Cognitive function estimation device, cognitive function estimation method, cognitive function estimation program, and cognitive function estimation system

Abstract

To provide a cognitive function estimation device capable of estimating a cognitive function of a subject easily and accurately.SOLUTION: A cognitive function estimation device includes a gait information acquisition part, an estimation parameter calculation part, and a determination part. The gait information acquisition part is configured to acquire gait information acquired by an inertial sensor attached to the foot of a subject, the gait information including at least one of information on acceleration of the foot of the walking subject and information on an angular speed. The estimation parameter calculation part is configured to calculate cognitive function estimation parameters on the basis of the gait information. The determination part is configured to determine presence or absence of mild cognitive impairment (MCI) of the subject on the basis of the cognitive function estimation parameters.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a cognitive function estimation device, a cognitive function estimation method, a cognitive function estimation program, and a cognitive function estimation system. [Background technology]

[0002] A method for easily determining whether or not a subject has dementia has been studied. Patent Document 1 discloses a dementia determination program that uses walking data based on waist acceleration information to determine whether or not a subject has dementia. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-87503 Summary of the Invention [Problem to be solved by the invention]

[0004] Mild cognitive impairment (MCI) is a condition in which cognitive functions such as memory and attention are impaired, but the condition has not yet reached dementia, and is in a state between normal and dementia. It is known that if appropriate measures are taken at the MCI stage, the recovery rate is higher than if treatment is administered after dementia has developed. Therefore, it is important to detect symptoms at the MCI stage. However, it is difficult to undergo testing at a hospital at the MCI stage due to the large burden on the person being tested. In addition, conventional simple methods for estimating dementia do not provide high accuracy.

[0005] The present invention aims to provide a cognitive function estimation device, a cognitive function estimation method, a cognitive function estimation program, and a cognitive function estimation system that can estimate a subject's cognitive function simply and accurately. [Means for solving the problem]

[0006] The cognitive function estimation device according to the present invention includes a walking information acquisition unit, an estimation parameter calculation unit, and a determination unit, the walking information acquisition unit is configured to acquire walking information obtained by an inertial sensor attached to a foot of the subject, the walking information includes at least one of information about the acceleration and information about the angular velocity of the subject's feet while walking; The estimated parameter calculation unit is configured to calculate a cognitive function estimated parameter based on the walking information, The determination unit is configured to determine the presence or absence of mild cognitive impairment (MCI) of the subject based on the cognitive function estimation parameter.

[0007] The cognitive function estimation method according to the present invention includes a walking information acquisition step, an estimation parameter calculation step, and a determination step, The step of acquiring walking information includes acquiring walking information measured by an inertial sensor attached to a foot of the subject; the walking information includes at least one of information about the acceleration and information about the angular velocity of the subject's feet while walking; the estimation parameter calculation step calculates a cognitive function estimation parameter based on the walking information; The determining step determines whether or not the subject has mild cognitive impairment (MCI) based on the cognitive function estimation parameters.

[0008] A cognitive function estimation program according to the present invention is a cognitive function estimation program for causing a computer to execute a walking information acquisition step, an estimation parameter calculation step, and a judgment step, The step of acquiring walking information includes acquiring walking information obtained by an inertial sensor attached to a foot of the subject; the walking information includes at least one of information about the acceleration and information about the angular velocity of the subject's feet while walking; the estimation parameter calculation step calculates a cognitive function estimation parameter based on the walking information; The determination step determines whether or not the subject has mild cognitive impairment (MCI) based on the cognitive function estimation parameters.

[0009] The cognitive function estimation system according to the present invention is a cognitive function estimation system including the cognitive function estimation apparatus according to the present invention and a measurement device, The measuring device includes an inertial sensor and a communication unit. The communication unit is capable of communicating with the cognitive function estimation device and is capable of transmitting the walking information to the cognitive function estimation device. Effect of the Invention

[0010] According to the present invention, it is possible to provide a cognitive function estimation device, a cognitive function estimation method, a cognitive function estimation program, and a cognitive function estimation system that can easily and accurately estimate the cognitive function of a subject. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram showing the configuration of a cognitive function estimation system according to one embodiment of the present invention. [Diagram 2] FIG. 2 is a block diagram showing the configuration of a cognitive function estimation device according to one embodiment of the present invention. [Diagram 3] FIG. 3 is a block diagram showing an example of the configuration of the measuring device. [Figure 4] FIG. 4 shows a walking subject fitted with a measurement device. [Diagram 5] FIG. 5 is a partially enlarged view of FIG. 4, showing an example of a mode of attachment of the measuring device. [Figure 6] FIG. 6 is a flowchart showing an example of processing in the cognitive function estimation device according to one embodiment of the present invention. [Figure 7] FIG. 7 is a block diagram showing an example of the configuration of a cognitive function estimation device according to another embodiment of the present invention. [Figure 8] FIG. 8 is a flowchart showing an example of processing in a cognitive function estimation device according to another embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, embodiments of the present invention will be described with reference to the drawings, but the present invention is not limited to these embodiments.

[0013] [Embodiment 1] FIG. 1 is a block diagram showing a cognitive function estimation system 100 according to an embodiment of the present invention. The cognitive function estimation system 100 according to an embodiment of the present invention includes a cognitive function estimation device 1 (hereinafter, referred to as the estimation device 1) and a measuring device 2. The estimation device 1 includes a walking information acquisition unit 11, an estimated parameter calculation unit 12, a determination unit 13, and an output unit 14. The measuring device 2 includes an inertial sensor 21 and a communication unit 22. As shown in FIG. 1, the communication unit 22 of the measuring device 2 can communicate with the estimation device 1 via, for example, a communication line network 3. However, the communication between the estimation device 1 and the measuring device 2 is not limited to this embodiment, and may be, for example, a wired connection using a cable or a wireless connection by wireless communication. As a result, the communication unit 22 can transmit walking information to the estimation device 1, which will be described later. The cognitive function estimation system 100 may include, for example, a plurality of measuring devices 2. Examples of the wireless connection include Wi-Fi (registered trademark), Bluetooth (registered trademark), and LPWA (Low Power Wide Area). As the wireless communication, either a form in which each device directly communicates (Ad Hoc communication) or an indirect communication via an access point can be used. The estimation device 1 of this embodiment may be incorporated into a server as a system. Also, the estimation device 1 of this embodiment may be a personal computer (PC) or a tablet terminal in which the program of the present invention is installed. Although not shown, the estimation device 1 can also be connected to an external terminal of a system administrator via a communication line network 3, for example, and the system administrator may manage the estimation device 1 from the external terminal.

[0014] The communication network 3 is not particularly limited and may be a known network, for example, a wired or wireless network. The communication network 3 may be, for example, an Internet line, a World Wide Web (WWW), a telephone line, a Local Area Network (LAN), Wi-Fi, or a Low Power Wide Area (LPWA).

[0015] 2 is a block diagram of a hardware configuration of the estimation device 1 according to this embodiment. The estimation device 1 includes, for example, a CPU (Central Processing Unit) 101, a memory 102, a bus 103, a storage device 104, an input device 106, a display 107, and a communication device 108. Each unit of the estimation device 1 is connected to each other via the bus 103 by its respective interface (I / F).

[0016] The CPU 101 cooperates with other components, for example, as a controller (system controller, I / O controller, etc.), and is responsible for overall control of the estimation device 1. In the estimation device 1, the CPU 101 executes, for example, a program 105 of the present invention and other programs, and also reads and writes various information. Specifically, for example, the CPU 101 functions as a walking information acquisition unit 11, an estimation parameter calculation unit 12, a determination unit 13, and an output unit 14. The estimation device 1 includes a CPU as a calculation device, but may include other calculation devices such as a GPU (Graphics Processing Unit) or an APU (Accelerated Processing Unit) instead of the CPU, or may include a combination of the CPU and these.

[0017] The memory 102 includes, for example, a main memory. The main memory is also called a primary storage device. When the CPU 101 performs processing, the memory 102 reads various operation programs, such as a program 105 of the present invention, stored in, for example, a storage device 104 (auxiliary storage device) described below. The CPU 101 then reads data from the memory 102, decodes it, and executes the program. The main memory is, for example, a RAM (random access memory). The memory 102 further includes, for example, a ROM (read only memory).

[0018] The bus 103 can also be connected to, for example, an external device. Examples of the external device include an external storage device (external database, etc.), a printer, etc. The estimation device 1 can be connected to a communication network 3 by, for example, a communication device 108 connected to the bus 103, and can also be connected to an external device via the communication network 3. The estimation device 1 can also be connected to a measurement device 2 via the communication device 108 and the communication network 3.

[0019] The storage device 104 is also called an auxiliary storage device, as opposed to a main memory (primary storage device). As described above, the storage device 104 stores an operation program including the program 105 of the present invention. The storage device 104 includes, for example, a storage medium and a drive for reading and writing data from and to the storage medium. The storage medium is not particularly limited, and may be, for example, a built-in or external type, and may be a hard disk (HD), SSD, FD (floppy (registered trademark) disk), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, memory card, or the like. The drive is not particularly limited. The storage device 104 may be, for example, a hard disk drive (HDD) in which the storage medium and the drive are integrated. When the estimation device 1 includes, for example, a storage unit, the storage device 104 functions as the storage unit.

[0020] The estimation device 1 further includes, for example, an input device 106 and a display 107. The input device 106 may be, for example, a pointing device such as a touch panel, a track pad, or a mouse; a keyboard; an imaging means such as a camera or a scanner; a card reader such as an IC card reader or a magnetic card reader; or a voice input means such as a microphone. The display 107 may be, for example, a display device such as an LED display or a liquid crystal display. In the first embodiment, the input device 106 and the display 107 are configured separately, but the input device 106 and the display 107 may be configured as an integrated device such as a touch panel display.

[0021] In the estimation device 1, the memory 102 and the storage device 104 can also store access information and log information from users, as well as information acquired from an external database (not shown).

[0022] Fig. 3 is a block diagram showing an example of a hardware configuration of the measuring device 2. As shown in Fig. 3, the measuring device 2 includes, for example, a CPU 201, a memory 202, a bus 203, a storage device 204, an input device 206, a communication device (communication unit 22), a display 207, and an inertial sensor 21. Each unit of the measuring device 2 is connected via the bus 203 by its respective interface (I / F). The description of each component of the measuring device 2 other than the inertial sensor 21 can be made by referring to the description of each component of the estimation device 1.

[0023] The inertial sensor 21 is a sensor capable of measuring acceleration, angular velocity, or both. The inertial sensor 21 is, for example, a six-axis sensor capable of measuring three-axis acceleration and three-axis angular velocity. The measuring device 2 is attached to the subject's foot and configured to enable the inertial sensor 21 to measure the acceleration, angular velocity, or both of the subject's foot.

[0024] Next, an example of a process for estimating cognitive function by the cognitive function estimation system according to this embodiment will be described.

[0025] FIG. 4 is a diagram showing a subject S whose cognitive function is to be estimated by this embodiment. First, the measuring device 2 is attached to the foot of the subject S. The measuring device 2 may be directly attached to the foot of the subject S, or may be indirectly attached by sandwiching a shoe, a sock, or tights. The specific location where the measuring device 2 is attached is not particularly limited, and the measuring device 2 may be attached to the instep, toe, heel, side, sole, etc. of the subject S. From the viewpoint of reducing the disturbance of the measurement data due to the impact during walking, etc., it is preferable to attach the measuring device 2 to the instep or side. FIG. 5 is an enlarged view of the area surrounded by the dashed line V in FIG. 4, and is a diagram showing an example of the manner in which the measuring device 2 is attached. In the manner shown in FIG. 5, the measuring device 2 is attached to a position corresponding to the instep of the shoe laces 30 of the subject S.

[0026] It is preferable that the measurement device 2 is attached to the subject's waist in addition to the subject's feet. By using gait information obtained from the movement of the subject's waist in addition, the accuracy of the estimation of cognitive function is further improved.

[0027] The measuring device 2 measures walking information of the subject. The "walking information" here is information that represents the walking state of the subject, and specifically, is information about the acceleration or angular velocity of a part of the subject's body when walking, measured by the inertial sensor 21. In this embodiment, the walking information includes at least one of information about the acceleration and information about the angular velocity of the subject's feet while walking. The walking information may further include information about the acceleration or angular velocity of the subject's waist.

[0028] The measuring device 2 transmits the measured walking information to the estimation device 1 via the communication network 3 by the communication unit 22. At this time, the measuring device 2 may transmit data of the acceleration or angular velocity of the subject's feet measured by the inertial sensor 21 directly to the estimation device 1 as walking information, or may extract data required for calculating cognitive function estimation parameters described below or calculate the data by calculation and transmit the data as walking information to the estimation device 1. Alternatively, some or all of the cognitive function estimation parameters described below may be calculated in the measuring device 2, and the calculated cognitive function estimation parameters may be transmitted to the estimation device 1 as walking information.

[0029] Next, processing by the estimation device 1 is started. FIG. 6 is a flowchart showing an example of processing in the estimation device 1. First, the walking information acquisition unit 11 of the estimation device 1 acquires walking information of the subject (S1, walking information acquisition step). Specifically, the walking information acquisition unit 11 acquires the walking information transmitted from the measuring device 2 via the communication line network 3 by the communication device 108. In addition, when the estimation device 1 includes a storage unit, the estimation device 1 may store the acquired walking information in the storage unit. When the storage unit of the estimation device 1 stores the walking information, the walking information acquisition unit 11 may read the walking information stored in the storage unit and acquire the walking information of the subject. In addition, when the walking information measured by the measuring device 2 is stored in a database outside the system, for example, the walking information acquisition unit 11 may acquire the walking information of the subject from the database.

[0030] Next, the estimated parameter calculation unit 12 calculates a cognitive function estimation parameter based on the acquired walking information (S2, estimated parameter calculation step). The cognitive function estimation parameter is various parameters for estimating the subject's cognitive function. Note that, when the cognitive function estimation parameter is calculated in the measuring device 2 and the cognitive function estimation parameter is transmitted to the estimation device 1 as walking information, the estimated parameter calculation unit may calculate the walking information as the cognitive function estimation parameter without performing any arithmetic processing.

[0031] Examples of the cognitive function estimation parameters include the subject's walking gait, stance time, ground contact time / stance time, heel lift time / stance time, walking speed, maximum swing speed, bed lift angle, foot trajectory length of one step, maximum heel height of one step, maximum toe height of one step, stride length, angular velocity waveform similarity, and acceleration waveform similarity. In particular, those in a state of MCI tend to walk in a so-called "sliding" state, in which the feet do not rise high and walk as if they are rubbing the ground. The "sliding" state is significantly reflected in parameters such as the maximum swing speed, bed lift angle, maximum heel height of one step, and maximum toe height of one step. Therefore, it is preferable that the estimation parameter calculation unit is configured to calculate at least one of the maximum swing speed, bed lift angle, maximum heel height of one step, and maximum toe height of one step, which are parameters that are likely to reflect the "sliding" state, as the cognitive function estimation parameters. With such a configuration, the accuracy of the cognitive function determination is further improved.

[0032] The cognitive function estimation parameters are described in detail below. In the following description, the x-axis represents the subject's walking direction (front-back direction), the y-axis represents the left-right direction, and the z-axis represents the height direction (up-down direction). The x-axis, y-axis, and z-axis are also shown in Figure 4.

[0033] Cadence is the number of steps per unit of time. A step is counted from when one foot touches the ground until it leaves the ground again.

[0034] Stance time is the time during the stance phase of a step, which is the period from when one foot is on the ground and the other foot leaves the ground until the other foot touches the ground again.

[0035] Contact time is the time it takes for the foot to touch the ground and then take off again, and can be rephrased as the time per step.

[0036] The heel-up time is the time from when the heel leaves the ground to when the entire foot leaves the ground, in other words, the time during which the toe is on the ground but the heel is off the ground.

[0037] The maximum swing velocity is the maximum velocity of the foot in the x-axis direction during the period from when the foot leaves the ground until when it touches down again (swing phase).

[0038] The bed-lift angle is the angle between the foot and the ground at the moment the toe leaves the ground during the foot-lift stage.

[0039] The foot path length of one step is the length of the path that the foot traces from when it leaves the ground until it touches down again, and is a value measured in three-dimensional space, not as a one-way projection. The maximum heel height of one step is the maximum height that the heel reaches from when it leaves the ground until it touches down again. The maximum toe height of one step is the maximum height that the toe reaches from when it leaves the ground until it touches down again.

[0040] Waveform similarity is an index that indicates the degree of similarity between multiple waveforms of acceleration or angular velocity along a certain axis when the waveforms are cut for each step and superimposed. In this specification, for example, acceleration waveform similarity along the x-axis is expressed as "x-axis acceleration waveform similarity," and angular velocity waveform similarity along the y-axis is expressed as "y-axis angular velocity waveform similarity."

[0041] The waveform similarity is calculated, for example, by the following procedure. First, the offset components of the waveform are removed, and the waveform Wj for each stride is identified by the peak value. Next, linear interpolation is performed so that the waveform for each step is composed of 100 points, to obtain Wj(s) (s is an integer between 1 and 100). Next, the strides Wj(s) are averaged to obtain Wmean(s) as shown in the following formula (i). Finally, the similarity γj between each stride Wj and Wmean is calculated using the following formula (ii), and the average value of the similarities γj is taken as the waveform similarity.

[0042]

number

[0043] Of the cognitive function estimation parameters mentioned above, stance time, ground contact time, heel lift time, maximum swing leg velocity, bed departure angle, maximum heel height per step, and maximum toe height per step cannot be obtained solely from walking information measured by an inertial sensor attached to the waist, but are parameters that can be obtained using walking information measured by inertial sensors attached to the feet.

[0044] Next, the determination unit 13 of the estimation device 1 determines whether or not the subject has MCI based on the cognitive function estimation parameters (S3, determination step).

[0045] The determination unit 13 determines whether or not the subject has MCI based on cognitive function estimation parameters such as gait, stance time, ground contact time / stance time, heel lift time / stance time, walking speed, maximum swing speed, bed-lifting angle, foot trajectory length of one step, maximum heel height of one step, maximum toe height of one step, stride length, angular velocity waveform similarity, and acceleration waveform similarity, using the following formula (1). n are cognitive function estimation parameters, and α1 to α n and β is the coefficient and p is the probability. α1~α n The coefficients of and β can be determined by performing a binomial logistic regression analysis, for example, as in the examples described later. In determining whether or not there is MCI, for example, the calculated cognitive function estimation parameters are substituted into formula (1) to obtain a probability p, and if p is 0.5 or more, it is determined that there is MCI, and if p is less than 0.5, it is determined that there is no MCI.

[0046]

number

[0047] Next, the output unit 14 outputs the result of the determination by the determination unit 13 (S4, output step). The output unit 14 may output the result of the determination to the display 107, for example, or may output the result to an external device via the communication device 108. The external device may be, for example, a printing machine such as a printer, a mobile terminal such as a tablet terminal, the measuring device 2, or the like. Then, the processing by the estimation device 1 ends.

[0048] When the output unit 14 outputs the determination result to the measuring device 2, the display 207 of the measuring device 2 may be capable of displaying the determination result.

[0049] According to this embodiment, since cognitive function is estimated using gait information including information related to the acceleration or angular velocity of the subject's feet, it is possible to accurately estimate whether or not the subject has MCI.

[0050] [Embodiment 2] Next, another embodiment of the present invention will be described. Fig. 7 is a block diagram showing a configuration of an example of an estimation device 1A of this embodiment. As shown in Fig. 7, the estimation device 1A further includes a cognitive function estimation unit 15 in addition to the configuration of the estimation device 1 described above. The hardware configuration of the estimation device 1A is the same as that of the estimation device 1 in Fig. 2, except that the CPU 101 has the configuration of the estimation device 1A in Fig. 7.

[0051] An example of processing in an estimation system including the estimation device 1A and the measuring device 2 of this embodiment will be described with reference to the flowchart of Fig. 8. Fig. 8 is a flowchart showing an example of processing (S1 to S3, S11, S4) of the estimation device 1A. An example of processing for estimating cognitive function according to this embodiment will be described below.

[0052] First, the walking information acquisition step S1, the estimation parameter calculation step S2, and the determination step S3 are performed in the same manner as the processing by the estimation device 1 described above to determine whether or not the subject has MCI.

[0053] When it is determined that the subject has MCI in the determination step S3 (YES in S3), the cognitive function estimation unit 15 of the estimation device 1A estimates the cognitive function level of the subject (S11, cognitive function estimation step).

[0054] The cognitive function estimation unit 15 can estimate the cognitive function level of the subject using, for example, a regression equation determined in advance. The cognitive function level can be estimated based on, for example, the revised Hasegawa Dementia Scale (HDS-R score) or the Mini-Mental State Examination (MMSE). For example, when based on the HDS-R score, first, the HDS-R scores of multiple subjects and cognitive function estimation parameters obtained from walking information are measured. Next, multiple regression analysis is performed using the HDS-R score as a dependent variable and the cognitive function estimation parameters as independent variables to determine the coefficients α1' to αn' and β' in the regression equation (2) that estimates the HDS-R score. The cognitive function estimation parameters of the subject are substituted into the regression equation (2) obtained in this way, whereby the cognitive function level of the subject (estimated HDS-R score) can be calculated.

[0055]

number

[0056] Then, the output unit 14 of the estimation device 1A outputs at least one of the result of the determination in the determination step S3 and the cognitive function level calculated in the cognitive function estimation step S11 (S4, output step). For example, the output unit 14 outputs the determination result and the cognitive function level for a subject determined to have MCI, and outputs the determination result for a subject determined not to have MCI (NO in the determination step S3). Then, the processing by the estimation device 1A ends.

[0057] According to this embodiment, in addition to determining whether or not the subject has MCI, the cognitive function level is also estimated, thereby making it possible to estimate the cognitive function of the subject in more detail.

[0058] [Embodiment 3] Yet another embodiment of the present invention relates to a cognitive function estimation method. The cognitive function estimation method according to this embodiment includes a walking information acquisition step S1, an estimation parameter calculation step S2, and a determination step S3 shown in the flowchart of Fig. 6. The cognitive function estimation method may further include an output step S4.

[0059] The walking information acquisition process S1 is a process of acquiring walking information measured by an inertial sensor 21 attached to the foot of the subject. The walking information includes at least one of information related to the acceleration and information related to the angular velocity of the foot of the subject while walking. The estimation parameter calculation process S2 is a process of calculating a cognitive function estimation parameter based on the walking information. The determination process S3 is a process of determining whether or not the subject has mild cognitive impairment (MCI) based on the cognitive function estimation parameter. The output process S4 is a process of outputting the result of the determination in the determination process S3.

[0060] As shown in the flowchart of FIG. 8, the cognitive function estimation method according to this embodiment may include a cognitive function estimation step S11 of estimating the cognitive function level of a subject who has been determined to have MCI in the determination step S3.

[0061] According to the cognitive function estimation method of this embodiment, it is possible to estimate the cognitive function of a subject simply and accurately. The specific aspects and preferred configurations of each step of the cognitive function estimation method of this embodiment are omitted here because the descriptions of the above-mentioned embodiments 1 and 2 are used.

[0062] [Embodiment 4] Yet another embodiment of the present invention relates to a cognitive function estimation program. The cognitive function estimation program according to this embodiment is a cognitive function estimation program for causing a computer to execute a walking information acquisition procedure, an estimation parameter calculation procedure, and a judgment procedure. The cognitive function estimation program may further cause the computer to execute an output procedure.

[0063] The gait information acquisition step is a step of acquiring gait information obtained by an inertial sensor attached to the subject's foot. The gait information includes at least one of information related to the acceleration and information related to the angular velocity of the subject's foot while walking. The estimation parameter calculation step is a step of calculating a cognitive function estimation parameter based on the gait information. The determination step is a step of determining whether or not the subject has mild cognitive impairment (MCI) based on the cognitive function estimation parameter.

[0064] The cognitive function estimation program according to this embodiment may further cause the computer to execute a cognitive function estimation procedure for estimating a cognitive function level of a subject who has been determined to have MCI in the assessment procedure.

[0065] According to the cognitive function estimation program of this embodiment, the cognitive function of the subject can be estimated simply and accurately. The walking information acquisition procedure, the estimated parameter calculation procedure, the judgment procedure, the output procedure, and the cognitive function estimation procedure of the cognitive function estimation program of this embodiment correspond to the walking information acquisition step S1, the estimated parameter calculation step S2, the judgment step S3, the output step S4, and the cognitive function estimation step S11 in the above-mentioned embodiment 1 and embodiment 2, respectively. The specific aspects and preferred configurations of each procedure are omitted here by citing the descriptions in the above-mentioned embodiment 1 and embodiment 2. In addition, the cognitive function estimation program according to this embodiment may be recorded in a computer-readable recording medium. The recording medium is, for example, a non-transitory computer-readable recording medium. The recording medium is not particularly limited, and examples thereof include a random access memory (RAM), a read-only memory (ROM), a hard disk (HD), an SSD, an optical disk, and a floppy (registered trademark) disk (FD). EXAMPLES

[0066] The present invention will be further described below with reference to examples, but the present invention is not limited to these examples.

[0067] [Example 1] (1) Subjects The subjects were 38 elderly people (23 men and 15 women) with an average age of 74 years (range 65-89 years). The subjects were explained the purpose and objectives of the study and how the results would be handled, and the study was conducted after obtaining informed consent. The study in this example was approved by the Ethics Committee of Ichinoseki National College of Technology.

[0068] (2) Measuring walking information and calculating cognitive function estimation parameters A measuring device with a 6-axis inertial sensor capable of measuring 3-axis acceleration and 3-axis angular velocity was attached to the shoelaces (at the instep) and waist of each subject with a clip. Each subject was instructed to walk normally. The walking distance was set to 10 m, and each subject was measured twice. Based on the walking information obtained, the following cognitive function estimation parameters were calculated: gait, stance time, ground contact time / stance time, heel lift time / stance time, walking speed, maximum swing speed, bed-lift angle, foot trajectory length per step, maximum heel height per step, maximum toe height per step, stride length, y-axis angular velocity waveform similarity (waist), and x-axis acceleration waveform similarity (waist).

[0069] (3) Measurement of cognitive function level The cognitive function level of each subject was measured using the Mini-Mental State Examination (MMSE), a screening test for cognition. The MMSE is a cognitive function test consisting of 11 items, including time orientation, place orientation, immediate and delayed recall of three words, calculation, object naming, sentence repetition, three-stage oral command, written command, sentence writing, and figure copying, with a maximum score of 30 points. According to the evaluation criteria of the MMSE, a score of 23 points or less is suspected of dementia, and a score of 27 points or less is suspected of mild cognitive impairment (MCI). In this embodiment, subjects with a score of 27 points or less were considered to have mild cognitive impairment (MCI), and subjects with a score of 30 points were considered to be healthy (Normal). There was no subject with a score of 28 or 29 points on the MMSE. In addition, the scores of all subjects who were considered to have MCI were within the range of 25 to 27 points, and there was no subject with a score of 23 points or less that was suspected of having dementia.

[0070] (4)Analysis First, we performed a binomial logistic regression analysis using the cognitive function estimation parameters of each subject as the independent variables and whether or not they had MCI as the dependent variable. As a result of the regression analysis, the following regression equation (3) was obtained.

[0071]

number

[0072] To verify the suitability of formula (3), we performed the Hosmer-Lemeshow test, with a p-value of 0.890. A p-value of 0.05 or higher is considered to be a good fit, so formula (3) can be said to be a good fit.

[0073] To confirm the judgment accuracy of formula (3), we estimated the cognitive function of each subject based on the cognitive function estimation parameters. The results are shown in Table 1. As shown in Table 1, the accuracy of formula (3) was 84.2%, indicating that the estimation accuracy of formula (3) is very high.

[0074] [Table 1]

[0075] [Comparative Example 1] Among the cognitive function estimation parameters for the 38 subjects in Example 1, only gait, walking speed, waveform similarity, and stride length were used to perform binomial logistic regression analysis in the same manner as in Example 1. Gait, walking speed, waveform similarity, and stride length are all parameters that can be calculated only from information obtained from an inertial sensor attached to the waist. As a result, the following regression equation (4) was obtained.

[0076]

number

[0077] Using formula (4), cognitive function was estimated based on the cognitive function estimation parameters of each subject in the same manner as in Example 1. The results are shown in Table 2. As shown in Table 2, the accuracy of formula (4) was 71.1%.

[0078] [Table 2]

[0079] From the results of Example 1 and Comparative Example 1, it was found that the accuracy of the prediction was greatly improved by using the estimated parameters calculated using the information obtained from the inertial sensor attached to the foot. In particular, the accuracy rate for the 14 subjects who were determined to have MCI by MMSE was 42.9% in Comparative Example 1 and 78.6% in Example 1, which was a significant improvement. If the subjects with MCI can be accurately determined, it is particularly useful because it can encourage the subjects with MCI to take appropriate measures before they develop dementia.

[0080] The present invention has been described above with reference to specific embodiments and examples, but the present invention is not limited to these examples, and is indicated by the claims, and is intended to include all modifications within the meaning and scope equivalent to the claims.

[0081] In the above embodiment, binomial logistic regression analysis was performed to calculate the cognitive function estimation parameters, but the cognitive function estimation parameters may be calculated using deep learning based on, for example, walking information and the cognitive function level. In the above embodiment, specific physical quantities such as gait pace and walking speed are used as the cognitive function estimation parameters, but the cognitive function estimation parameters do not need to be specific physical quantities and may be any parameters for estimating cognitive function.

[0082] In the above embodiment, the inertial sensor 21 is attached to the measuring device 2, and walking information is sent from the measuring device 2 to the estimation device 1 through the communication unit 22. However, the estimation device 1 may be equipped with the inertial sensor 21. For example, the estimation device 1 may be a smartphone equipped with an inertial sensor. [Explanation of symbols]

[0083] 1. Cognitive function estimation device 2. Measuring Device 3. Communication Network 11 Walking information acquisition unit 12 Estimation parameter calculation section 13 Judgment section 14 Output section 15 Cognitive Function Estimation Division 21 Inertial Sensor 22 Communications Department 30 Shoes 100 Cognitive Function Estimation System 101,201 CPU 102,202 Memory 103,203 Bus 104,204 Storage device 105 Programs 106,206 Input Devices 107,207 Display S Subject

Claims

1. A cognitive function estimation device including a walking information acquisition unit, an estimation parameter calculation unit, and a determination unit, the walking information acquisition unit is configured to acquire walking information obtained by an inertial sensor attached to a foot of the subject, the walking information includes at least one of information about the acceleration and information about the angular velocity of the subject's feet while walking; The estimated parameter calculation unit is configured to calculate a cognitive function estimated parameter based on the walking information, The determination unit is configured to determine the presence or absence of mild cognitive impairment (MCI) of the subject based on the cognitive function estimation parameter. Cognitive function estimation device.

2. the estimation parameter calculation unit is configured to calculate, as the cognitive function estimation parameter, at least one selected from a group consisting of a maximum swing leg speed, a bed take-off angle, a maximum heel height per step, and a maximum toe height per step, based on the walking information; The determination unit is configured to determine the presence or absence of mild cognitive impairment (MCI) of the subject based on the cognitive function estimation parameters including at least one selected from the group consisting of the maximum swing leg velocity, the bed departure angle, the maximum heel height of the step, and the maximum toe height of the step. The cognitive function estimation device according to claim 1 .

3. Furthermore, a cognitive function estimation unit is provided, The cognitive function estimation device according to claim 1 or 2, wherein the cognitive function estimation unit estimates a cognitive function level of a subject determined to have mild cognitive impairment (MCI) based on the cognitive function estimation parameters.

4. The cognitive function estimation device according to claim 1 or 2, wherein the walking information further includes at least one of information regarding acceleration and information regarding angular velocity of the subject's waist measured by an inertial sensor attached to the subject's waist.

5. A cognitive function estimation method including a walking information acquisition step, an estimation parameter calculation step, and a determination step, The step of acquiring walking information includes acquiring walking information measured by an inertial sensor attached to a foot of the subject; the walking information includes at least one of information about the acceleration and information about the angular velocity of the subject's feet while walking; the estimation parameter calculation step calculates a cognitive function estimation parameter based on the walking information; The determining step determines whether or not the subject has mild cognitive impairment (MCI) based on the cognitive function estimation parameter. Cognitive function estimation method.

6. A cognitive function estimation program for causing a computer to execute a walking information acquisition step, an estimation parameter calculation step, and a judgment step, The step of acquiring walking information includes acquiring walking information obtained by an inertial sensor attached to a foot of the subject; the walking information includes at least one of information about the acceleration and information about the angular velocity of the subject's feet while walking; the estimation parameter calculation step calculates a cognitive function estimation parameter based on the walking information; The determination step determines whether or not the subject has mild cognitive impairment (MCI) based on the cognitive function estimation parameter. Cognitive function estimation program.

7. A cognitive function estimation system comprising the cognitive function estimation apparatus according to claim 1 or 2 and a measurement device, The measuring device includes an inertial sensor and a communication unit. The communication unit is capable of communicating with the cognitive function estimation device and transmitting the walking information to the cognitive function estimation device. Cognitive function estimation system.

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