Cognitive intervention method and device, wearable equipment and storage medium

Abstract

The invention provides a cognitive intervention method and device, wearable equipment and a storage medium. The wearable device comprises an audio output assembly, a light output assembly, a transcranial electrical stimulation assembly and a display assembly, and the cognitive intervention method comprises the steps that after a user wears the wearable device, the audio output assembly, the light output assembly, the transcranial electrical stimulation assembly and the display assembly are controlled to execute cognitive intervention measures. And adjusting the cognitive intervention measures in response to user feedback. The cognitive competence of the user can be effectively improved, and meanwhile side effects possibly caused by some stimuli can be reduced.

Description

Technical Field

[0001] The present disclosure relates to the technical field of medical equipment, and in particular to a cognitive intervention method, apparatus, wearable device and storage medium. Background Art

[0002] Alzheimer's disease (AD) is the most common neurodegenerative disease, which is more common in the elderly. It is mainly manifested by the gradual loss of cognitive function in patients. The pathological hallmarks of Alzheimer's disease include amyloid-β (Aβ) and phosphorylated Tau protein. Abnormal neural activity can also aggravate the deterioration of AD and eventually destroy the neural circuits of advanced cognitive functions. The latest research shows that the use of photoacoustic stimulation of a certain frequency can reduce the accumulation of amyloid-β (Aβ) and phosphorylated Tau protein, thereby alleviating or even curing Alzheimer's disease. However, current phototherapy devices all use flickering light of a fixed frequency to provide visual stimulation to patients. However, since the visual stimulation of flickering light of a fixed frequency may be too strong for some patients, it is easy to cause side effects such as dizziness, vomiting, and headaches. Therefore, patients are less comfortable when using phototherapy equipment, and it is difficult to use phototherapy equipment to treat patients in daily life, resulting in poor treatment effects on patients' cognitive functions. Summary of the invention

[0003] In view of this, the present disclosure provides a cognitive intervention method, apparatus, wearable device and storage medium.

[0004] According to a first aspect of the present disclosure, a cognitive intervention method is provided, wherein the cognitive intervention method is applied to a wearable device, wherein the wearable device includes an audio output component, a light output component, a transcranial electrical stimulation component, and a display component; the cognitive intervention method includes: After the user wears the wearable device, the audio output component, the light output component, the transcranial electrical stimulation component and the display component are controlled to perform cognitive intervention measures, wherein the cognitive intervention measures include applying acoustic stimulation, light stimulation, transcranial electrical stimulation and displaying cognitive training content to the user: The cognitive intervention is adjusted in response to user feedback.

[0005] In some embodiments of the first aspect of the present disclosure, adjusting the cognitive intervention measures in response to user feedback includes: constructing a cognitive ability curve in response to user feedback, wherein the cognitive ability curve is used to indicate the real-time change trend of one or more cognitive ability indicators of the user and / or the real-time change trend of the user's comprehensive cognitive ability; and adjusting the cognitive intervention measures according to the cognitive ability curve.

[0006] In some embodiments of the first aspect of the present disclosure, constructing a cognitive ability curve in response to user feedback includes: obtaining user feedback data, wherein the user feedback data includes: EEG data and motion data; using the user feedback data to obtain scores of one or more cognitive ability indicators of the user; and fitting the cognitive ability curve based on the scores of one or more cognitive ability indicators of the user.

[0007] In some embodiments of the first aspect of the present disclosure, the wearable device also includes an inertial measurement unit IMU and an EEG component; the acquisition of user feedback data includes: during the execution of the cognitive intervention measures, controlling the EEG component to collect the user's EEG data, and controlling the IMU to measure the user's body movement data.

[0008] In some embodiments of the first aspect of the present disclosure, the controlling the audio output component, the light output component, the transcranial electrical stimulation component, and the display component to perform cognitive intervention measures comprises: Controlling the audio output component to output 40 Hz binaural beats to apply acoustic stimulation to the user; Controlling the light output assembly to emit light alternately at 40 Hz to apply light stimulation to the user; Controlling the transcranial electrical stimulation component to apply transcranial alternating current stimulation to the user's scalp; The display component is controlled to display predetermined cognitive training content to the user.

[0009] In some implementations of the first aspect of the present disclosure, the audio output component includes an LED array; and controlling the audio output component to output 40 Hz double sound beats to apply acoustic stimulation to the user includes one or more of the following: Control all LED lights in the LED array to flash 40 times at a flashing frequency of 40 Hz; The LED array is controlled to flash in a checkerboard-like staggered manner at a flashing frequency of 40 Hz.

[0010] In some embodiments of the first aspect of the present disclosure, adjusting the cognitive intervention measure in response to user feedback comprises: In response to user feedback, adjust one or more of the following: The cognitive training content; stimulation parameters of the audio output component; stimulation parameters of the light output assembly; stimulation parameters of the transcranial electrical stimulation component; The execution order of the cognitive training content, the sound stimulation, the light stimulation and the transcranial electrical stimulation.

[0011] According to a second aspect of the present disclosure, a cognitive intervention device is provided, which is applied to a wearable device, wherein the wearable device includes an audio output component, a light output component, a transcranial electrical stimulation component, and a display component; the cognitive intervention device includes: A cognitive intervention execution unit is used to control the audio output component, the light output component, the transcranial electrical stimulation component and the display component to execute cognitive intervention measures after the user wears the wearable device. The cognitive intervention measures include applying acoustic stimulation, light stimulation, transcranial electrical stimulation and displaying cognitive training content to the user: A cognitive intervention adjustment unit is used to adjust the cognitive intervention in response to user feedback.

[0012] According to a third aspect of the present disclosure, a wearable device is provided, comprising: an audio output component, a light output component, a transcranial electrical stimulation component, a display component, a processor and a memory storing a program, wherein the program comprises instructions, and when the instructions are executed by the processor, the processor executes the above-mentioned method.

[0013] According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium storing a program, wherein the program includes instructions, and when the instructions are executed by one or more processors, the computing device executes the above method.

[0014] It can be seen from the above technical solutions that the embodiments of the present disclosure can apply cognitive intervention measures including sound stimulation, light stimulation, transcranial electrical stimulation and cognitive training content to users through wearable devices, and can dynamically adjust cognitive intervention measures in a timely manner by continuously monitoring user feedback (that is, changes in their cognitive abilities), thereby effectively improving the user's cognitive abilities while reducing the side effects of certain stimuli. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 An exemplary structural diagram of a wearable device provided in an embodiment of the present disclosure; Figure 2 An example diagram of the appearance and wearing of a wearable device provided in an embodiment of the present disclosure; Figure 3 A flowchart of a cognitive intervention method provided by an embodiment of the present disclosure; Figure 4A schematic diagram of the structure of a cognitive intervention device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0018] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments, and are not intended to limit the present disclosure. The singular forms "a", "said" and "the" used in the embodiments of the present disclosure and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0019] As used herein, the words "if," "if," and the like may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0020] For ease of understanding, the wearable device 100 provided in the embodiment of the present disclosure is first described in detail below.

[0021] Figure 1 A schematic diagram of the structure of a wearable device 100 provided in an embodiment of the present disclosure is shown. Figure 2 An example diagram showing the appearance and wearing state of the wearable device 100 provided in an embodiment of the present disclosure is shown.

[0022] See also Figure 1 and Figure 2 , the wearable device 100 may include: a processor 101, a memory 102, an audio output component 103, an optical output component 104, a transcranial electrical stimulation component 105 and a display component 106, and the memory 102, the audio output component 103, the optical output component 104, the transcranial electrical stimulation component 105 and the display component 106 are respectively connected to the processor 101. Among them, the memory 102 stores a program, and the program includes instructions, and the instructions are executed by the above-mentioned processor 101 to implement the process of the cognitive intervention method shown in the following embodiments of the present disclosure and / or the program units corresponding to each unit in the cognitive intervention device.

[0023] In a specific application, the number of processors 101 may be one or more, and the type may be but is not limited to a microprocessor 101 (MCU), a central processing unit 101 (CPU), a graphics processor or other types.

[0024] In a specific application, the processor 101, the memory 102, the audio output component 103, the optical output component 104, the transcranial electrical stimulation component 105, the display component 106, the IMU 107 described below, the EEG component 108 described below, and other components may be connected via a bus. For example, these components may be connected to each other using different buses.

[0025] The memory 102 is a computer-readable storage medium provided by the present disclosure, which can be used to store non-transient software programs, non-transient computer executable programs and units, such as the following in the embodiments of the present disclosure: Figure 3 The processor 101 executes the following embodiments by running the non-transient software programs, instructions and units stored in the memory 102. Figure 3 The procedures, instructions and units corresponding to the cognitive intervention methods shown.

[0026] The above-mentioned programs (also referred to as software, software applications, or codes) include machine instructions for programmable processor 101, and these computer programs can be implemented using object-oriented programming languages, assembly or machine languages.

[0027] With the development of time and technology, the meaning of medium is becoming more and more extensive, and the dissemination path of computer programs is no longer limited to tangible media, but can also be downloaded directly from the Internet, etc. Any combination of one or more computer-readable storage media can be used. Computer-readable storage media can be used but not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media (non-exhaustive list) include: portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories 102 (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, computer-readable storage media can be any tangible medium containing or storing programs, which can be used by or in combination with instruction execution systems, devices or devices.

[0028] The transcranial electrical stimulation component 105 can be used to apply current to specific areas of the user's brain through electrodes to regulate the neural activity of the user's brain, thereby improving brain function and achieving the purpose of treating cognitive disorders such as AD.

[0029] In some examples, the transcranial electrical stimulation component 105 may include: electrodes, wires, and stimulators. The electrodes may be used to deliver current to specific areas of the user's scalp and brain, the wires may be used to connect the electrodes and the stimulator to deliver current between the electrodes and the stimulator, and the stimulator may be used to control the intensity, direction, and duration of the current.

[0030] See also Figure 2 , the electrodes can be mounted on the fixture 202 so that after the user wears the wearable device 100, the electrodes contact multiple designated areas of the user's scalp to cover a larger brain area, so that the electrodes can transmit current to the user's scalp and cerebral cortex.

[0031] In specific applications, the electrodes in the transcranial electrical stimulation component 105 can be made of conductive materials such as silver, gold or copper, or can be made of biocompatible materials such as medical grade silicone, silver / silver chloride (Ag / AgCl) or stainless steel to ensure safety and minimize skin irritation. The size and shape of the electrodes of the transcranial electrical stimulation component 105 can be flexibly selected as needed, and can be adapted according to the target area to be stimulated and the user's head shape. For example, the electrodes of the transcranial electrical stimulation component 105 can be designed to cover a larger brain area, or can be designed to target a specific area more accurately.

[0032] The electrode position of the transcranial electrical stimulation component 105 is crucial to the effect of transcranial alternating current stimulation (tACS). In specific applications, the electrodes of the transcranial electrical stimulation component 105105 can be placed at specific locations on the scalp according to the desired stimulation area, for example, by using a clamp 202, and the electrode position of the transcranial electrical stimulation component 105105 can be determined by referring to the international 10-20 system.

[0033] In specific applications, the stimulator can be single-channel stimulation or multi-channel stimulation, supporting one or more stimulation modes such as transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS) and / or transcranial random noise stimulation (tRNS). Exemplarily, the stimulator may include: a control unit, a power module, an electrode interface and a safety protection circuit, the control unit is responsible for setting and adjusting the stimulation parameters, the power module is used to provide a stable current output to the electrode according to the stimulation parameters to ensure the continuity and stability of the stimulation, the electrode interface is used to connect the electrodes of the transcranial electrical stimulation component 105105, and the safety protection circuit is responsible for monitoring the impedance and current state of the electrodes of the transcranial electrical stimulation component 105105 to prevent overload and electric shock.

[0034] In other embodiments, the transcranial electrical stimulation component 105 may also include auxiliary components such as impedance monitoring, safety monitoring, and MRI compatibility. The specific structure of the transcranial electrical stimulation component 105 is not limited in the embodiments of the present disclosure.

[0035] The audio output component 103 can be used to convert the electrical signal into a sound signal and output it. Figure 2 , the audio output component 103 may include earphones 201. In other examples, the audio output component 103 may also include a speaker array or other similar components. In addition, the audio output component 103 may also include components such as an amplifier and a filter. The specific structure of the audio output component 103 is not limited in the embodiment of the present disclosure.

[0036] The light output component 104 can be used to convert an electrical signal into an optical signal and output it. In some examples, the light output component 104 can include a regularly arranged LED array. Figure 2 , the LED array can be set Figure 2 The LEDs are arranged on the eye mask portion 203 in a regular arrangement. For example, the LED array can be arranged in a rectangular shape or in other forms. In addition, the light output assembly 104 can also include components such as an amplifier and a filter. The specific structure of the light output assembly 104 is not limited in the embodiment of the present disclosure.

[0037] The display component 106 can be used to convert the electrical signal into an image signal and output it. The display component 106 may include a display screen that can display predetermined cognitive training content to the user. Here, the cognitive training content can be flexibly set as needed and flexibly adjusted according to user feedback. The cognitive training content can be, but is not limited to, cognitive training videos, cognitive training games, cognitive training tasks, etc. For example, the cognitive training content can adopt the multi-field cognitive training mentioned in the "China Guide to Cognitive Training (2022 Edition)".

[0038] In specific applications, the display screen may be, but is not limited to, a touch display screen or other types, which is not limited in the embodiments of the present disclosure. The display component 106 may also include other components in addition to the display screen, and the specific structure of the display component 106 is not limited in the embodiments of the present disclosure.

[0039] In some embodiments, see Figure 1 The wearable device 100 may further include: an inertial measurement unit 107 (IMU) and an EEG component 108, which are respectively connected to the processor 101. The inertial measurement unit 107 may be used to measure the motion data of the user in response to the cognitive intervention measures, and the EEG component 108 may be used to collect the EEG data generated by the user in response to the cognitive intervention measures.

[0040] When a user wears the wearable device 100 , the IMU 107 may be used to measure the user's body motion data.

[0041] IMU 107 may include an accelerometer, a gyroscope, and a magnetometer. The accelerometer can be used to measure the linear acceleration of an object in the three axes of X, Y, and Z, that is, the acceleration change of the object in space. The gyroscope can be used to measure the angular velocity of an object around the three axes of X, Y, and Z, that is, the rotation speed of the object. The magnetometer can be used to measure the direction of an object relative to the earth's magnetic field, and provide magnetic field strength and direction information for determining the heading of the object. When the wearable device 100 is worn on the user's body (e.g., head), the "object" here refers to the user's body (e.g., head).

[0042] The body motion data measured by IMU 107 may include, but are not limited to, acceleration data, angular velocity data, and magnetic field data. Acceleration data includes acceleration values ​​on three orthogonal axes (i.e., X, Y, and Z axes), typically in units of g (acceleration due to gravity). Angular velocity data may include angular velocity values ​​around three orthogonal axes (i.e., X, Y, and Z axes), typically in units of degrees per second (° / s) or radians per second (rad / s). Magnetic field data may include magnetic field intensity values ​​and magnetic field directions on three orthogonal axes (X, Y, and Z axes), typically in units of microteslas (μT). Each value in the acceleration data, angular velocity data, and magnetic field data is accompanied by a timestamp that indicates the time when the value was measured.

[0043] In some examples, the EEG component 108 may include components such as electrodes, electrode caps, and amplifiers. The electrodes are placed at designated locations or designated areas on the user's scalp to collect EEG signals. The electrodes are embedded in the electrode caps, and the electrode caps can position the electrodes according to the international 10-20 system to cover different areas of the brain. The amplifier is used to sample and amplify the EEG signals collected by the electrodes and convert the analog signals into digital signals to obtain EEG data, which are sent to the processor 101 or the memory 102. In addition to the above-mentioned transcranial electrical stimulation component 105, audio output component 103, light output component 104, display component 106, IMU 107, EEG component 108 and other components, the wearable device 100 may also include one or more of the following components: an eye movement detection component, a gesture detection component, one or more physical buttons, a tactile feedback device, etc.

[0044] It should be noted that Figure 1 and Figure 2 This is only an exemplary structure of the wearable device 100 and is not intended to limit the present disclosure. Those skilled in the art should understand that the types of components in the wearable device 100 and the specific structures of each component can be flexibly adjusted as needed in specific applications.

[0045] Figure 3 1 shows a flow chart of a cognitive intervention method provided by an embodiment of the present disclosure. The cognitive intervention method can be applied to the aforementioned wearable device 100 and executed by the wearable device 100. Figure 3 The cognitive intervention method of the embodiment of the present disclosure may include the following steps: Step 301, after the user wears the wearable device 100, the audio output component 103, the light output component 104, the transcranial electrical stimulation component 105 and the display component 106 are controlled to perform cognitive intervention measures; The cognitive intervention measures include applying acoustic stimulation, light stimulation, transcranial electrical stimulation and displaying cognitive training content to the user. In some embodiments, the cognitive intervention measures may specifically include: 1) controlling the audio output component 103 to output 40Hz double sound beats to apply acoustic stimulation to the user; 2) controlling the light output component 104 to emit 40Hz alternating light to apply light stimulation to the user; 3) controlling the transcranial electrical stimulation component 105 to apply transcranial alternating current stimulation to the user's scalp; 4) controlling the display component 106 to display predetermined cognitive training content to the user.

[0046] When the transcranial electrical stimulation component 105 is controlled to apply transcranial alternating current stimulation to the user's scalp, a gamma wave of 40Hz potential in the brain can be induced, also known as 40Hz transcranial alternating current stimulation (40 Hz-tACS). 40Hz transcranial alternating current stimulation can regulate the oscillatory activity of the cerebral cortex, especially the activity of gamma waves (γ waves). The frequency range of gamma waves is 30-80 Hz, among which 40Hz gamma waves play an important role in cognitive processing such as information processing, working memory and episodic memory. 40Hz transcranial alternating current stimulation shows good potential in improving cognitive function, especially in the treatment of Alzheimer's disease and mild cognitive impairment. 40Hz binaural beats can effectively improve cognitive function, emotion regulation ability and concentration. Specifically, when the audio output component 103 is controlled to output 40Hz binaural beats, one ear of the user will receive a sound slightly lower than 40Hz, while the other ear will receive a sound slightly higher than 40Hz. The user's brain will fuse these two sounds to produce a 40Hz beat, which is related to gamma waves in the brain. Gamma waves are usually related to cognitive function, information processing and consciousness state. Therefore, by controlling the audio output component 103 to output 40Hz binaural beats, cognitive function can be effectively improved.

[0047] In some implementations, when the audio output component 103 is controlled to output 40 Hz double-tone beats, the audio output component 103 can be controlled to simultaneously output a preset background audio to apply acoustic stimulation to the user. Thus, in applying the acoustic stimulation of 40 Hz double-tone beats, some soothing background music is combined to directly compound the double-tone beat signal with the audio signal of the music, which can effectively reduce the user's discomfort with 40 Hz double-tone beats, thereby improving cognitive function through acoustic stimulation while improving user experience.

[0048] Cognitive training content may include, but is not limited to, memory training, attention training, logical reasoning training, etc. For example, a series of pictures or number sequences may be displayed, requiring the user to recall or sort them after viewing.

[0049] In some embodiments, the audio output component 103 includes an LED array. When the light output component 104 is controlled to emit light alternately at 40 Hz, 40 Hz refers to the flashing frequency of the LED lights in the light output component 104. The selection of this frequency is related to the visual perception of the human eye and can achieve specific physiological and psychological effects, thereby improving cognitive function.

[0050] In some embodiments, the audio output component 103 includes an LED array, and the working mode of controlling the light output component 104 to alternately emit light at 40 Hz to apply light stimulation to the user may include one or more of the following: 1) controlling all LED lights in the LED array to flash 40 times at a flashing frequency of 40 Hz to provide the user with a predetermined 80 pictures; 2) controlling the LED array to flash in a checkerboard-like staggered manner at a flashing frequency of 40 Hz. Thus, light stimulation of alternating light at 40 Hz can be achieved through two modes. For example, one of them can be selected for light stimulation, or the two working modes can be switched as needed to achieve light stimulation.

[0051] When implementing cognitive intervention measures, the aforementioned four measures (i.e., sound stimulation, light stimulation, transcranial alternating current stimulation, and display of cognitive training content) can be performed simultaneously or in a certain order. In specific applications, the execution order of the aforementioned four measures can be flexibly configured as needed. For example, transcranial alternating current stimulation can be set after sound stimulation and light stimulation, and sound stimulation and light stimulation can be performed at the same time, and cognitive training content can be performed continuously during the process or after transcranial alternating current stimulation.

[0052] Step 302, adjusting cognitive intervention measures in response to user feedback.

[0053] Specifically, a cognitive ability curve can be constructed in response to user feedback, and cognitive intervention measures can be adjusted according to the cognitive ability curve. Among them, the cognitive ability curve can be used to indicate the real-time change trend of one or more cognitive ability indicators of the user and / or the real-time change trend of the user's comprehensive cognitive ability. Thus, the real-time change trend of the user's cognitive ability can be obtained by continuously monitoring the user's feedback on cognitive intervention measures, and the cognitive intervention measures can be dynamically adjusted according to the real-time change trend of the user's cognitive ability, forming a closed loop of "cognitive intervention-user feedback-cognitive intervention", while effectively improving the effect of cognitive ability intervention, it can also avoid the cognitive intervention measures from causing side effects such as dizziness, vomiting, nausea, etc. to the user.

[0054] In some implementations, the process of constructing a cognitive ability curve in response to user feedback may include: obtaining user feedback data, which may include EEG data and motion data; obtaining scores of one or more cognitive ability indicators of the user using the user feedback data; and obtaining a cognitive ability curve based on the scores of one or more cognitive ability indicators of the user. Thus, the user's performance on various cognitive ability indicators can be accurately, efficiently and comprehensively evaluated by integrating various types of user feedback data, and the cognitive ability curve can be obtained by analyzing the changes in the user's performance on various cognitive ability indicators over time, which can improve the accuracy and real-time performance of the cognitive ability curve and further improve the effectiveness of cognitive intervention measures.

[0055] In the process of performing cognitive intervention measures to implement acoustic stimulation, light stimulation, transcranial brain stimulation and display cognitive training content to the user, the EEG component 108 is controlled to collect the user's EEG data, and the IMU 107 is controlled to measure the user's motion data. Thus, user feedback can be obtained by monitoring the user's body movements and brain waves when performing cognitive training, so as to comprehensively evaluate the user's cognitive level.

[0056] During the process of executing cognitive intervention measures, that is, when the user is undergoing cognitive training, the EEG component 108 is controlled to collect EEG data to monitor the electrical activity of the user's brain in real time. The power changes of specific frequency bands (such as α waves, β waves, γ waves, etc.) in the EEG data are related to the cognitive state. The EEG data can provide information about the cognitive state in terms of attention, memory, calculation, thinking, hearing, language, social cognition, etc. EEG data may include one or more of the following: 1) EEG data contains information such as event-related potentials (ErrPs), which is related to the user's concentration and distraction of attention; 2) EEG data contains information on specific bands such as theta waves and alpha waves, which is related to the user's memory process; 3) EEG data contains information on activities in the prefrontal region, which is related to the user's computing ability; 4) EEG data contains information on specific bands such as beta waves and gamma waves, which is related to the user's thinking ability; 5) EEG data contains information on the user's EEG response when hearing sounds of different frequencies and intensities, which is related to the user's auditory sensitivity and recognition ability; 6) EEG data contains information on the user's EEG response when hearing voice stimuli, which is related to the user's language ability; 7) EEG data contains information on the user's EEG activity during social interaction tasks (e.g., interacting with others, assessing others' emotions or intentions), which is related to the user's social cognitive ability.

[0057] In the process of executing cognitive intervention measures, that is, when the user is performing cognitive training, the IMU 107 is controlled to measure the user's body motion data in real time. The body motion data may include but is not limited to information such as rotation, shaking, and movement of parts such as the head and hands. The body motion data can reflect the movement trajectory and posture changes of the user's body (for example, head, hands, etc.) when the user is performing cognitive training. These movement trajectories and posture changes can accurately reflect the user's body stability, movement coordination, and reaction speed, and the user's body stability, movement coordination, and reaction speed during cognitive training accurately reflect the user's concentration, reaction ability, and other characteristics. For example, the stability of the user's head movement and the accuracy of the user's hand movement can be judged by analyzing the body motion data, thereby indirectly evaluating the user's concentration and reaction ability. If the user's head movement is relatively stable and the hand movement is accurate when completing the memory task, it means that the user is focused and responsive. For another example, when performing cognitive intervention measures such as balance training and gait training, the body motion data measured by the IMU 107 can provide accurate data about body posture and its movement pattern, which more accurately reflects the user's concentration, reaction ability, and other characteristics.

[0058] The user's cognitive ability indicators may include, but are not limited to, one or more of the following: attention, memory, calculation, thinking, hearing, language, and social cognition. In specific applications, it may be necessary to flexibly set or adjust the cognitive ability indicators involved in the cognitive ability curve.

[0059] In some implementations, a pre-trained cognitive ability assessment model can be used to obtain the user's score on one or more cognitive ability indicators using user feedback data. The cognitive ability assessment model can be implemented as, but not limited to, a machine learning model such as a neural network or a support vector machine.

[0060] In some examples, the process of obtaining a score through a cognitive ability assessment model may include: extracting the user's cognitive state characteristics from the user's EEG data and motion data (for example, the power spectrum density of a specific frequency band (such as α waves, β waves), the connection strength between brain regions, etc. can reflect the cognitive state information), extracting the user's body reaction characteristics from the body movement data (for example, such as movement amplitude, movement frequency, acceleration changes, etc. can reflect the characteristics of body stability, body coordination and / or reaction speed), and using cognitive state characteristics and body reaction characteristics to classify and predict cognitive states to obtain the scores of various cognitive ability indicators of the user. In this example, the cognitive ability assessment model may include a feature extraction module and a classification prediction module, and the cognitive-related features and action features are extracted by the feature extraction module, and then the cognitive-related features and action features are processed by the classification prediction module to obtain the scores of various cognitive ability indicators of the user. In specific applications, the feature extraction module and the classification prediction module can be implemented by different machine learning models, respectively. For example, the feature extraction module can be implemented by a neural network, and the classification prediction module can be implemented by a support vector machine. The specific structure and implementation method of the cognitive ability assessment model are not limited by the embodiments of the present disclosure.

[0061] In some examples, the process of obtaining a score through a cognitive ability assessment model may include: using a data fusion algorithm such as weighted average, principal component analysis (PCA), etc. to perform multimodal data fusion on EEG data and body movement data to obtain fused data, and extracting and classifying the fused data to obtain scores of various cognitive ability indicators of the user. In this example, the cognitive ability assessment model may include a fusion module, a feature extraction module, and a classification prediction module. The fusion model is used to perform multimodal data fusion on EEG data and body movement data to obtain fused data. The feature extraction module can be used to extract features from the fused data to obtain comprehensive cognitive features. The comprehensive cognitive features include cognitive state features and related features of physical reactions (for example, features such as movement amplitude, movement frequency, acceleration changes, etc. that can reflect physical stability, physical coordination, and / or reaction speed). The classification prediction module can be used to perform cognitive state classification prediction using the comprehensive cognitive features to obtain scores of various cognitive ability indicators of the user. Specifically, the fusion module, the feature extraction module, and the classification prediction module can be implemented by independent machine learning models or parts of machine learning models, respectively.

[0062] From the above, we can more accurately evaluate the changes in users' cognitive levels through the combination of multimodal data such as body movement data and EEG data, so as to effectively adjust cognitive intervention measures to provide users with personalized cognitive training, improve the effect of cognitive training, and accelerate the improvement of users' cognitive functions.

[0063] In specific applications, a public data set containing cognitive ability experimental data of various users can be used to train a cognitive ability assessment model so that it can analyze the user's body movement data and EEG data to assess the user's cognitive ability. Specifically, a public data set containing cognitive ability experimental data of various users can be used to construct a training data set, a test data set, and a validation data set, determine the assessment dimensions of the cognitive ability assessment model (i.e., which cognitive ability indicators), and construct a cognitive ability assessment model by improving an existing machine learning model. The training data set is used to train the cognitive ability assessment model to determine the parameters of the cognitive ability assessment model, and the test data set is used to test the performance of the cognitive ability assessment model to ensure that the performance of the cognitive ability assessment model meets the accuracy requirements, and then the validation data set is used to verify the assessment ability of the cognitive ability assessment model. Among them, the cognitive ability experimental data of various users include the EEG data and body movement data of various users for cognitive intervention measures and the true scores of their various cognitive ability indicators. These true scores can be, but are not limited to, the scores obtained using cognitive function tools such as the Montreal Cognitive Assessment (MoCA) scale and the Alzheimer's Disease Assessment Scale (ADAS-cog).

[0064] In specific applications, as new data are collected, the parameters of the cognitive ability assessment model can be continuously updated to improve the accuracy and applicability of the cognitive ability curve.

[0065] The cognitive ability curve includes the user's cognitive ability index scores at different time points. The horizontal axis of the cognitive ability curve represents time, and the vertical axis of the cognitive ability curve represents the score of the cognitive ability index. The cognitive ability curve can reflect the user's ability in a specific cognitive field (i.e., cognitive abilities such as memory and attention) and / or the trend of comprehensive cognitive ability over time. If the cognitive ability curve shows an upward trend, it means that the corresponding cognitive ability has improved. If the cognitive ability curve tends to be flat or downward, it means that the corresponding cognitive ability has not been improved or has declined, and the intervention measures for this cognitive ability need to be adjusted. It can be seen that the cognitive ability curve reflects the development, maintenance or decline of the user's cognitive ability in the process of implementing cognitive intervention measures.

[0066] The cognitive ability curve can be linear or nonlinear, single-dimensional or multi-dimensional.

[0067] For example, if the cognitive ability index includes 7 items, namely attention, memory, calculation, thinking, hearing, language, and social cognition, the cognitive ability curve can be a single dimension, which indicates the trend of the user's comprehensive cognitive ability index changing over time. Specifically, the sum of the scores of the above cognitive ability indicators is used as the score of the comprehensive cognitive ability indicator, and the scores of the comprehensive cognitive ability indicators at each time node in the current cycle are used to fit the cognitive ability curve of the current cycle. The cognitive ability curve can reflect the trend of the comprehensive cognitive ability indicators changing over time in the current cycle.

[0068] For another example, if the cognitive ability indicators include seven items, namely attention, memory, calculation, thinking, hearing, language, and social cognition, the cognitive ability curve can have seven dimensions, each dimension corresponds to a cognitive ability indicator, and the scores of each cognitive ability indicator at each time node in the current cycle are fitted to seven curves respectively. Each curve reflects the trend of a cognitive ability indicator over time.

[0069] For another example, if the predetermined cognitive ability indicators include 7 items, namely attention, memory, calculation, thinking, hearing, language, and social cognition, the cognitive ability curve may have 8 dimensions, including 7 dimensions corresponding to the above 7 cognitive ability indicators and 1 dimension corresponding to the aforementioned comprehensive cognitive ability indicator.

[0070] Of course, the cognitive ability curve may also be in other forms, which is not limited in the embodiments of the present disclosure.

[0071] In step 302, cognitive intervention measures can be dynamically adjusted through the cognitive ability curve. Specifically, one or more of the following can be adjusted according to the cognitive ability curve: 1) cognitive training content (e.g., difficulty, specific cognitive ability related); 2) stimulation parameters of the audio output component 103 (e.g., stimulation duration, working frequency, time interval of periodic sound stimulation, etc.); 3) stimulation parameters of the light output component 104 (e.g., working mode, working frequency, working duration, time interval of periodic light stimulation, etc.); 4) stimulation parameters of the transcranial electrical stimulation component 105 (e.g., working mode, working frequency, working duration, time interval of periodic transcranial electrical stimulation, etc.); 5) the execution order of cognitive training content, sound stimulation, light stimulation and transcranial electrical stimulation (e.g., changing simultaneous execution to sequential execution, cognitive training content, sound stimulation, light stimulation are executed simultaneously and transcranial electrical stimulation is executed after these stimulations are completed).

[0072] In some examples, cognitive intervention measures can be adjusted by the change in the comprehensive cognitive ability index score in the cognitive ability curve. Specifically, if the change rate of the comprehensive cognitive ability index score in the current cycle is positive and the increase in the score exceeds the first predetermined threshold, it indicates that the user's cognitive ability has been effectively improved, and one or more of the following can be adjusted: 1) Adjust the stimulation parameters of the transcranial electrical stimulation component 105 to reduce the intensity and / or duration of the transcranial electrical stimulation; 2) Adjust the stimulation parameters of the light output component 104 to reduce the intensity and / or duration of the light stimulation; 3) Adjust the stimulation parameters of the audio output component 103 to reduce the intensity and / or duration of the sound stimulation; 4) Change the cognitive training content to reduce the difficulty of the cognitive training content, thereby improving cognition while reducing the side effects of cognitive intervention measures.

[0073] If the rate of change of the comprehensive cognitive ability index score in the current cycle is positive but the increase in the score does not exceed the first predetermined threshold, it indicates that the user's cognitive ability has improved but has not been effectively improved, and the current cognitive intervention measures can be kept unchanged.

[0074] If the rate of change of the comprehensive cognitive ability index score in the current cycle is negative or remains unchanged, it indicates that the user's cognitive ability has not improved, and one or more of the following adjustments can be made: 1) adjusting the stimulation parameters of the transcranial electrical stimulation component 105 to increase the intensity and / or duration of the transcranial electrical stimulation; 2) adjusting the stimulation parameters of the light output component 104 to increase the intensity and / or duration of the light stimulation; 3) adjusting the stimulation parameters of the audio output component 103 to increase the intensity and / or duration of the sound stimulation; 4) changing the cognitive training content to increase the difficulty of the cognitive training content, so as to achieve the purpose of effectively improving the user's cognitive ability.

[0075] In some examples, cognitive intervention measures can be adjusted by the changes in the scores of specific cognitive ability indicators in the cognitive ability curve. The adjustment method is the same as the adjustment method of the aforementioned comprehensive cognitive ability indicators, adjusting the parameters (intensity and duration) of the acoustic and optical stimulation and the difficulty of the cognitive training content. The specific implementation process will not be repeated here.

[0076] If the scores of specific cognitive ability indicators show a downward trend, the intensity of sound stimulation, light stimulation and / or transcranial electrical stimulation can be strengthened or new cognitive training content can be added in cognitive intervention measures.

[0077] If the cognitive ability curve shows that the score of the user's comprehensive cognitive ability index increases continuously and steadily, the intensity of sound stimulation, light stimulation and / or transcranial electrical stimulation in the cognitive intervention measures can be reduced, or the frequency of the cognitive intervention measures can be gradually reduced, or the execution order of cognitive training content, sound stimulation, light stimulation and transcranial electrical stimulation in the cognitive intervention measures can be adjusted.

[0078] If the cognitive ability curve identifies that the user's score on a specific cognitive ability index shows a downward trend or its average value is lower than the second predetermined threshold (i.e., normal value), it indicates that the user has obstacles in this specific cognitive ability, and cognitive intervention measures can be adjusted to meet the user's personalized needs. For example, if the average value of the user's memory ability index score is lower than the normal value, memory training can be added to the cognitive intervention measures by adjusting any one or more of the above five items to specifically improve the user's memory ability.

[0079] If it is identified through the cognitive ability curve that the user's attention index score shows a downward trend or its average value is lower than the third predetermined threshold (i.e., the normal value of attention), it indicates that the user's attention is not focused, and the difficulty of the attention training task in the cognitive intervention measure can be increased by adjusting any one or more of the above five items.

[0080] As can be seen from the above, the embodiment of the present disclosure can apply cognitive intervention measures including sound stimulation, light stimulation, transcranial electrical stimulation and cognitive training content to the user through the wearable device 100, and can dynamically adjust the cognitive intervention measures in a timely manner by continuously monitoring the user's feedback (that is, changes in their cognitive abilities), thereby effectively improving the user's cognitive abilities while reducing the side effects of certain stimuli.

[0081] Figure 4 1 shows a schematic diagram of the structure of a cognitive intervention device provided by an embodiment of the present disclosure, wherein the cognitive intervention device is applied to a wearable device 100, and the wearable device 100 includes an inertial measurement unit 107. Figure 4 , the cognitive intervention device 400 may include: The cognitive intervention measure execution unit 401 is used to control the audio output component 103, the light output component 104, the transcranial electrical stimulation component 105 and the display component 106 to execute cognitive intervention measures after the user wears the wearable device 100. The cognitive intervention measures include applying acoustic stimulation, light stimulation, transcranial electrical stimulation and displaying cognitive training content to the user: The cognitive intervention measure adjustment unit 402 is used to adjust the cognitive intervention measure in response to user feedback.

[0082] In some embodiments, the cognitive intervention measure adjustment unit 402 can be specifically used to: construct a cognitive ability curve in response to user feedback, the cognitive ability curve is used to indicate the real-time change trend of one or more cognitive ability indicators of the user and / or the real-time change trend of the user's comprehensive cognitive ability; and adjust cognitive intervention measures according to the cognitive ability curve.

[0083] In some embodiments, the cognitive intervention measure adjustment unit 402 can be specifically used to construct a cognitive ability curve in response to user feedback in the following manner: obtaining user feedback data, the user feedback data including: EEG data and motion data; using the user feedback data to obtain scores of one or more cognitive ability indicators of the user; and fitting a cognitive ability curve based on the scores of one or more cognitive ability indicators of the user.

[0084] In some implementations, the cognitive intervention measure execution unit 401 may also be used to control the EEG component 108 to collect the user's EEG data and control the IMU 107 to measure the user's body movement data during the execution of the cognitive intervention measure.

[0085] In some embodiments, the cognitive intervention measure execution unit 401 can be used to control the audio output component 103, the light output component 104, the transcranial electrical stimulation component 105 and the display component 106 to execute cognitive intervention measures in the following manner: control the audio output component 103 to output 40 Hz double beats to apply sound stimulation to the user; control the light output component 104 to emit 40 Hz alternating light to apply light stimulation to the user; control the transcranial electrical stimulation component 105 to apply transcranial alternating current stimulation to the user's scalp; and control the display component 106 to display predetermined cognitive training content to the user.

[0086] In some embodiments, the cognitive intervention measure adjustment unit 402 can be specifically used to adjust one or more of the following in response to user feedback: cognitive training content; stimulation parameters of the audio output component 103; stimulation parameters of the light output component 104; stimulation parameters of the transcranial electrical stimulation component 105; and the execution order of cognitive training content, acoustic stimulation, light stimulation, and transcranial electrical stimulation.

[0087] For other technical details of the cognitive intervention device 400, please refer to the section of the cognitive intervention method above, which will not be repeated here. In specific applications, the cognitive intervention device 400 can be implemented by the wearable device 100 of this article, or can be implemented as software in the wearable device 100.

[0088] In addition, an embodiment of the present disclosure also provides a computer-readable storage medium on which a computer program is stored. The program includes instructions, and the instructions implement the steps of the aforementioned cognitive intervention method when executed by one or more processors 101 in an electronic device such as a wearable device.

[0089] The technical solution provided by the present disclosure is described in detail above. The principles and implementation methods of the present disclosure are described in detail using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present disclosure. At the same time, for those skilled in the art, according to the idea of ​​the present disclosure, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present disclosure.

[0090] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A cognitive intervention method, characterized in that: The cognitive intervention method is applied to a wearable device, wherein the wearable device includes an audio output component, a light output component, a transcranial electrical stimulation component, and a display component; the cognitive intervention method includes: After the user wears the wearable device, the audio output component, the light output component, the transcranial electrical stimulation component and the display component are controlled to perform cognitive intervention measures, wherein the cognitive intervention measures include applying acoustic stimulation, light stimulation, transcranial electrical stimulation and displaying cognitive training content to the user: The cognitive intervention is adjusted in response to user feedback.

2. The method according to claim 1, characterized in that The adjusting the cognitive intervention in response to user feedback comprises: constructing a cognitive ability curve in response to user feedback, the cognitive ability curve being used to indicate a real-time change trend of one or more cognitive ability indicators of the user and / or a real-time change trend of the user's comprehensive cognitive ability; and, The cognitive intervention measure is adjusted according to the cognitive ability profile.

3. The method according to claim 2, characterized in that The step of constructing a cognitive ability curve in response to user feedback includes: Acquiring user feedback data, wherein the user feedback data includes: EEG data and movement data; Using the user feedback data to obtain scores of one or more cognitive ability indicators of the user; The cognitive ability curve is obtained by fitting based on the scores of one or more cognitive ability indicators of the user.

4. The method according to claim 3, characterized in that The wearable device also includes an inertial measurement unit IMU and an electroencephalogram component; The obtaining of user feedback data includes: in the process of executing the cognitive intervention measure, controlling the EEG component to collect the user's EEG data, and controlling the IMU to measure the user's body movement data.

5. The method according to claim 1, characterized in that The controlling audio output component, light output component, transcranial electrical stimulation component and display component to perform cognitive intervention measures includes: Controlling the audio output component to output 40 Hz binaural beats to apply acoustic stimulation to the user; Controlling the light output assembly to emit light alternately at 40 Hz to apply light stimulation to the user; Controlling the transcranial electrical stimulation component to apply transcranial alternating current stimulation to the user's scalp; The display component is controlled to display predetermined cognitive training content to the user.

6. The method according to claim 5, characterized in that The audio output component includes an LED array; and controlling the audio output component to output 40 Hz double sound beats to apply sound stimulation to the user includes one or more of the following: Control all LED lights in the LED array to flash 40 times at a flashing frequency of 40 Hz; The LED array is controlled to flash in a checkerboard-like staggered manner at a flashing frequency of 40 Hz.

7. The method according to any one of claims 1 to 6, characterized in that: The adjusting the cognitive intervention in response to user feedback comprises: In response to user feedback, adjust one or more of the following: The cognitive training content; stimulation parameters of the audio output component; stimulation parameters of the light output assembly; stimulation parameters of the transcranial electrical stimulation component; The execution order of the cognitive training content, the sound stimulation, the light stimulation and the transcranial electrical stimulation.

8. A cognitive intervention device, characterized in that: The cognitive intervention device is applied to a wearable device, wherein the wearable device comprises an audio output component, a light output component, a transcranial electrical stimulation component and a display component; the cognitive intervention device comprises: A cognitive intervention execution unit is used to control the audio output component, the light output component, the transcranial electrical stimulation component and the display component to execute cognitive intervention measures after the user wears the wearable device. The cognitive intervention measures include applying acoustic stimulation, light stimulation, transcranial electrical stimulation and displaying cognitive training content to the user: A cognitive intervention adjustment unit is used to adjust the cognitive intervention in response to user feedback.

9. A wearable device, characterized in that: include: An audio output component, an optical output component, a transcranial electrical stimulation component, a display component, a processor, and a memory storing a program, wherein the program includes instructions, and when the instructions are executed by the processor, the processor executes the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a program, wherein the program comprises instructions, and when the instructions are executed by one or more processors, the instructions cause the computing device to execute the method according to any one of claims 1 to 7.

Images