JP7448904B1 - Brain dysfunction evaluation system - Google Patents

Abstract

An object of the present invention is to provide a system that can evaluate the degree of brain dysfunction with high precision, safely, and easily without the need to wear instruments. [Solution] A storage unit that stores example data for illustrating repeated finger movements as an image, a display unit that displays video data corresponding to the example data, a sensor that measures the movement of a subject, and a display unit. a control calculation unit that instructs the display of example data to the computer and calculates the acquired measurement data; the control calculation unit has a video period in which the subject exercises in accordance with the video; From the measurement data for a fixed period, extract a stimulated motion frequency component that is the frequency component of the stimulating motion that repeats the finger movement, and a non-stimulated motion component that is the sum of one or more components from frequencies other than the stimulated motion frequency component. The present invention has a calculation method for calculating the ratio of the stimulated motion extra-component and the stimulated motion frequency component, and uses the value calculated by the calculation method as an evaluation value of brain dysfunction. [Selection diagram] Figure 1

Description

TECHNICAL FIELD The present invention relates to a brain dysfunction evaluation system, and more particularly, to a technique for easily performing brain dysfunction evaluation.

BACKGROUND ART Conventionally, as a method for evaluating the degree of brain dysfunction such as dementia, methods of analysis based on question-type tests such as MMSE (Mini Mental State Examination) and motor functions such as finger tapping movements are known.
However, although MMSE is relatively short, it requires asking questions to the subject for more than ten minutes, and the finger tapping action requires the user to wear an instrument, making it a long process.
Therefore, there is a need for a system that can evaluate the degree of brain dysfunction in a short period of about several tens of seconds without having to wear any equipment.

Various techniques have been proposed in the past to address such problems. For example, a system for evaluating brain dysfunction based on finger movements has been proposed (see Patent Document 1) and has become a publicly known technology.
More specifically, by calculating the difference in the motor function of both hands during bimanual coordination movements, the degree of brain dysfunction such as cognitive decline can be easily evaluated. For each movement waveform, a feature related to the variation in the tapping time interval is generated, and the difference is used as a two-hand difference feature to evaluate brain dysfunction.
However, in the configuration according to this prior art, a sensor must be attached to the subject's thumb, index finger, etc., which is a roundabout way, and as a whole, the burden on the subject is large, and the above-mentioned problem has not been solved.

JP2016-49282A

In view of the above-mentioned problems such as asking the subject many questions and wearing equipment, which increases the burden on the subject, the present invention has been developed to provide a highly accurate and safe method that does not require the subject to wear any equipment. The object of the present invention is to provide a system that can easily evaluate the degree of brain dysfunction.

In order to solve the above-mentioned problems, the present invention provides a system capable of evaluating the degree of brain dysfunction, which includes a storage unit that stores example data for illustrating repeated hand and finger movements as images; The control calculation unit is composed of a display unit that displays corresponding video data, a sensor that measures the subject's movement, and a control calculation unit that instructs the display unit to display example data and performs calculations on the acquired measurement data. has a video period in which the subject moves in imitation of the video, and from the measurement data of the video period, the component of the stimulation movement frequency, which is the frequency component of the stimulation motion that repeats the finger movement, and the stimulation motion It has a calculation method that extracts a non-stimulated motion component, which is the sum of one or more components from frequencies other than frequency components, and calculates the ratio of the non-stimulated motion component and the stimulated motion frequency component, and Measures will be taken to use the value as an evaluation value for brain dysfunction.

The present invention also provides a storage unit that stores example data for illustrating repeated hand and finger movements as an image, a display unit that displays video data corresponding to the example data, and a sensor that measures the movement of the subject. It is composed of a control calculation unit that instructs the display unit to display example data and performs calculations on the acquired measurement data, and the control calculation unit includes a video period in which the subject exercises according to the video, and a control calculation unit that instructs the display unit to display example data and performs calculations on the acquired measurement data. After stopping, there is a non-video period in which the subject continues to exercise in imitation of the video during the video period, and from the measurement data of the non-video period, it is a frequency component of the stimulation motion in which the finger movement is repeated. Calculation of extracting a stimulated motion extra-component, which is the sum of a stimulated motion frequency component and one or more components from frequencies other than the stimulated motion frequency component, and calculating the ratio of the stimulated motion non-component and the stimulated motion frequency component. A method is adopted in which the value calculated by the calculation method is used as the evaluation value of brain dysfunction.

Furthermore, the present invention adopts a method of dividing the measurement period into a plurality of unit periods and using a representative value of the values calculated by the calculation method for each unit period as the evaluation value of brain dysfunction.

Still further, the present invention employs means in which the representative value is an average value.

Furthermore, the present invention employs means in which the representative value is a median value.

Still further, the present invention employs means in which the length of the unit period is one cycle or more of repeated finger movements.

Furthermore, the present invention employs means in which the video presence period is 8 seconds or more, and the video non-video period is 10 seconds or more.

Still further, the present invention employs means for obtaining measurement data of the no-video period only from the first period when the no-video period is divided into a plurality of periods.

Furthermore, the present invention adopts a method in which repeated finger movements are performed on the fingers of both hands, a representative value is calculated for each finger of both hands, and the better or worse of the calculated values is used as the evaluation value.

Still further, the present invention employs measures for use in assessing cognitive impairment.

The present invention also employs measures for use in the evaluation of mild cognitive impairment.

According to the brain dysfunction evaluation system according to the present invention, evaluation of brain dysfunction, particularly cognitive impairment and mild cognitive impairment, can be performed without asking the subject many questions or attaching equipment. It has the excellent effect of contributing to reducing the burden on the subject and allowing highly accurate, safe, and simple evaluation.

FIG. 1 is a system block diagram showing an embodiment of a brain dysfunction evaluation system according to the present invention. FIG. 2 is a schematic diagram showing the procedure for implementing the brain dysfunction evaluation system according to the present invention. FIG. 2 is an explanatory diagram showing measurement data of the brain dysfunction evaluation system according to the present invention. FIG. 2 is an explanatory diagram showing measurement data when implementing the brain dysfunction evaluation system according to the present invention. FIG. 2 is a graph diagram showing frequency components of data of the brain dysfunction evaluation system according to the present invention. It is a schematic diagram which shows the procedure of other implementation of the brain dysfunction evaluation system based on this invention.

The main feature of the brain dysfunction evaluation system according to the present invention is that it is possible to perform brain dysfunction evaluation simply, safely, objectively, and quantitatively in a short time.
Hereinafter, embodiments of the brain dysfunction evaluation system according to the present invention will be described based on the drawings.
The overall configuration and the configuration of each part of the brain dysfunction evaluation system shown below are not limited to the following examples, but are within the scope of the technical idea of the present invention, that is, can exhibit the same effects. This can be changed as appropriate within the scope of the configuration.

The present invention will be explained according to FIGS. 1 to 6.
FIG. 1 is a system block diagram showing an embodiment of the brain dysfunction evaluation system according to the present invention. FIG. 2 is a schematic diagram showing the procedure for implementing the brain dysfunction evaluation system according to the present invention. FIG. 3 is an explanatory diagram showing measurement data from a sensor by the brain dysfunction evaluation system according to the present invention. FIG. 4 is an explanatory diagram showing measurement data from a sensor over time when the brain dysfunction evaluation system according to the present invention is implemented. FIG. 5 is a histogram diagram showing frequency components of data of the brain dysfunction evaluation system according to the present invention. FIG. 6 is a schematic diagram showing another implementation procedure of the brain dysfunction evaluation system according to the present invention.

The evaluation device 1 is a system for evaluating brain dysfunction. This system measures and analyzes the subject's movements to evaluate the degree of brain dysfunction of the subject, and is particularly useful for cognitive disorders whose progress is difficult to grasp and for mild cognitive impairments in the early stages of cognitive disorders. (MCI) evaluation can be quantified.
The evaluation device 1 mainly includes a sensor 100, a display section 110, a control calculation section 130, and a storage section 140.

The sensor 100 measures the movement of the subject's hand, and measures the movement of the hand and fingers as a rotation angle. The subject holds out both hands or one hand, and with the hand naturally spread, repeats the action of turning the palm over. The speed of rotation is the speed that follows the image displayed as a guide. The rotation speed of the guide is approximately 1 second per cycle. If, for example, a frequency component up to about 25 Hz is to be measured as a frequency component of finger movement by a sensor, sampling at a cycle of 50 Hz or more, which is twice 25 Hz, is required. In this embodiment, sampling is performed at 100 Hz with a margin.
Furthermore, in order to reduce the burden on the subject, the sensor is preferably of a type that does not require a marker or the like to be attached to the subject's hand.
A specific example is an infrared sensor such as LEAP MOTION (registered trademark of LEAP MOTION, Inc.) in terms of performance and price. By placing LEAP MOTION several tens of centimeters away from the subject's hand, it is possible to measure the movement of the subject's hand in detail in three dimensions without performing any special processing on the subject's hand. For example, the sensor 100 can extract translation data on three axes and rotation data on three axes of hand movement, but in the present invention, when evaluating the pronation/supination movement of the hand, the arm of the palm direction vector is extracted as the axis. We use one-dimensional information only about the rotation around the . Measurement data is generated for each left and right hand.

The display unit 110 is a part that shows an example video, which is video data corresponding to example data serving as a reference for hand movements, to the subject, and displays measurement results and evaluation values. It is preferable that the display unit 110 is located at a position where the subject can comfortably view it while performing repetitive hand movements. This is because such a position allows the user to concentrate on repetitive movements of the hand. The size is preferably such that the subject can confirm it without squinting. In addition, in order to display an image of the hand repeatedly moving the hand, it is preferable that the size of the hand on the display unit 110 be closer to the actual size of the hand to reduce the sense of discomfort.

Note that it is also possible to consider a mode including a speaker 120 that can provide audio guidance to the subject for the test, notify the start and end of the test, and notify measured values and evaluation values. Notifications can be made using the display unit 110, but notifications can be made more effective by giving voice notifications, and are more effective for people with poor eyesight than notifications using the display unit 110.
Furthermore, it is often easier for subjects to understand the measurement itself if it is explained audibly. For example, if the subject is given voice instructions such as ``Please move your hand in the same way as the display'' or ``Please continue to move your hand even if the display disappears'', the subject will be able to proceed with the task more smoothly.

The control calculation unit 130 is a part that controls the entire evaluation device 1, performs data calculations, and the like. In other words, it instructs the display unit 110 to display the example data and performs calculations on the acquired measurement data 230.
The controls include starting the evaluation device 1, instructing the display of measurement guidance, instructing the display unit 110 to display an example image, instructing the speaker 120 to produce sound, instructing the sensor 100 to operate, and storing the acquired data in the storage unit 140. Instructs to store the measurement value and display the evaluation value on the display unit 110, etc.
The acquired measurement data 230 is divided and managed for each period.
The measurement data 230 of each period is divided into measurement unit data 240 of 1 second and stored in the storage unit 140.
An evaluation value is calculated in period units. An evaluation value can be calculated for any period.
For a predetermined period, the relationship between the stimulation movement frequency 250, which is the frequency of finger repetition, and the measurement data 230 is calculated for each measurement unit data 240. A representative value is calculated from the plurality of calculated values and is used as an evaluation value.
When measuring both the left and right fingers, calculate the relationship between the stimulation movement frequency 250 and the measurement data 230 in units of measurement unit data 240 for each left and right hand, calculate a representative value, and process the two representative values as appropriate. This is the final evaluation value.

Each operation and process of the control calculation unit 130 will be explained. The instruction to display the measurement guidance is as follows: ``Motor evaluation measurement will be performed.Put both hands out in front of you, and rotate your hands in the same way as the hands on the screen.After 10 seconds, the image will be displayed.'' will disappear, but please rotate your hand for 15 seconds even after the image disappears.'' The subject's transition to measurement can be made smoother. In response to an instruction to display an example image on the display section 110, the video data 210 in the storage section 140 is displayed on the display section 110 as a moving image. The video data 210 may be video data or a collection of still image data. The subject is taught how to pronate and supinate the hand. The sensor 100 is instructed to start and stop measurement. During measurement, data is received from the sensor 100 and stored in the storage unit 140 as measurement data 230.
After the measurement is started and 10 seconds, which is the period 1, has elapsed, the display on the display unit 110 is stopped. At this point, the data of the sensor 100 is grouped as period 1 data and stored in the storage unit 140 as a group of measurement unit data 240 in units of 1 second. Thereafter, data from the sensor 100 is stored in one group as data for periods 2 to 4 every 5 seconds in the storage unit 140 as a group of measurement unit data 240 in units of 1 second.
15 seconds after the video is turned off, a message such as "Thank you for your hard work. Measurement is complete" is displayed on the display unit 110, and audio is also output.
After the measurement is completed, the relationship between the stimulation motion frequency 250 and the measurement unit data 230 is calculated, a representative value is calculated, and an evaluation value is obtained.
In this way, the control calculation unit 130 distinguishes between a video period in which the subject exercises in accordance with the video, and a non-video period in which the subject continues to exercise in accordance with the video in the video period after stopping the video. In addition, from the measurement data of the non-video period, the stimulated movement frequency component, which is the frequency component of the stimulating movement that repeats finger movements, and the stimulated movement extraneous component, which is the sum of one or more components from frequencies other than the stimulated movement frequency component. It has a calculation method that extracts the stimulated movement extra-component and the stimulated movement frequency component (a value obtained by dividing the stimulated movement extra-component by the stimulated movement frequency component), and calculates the value calculated by the calculation method as a function of brain function. This is the evaluation value of the failure or the original data of the evaluation value.
Note that the video presence period is at least 8 seconds or more, preferably 10 seconds or more. A measurement period of at least 3 seconds is required for the subject to recognize the movement shown in the video and to start moving in accordance with that movement, and a measurement period of at least 5 seconds is required to evaluate the subject's movement in accordance with the video. . Further, the video non-period is at least 10 seconds or more, preferably 15 seconds or more. This is because if it is shorter than 10 seconds, there will be insufficient measurement data for evaluating changes in the subject's movement after the stimulus image disappears.

The storage unit 140 is a part that stores programs and data. Consists of non-volatile memory and volatile memory. The nonvolatile memory stores data whose values have been determined, such as a program, example data 200 for illustrating repeated hand and finger movements as an image, and video data 210 corresponding to the example data.
The program is a part that describes the procedure for controlling the evaluation device 1. It may be incorporated when the evaluation device 1 is manufactured, or may be installed later as an application as appropriate.
The example data 200 and the video data 210 are data that serve as a guide for making the subject exercise. Therefore, the example data 200 and the video data 210 must always be stored in the evaluation device 1. The measurement data 230, the measurement unit data 240, the fundamental frequency component 280, and the HIGH frequency component 290 are data that is generated and calculated each time a measurement is performed, and therefore are stored in a volatile memory.

The example data 200, video data 210, measurement data 230, and measurement unit data 240 will be explained. The example data 200 is a value indicating the angle of the finger when pronating and supinating the finger, and indicates that pronation/supination is one cycle and one cycle occurs in one second. The waveform is a continuous sine waveform. As example data, data for at least half of one period is used, and data is used as appropriate, such as repeating it as many times as necessary. The example data is also referred to as ideal stimulation motion data.
The video data 210 is a collection of videos showing the actual positions of fingers corresponding to the example data 200. For one second and one period of the example data, a group of images for movement in one direction during at least one reciprocation of pronation and supination of the finger is provided and used as appropriate. The video group may be a collection of still images or one cycle of video data.
The measurement data 230 is data for each period, and the measurement unit data 240 is data obtained by further dividing the measurement data 230.
In order to extract frequency components up to about 25 Hz, the data sampling frequency needs to be at least 50 Hz. In this embodiment, the sampling frequency is 100 Hz. Therefore, this is a data group measured at 0.01 second intervals.

When calculating the evaluation value of brain dysfunction, it is possible to process the measurement data 230, which is data for the entire period, at once for the period to be measured, but it is necessary to eliminate movements that are close to noise, such as sudden fluctuations. Therefore, processing is performed for each measurement unit data 240.
The length of the unit time to be divided is equal to or longer than one cycle of repeated finger movements. This is because when FFT is used for processing, the fundamental frequency cannot be extracted sufficiently unless there is a period longer than the period of the fundamental frequency.
In this embodiment, the measurement data 230 is divided into units of 1 second each as measurement unit data 240, which is a measurable period of 1 Hz, which is the fundamental frequency.
In other words, the measurement period of measurement data is divided into a plurality of unit periods.
For each unit period, the characteristic amount of the measured value for the ideal stimulation exercise is calculated, and the representative value thereof is used as the evaluation value of brain dysfunction.

FIG. 3 is a graph showing an ideal stimulation movement, which is an ideal pronation/supination movement of the fingers, and an angle value of the fingers in an example of the test subject's reaction movement corresponding to the ideal stimulation movement. The horizontal axis is time. The vertical axis is the rotation angle value of the fingers.
The ideal stimulation motion is a sinusoidal waveform and a single frequency of 1 Hz.
During the first 10 seconds, which is a video period, the subject responds according to the video data of the ideal stimulation movement, and during the latter 15 seconds, which is a non-video period, the video data of the ideal stimulation movement is stopped, and the subject responds to the ideal stimulation movement. This is a waveform obtained by estimating and memorizing the stimulus movement and responding to it.
For the first 10 seconds, the stimulated movement image is presented, so a healthy person will not notice that the reaction movement is significantly different from the ideal stimulated movement. However, since the presentation of the stimulated motion image is stopped after 10, the difference between the reaction motion and the ideal stimulated motion is accumulated without being corrected, and the mutual difference increases.
In the first half, the participants imitate the ideal stimulus movement while performing a similar movement, so the memory-related parts of the brain are less involved. In the latter half, the movement is performed based on the memory of the ideal stimulus movement seen in the past, so the memory-related parts of the brain become more involved.
Therefore, it can be assumed that subjects whose deviation from the ideal stimulated movement becomes large in the latter half of the period have an impairment in short-term memory, which is the memory of the previous movement, which is one of the characteristics of cognitive impairment and mild cognitive impairment. can.

As a method of calculating the relationship between the stimulation movement frequency 250 and the measurement data 230, there is a method of calculating the value of NSM (Non Smoothness Measure).
NSM is a method of quantifying how much the measurement unit data, which is the movement of the subject, differs from the sine wave when the stimulus movement is a sine wave.
The measurement unit data 240 is converted into frequency components, and the ratio of signal strength between the stimulation movement frequency 250 and other higher frequencies is calculated.

A specific NSM calculation procedure will be explained.
The measurement unit data 240 is converted into signal strength and phase for each frequency component by FFT (fast fourier transform). In this example, since the fingers are rotated at a cycle of 1 Hz, the stimulation movement frequency 250, which is the fundamental frequency, is 1 Hz.
The frequency components include 1, 2, 3, 4, 5, 6, 7, 8, 9...25 Hz components.
An example of a frequency component graph of signal strength is shown in FIG. The stimulation movement frequency 250, which is the fundamental frequency, is 1 Hz. 2, 3, 4, 5, 6, 7, 8, 9...25Hz becomes the HIGH frequency 260. The total signal strength of the HIGH frequency components becomes the HIGH frequency component.

If the signal strength component value of frequency n is pn, the value of NSM (Non Smoothness Measure) is
NSM=(p2+p3+p4+...+p25)/p1...(1)
In (1), pn is the signal strength of the nHz component.
This is the value obtained by dividing the HIGH frequency component, which is the total signal strength of the frequency components p2 to p25, by the signal strength value of p1, which is the fundamental frequency.
If the measurement data 230 has the same waveform as the ideal stimulated movement, the values of p2 to p25 are zero, so the NSM is zero. The larger the gap from the ideal stimulated movement, the larger the values of p2 to p25. , the NSM value increases.
In other words, the larger the NSM value, the larger the ratio of the amount of blurring components with respect to the reference.
The NSM value is calculated in units of 240 units of measurement data, which are data in units of 1 second.
In this example, all values from p2 to p25 are added, but only components considered to be effective for testing may be added, or weighting may be changed for each frequency.

Since NSM observes the subject's reaction movement based on the ideal stimulation movement, measurement is started by displaying the ideal stimulation movement as an example image.
For example, if the example image is not displayed, the subject will react with his or her own movement cycle because there is no standard. Then, the standard of measurement tends to be to look at how many times something can be done per unit of time.
Subjects are forced to move their hands and fingers as fast as possible, often resulting in fatigue. Displaying example images is also effective in reducing the burden on the subject.
In this embodiment, the period of the example data is set to one second period. This is because if the cycle is short, the burden on elderly people and the like will increase. If the period is 1 second, the period is relatively slow and the subject can follow it sufficiently.
By setting such a value, the measurement can be easily performed on many people, from children to the elderly, without any mental burden.

After calculating the relationship between the stimulation movement frequency and the measurement data using NSM for each measurement unit data, a representative value is calculated for the NSM value group.
For example, when five NSM values are calculated, a representative value is calculated from them.
Possible representative values include an average value, median value, and minimum value.
It is also conceivable to use standard deviation values and fluctuation ranges. By using the standard deviation value, the stability of the subject's movement can be evaluated. If it is a healthy person, the variation is small, so the standard deviation value will be small. This is because the standard deviation value increases as the degree of disability increases.
By using the variation range, it is possible to grasp the maximum range of variation in the subject's finger movements. For a healthy person, the maximum width of blurring will be extremely small, but the greater the degree of disability, the greater the variation.
If the representative value is an average value, calculate the average of five NSM values.
If the measurement data 230 is relatively stable, such as when moving your fingers while looking at an example image, the average value is effective as a representative value because the NSM value has little variation, less blurring, and less noise. be.
If the measurement data is relatively unstable, such as when you move your fingers without looking at the example image, there will be variations in the NSM values and large vertical fluctuations, so the median value that can exclude the vertical fluctuations may be It is valid.

Furthermore, instead of simply using the left and right measured values as evaluation values, the difference between the left and right values and its absolute value may be taken. By using such values, it may be possible to estimate the type of brain dysfunction based on the difference in left and right movements.

The measurement procedure will be explained with reference to FIGS. 2 and 4.
FIG. 2 schematically shows the state of the measurement. Measurements are taken on both hands as shown.
Although it is possible to measure with only either the left or right hand, generally speaking, when the left and right fingers are moved at the same time, the left and right movements are very well synchronized. Additionally, when moving the left and right hands in synchronization, motor coordination performance improves. This is because, in the case of bimanual movements, a wider range of brain functions operate in conjunction with the coordination of both hands, and motor control functions are thought to improve. On the other hand, if the movement is performed with only one hand, the ability to coordinate movement is considered to deteriorate.
FIG. 4 is a graph of exemplary data and measured data for periods 1 to 4. The horizontal axis is time. The vertical axis is the rotation angle value of the hand and fingers, and represents the waveform of the rotation angle of the hand of the example data in each period and the waveform of the rotation angle of the test subject's hand. The serial number written above the waveform is a measurement unit data number 220 in which the measurement unit data is arranged in order. In this embodiment, each second is 25 seconds, so the number ranges from 1 to 25.
The waveform data for each period is waveform data 230, and the data for each second in each period is waveform unit data 240.

The measurement is performed in four consecutive periods. Period 1 is a video period, and the test subject pronates and supinates the test subject's right hand RH and the test subject's left hand LH, following the video data 210 on the display unit 110 (upper part of FIG. 2).
According to FIG. 4, the subject rotates his/her hand following the example data 200 and performs an action similar to the example data 200. Therefore, the measured data 230 is in a form close to the example data.
Note that the measurement data 230 includes both right-hand data and left-hand data.

Continuing from period 1, periods 2, 3, and 4 are performed.
Periods 2, 3, and 4 are periods without images, and the subject continues to pronate and supinate the hand even after the image on the display unit 110 disappears. (Figure 2 bottom).
Period 1 has the purpose of allowing the subject to get used to the movements and memorizing the images, so it is necessary to be 8 seconds or more. In this embodiment, the time is set to 10 seconds to allow for some margin.
Periods 2, 3, and 4 are portions where measurement work is performed. Period 2 is a period during which measurement is performed and requires a period of 5 seconds. Periods 3 and 4 are periods in which the hand is continuously pronated and supinated.
The total period of periods 2, 3, and 4 is suitably 10 seconds or more. In this embodiment, periods 2, 3, and 4 are all set to 5 seconds, making a total of 15 seconds, taking into consideration a margin.
According to FIG. 4, during periods 2, 3, and 4, which are non-image periods, the subject rotates his/her hand so as to perform a motion similar to the example data 200 while remembering the image of the example data 200. Therefore, the measurement data 230 is in a form that follows the example data in memory, and the difference from the example data is greater than in period 1.

In this embodiment, the waveform data 230 of period 2 is used to generate the evaluation value. That is, the measurement data of the no-video period used for generating the evaluation value is obtained only from period 2, which is the first period when the no-video period is divided into a plurality of segments.
Among brain dysfunctions, it is important to check short-term memory for cognitive impairment and mild cognitive impairment.
Period 2 is a period immediately after the stimulus image disappears, so it is very short-term, but it is a part where the hand is moved relying on motor memory. Therefore, the data of period 2 can be said to be effective data for confirming the correlation between memory and exercise.
Furthermore, when period 2 is used as the measurement data, periods 3 and 4 may be used as supplementary data, and by instructing the subject to move their fingers up to periods 3 and 4, it is possible to This method is effective because good data on the first half can be obtained even for subjects who become sluggish.

The right-hand and left-hand waveform unit data 240 of period 2 are selected. The waveform unit data 240 has five waveform unit data numbers 11 to 15. There are 5 pieces each.
The NSM value is determined for these.
For the determined NSM values, the median value is determined as a representative value for each of the right and left hands.
As the evaluation value, either the representative value of the left or right hand may be selected, or a value derived from both may be used.

In healthy people, the movements of the left and right hands are extremely well synchronized. Therefore, since the movements of the left and right hands are almost the same, the representative values for the left and right hands are naturally close to each other. Therefore, if the representative value is sufficiently good, it is sufficient to use either the left or right value.
In the case of testing for mild cognitive impairment (MCI), it is assumed that there is not much difference between the left and right sides, so we focus on the measured values on the right side, where the measurements are better. If it is guaranteed that the measurements are good, it may be possible to analyze both the left and right sides and use the better or worse of the left and right measured values as the evaluation value.
Based on the calculated evaluation values, the test subject will be advised to consult a doctor or have regular tests performed.

As described above, according to this embodiment, it is possible to evaluate brain dysfunction, particularly cognitive impairment and mild cognitive impairment, without asking the subject many questions or wearing any equipment. Therefore, the burden on the subject is small and evaluation can be performed easily.

Further, according to this embodiment, it is possible to numerically evaluate cognitive impairment and mild cognitive impairment in just 25 seconds, and the testing efficiency can be improved.

Furthermore, according to this embodiment, since images are used as a measurement standard, there is little variation among individuals, and data can be collected with high accuracy.

Furthermore, according to this embodiment, since the operation is repeated once every second, even the elderly can perform the operation easily.

As an example, an exercise of repeating pronation and supination of the fingers has been described, but the present invention may be applied to other movements as long as the movements of the fingers are repeated. For example, as shown in FIG. 6, it may be a motion of touching and separating the thumb and index finger.
First, as shown in the upper part of FIG. 6, a model image is displayed on the screen 110, and the subject follows the pattern by touching and separating the thumb and index finger. The subject's finger movements are processed through a camera and converted into waveforms.
Since the test subject only has to move their fingers slowly following the model image, the mental burden on the test subject can be reduced.

As an example, an example using the measurement data of period 2 has been described, but by using period 1, the measurement time for the subject can be shortened.
When measuring in a period without examples, it takes a total of about 18 to 25 seconds, but in the case of only examples, it only takes about 8 to 10 seconds, which further reduces the burden on the subject. can.
Note that the measurement time is required to be at least 8 seconds or more in view of the test subject's familiarity with finger movements and the accuracy of measurement, but in this example below, it is set to 10 seconds to allow for some margin.

The measurement is only for period 1, 10 seconds. Period 1 is a video period, and the test subject pronates and supinates the test subject's right hand RH and the test subject's left hand LH, following the video data 210 on the display unit 110 (upper part of FIG. 2).
According to FIG. 4, the measurement is completed in period 1, so the measurement is completed by simply having the subject rotate his hand and perform a motion similar to the example data 200 for 10 seconds, following the example data 200.
NSM values are calculated for each of the right-hand and left-hand waveform unit data 240 of period 1, 10 pieces each, 20 pieces in total. Calculate representative values for each of the right and left hands. Since the operation is based on example data and there is often little variation, the representative value is an average value.
The evaluation value is calculated using either or both of the representative values of the right hand and the left hand.

Subjects can undergo tests for brain dysfunction such as cognitive impairment and mild cognitive impairment quickly in just 10 seconds, lowering the hurdles to testing and increasing the number of opportunities for testing. .
If the test determines that a doctor's confirmation is required, a notification to that effect can be sent to prompt a diagnosis.

Since the evaluation system according to the present invention can be tested in a short time, it is possible to periodically test a specific individual and check for changes in evaluation values, thereby detecting subtle changes in brain dysfunction. can be confirmed.
Therefore, it is also beneficial to incorporate it into regular health checkups.

The present invention relates to an evaluation system for brain dysfunction, and one of its features is that the evaluation of brain dysfunction can be quantified.
Dementia is one type of brain dysfunction. Dementia is a disease that progresses gradually and is difficult to completely cure. Therefore, the present invention, which can quantify the progress status, is effective as a system for determining and evaluating dementia.
Furthermore, it is even more difficult to identify mild cognitive impairment (MCI), which is the early stage of dementia, than dementia.
According to the present invention, it is possible to determine whether or not a test for mild cognitive impairment (MCI) is necessary in a short time.
By using the brain dysfunction evaluation system of the present invention during regular medical checkups, etc., it is possible to contribute to early detection of dementia.
In addition, by managing brain dysfunction evaluation values for each individual, it is possible to confirm the degree of progress of brain dysfunction, dementia, and mild cognitive impairment (MCI) with high accuracy. This can contribute to the early detection of (MCI).

It is understood that the brain dysfunction evaluation system according to the present invention has great industrial applicability as a system that easily and accurately evaluates brain dysfunction, particularly cognitive impairment and mild cognitive impairment.

1 Evaluation device 100 Sensor 110 Display section 120 Speaker 130 Control calculation section 140 Storage section 200 Example data 210 Video data 220 Measurement unit data number 230 Measurement data 240 Measurement unit data 250 Stimulation movement frequency (fundamental frequency)
260 HIGH frequency LH Subject's left hand RH Subject's right hand

Claims (11)

A brain dysfunction evaluation system,
A storage unit that stores example data for illustrating repetitive finger movements as an image, a display unit that displays video data corresponding to the example data, a sensor that measures the exercise of the subject, and example data to the display unit. a control calculation unit that performs display instructions and calculations of the acquired measurement data;
The control calculation unit has a video period in which the subject exercises in imitation of the video, and
From the measurement data of the video period, a stimulated motion frequency component that is a frequency component of the stimulating motion that repeats the finger movement, and a stimulated motion extra-component that is the sum of one or more components from frequencies other than the stimulated motion frequency component. and has a calculation method for calculating the ratio of the stimulated motion extra-component and the stimulated motion frequency component,
A brain dysfunction evaluation system characterized in that a value calculated by the calculation method is used as an evaluation value of brain dysfunction. A brain dysfunction evaluation system,
A storage unit that stores example data for illustrating repetitive finger movements as an image, a display unit that displays video data corresponding to the example data, a sensor that measures the exercise of the subject, and example data to the display unit. a control calculation unit that performs display instructions and calculations of the acquired measurement data;
The control calculation unit includes a video period during which the subject exercises in imitation of the video;
After stopping the video, there is a non-video period in which the subject continues to exercise in imitation of the video during the video period, and
From the measurement data of the non-video period, a stimulated movement frequency component which is a frequency component of the stimulating movement that repeats the finger movement, and a stimulated movement extraneous component which is the sum of one or more components from frequencies other than the stimulated movement frequency component. and has a calculation method for calculating the ratio of the stimulated motion extra-component and the stimulated motion frequency component,
A brain dysfunction evaluation system characterized in that a value calculated by the calculation method is used as an evaluation value of brain dysfunction. Divide the measurement period of the measurement data into a plurality of unit periods,
3. The brain dysfunction evaluation system according to claim 1, wherein a representative value of the values calculated by the calculation method for each unit period is used as an evaluation value of brain dysfunction. The brain dysfunction evaluation system according to claim 3, wherein the representative value is an average value. The brain dysfunction evaluation system according to claim 3, wherein the representative value is a median value. 4. The brain dysfunction evaluation system according to claim 3, wherein the length of the unit period is one or more cycles of the repetitive hand and finger movements. 3. The brain dysfunction evaluation system according to claim 2, wherein the video period is 8 seconds or more, and the video non-video period is 10 seconds or more. 3. The brain dysfunction evaluation system according to claim 2, wherein the measurement data of the non-video period is obtained only from the first period when the non-video period is divided into a plurality of periods. 4. The operation of repeating finger movements is performed for the fingers of both hands, a representative value is calculated for each finger of both hands, and a better or worse calculated value is used as the evaluation value. Described brain dysfunction evaluation system. The brain dysfunction evaluation system according to claim 3, which is used for evaluating cognitive impairment. The brain dysfunction evaluation system according to claim 3, which is used for evaluation of mild cognitive impairment.