Alzheimer's disease risk monitoring and training device and method

Abstract

The present invention is applicable to the fields of risk monitoring and health care technology, and provides a device and method for monitoring and training Alzheimer's disease risk. The device comprises at least: a test training unit and a control unit; the test training unit comprises a test training platform, a signal light, a touch switch, and a reaction suppression switch, wherein two or more signal lights of different colors are arranged on the test training platform, and the touch switch and reaction suppression switch are also arranged on the test training platform; the signal light has a built-in pressure sensor for collecting pressure data on the signal light when the signal light is subjected to force; the control unit is electrically connected to the touch switch, signal light, reaction suppression switch, and pressure sensor. The present invention can be widely used in reaction time assessment to evaluate Alzheimer's disease status, and the reaction time assessment results can be used to provide targeted training plans to monitor and train Alzheimer's disease, thereby delaying the progression of Alzheimer's disease.

Description

Technical Field

[0001] The present invention belongs to the technical field of risk monitoring and health care, and provides a device and method for monitoring and training Alzheimer's disease risk. Background Art

[0002] Mild cognitive impairment (MCI), as a transitional stage of Alzheimer's disease (AD), has become a hot topic in Alzheimer's disease research. Exploring practical, low-cost, and quantifiable indicators is of great value in identifying and screening high-risk groups.

[0003] Reaction time refers to the time elapsed from stimulus presentation to the onset of an external response and is related to the state of preparation prior to stimulus presentation. As a reliable indicator of psychological activity, reaction time can measure the excitatory and inhibitory functions of the cerebral cortex and analyze various psychological activities, including perception, attention, learning and memory, thinking, and personality differences. Researchers from Tzu Chi Hospital used the Cognitive Screening Instrument (CASI) to assess the cognitive performance of study participants, using computer-controlled simple reaction time (SRT) and inhibition reaction time (FRT) tasks. The nonparametric Kruskal-Wallis test was used to compare CASI scores, SRT, and FRT. Pearson's partial correlation coefficient was used to assess the relationship between CASI scores and the inverse-transformed SRT and inverse-transformed FRT. Finally, it was concluded that the FRT test can be used as a criterion for cognitive decline in patients with mild cognitive impairment.

[0004] However, the above-mentioned prior art is only a method for simply measuring the simple overall reaction time, and has defects in the accuracy of the test results, which urgently needs to be improved. Summary of the Invention

[0005] The purpose of the embodiment of the present invention is to provide a device for monitoring and training the risk of Alzheimer's disease. Different from the traditional method of simply measuring simple overall reaction time, it distinguishes between simple reaction time, selection reaction time, discrimination reaction time, inhibition reaction time, reaction flexibility and inhibition reaction time, and distinguishes between neural reaction time and motor reaction time in the overall reaction time. At the same time, it also measures the execution accuracy, comprehensively monitoring the risk of Alzheimer's disease from multiple angles and levels, and training the cognitive flexibility of the elderly.

[0006] The embodiment of the present invention is implemented as follows: a device for monitoring and training Alzheimer's disease risk, the device comprising at least: a test training unit, a control unit;

[0007] The test training unit includes a test training platform, a signal light, a touch switch, and an inhibitory reaction switch. Two or more signal lights with different colored lights are arranged on the test training platform, and the touch switch and the inhibitory reaction switch are also arranged on the test training platform. The signal light has a built-in pressure sensor for collecting pressure data on the signal light when the signal light is subjected to force;

[0008] The control unit is electrically connected to the touch switch, the signal light, the reaction suppression switch and the pressure sensor;

[0009] The control unit is used to determine the subject's reaction time and reaction flexibility based on the reaction changes generated by the subject operating the touch switch, the signal light, and the reaction inhibition switch during the test, and to determine the subject's execution accuracy in combination with the pressure data collected by the pressure sensor to classify the subject's reaction time and movement time grade;

[0010] The control unit is further configured to select a first training program based on the classified reaction time and movement time, execute the first training program, and provide a training evaluation result after the first training program is executed;

[0011] Among them, the reaction change representation includes: neural reaction time, action reaction time, and inhibition reaction time; the neural reaction time is obtained by using the time difference between the moment the signal light turns on and the moment the touch switch state is changed; the action reaction time is obtained by using the time difference between the moment the touch switch state is changed and the moment the pressure sensor collects the pressure data; the inhibition reaction time is obtained by using the time difference between the moment the touch switch state is changed and the moment the inhibition reaction switch state is changed.

[0012] In order to facilitate the implementation of the Alzheimer's disease risk monitoring and training device, reduce the implementation difficulty and expand the scope of implementation, another object of the embodiment of the present invention is to provide an Alzheimer's disease risk monitoring and training method for use with the Alzheimer's disease risk monitoring and training device, the Alzheimer's disease risk monitoring and training method comprising the following steps:

[0013] Establishing a reference sample library, and dividing the reaction time and movement time classification catalog based on the reference sample library and the cognitive function screening tool;

[0014] Obtaining relevant change parameters generated by the subject during the cognitive assessment scale test, and determining the subject's reaction time, reaction flexibility, and execution accuracy based on the relevant change parameters;

[0015] Based on the reaction time and movement time classification catalog, determining a reaction time and movement time classification representing the degree of dementia of the subject according to the reaction time, reaction flexibility and execution accuracy of the subject;

[0016] selecting a first training program for the subject from a preset program library according to the determined reaction time and movement time classification;

[0017] Obtaining relevant change parameters generated by the subject during the execution of the first training program; and inputting the obtained relevant change parameters into a specified training scoring mechanism to obtain a scoring value of the training evaluation result;

[0018] Among them, the reaction time and movement time classification includes: reaction time level, reaction flexibility level and execution accuracy level; the relevant change parameters at least include: neural reaction time, action reaction time, inhibition reaction time and pressure data.

[0019] The Alzheimer's disease risk monitoring and training device provided by the embodiment of the present invention has the following beneficial effects compared with the prior art:

[0020] Based on existing classic cognitive function screening tools or cognitive function test scales, neural reaction time and motor reaction time are obtained by tapping a signal light, pressure data generated by tapping is obtained through a pressure sensor, and reaction time evaluation results of Alzheimer's disease patients are obtained using the Alzheimer's Reaction Time Assessment Scale. Based on the reaction time evaluation results, a training plan that can be executed in the device is given, and the training results are evaluated;

[0021] This method differs from the traditional method of simply measuring simple overall reaction time. It further distinguishes simple reaction time, choice reaction time, discrimination reaction time, inhibition reaction time, reaction flexibility and inhibition reaction time. It also distinguishes neural reaction time and motor reaction time in overall reaction time. The test also measures execution accuracy, comprehensively monitoring the risk of dementia from multiple angles and levels, and training the cognitive flexibility of the elderly.

[0022] The present invention can be widely used in reaction time assessment to evaluate the condition of Alzheimer's disease, and the reaction time assessment results can be used to provide targeted training programs to monitor and train Alzheimer's disease, thereby delaying the progression of Alzheimer's disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a structural diagram of a device for monitoring and training Alzheimer's disease risk provided in this embodiment;

[0024] Figure 2 A flowchart of steps for performing Alzheimer's disease risk monitoring training in one embodiment;

[0025] Figure 3 A structural block diagram of an embedded control system and a host computer control system in a control unit provided in this embodiment;

[0026] Figure 4 It is a structural block diagram of the host computer control system in another embodiment;

[0027] Figure 5 A software framework diagram of a device for monitoring and training Alzheimer's disease risk provided in this embodiment;

[0028] Figure 6 An execution logic diagram for testing and training a subject in one embodiment;

[0029] Figure 7 This is a flow chart of a method for monitoring and training Alzheimer's disease risk provided in this embodiment. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0031] The present invention has been observed in practice: researchers at Soochow University used the EP206-P motion reaction time meter to measure and compare the total reaction time, average reaction time, total motion time, and average motion time of 691 healthy elderly people and patients with Alzheimer's disease over a three-month period. The experimental data were statistically analyzed using receiver operating characteristic (ROC) curves, with the cutoff point corresponding to the highest sensitivity and specificity value being used as the optimal clinical diagnostic threshold. New variables were used to calculate the sensitivity, specificity, false positive rate, false negative rate, positive predictive value, negative predictive value, and accuracy of single and combined tests for mild cognitive impairment. It was found that the thinking speed, ability to respond to stimuli, and movement speed of patients with MCI and AD have all declined, and that reaction time and motion time can be used as methods for distinguishing and assisting in the diagnosis of MCI and AD.

[0032] Studies have shown that simple reaction speed training can specifically improve the elderly's central reaction speed, motor processing ability, and attentional focus, thereby delaying cognitive aging and improving cognitive function. Researchers at Tongji University observed the effects of simple reaction speed training on the executive functions of elderly people with mild cognitive impairment (MCI) and concluded that simple reaction speed training, through targeted training of central reaction speed, motor processing ability, and attentional focus, can improve serum BDNF (brain-derived neurotrophic factor) levels, executive function, and brain nerve function in elderly people with MCI, effectively delaying cognitive decline and serving as an important intervention for the early prevention of dementia.

[0033] Based on the above research, the present invention provides a cognitive flexibility-based Alzheimer's disease risk monitoring and training device and method for monitoring and training, which can also be widely used in Alzheimer's disease risk monitoring. It uses reaction time and strength measurement results to provide targeted training plans for application in the field of health rehabilitation.

[0034] In one embodiment, the present invention can be implemented by Figure 1 The structure shown is implemented; specifically: Figure 1 A structural diagram of an Alzheimer's disease risk monitoring and training device provided by an embodiment of the present invention may specifically include: a test training unit, a control unit;

[0035] The test training unit includes a test training platform 10, a signal light 1, a touch switch 2, and a reaction suppression switch 3. Two or more signal lights 1 with different colored lights are arranged on the test training platform 10, and the touch switch 2 and reaction suppression switch 3 are also arranged on the test training platform 10; the signal light 1 has a built-in pressure sensor 6 for collecting pressure data on the signal light when the signal light 1 is subjected to force;

[0036] The control unit is electrically connected to the touch switch 2, the signal light 1, the reaction suppression switch 3 and the pressure sensor 6;

[0037] The control unit is used to determine the subject's reaction time and reaction flexibility based on the reaction changes generated by the subject operating the touch switch 2, the signal light 1 and the reaction suppression switch 3 during the test, and to determine the subject's execution accuracy in combination with the pressure data collected by the pressure sensor 6 to classify the subject's reaction time and movement time grade;

[0038] The control unit is further configured to select a first training program based on the classified reaction time and movement time, execute the first training program, and provide a training evaluation result after the first training program is executed;

[0039] Among them, the reaction change representation includes: neural reaction time, action reaction time, and inhibition reaction time; the neural reaction time is obtained by using the time difference between the moment when the signal light 1 lights up and the moment when the touch switch 2 changes state; the action reaction time is obtained by using the time difference between the moment when the touch switch 2 changes state and the moment when the pressure sensor 6 collects pressure data; the inhibition reaction time is obtained by using the time difference between the moment when the touch switch 2 changes state and the moment when the inhibition reaction switch 3 changes state.

[0040] In this embodiment, the reaction change representation is obtained by tapping the signal light 1, and the reaction change representation can be divided into neural reaction time and action reaction time. The neural reaction time can be characterized by simple reaction time, selection reaction time, discrimination reaction time, inhibition reaction time, reaction flexibility, etc., and the action reaction time is characterized by execution accuracy; the pressure data generated by tapping the signal light 1 is obtained by the pressure sensor 6, and the reaction time evaluation result of the Alzheimer's disease reaction time assessment scale (or cognitive assessment scale) is obtained by referring to the Alzheimer's disease reaction time assessment scale (which can be measured online or in advance), and according to the reaction time evaluation situation, a training program that can be executed in the device is given, and the training is evaluated. The training results are evaluated; the present invention is different from the traditional method of simply measuring simple overall reaction time. It further distinguishes simple reaction time, selection reaction time, discrimination reaction time, inhibition reaction time, reaction flexibility and inhibition reaction time, distinguishes neural reaction time and action reaction time in the overall reaction time, and measures the execution accuracy during the test. It monitors the risk of Alzheimer's disease from multiple angles and levels, and trains the cognitive flexibility of the elderly; this embodiment can be widely used in reaction time evaluation, used to evaluate the condition of Alzheimer's disease, and use the reaction time evaluation results to provide targeted training plans, monitor and train Alzheimer's disease, so as to delay the progression of Alzheimer's disease.

[0041] like Figure 1 As shown, in an example of this embodiment, the touch switch 2 can be an electronic touch panel, or a control key or button, and the reaction suppression switch 3 can be a row of reaction suppression buttons, but this embodiment is not limited thereto.

[0042] In one example of this embodiment, for health care and rehabilitation training for elderly dementia, a training library can be established based on empirical data, big data, or by experienced workers. The training library includes a series of training programs corresponding to each level in the reaction time and movement time classification. Generally, in this embodiment, the training program applicable to the current classification result is referred to as the first training program. Of course, there can be a second training program in the training library that is equivalent to the first training program; the second training program can serve as a backup, and the first training program is preferred. Specifically, according to existing means, for example, a normal and patient reaction time sample library and a normal and patient movement time sample library can be established based on empirical data or big data. In the normal and patient reaction time samples, the elderly's reaction time classification is combined with cognitive function screening tools, including the elderly's simple reaction time classification, selection reaction time classification, discrimination reaction time classification, and reaction flexibility classification. In the normal and patient movement time sample library (or normal and patient slapping force sample library), the execution accuracy is graded according to different degrees of dementia; the normal and patient reaction time sample library and the normal and patient movement time sample library together constitute a reference sample library. Patients with Alzheimer's disease were given training programs to improve the risk of Alzheimer's disease in terms of simple reaction time grading, choice reaction time grading, discrimination reaction time grading, reaction flexibility and execution accuracy.

[0043] like Figure 1 、 Figure 2 As shown, in an example of this embodiment, when conducting Alzheimer's disease risk monitoring training, after turning on the device, the subject's hand is placed on the touch switch 2, which is the initial moment or preparation stage. The neural response time is obtained by using the time difference between the moment when the signal light 1 lights up and the moment when the touch switch 2 changes state, which is recorded as T1; the action response time is obtained by using the time difference between the moment when the touch switch 2 changes state and the moment when the pressure sensor 6 collects pressure data, which is recorded as T2; the inhibitory response time is obtained by using the time difference between the moment when the touch switch 2 changes state and the moment when the inhibitory response switch 3 changes state, which is recorded as T3; the slapping force data N is collected by the pressure sensor 6 and analog-to-digital conversion is performed;

[0044] Furthermore, by tapping a single light 1 at a known position five times, the neural response times STr1, STr2, STr3, STr4, STr5 and the action response times STs1, STs2, STs3, STs4, STs5 are obtained; by tapping a single light 1 at an unknown position five times, the neural response times CTr1, CTr2, CTr3, CTr4, CTr5 and the action response times CTs1, CTs2, CTs3, CTs4, CTs5 are obtained; by tapping a signal light 1 of a specified color five times, the neural response times DTr1, DTr2, DTr3, DTr4, DTr5 and the action response times DTs1, DTs2, DTs3, DTs4, DTs5 are obtained. s5, the position of the signal light 1 is unknown, the number of signal lights 1 that light up is greater than 2 and the colors are different; use the inhibitory reaction switch corresponding to the single signal light 1 at the unknown position to tap five times to obtain the inhibitory neural reaction times Ti1, Ti2, Ti3, Ti4, and Ti5; use the single signal light at the specified position to tap five times to obtain the neural reaction times FTr1, FTr2, FTr3, FTr4, and FTr5 and the action reaction times FTs1, FTs2, FTs3, FTs4, and FTs5, and tap the single signal light at the unknown position again to obtain the neural reaction time FTr6 and the action reaction time FTs6; use the specified force to tap the signal light at the known position six times to obtain the sixth tapping force N6; then:

[0045] Simple reaction time TRT is:

[0046] TRT=(STr1+STr2+STr3+STr4+STrS+STs1+STs2+STs3+STs4+STs5) / 5 (1);

[0047] The TSRT for the choice reaction is:

[0048] TSRT=(CTr1+CTr2+CTr3+CTr4+CTr5+CTs1+CTs2+CTs3+CTs4+CTs5) / 5 (2);

[0049] Discrimination reaction time TDRT:

[0050] TDRT=(DTr1+DTr2+DTr3+DTr4+DTr5+DTs1+DTs2+DTs3+DTs4+DTs5) / 5 (3);

[0051] When the reaction is suppressed, TIRT is:

[0052] TIRT=(Ti1+Ti2+Ti3+Ti4+Ti5) / 5 (4);

[0053] The reaction flexibility Tf is:

[0054] Tf=(FTr6+FTs6)-(FTr1+FTr2+FTr3+FTr4+FTr5+FTs1+FTs2+FTs3+FTs4+FTs5) / 5(5);

[0055] The execution accuracy N is: N6.

[0056] The test methods for each item are shown in Table 1 below;

[0057] Table 1 is the test directory of each project

[0058]

[0059]

[0060] The reaction time, reaction flexibility and execution accuracy of the elderly obtained from the above test are compared with the reaction time grade, reaction flexibility grade and execution accuracy grade of the elderly classified by the reference sample library to determine the reaction time grade, reaction flexibility grade and execution accuracy grade of the subject, and obtain the corresponding Alzheimer's disease monitoring training program, for example: the first training program; the subject is trained according to the corresponding Alzheimer's disease monitoring training program; in addition, the training results can be evaluated and the evaluation results can be given during or after the training, and further, the training results can be archived.

[0061] like Figure 1 、 Figure 3 As shown, in one embodiment, the control unit includes an embedded control system 5 and a host computer control system 20;

[0062] It is understandable that the Alzheimer's disease risk monitoring and training device is composed of a hardware part and a software part. Figure 1 As shown, the software part is as follows Figure 5 As shown, in addition, Figure 6 As shown, it is an execution logic diagram of the test training for the subject in this embodiment, which can provide inspiration for the implementation of this embodiment and will not be described in detail here.

[0063] In this embodiment, the embedded control system 5 includes a data processing module 202 and a power module 203. The data processing module 202 is connected to the signal light 1, the touch switch 2, and the suppression reaction switch 3 via the input / output module 201. The data processing module 202 is connected to the pressure sensor 6 via the analog-to-digital converter 4. The power module 203 is used to provide electrical energy.

[0064] Among them, the embedded control system 5 can adopt a microprogram controller or a PLC controller. Taking the PLC controller as an example, the microprocessor (CPU) in the PLC controller can be used as the above-mentioned data processing module 202; used for automatic control of the lighting or light color conversion of the signal light 1. In some examples, the PLC controller has unit modules such as instruction and data memory, input and output units, power supply modules, digital simulation modules, etc., which can be used as the above-mentioned input and output modules 201, power supply modules 203 and analog-to-digital converter 4, etc.

[0065] In this embodiment, the host computer control system 20 includes a communication module 301, a data storage module 302, and a data analysis module 303. The communication module 301 connects the data processing module 202 and the data storage module 302, and the data analysis module 303 is connected to the data storage module 302 for realizing test and training analysis.

[0066] In some examples, the communication module 301 includes a WIFI module, an Ethernet port, a serial port, and a USB port. The WIFI module and Ethernet port are used for data communication over a local area network or the Internet. The serial port and USB port communicate data with the embedded control system 5 via data acquisition and transmission components. The data storage module 302 can use a conventional memory, the capacity of which can be selected based on actual needs. The data analysis module 303 can be implemented through training using an existing deep learning model. This deep learning model can compare the different degrees of dementia of elderly patients classified by a reference sample library and cognitive function screening tools with the reaction time grade, reaction flexibility grade, and execution accuracy grade generated by the test training unit, and provide the subject's reaction time grade, reaction flexibility grade, and execution accuracy grade. In this way, analysis of testing and training can be achieved.

[0067] In one example, the deep learning model is a deep learning neural network, including but not limited to a VGG model, a residual convolutional neural network model (ResNet), a Mobilenet model, a DenseNet model, etc. During the training of the deep learning model, an optimizer can be used to adaptively adjust parameters such as the activation function and loss function of the deep learning model to facilitate faster completion of the training of the deep learning model.

[0068] Wherein the optimizer includes one or more of a stochastic gradient descent optimizer, an adaptive gradient optimizer, and an exponentially weighted moving average optimizer. It is understood that the optimizer includes but is not limited to a stochastic gradient descent optimizer (SGD), an adaptive gradient optimizer (AdaGrad), and an exponentially weighted moving average optimizer (RMSProp). In a preferred embodiment, the optimizer used is a stochastic gradient descent optimizer (SGD).

[0069] In one aspect of this embodiment, the input / output module 201 is a conventional transmission interface. For low-latency transmission of signals from the signal light 1, the touch switch 2, and the suppression reaction switch 3, a high-speed data transmission line is preferred. The analog-to-digital converter 5 can be a conventional AD converter. The power module 203 can be a rechargeable battery or a conventional lithium battery, preferably capable of providing a 3.8V voltage output.

[0070] like Figure 3 、 Figure 4 As shown, in one case of this embodiment, the host computer control system includes a communication module 301, a data storage module 302, a data analysis module 303, a display module 304 and a voice module 305. The communication module 301 is connected to the data processing module 202 and the data storage module 302, and the data analysis module 303 is respectively connected to the data storage module 302, the display module 304 and the voice module 305, for realizing the analysis, display and broadcast of testing and training.

[0071] In this embodiment, the display module 304 may be a host computer or an embedded touch screen provided on the test training platform. The voice module 305 may include a microphone and a speaker.

[0072] like Figure 5 As shown, in one embodiment, the data processing module 202 is pre-installed with a reaction time testing module 103 and a reaction time training module 104;

[0073] The reaction time testing module 103 is used to determine the reaction time and reaction flexibility of the subject based on the reaction changes generated by the subject operating the touch switch 2, the signal light 1 and the reaction suppression switch 3 during the test, and to determine the execution accuracy of the subject in combination with the pressure data collected by the pressure sensor 6 to classify the reaction time and movement time grade of the subject;

[0074] The reaction time training module 104 is used to select a first training program according to the classified reaction time and movement time levels, execute the first training program, and provide a training evaluation result after the first training program is executed.

[0075] In one example of this embodiment, the reaction time testing module 103, its testing process can be referred to in Table 1. Similarly, the reaction time training module 104 can provide the test training items (see Table 1) corresponding to the first training program through the host computer control system, and complete the test training tasks according to the prompts.

[0076] After the training is completed, the subject's training can be scored, and the first training program can be adjusted or optimized based on the scoring result.

[0077] In this embodiment, during the test, the subject uses the time difference between the moment the signal light 1 lights up and the moment the touch switch 2 switches state to obtain the neural reaction time T1; uses the time difference between the moment the touch switch 2 switches state to obtain the pressure data and the moment the pressure sensor 6 collects the action reaction time T2; uses the time difference between the moment the touch switch 2 switches state to obtain the inhibition reaction time T3; uses the pressure sensor 6 and the AD converter to collect the slap force data N;

[0078] By tapping a single light at a known location 1 five times, the neural response times STr1, STr2, STr3, STr4, STr5 and the action response times STs1, STs2, STs3, STs4, STs5 were obtained; by tapping a single light at an unknown location 1 five times, the neural response times CTr1, CTr2, CTr3, CTr4, CTr5 and the action response times CTs1, CTs2, CTs3, CTs4, CTs5 were obtained; by tapping a signal light of a specified color 1 five times, the neural response times DTr1, DTr2, DTr3, DTr4, DTr5 and the action response times DTs1, DTs2, DTs3, DTs4, DTs 5. The position of the signal light 1 is unknown, and the number of signal lights 1 that light up is greater than 2 and the colors are different; use the inhibitory reaction switch corresponding to the single signal light 1 at the unknown position to tap five times to obtain the inhibitory neural reaction times Ti1, Ti2, Ti3, Ti4, and Ti5; use the single signal light at the specified position to tap five times to obtain the neural reaction times FTr1, FTr2, FTr3, FTr4, and FTr5 and the action reaction times FTs1, FTs2, FTs3, FTs4, and FTs5, and tap the single signal light at the unknown position again to obtain the neural reaction time FTr6 and the action reaction time FTs6; use the specified force to tap the signal light at the known position six times to obtain the sixth tapping force N6;

[0079] Afterwards, using the above formulas (1) to (5), the following are calculated: simple reaction time TRT, selection reaction time TSRT, discrimination reaction time TDRT, inhibition reaction time TIRT, and reaction flexibility Tf; and adding the measured execution accuracy N as N6, the reaction time and movement time grade of the subject can be classified; and the first training program can be selected based on the reaction time and movement time grade, and then the first training program is executed, and a training evaluation result is given after the first training program is executed.

[0080] In an example of this embodiment, the data processing module 202 can be pre-installed with a login / test module 101 and a personal center 105, and the user's basic information, such as name, gender, age, ID number, user name and password, can be registered or registered through the login / test module 101; the personal center 105 is used to record the changing trend of the subject's cognitive level, training results, evaluation results, etc.

[0081] In an example of this embodiment, during the execution of the first training program, the simple reaction time TRT, the selection reaction time TSRT, the discrimination reaction time TDRT, the inhibition reaction time TIRT, and the reaction flexibility Tf can also be monitored and calculated, and based on the difference in reaction time before and after training, the deviation in the training execution process can be corrected and the training effect can be obtained.

[0082] In one embodiment, the data processing module 202 is also pre-installed with a CASI test module 102, and the CASI test module 102 is used to test the cognitive function level of the subject.

[0083] The CASI test module 102 is constructed based on a cognitive assessment scale, and the cognitive function level of the subject can be obtained by conducting a questionnaire based on the cognitive assessment scale.

[0084] As described above, this embodiment allows the user (or subject) to register basic information through the login / registration module 101, obtain the user's cognitive function level through the CASI test module 102, and obtain the user's neural reaction time and action reaction time under the cognitive neuroscience task paradigm through the reaction time test module 103. The neural reaction time is calculated by recording the time from the random task signal light 1 to the hand leaving the starting point (touching switch 2). This time indicates that the user has completed the cognitive decision for the specific task. The action reaction time specifically refers to the time from the user's hand leaving the starting point to the signal light 1 turning off. It indicates the time required for the user to perform the task and reflects the sensitivity of the execution. Changes in the user's cognitive function and executive function are monitored; the reaction time training module 104 improves the user's cognitive function and executive function through training in cognitive neuroscience tasks. The personal center 105 is used to record the changing trend of the user's cognitive level.

[0085] In one embodiment, the Alzheimer's disease risk monitoring and training device further comprises: a program optimization module;

[0086] The program optimization module is used to read the training evaluation results stored in the data storage module 302, determine the differences between previous training evaluation results, and optimize the first training program according to the differences.

[0087] In this embodiment, the solution optimization module includes: a data calling unit, a difference comparison unit and a solution optimization unit;

[0088] The data calling unit is used to read the training evaluation results stored in the data storage module;

[0089] The difference comparison unit is used to determine the differences in the results of previous training assessments;

[0090] The scheme optimization unit is used to optimize the first training scheme according to the difference.

[0091] In an example of this embodiment, the data calling unit is a data calling interface, and the difference comparison unit includes a digital converter and a comparator. The reaction time, reaction flexibility and execution accuracy generated by the test and training are converted into numerical values ​​through the digital converter. Thereafter, the difference between the converted values ​​is compared through the comparator. When the difference exceeds a set threshold, the first training plan can be optimized; the optimization can be performed one by one for the reaction time, reaction flexibility and execution accuracy with differences, and it is not necessary to adjust all reaction times, reaction flexibility and execution accuracy.

[0092] Generally, the program optimization unit may be a modifier, which may modify or replace part of the content of the original first training program, or even modify or replace the first training program.

[0093] In an example of this embodiment, the solution optimization module is set in the host computer control system, so that the training evaluation results stored in the data storage module can be directly called (read); it is convenient and fast, and does not affect the data transmission or exchange between the embedded control system and the host computer control system.

[0094] like Figure 7 As shown, in another embodiment, a method for monitoring and training the risk of Alzheimer's disease is used in the aforementioned Alzheimer's disease risk monitoring and training device, and the method for monitoring and training the risk of Alzheimer's disease comprises the following steps:

[0095] Establishing a reference sample library, and dividing the reaction time and movement time classification catalog based on the reference sample library and the cognitive function screening tool;

[0096] Obtaining relevant change parameters generated by the subject during the cognitive assessment scale test, and determining the subject's reaction time, reaction flexibility, and execution accuracy based on the relevant change parameters;

[0097] Based on the reaction time and movement time classification catalog, determining a reaction time and movement time classification representing the degree of dementia of the subject according to the reaction time, reaction flexibility and execution accuracy of the subject;

[0098] selecting a first training program for the subject from a preset program library according to the determined reaction time and movement time classification;

[0099] Obtaining relevant change parameters generated by the subject during the execution of the first training program; and inputting the obtained relevant change parameters into a specified training scoring mechanism to obtain a scoring value of the training evaluation result;

[0100] Among them, the reaction time and movement time classification includes: reaction time level, reaction flexibility level and execution accuracy level; the relevant change parameters at least include: neural reaction time, action reaction time, inhibition reaction time and pressure data.

[0101] In the specific implementation of this embodiment, Figure 1 As shown, the subject first performs a preparatory action, with one hand placed on the touch switch 2, waiting for the host computer control system 20 to prompt that it is ready. After being ready, the subject waits for the host computer control system 20 to prompt the content of the test training project (see Table 1), and the subject completes the test training task according to the prompt content; during the test training process, the embedded control system automatically obtains and processes the corresponding related change parameters; to determine the reaction time, reaction flexibility and execution accuracy of the subject, and then determine the reaction time and movement time grade that characterizes the degree of dementia of the subject, that is, the reaction time grade, reaction flexibility grade and execution accuracy grade, etc.

[0102] In one embodiment, the reference sample library includes a normal and patient reaction sample library, and a normal and patient exercise sample library.

[0103] The construction of the reaction time sample library of normal and patients and the movement time sample library of normal and patients refer to the above embodiments and will not be repeated here.

[0104] In one embodiment, the training scoring mechanism satisfies:

[0105]

[0106]

[0107] Among them, s t is the score value of the training evaluation result, T ais the patient's reaction time; T0 is the normal reaction time; s m is the highest single score, s m =100 / N t ; N t is the number of scoring times, N t =m×n; m is the number of training groups, n is the number of training times in each group.

[0108] In the host computer control system 20, the score value s t , judge the training effect of the subject, and can use the score value s t Adjust the plan for the next test and training.

[0109] In one embodiment, after the step of establishing the reference sample library, an association is established between the solution library and the reference sample library, specifically including:

[0110] In the reaction time sample library of normal and patient samples, reaction time is graded in combination with cognitive function screening tools, and the reaction time grades are divided into simple reaction time grade, choice reaction time grade, discrimination reaction time grade and reaction flexibility grade;

[0111] In the sample library of normal and patient exercisers, the execution accuracy is graded according to different degrees of dementia;

[0112] Corresponding training plans are given for simple reaction time grading, choice reaction time grading, discrimination reaction time grading, reaction flexibility grading and execution accuracy grading to establish a connection with the plan library.

[0113] The association between such training programs and program libraries follows the reaction time grading and execution accuracy grading according to different degrees of dementia; the reaction time grading is divided into: simple reaction time grading, choice reaction time grading, discrimination reaction time grading, and reaction flexibility grading; therefore, the simple reaction time grading, choice reaction time grading, discrimination reaction time grading, reaction flexibility and execution accuracy of the subjects are given dementia monitoring training programs respectively, and before the test, cognitive function screening tools such as the measurement of cognitive assessment scales, test data during the test, and training data during the training process form a closed loop to realize the risk monitoring training of dementia for the subjects, so as to better improve the degree of dementia or avoid the risk of dementia.

[0114] The present embodiment provides a device for monitoring and training the risk of Alzheimer's disease, and provides a method for monitoring and training the risk of Alzheimer's disease based on the device. The device for monitoring and training the risk of Alzheimer's disease obtains a reaction change representation by tapping a signal light 1 during testing and training. The reaction change representation can be divided into neural reaction time and action reaction time. The neural reaction time can be characterized by simple reaction time, selection reaction time, discrimination reaction time, inhibition reaction time, reaction flexibility, etc. The action reaction time can be characterized by execution accuracy. The pressure data generated by tapping the signal light 1 is obtained by a pressure sensor 6, and the reaction time evaluation result of the Alzheimer's disease patient is obtained by referring to the cognitive assessment scale, and the reaction time is evaluated according to the reaction time. Evaluation situation, provide a training program that can be executed in the device, and evaluate the training results; the present invention is different from the traditional method of simply measuring simple overall reaction time, further distinguishes simple reaction time, selection reaction time, discrimination reaction time, inhibition reaction time, reaction flexibility and inhibition reaction time, distinguishes neural reaction time and action reaction time in the overall reaction time, and measures the execution accuracy during the test, comprehensively monitors the risk of Alzheimer's disease from multiple angles and levels, and trains the cognitive flexibility of the elderly; this embodiment can be widely used in reaction time evaluation, used to evaluate the situation of Alzheimer's disease, and use the reaction time evaluation results to provide targeted training programs, monitor and train Alzheimer's disease, so as to delay the progression of Alzheimer's disease.

[0115] It should be noted that Figure 5 The device for monitoring and training Alzheimer's disease risk shown can be implemented by a computer program installed in a computer device; the computer device includes a processor, memory, a network interface, an input device, and a display screen connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor may implement the method for monitoring and training Alzheimer's disease risk. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor may implement the method for monitoring and training Alzheimer's disease risk. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen. The input device of the computer device may be a touch layer covering the display screen, or may be a key, trackball, or touchpad provided on the computer device housing, or may be an external keyboard, touchpad, or mouse.

[0116] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0117] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0118] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0119] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0120] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A device for monitoring and training Alzheimer's disease risk, characterized in that: The Alzheimer's disease risk monitoring and training device at least comprises: a test training unit and a control unit; The test training unit includes a test training platform, a signal light, a touch switch, and an inhibitory reaction switch. Two or more signal lights with different colored lights are arranged on the test training platform, and the touch switch and the inhibitory reaction switch are also arranged on the test training platform. The signal light has a built-in pressure sensor for collecting pressure data on the signal light when the signal light is subjected to force; The control unit is electrically connected to the touch switch, the signal light, the reaction suppression switch and the pressure sensor; The control unit is used to determine the subject's reaction time and reaction flexibility based on the reaction changes generated by the subject operating the touch switch, the signal light, and the reaction inhibition switch during the test, and to determine the subject's execution accuracy in combination with the pressure data collected by the pressure sensor to classify the subject's reaction time and movement time grade; The control unit is further configured to select a first training program based on the classified reaction time and movement time, execute the first training program, and provide a training evaluation result after the first training program is executed; Among them, the reaction change representation includes: neural reaction time, action reaction time, and inhibition reaction time; the neural reaction time is obtained by using the time difference between the moment the signal light turns on and the moment the touch switch state is changed; the action reaction time is obtained by using the time difference between the moment the touch switch state is changed and the moment the pressure sensor collects the pressure data; the inhibition reaction time is obtained by using the time difference between the moment the touch switch state is changed and the moment the inhibition reaction switch state is changed.

2. The Alzheimer's disease risk monitoring and training device according to claim 1, characterized in that: The control unit includes an embedded control system and a host computer control system; The embedded control system includes a data processing module and a power supply module. The data processing module is connected to the signal light, touch switch, and suppression reaction switch through an input and output module. The data processing module is connected to the pressure sensor through an analog-to-digital converter. The power supply module is used to provide electrical energy. The host computer control system includes a communication module, a data storage module, a data analysis module, a display module and a voice module. The communication module is connected to the data processing module and the data storage module. The data analysis module is respectively connected to the data storage module, the display module and the voice module to realize the analysis, display and broadcast of testing and training.

3. The Alzheimer's disease risk monitoring and training device according to claim 2, characterized in that: The data processing module is pre-installed with a reaction time testing module and a reaction time training module; The reaction time testing module is used to determine the reaction time and reaction flexibility of the subject based on the reaction changes generated by the subject operating the touch switch, signal light and reaction inhibition switch during the test, and to determine the execution accuracy of the subject in combination with the pressure data collected by the pressure sensor to classify the reaction time and movement time grade of the subject; The reaction time training module is used to select a first training program according to the classified reaction time and movement time levels, execute the first training program, and provide a training evaluation result after the first training program is executed.

4. The Alzheimer's disease risk monitoring and training device according to claim 3, characterized in that: The data processing module is also pre-installed with a CASI test module, which is used to test the cognitive function level of the subject.

5. The Alzheimer's disease risk monitoring and training device according to claim 3, characterized in that: The Alzheimer's disease risk monitoring and training device further includes: a program optimization module; The program optimization module is used to read the training evaluation results stored in the data storage module, determine the differences between previous training evaluation results, and optimize the first training program according to the differences.

6. The Alzheimer's disease risk monitoring and training device according to claim 5, characterized in that: The solution optimization module includes: a data calling unit, a difference comparison unit and a solution optimization unit; The data calling unit is used to read the training evaluation results stored in the data storage module; The difference comparison unit is used to determine the differences in the results of previous training assessments; The scheme optimization unit is used to optimize the first training scheme according to the difference.

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