, so the mobile terminal 1 obtains the probability of falling by multiplying the compensation failure probability by the disturbance occurrence probability.

[0068] The probability of falling will be described in more detail. As explained above, the probability of falling is composed of the probability of disturbance occurrence and the probability of compensation failure, and the probability of disturbance occurrence is composed of the probability of tripping and the probability of slipping. Therefore, the indices (parameters) used to calculate the probability of falling include the MTC, friction coefficient, minimum vibration strength, and reaction time, and prescriptions for improving each of these indices can be set in advance. Figure 16 is a diagram for explaining the process of obtaining prescription information for improving the risk of falling.

[0069] In FIG. 16, the probability of falling R is composed of the probability of disturbance occurrence Ra and the probability of compensation failure Rb, and the index used to calculate the probability of disturbance occurrence Ra is the MTC (S1), and the index used to calculate the coefficient of friction S2 and the probability of compensation failure Rb is the minimum vibration strength S3 and reaction time S4. In addition, the prescription information indicates that in order to improve the MTC (S1), muscle training around the ankle and hip joints is necessary, and in order to improve the coefficient of friction S2, it is necessary to learn a walking pattern that is not slippery and to wear shoes with a high coefficient of friction. In addition, the prescription information indicates that in order to improve the minimum vibration strength S3, it is necessary to sharpen the senses through foot care, and in order to improve the reaction time S4, it is necessary to train reflexes.

[0070] 16 is stored in the storage 13 of the mobile terminal 1 or the server 3. The mobile terminal 1 can find the index that has the greatest effect on the calculated fall probability R, and output the prescription information corresponding to the index on the display to encourage the subject P to make improvements.

[0071] [Update of evaluation criteria based on environmental information] When each of a plurality of subjects wears the smart shoes 2 and walks while holding the mobile terminal 1, walking data can be collected by the server 3 in various environments. Using this collected walking data, the fall risk assessment system may update the assessment criteria for the fall risk based on the environmental information. Fig. 17 is a diagram for explaining the fall risk assessment system 100 that updates the assessment criteria.

[0072] In the fall risk assessment system 100, as shown in FIG. 17, the server 3 can communicate with each of the mobile terminals 1a of subject Pa who is walking while wearing smart shoes 2a, the mobile terminal 1b of subject Pb who is walking while wearing smart shoes 2b, and the mobile terminal 1 of subject Pc who is walking while wearing smart shoes 2c.

[0073] The falls of subjects Pa-Pc can be detected from, for example, abnormal values ​​of acceleration obtained from smart shoes 2a-2c (sensor modules). Specifically, when subject Pb trips and falls at a certain location, smart shoes 2b detects abnormal value W1 where the acceleration value suddenly drops. When smart shoes 2b detect abnormal value W1, mobile terminal 1b detects that subject Pb tripped and fell, and transmits the walking data to server 3 together with environmental information such as location information at the time of detection, time, and weather.

[0074] In addition, if the subject Pc slips and falls at a certain location, the smart shoes 2c detect an abnormal value W2 where the acceleration value exceeds a threshold and rises sharply. When the smart shoes 2c detect the abnormal value W2, the mobile terminal 1c detects that the subject Pc has slipped and fallen, and transmits the walking data to the server 3 together with environmental information such as the location information, time, and weather at the time of detection.

[0075] In this way, the fall risk assessment system 100 can collect walking data in various environments using the server 3, and can use the collected walking data to update the assessment criteria for the fall risk based on environmental information. For example, the server 3 updates the MTC threshold L for calculating the stumbling probability at the location where the subject Pb stumbled and fell to the MTC at the time the subject Pb stumbled. The MTC threshold L may be changed depending on environmental information such as the time of day and weather when the subject Pb stumbled.

[0076] Furthermore, the server 3 updates the friction coefficient threshold N for calculating the slip probability at the location where the subject Pc slipped and fell to the friction coefficient at the time when the subject Pc slipped. The friction coefficient threshold N may be changed depending on environmental information such as the time of day and weather when the subject Pc slipped.

[0077] The server 3 stores the fall risk assessment criteria set for each piece of environmental information, and updates the fall risk assessment criteria based on information on the subject Pb tripping and falling, and information on the subject Pc slipping and falling. The computer 4 can set or edit the fall risk assessment criteria for each piece of environmental information stored in the server 3. Therefore, the computer 4 can plot on a map locations with high fall risk where the thresholds L and N are equal to or greater than a certain value. The computer 4 can also plot on a map locations with high fall risk by environment, such as by time of day or weather.

[0078] [Variations] (1) In the above embodiment, the fall risk assessment device calculates the fall risk based on the walking data, taking into account the fall risk assessment criteria set for each piece of environmental information. However, the present invention is not limited to this, and the fall risk assessment device may set a fall risk assessment criterion for each emotion of the subject, and calculate the fall risk based on the walking data, taking into account the set evaluation criterion. For example, the subject's current emotion is selected on the mobile terminal, and the fall risk is calculated based on the evaluation criterion corresponding to the emotion selected for the walking data at that time. In addition, the fall risk assessment device may evaluate the fall risk by calculating the proportion of walking data when the subject's emotion is depressed that is equal to or lower than the threshold for when the emotion is depressed.

[0079] (2) In the above embodiment, the subject's movements were measured using smart shoes 2 incorporating a sensor module 21 (including a three-axis acceleration sensor and a three-axis angular velocity sensor) in the shoes, but the sensor module 21 may be attached at any position where walking data of the subject can be acquired. In the above embodiment, a total of two sensor modules 21 are used by providing one type of sensor in each of the left and right shoes, but one sensor module 21 may be provided in either one of the left and right shoes to use one sensor module 21. Also, a plurality of different types of sensors may be used. Furthermore, even if a sensor module 21 is provided in each of the left and right shoes, the fall risk assessment device 10 may be configured to receive walking data from one of the sensor modules 21.

[0080] (3) In the above embodiment, it has been explained that in order to simply measure the minimum vibration intensity and reaction time of the subject P, it is necessary to incorporate a vibration device capable of applying vibration to the sole of the subject P using the sensor module 21 or another module into the smart shoes 2. However, the reaction time of the subject P may be measured by another method. Specifically, there is a method of measuring the reaction time of the subject P using a reaction bar. First, the subject P sits on a chair and extends his / her dominant hand from the edge of the desk and slightly opens his / her hand. The stick is set so that the bottom end of the stick is located between the thumb and index finger and at the top end of the thumb. The person measuring the stick says, "I'm going to drop the stick now. I don't know when it will drop, so if you think it has dropped, please grab it as quickly as possible. Here I go." The stick is dropped at an appropriate timing. The scale on the top end of the thumb is read when the stick is dropped and gripped. Note that care should be taken not to take the arm off the platform when gripping it. The same measurement is performed twice, and each is recorded to determine the reaction time of the subject P.

[0081] There is also another method of measuring subject P's reaction time, using a whole body reaction measuring device. Subject P stands on a mat (jumping platform) that can detect pressure, with knees slightly bent. The measurer says, "When the light in front of you lights up, please jump lightly." The light comes on at an appropriate time. After practicing once or twice, the measurement is carried out five times, and the average of the five measurements is recorded as the result to determine subject P's reaction time.

[0082] [Aspects] (1) A fall risk assessment device according to the present disclosure includes: A fall risk assessment device for assessing a fall risk of a subject, comprising: an input unit for receiving walking data of a subject and environmental information of the subject; A calculation unit that calculates a fall risk based on walking data received by an input unit; an output unit that outputs the fall risk calculated by the calculation unit, The calculation unit is The risk of falling is calculated based on walking data, taking into account the fall risk assessment criteria set for each piece of environmental information.

[0083] As a result, the fall risk assessment device according to the present disclosure determines the fall risk based on walking data, taking into account the fall risk assessment criteria set for each piece of environmental information, and is therefore able to assess the subject's risk of falling in everyday life.

[0084] (2) A fall risk assessment device according to (1), The environmental information includes at least one of the following information: the time of day when the person walks, the place where the person walks, the weather when the person walks, and the temperature and humidity when the person walks.

[0085] (3) A fall risk assessment device according to (1) or (2), The evaluation criteria include information on thresholds for evaluating the risk of falling and ranges of gait data for evaluating the risk of falling.

[0086] (4) A fall risk assessment device according to any one of (1) to (3), The calculation unit calculates at least one probability of the subject tripping, slipping, or unsteadiness based on the walking data, and determines the risk of falling based on the calculated probability.

[0087] (5) A fall risk assessment device according to (4), The calculation unit calculates the probability of tripping, slipping, and wobbling, calculates the probability of disturbance occurrence by multiplying each of the probability of tripping, slipping, and wobbling by a weighting factor, and determines the risk of falling based on the probability of disturbance occurrence.

[0088] (6) A fall risk assessment device according to (4) or (5), The probability of tripping is calculated based on information regarding the distance between the toes and the floor surface during the swing phase, the probability of slipping is calculated based on information regarding friction with the floor surface, and the probability of wobbling is calculated based on information regarding fluctuations in gait.

[0089] (7) A fall risk assessment device according to any one of (1) to (6), The calculation unit calculates a compensation failure probability at which a compensatory action taken by the subject to avoid a fall will fail, and determines the risk of falling by taking the compensation failure probability into consideration.

[0090] (8) A fall risk assessment device according to (7), The probability of compensation failure is calculated based on the minimum vibration intensity that the subject can feel on the sole of the foot and the subject's reaction time.

[0091] (9) A fall risk assessment device according to any one of (1) to (8), The calculation unit calculates prescription information for improving the risk of falling based on each calculated probability, The output unit outputs the prescription information.

[0092] (10) A fall risk assessment system according to the present disclosure includes a measuring device that measures walking data, a storage device that stores fall risk assessment criteria set for each piece of environmental information, and a fall risk assessment device described in any one of (1) to (9).

[0093] (11) A fall risk assessment system according to (10), The measurement device includes at least one sensor that measures acceleration in three axes and angular velocity in three axes.

[0094] (12) A fall risk assessment system according to (11), The sensors are placed in shoes worn by the subject.

[0095] (13) A fall risk assessment system according to any one of (10) to (12), The storage device updates the assessment criteria based on the walking data and environmental information received by the fall risk assessment device.

[0096] (14) The fall risk assessment method according to the present disclosure includes: A fall risk assessment method for assessing a fall risk of a subject, comprising: receiving walking data of the subject measured by a measuring device and environmental information of the subject; determining a fall risk based on the walking data in consideration of a fall risk evaluation criterion set for each piece of environmental information; and outputting the determined risk of falling.

[0097] The embodiments disclosed herein should be considered to be illustrative and not restrictive in all respects. The scope of the present invention is defined by the claims, not the above description, and is intended to include all modifications within the scope and meaning equivalent to the claims. [Explanation of symbols]

[0098] 1 Calculation device, 2 Smart shoes, 3 Server, 4 Computer, 11 Processor, 12 Memory, 13 Storage, 14 Input interface, 15 Output interface, 16 Communication interface, 17 Media reading device, 18 Processor bus, 21 Sensor module, 100 Fall risk assessment system, 131 Fall risk assessment program, 132 Compensation failure probability calculation program, 133 Gait data, 134 Evaluation criteria.

Claims

1. A fall risk assessment device for assessing a fall risk of a subject, comprising: An input unit that receives walking data of the subject and environmental information of the subject; a calculation unit for calculating the risk of falling based on the walking data received by the input unit; an output unit that outputs the risk of falling calculated by the calculation unit, The calculation unit is A fall risk assessment device that determines the fall risk based on the walking data, taking into consideration the fall risk assessment criteria that are set for each of the environmental information.

2. The fall risk assessment device according to claim 1 , wherein the environmental information includes at least one of information on the time of day when walking, the place where walking is performed, the weather when walking, and the temperature and humidity when walking.

3. The fall risk assessment device according to claim 1 , wherein the assessment criteria include information on a threshold for assessing the fall risk and a range of the gait data for assessing the fall risk.

4. The fall risk assessment device according to claim 1 , wherein the calculation unit calculates at least one probability of the subject tripping, slipping, or wobbling based on the walking data, and determines the fall risk based on the probability.

5. 5. The fall risk assessment device of claim 4, wherein the calculation unit calculates the tripping probability, the slipping probability, and the wobbling probability, calculates a disturbance occurrence probability by multiplying each of the tripping probability, the slipping probability, and the wobbling probability by a weighting, and determines the fall risk based on the disturbance occurrence probability.

6. 5. The fall risk assessment device of claim 4, wherein the tripping probability is calculated based on information regarding the distance between the toe and the floor surface during the swing phase, the slipping probability is calculated based on information regarding friction with the floor surface, and the wobbling probability is calculated based on information regarding fluctuations in gait.

7. The fall risk assessment device according to claim 4 , wherein the calculation unit calculates a compensation failure probability that a compensation action taken by the subject to avoid a fall will fail, and determines the fall risk by taking the compensation failure probability into consideration.

8. The fall risk assessment device according to claim 7 , wherein the compensation failure probability is calculated based on a minimum vibration intensity that the subject can feel on the soles of the feet and a reaction time of the subject.

9. The calculation unit calculates prescription information for improving the risk of falling based on each calculated probability, The fall risk assessment device according to claim 7 , wherein the output unit outputs the prescription information.

10. A measuring device for measuring the walking data; A storage device that stores the evaluation criteria for the fall risk that are set for each of the environmental information; A fall risk assessment system comprising the fall risk assessment device according to any one of claims 1 to 9.

11. The fall risk assessment system of claim 10 , wherein the measurement device includes at least one sensor that measures three-axis acceleration and three-axis angular velocity.

12. The fall risk assessment system according to claim 11 , wherein the sensor is provided in a shoe worn by the subject.

13. The fall risk assessment system according to claim 10 , wherein the storage device updates the assessment criteria based on the walking data and the environmental information received by the fall risk assessment device.

14. A fall risk assessment method for assessing a fall risk of a subject, comprising: receiving walking data of the subject measured by a measurement device and environmental information of the subject; determining the risk of falling based on the walking data in consideration of an evaluation criterion for the risk of falling set for each of the environmental information; and outputting the determined risk of falling.

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