A cognitive behavior processing method and device based on cognitive impairment
Patent Information
- Application Number
- CN202680000531.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2025-03-05
- Filing Date
- 2026-03-04
- Publication Date
- 2026-11-06
AI Technical Summary
Existing methods for recording or processing cognitive behaviors are subject to external limitations, impacting users' mental health and quality of life.
By acquiring raw behavioral data of the users to be monitored, data processing is performed to obtain interaction parameter values, and the complexity of cognitive behavioral activities is dynamically adjusted based on these parameters to display new cognitive behavioral activities and operation instructions to promote users' cognitive behavioral operations.
It effectively improved users' cognitive abilities and enhanced their mental health and quality of life.
Smart Images
Figure CN123057552A_ABST
Abstract
Description
A cognitive behavioral processing method and device based on cognitive impairment
[0001] This application claims priority to patent application filed on March 5, 2025, at the Patent Registry of the Intellectual Property Department of the Hong Kong Special Administrative Region Government, application number 32025104305.7, entitled “A method and apparatus for processing cognitive behavior based on cognitive impairment”, the entire contents of which are incorporated herein by reference. Technical Field
[0002] This application belongs to the field of rehabilitation medicine technology, and in particular relates to a cognitive behavioral processing method and device based on cognitive impairment. Background Technology
[0003] In recent years, with the development of science and technology and the increase in human lifespan, the number of diseases involving cognitive problems has been increasing year by year.
[0004] However, existing cognitive behavioral recording or processing methods are often subject to external constraints, such as limitations of timing and location, and a limited variety of multi-sensory stimuli, which affect users' mental health and quality of life. Summary of the Invention
[0005] This application provides a cognitive behavior processing method and apparatus based on cognitive impairment, which can ultimately and effectively assist users in improving their cognitive abilities and enhancing their mental health and quality of life.
[0006] In a first aspect, embodiments of this application provide a cognitive behavioral processing method based on cognitive impairment, comprising:
[0007] Acquire raw behavioral data of the user to be monitored, wherein the raw behavioral data includes at least the interaction data of the user to be monitored with respect to the current cognitive behavioral activities configured in the terminal device, and the current cognitive behavioral activities are used to promote the cognitive behavioral operations of the user to be monitored.
[0008] The raw behavioral data is processed to obtain the interaction parameter values of the user to be monitored for the current cognitive behavioral activity;
[0009] The complexity of the current cognitive behavior activity is dynamically adjusted based on the interaction parameter values to obtain a new current cognitive behavior activity.
[0010] The terminal device displays new current cognitive behavioral activities and behavioral operation instructions on its display panel. The behavioral operation instructions are used to instruct the monitored user to perform cognitive behavioral operations based on the new current cognitive behavioral activities.
[0011] In some embodiments, the complexity of the current cognitive behavioral activity is dynamically adjusted based on the interaction parameter value to obtain a new current cognitive behavioral activity, including:
[0012] Obtain the preset maximum and minimum parameter thresholds;
[0013] Based on the interaction parameter values, the maximum parameter threshold, and the minimum parameter threshold, the complexity of the current cognitive behavioral activity is dynamically adjusted to obtain a new current cognitive behavioral activity.
[0014] In some embodiments, the complexity of the current cognitive behavioral activity is dynamically adjusted based on the interaction parameter value, the maximum parameter threshold, and the minimum parameter threshold to obtain a new current cognitive behavioral activity, including:
[0015] If the interaction parameter value is greater than the maximum parameter threshold, the complexity of the current cognitive behavior activity is increased to obtain a new current cognitive behavior activity;
[0016] If the interaction parameter value is less than the minimum parameter threshold, the complexity of the current cognitive behavior activity is reduced to obtain a new current cognitive behavior activity.
[0017] If the interaction parameter value is greater than the minimum parameter threshold and less than the maximum parameter threshold, then the complexity of the current cognitive behavior activity is maintained, and the current cognitive behavior activity is taken as the new current cognitive behavior activity.
[0018] In some embodiments, the raw behavioral data is processed to obtain interaction parameter values of the user to be monitored in response to the current cognitive behavioral activity, including:
[0019] The original behavioral data is preprocessed to obtain preprocessed behavioral data. The preprocessing includes: anomaly deletion processing and interpolation processing.
[0020] The preprocessed behavioral data is processed by feature calculation to obtain the interaction parameter values of the user to be monitored for the current cognitive behavioral activity.
[0021] In some embodiments, feature calculation processing is performed on the preprocessed behavioral data to obtain the interaction parameter values of the user to be monitored in response to the current cognitive behavioral activity, including:
[0022] Feature extraction is performed on the preprocessed behavioral data to obtain the state data and time series data of the user to be monitored. The state data is used to characterize the interaction state between the user to be monitored and the active object in the current cognitive behavior activity, and the time series data is used to characterize the time and order in which the user to be monitored completes the current cognitive behavior activity.
[0023] Based on state data and time series data, calculate the interaction parameter values of the user to be monitored for the current cognitive behavior activity. The interaction parameter values include hit rate and / or average speed.
[0024] In some embodiments, the original behavioral data is preprocessed to obtain preprocessed behavioral data, including:
[0025] The original behavioral data is subjected to anomaly deletion processing to obtain anomaly-processed data.
[0026] Interpolation is performed on the missing data in the data after anomaly handling to obtain preprocessed behavioral data.
[0027] In some embodiments, the behavioral instruction information includes cognitive outcome information of the user to be monitored, which is used to provide sensory stimulation to the user by performing gardening behavioral actions.
[0028] Secondly, embodiments of this application provide a cognitive behavioral processing device based on cognitive impairment, comprising:
[0029] The acquisition module is used to acquire the raw behavioral data of the user to be monitored. The raw behavioral data includes at least the interaction data of the user to be monitored with respect to the current cognitive behavioral activities configured in the terminal device. The current cognitive behavioral activities are used to promote the cognitive behavioral operations of the user to be monitored.
[0030] The data processing module is used to process the raw behavioral data to obtain the interaction parameter values of the user to be monitored for the current cognitive behavioral activity;
[0031] The adjustment module is used to dynamically adjust the complexity of the current cognitive behavior activity based on the interaction parameter value to obtain a new current cognitive behavior activity.
[0032] The display module is used to display new current cognitive behavioral activities and behavioral operation instructions on the display panel of the terminal device. The behavioral operation instructions are used to instruct the user to be monitored to perform cognitive behavioral operations based on the new current cognitive behavioral activities.
[0033] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any of the first aspects.
[0034] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in any of the first aspects.
[0035] Fifthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to execute any of the methods described in the first aspect above.
[0036] This application provides a method and apparatus for cognitive behavior processing based on cognitive impairment. The method includes: acquiring raw behavioral data of a user to be monitored, wherein the raw behavioral data includes at least interaction data of the user to be monitored regarding current cognitive behavioral activities configured in a terminal device, the current cognitive behavioral activities being used to facilitate cognitive behavioral operations of the user to be monitored; processing the raw behavioral data to obtain interaction parameter values of the user to be monitored regarding the current cognitive behavioral activities; dynamically adjusting the complexity of the current cognitive behavioral activities based on the interaction parameter values to obtain new current cognitive behavioral activities; and displaying the new current cognitive behavioral activities and behavioral operation instruction information on the display panel of the terminal device, wherein the behavioral operation instruction information is used to instruct the user to be monitored to perform cognitive behavioral operations based on the new current cognitive behavioral activities. By using the above technical solution, and dynamically adjusting the complexity of the current cognitive behavioral activities based on the interaction parameter values of the user to be monitored regarding the current cognitive behavioral activities, the user to be monitored can better perform cognitive behavioral operations based on the new current cognitive behavioral activities, effectively improving the user's cognitive ability and enhancing the user's mental health and quality of life. Attached Figure Description
[0037] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0038] Figure 1 is a flowchart illustrating a cognitive behavior processing method based on cognitive impairment according to an embodiment of this application;
[0039] Figure 2 is a flowchart illustrating a cognitive behavior processing method based on cognitive impairment according to another embodiment of this application;
[0040] Figure 3 is a schematic diagram of the overall process of a cognitive behavior processing method based on cognitive impairment provided in another embodiment of this application;
[0041] Figure 4 is a flowchart illustrating a process for dynamically adjusting the complexity of current cognitive behavioral activities according to another embodiment of this application;
[0042] Figure 5 is a flowchart illustrating a gardening activity according to another embodiment of this application;
[0043] Figure 6 is a schematic diagram of the framework of a cognitive behavior processing method based on cognitive impairment provided in another embodiment of this application;
[0044] Figure 7 is a structural block diagram of a cognitive behavior processing device based on cognitive impairment provided in an embodiment of this application;
[0045] Figure 8 is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation
[0046] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.
[0047] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.
[0048] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.
[0049] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."
[0050] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0051] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.
[0052] It should be noted that the information collection process (such as the facial image collection process, the behavioral data collection process, etc.) / feature extraction process involved in this application is carried out with the user's knowledge and permission. That is, the information collection process / feature extraction process complies with the requirements of laws and regulations and does not constitute an act that harms the public interest.
[0053] The cognitive behavior processing method based on cognitive impairment provided in this application can be applied to terminal devices such as mobile phones, tablets, wearable devices, in-vehicle devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, and personal digital assistants (PDAs). This application does not impose any restrictions on the specific type of terminal device.
[0054] Figure 1 is a flowchart illustrating a cognitive behavior processing method based on cognitive impairment according to an embodiment of this application. It is provided as an example and not as a limitation. This method can be applied to a terminal device. As shown in Figure 1, the method includes:
[0055] S101. Obtain the raw behavioral data of the user to be monitored, wherein the raw behavioral data includes at least the interaction data of the user to be monitored with respect to the current cognitive behavioral activities configured in the terminal device, and the current cognitive behavioral activities are used to promote the cognitive behavioral operations of the user to be monitored.
[0056] The user to be monitored can be considered as someone with low cognitive level or cognitive function deficits who needs cognitive behavior monitoring, such as a user with cognitive impairment or other types of users. The raw behavioral data can be the original behavioral data collected for the user to be monitored. For example, the raw behavioral data can include physical data sensed by the user in the real world or interactive behavioral data. For example, it can be physical data between the user to be monitored and the real world collected by IoT devices. For example, when the IoT device is a camera, the physical data can include image data collected by the camera. Or, when the IoT device is a smartwatch, the physical data can include data such as motion, heart rate, and blood oxygen saturation collected by the smartwatch.
[0057] Raw behavioral data may also include the user's interaction data in the electronic world, such as the user's interaction data with the current cognitive behavioral activities configured within the terminal device. These current cognitive behavioral activities can be a series of activities related to cognitive behavior, which can be used to promote the user's cognitive behavioral operations, such as encouraging the user to slow down the decline in their cognitive level by performing certain behavioral operations (such as thinking). The type of current cognitive behavioral activity is not limited; for example, it could refer to a mini-game or baseline activity developed for the user to promote their cognitive behavioral operations.
[0058] S102. Process the raw behavioral data to obtain the interaction parameter values of the user to be monitored for the current cognitive behavioral activity.
[0059] Interaction parameter values can be used to characterize the interaction results of the monitored user in response to the current cognitive behavior activity. Specific parameters are not limited, such as hit rate, average speed, or other parameters that can characterize the interaction results.
[0060] After obtaining the raw behavioral data of the user to be monitored, the raw behavioral data can be processed to obtain the interaction parameter values of the user to be monitored for the current cognitive behavioral activities. This embodiment does not limit the specific data processing process. The data can be processed according to the actual situation of the raw behavioral data, as long as the interaction parameter values of the user to be monitored can be obtained.
[0061] S103. Dynamically adjust the complexity of the current cognitive behavior activity based on the interaction parameter value to obtain a new current cognitive behavior activity.
[0062] It can be assumed that current cognitive activities can have different levels of complexity. For example, different levels can correspond to current cognitive activities with different levels of complexity. The complexity of current cognitive activities can be represented by continuous spectral values, and different spectral values can uniquely correspond to current cognitive activities with different levels of complexity.
[0063] Specifically, this embodiment can dynamically adjust the complexity of the current cognitive behavior activity based on the obtained interaction parameter values to obtain a new current cognitive behavior activity. This enables the continuous updating of the complexity of the current cognitive behavior activity according to the cognitive level of the user being monitored, thereby promoting the user's cognitive behavior. The specific process of dynamically adjusting the complexity of the current cognitive behavior activity is not limited. For example, it can directly output a new current cognitive behavior activity based on an adjustment model, which can be a pre-trained neural network model used to dynamically adjust the complexity of the current cognitive behavior activity; alternatively, it can implement the dynamic adjustment of the complexity of the current cognitive behavior activity according to a certain adjustment strategy, which can be configured by relevant personnel according to actual needs.
[0064] In some embodiments, the complexity of the current cognitive behavioral activity is dynamically adjusted based on the interaction parameter value to obtain a new current cognitive behavioral activity, including:
[0065] Obtain the preset maximum and minimum parameter thresholds;
[0066] Based on the interaction parameter values, the maximum parameter threshold, and the minimum parameter threshold, the complexity of the current cognitive behavioral activity is dynamically adjusted to obtain a new current cognitive behavioral activity.
[0067] The maximum parameter threshold can be the maximum critical value of the interaction parameter, used to determine whether to increase the complexity of the current cognitive behavior activity; the minimum parameter threshold can be the minimum critical value of the interaction parameter, used to determine whether to decrease the complexity of the current cognitive behavior activity. The specific values of the maximum and minimum parameter thresholds can be set in advance based on empirical values.
[0068] In a specific implementation, the complexity of the current cognitive behavior activity can be dynamically adjusted by comparing the interaction parameter value with the obtained maximum and minimum parameter thresholds. Alternatively, the complexity can be dynamically adjusted by performing certain calculations on the interaction parameter value, the maximum parameter threshold, and the minimum parameter threshold. This embodiment does not limit this approach.
[0069] In some embodiments, the complexity of the current cognitive behavioral activity is dynamically adjusted based on the interaction parameter value, the maximum parameter threshold, and the minimum parameter threshold to obtain a new current cognitive behavioral activity, including:
[0070] If the interaction parameter value is greater than the maximum parameter threshold, the complexity of the current cognitive behavior activity is increased to obtain a new current cognitive behavior activity;
[0071] If the interaction parameter value is less than the minimum parameter threshold, the complexity of the current cognitive behavior activity is reduced to obtain a new current cognitive behavior activity.
[0072] If the interaction parameter value is greater than the minimum parameter threshold and less than the maximum parameter threshold, then the complexity of the current cognitive behavior activity is maintained, and the current cognitive behavior activity is taken as the new current cognitive behavior activity.
[0073] In a specific implementation, taking three randomly selected, non-repeating designated games as an example, dynamically adjusting the current cognitive behavior activity can include: first, the user to be monitored plays one of the designated games; the terminal device acquires the user's interaction data for one or more designated games, and processes the data to obtain the user's interaction parameter value; if the interaction parameter value is greater than the maximum parameter threshold, the complexity of the current cognitive behavior activity can be increased, meaning the current designated game is considered passed, and a more complex next designated game can be played; if the interaction parameter value is less than the minimum parameter threshold, the complexity of the current cognitive behavior activity needs to be reduced, meaning the current designated game is considered failed, and the previous designated game needs to be played; if the interaction parameter value is greater than the minimum parameter threshold and less than the maximum parameter threshold, the complexity of the current cognitive behavior activity needs to be maintained, and the user continues playing the current designated game.
[0074] S104. Display new current cognitive behavioral activities and behavioral operation instructions on the display panel of the terminal device, wherein the behavioral operation instructions are used to instruct the user to be monitored to perform cognitive behavioral operations based on the new current cognitive behavioral activities.
[0075] After determining the new current cognitive behavioral activity through the above steps, the new current cognitive behavioral activity and behavioral operation instruction information can be displayed on the display panel of the terminal device. The behavioral operation instruction information can be used to instruct the user to be monitored to perform cognitive behavioral operations based on the new current cognitive behavioral activity. The specific content of the behavioral operation instruction information is not limited. For example, the behavioral operation instruction information can include instruction information related to the new current cognitive behavioral activity, such as displaying the activity reward title for the user to be monitored and prompting the user to continue a new round of current cognitive behavioral activity to obtain a higher reward title; it can also include other types of instruction information, such as the terminal device can output cognitive result information of the current cognitive behavioral activity, which is used for the user to be monitored to perform sensory stimulation by taking gardening behavioral operations.
[0076] As an example, this embodiment can involve multi-sensory observation in technology-enabled activities. For instance, technology can be used to record multi-sensory gardening and cognitive activities to stimulate the cognitive level of individuals with declining cognitive abilities. Specifically, by collecting various information about the user being monitored, caregivers can understand the user's needs and assist them in performing gardening activities to stimulate their senses. For example, if the user's goal is to stimulate their vision, hearing, and smell; to focus on the appearance of Internet of Things (IoT) devices; and to encourage interaction between individuals with declining cognitive abilities and caregivers, caregivers can assist the user in paying attention to differences in the LED light of IoT devices; focusing on the sound of water; smelling seeds to see if different seeds have different scents; and sharing their observations.
[0077] This embodiment provides a cognitive behavior processing method based on cognitive impairment. The method acquires raw behavioral data of a user to be monitored, including at least interaction data of the user's current cognitive behavioral activities configured within a terminal device. These current cognitive behavioral activities facilitate cognitive behavior operations for the user. The raw behavioral data is processed to obtain interaction parameter values for the user's current cognitive behavioral activities. The complexity of the current cognitive behavioral activities is dynamically adjusted based on these interaction parameter values to obtain new current cognitive behavioral activities. The new current cognitive behavioral activities and behavioral operation instructions are displayed on the terminal device's display panel, whereby the behavioral operation instructions instruct the user to perform cognitive behavior operations based on the new current cognitive behavioral activities. By dynamically adjusting the complexity of the current cognitive behavioral activities based on the user's interaction parameter values, this method enables the user to better perform cognitive behavior operations based on the new current cognitive behavioral activities, effectively improving the user's cognitive abilities and enhancing their mental health and quality of life.
[0078] Figure 2 is a flowchart illustrating a cognitive behavior processing method based on cognitive impairment according to another embodiment of this application. This embodiment further optimizes the original behavioral data processing to obtain interaction parameter values of the user to be monitored for the current cognitive behavior activity by: preprocessing the original behavioral data to obtain preprocessed behavioral data; the preprocessing includes: anomaly removal processing and interpolation processing; and performing feature calculation processing on the preprocessed behavioral data to obtain the interaction parameter values of the user to be monitored for the current cognitive behavior activity. As shown in Figure 2, the method includes:
[0079] S201. Obtain the raw behavioral data of the user to be monitored, wherein the raw behavioral data includes at least the interaction data of the user to be monitored with respect to the current cognitive behavioral activities configured in the terminal device, and the current cognitive behavioral activities are used to promote the cognitive behavioral operations of the user to be monitored.
[0080] S202. Preprocess the original behavioral data to obtain preprocessed behavioral data. Preprocessing includes: abnormal deletion processing and interpolation processing.
[0081] This step can perform data cleaning on the acquired raw behavioral data. Data cleaning methods can include anomaly removal and interpolation. For example, the raw behavioral data can first be anomaly removed to obtain anomaly-removed data. Then, anomaly detection can be performed on the raw behavioral data, and the detected anomalies can be deleted. Anomaly detection methods can include statistical standard deviation. That is, if the interaction data consists of 5 interactions of a user to be monitored for a certain cognitive behavior activity, and 4 of the 5 interactions take 90-110 seconds to complete the current cognitive behavior activity, and 1 interaction takes 300 seconds to complete the current cognitive behavior activity, then the interaction data corresponding to the 300-second interaction is considered anomaly data.
[0082] Furthermore, if the data is missing after anomaly handling and subsequent data processing cannot be successfully performed, interpolation can be performed on the missing data in the anomaly handling data. For example, the missing data can be filled in according to existing interpolation methods to obtain preprocessed behavioral data.
[0083] S203. Perform feature calculation on the preprocessed behavioral data to obtain the interaction parameter values of the user to be monitored for the current cognitive behavioral activity.
[0084] Feature calculation methods can be implemented by directly outputting the interaction parameter values of the monitored user for the current cognitive behavior activity based on a neural network model, or by first extracting features from the preprocessed behavioral data to obtain the user's state data and time-series data. The state data characterizes the interaction state between the monitored user and the activity objects within the current cognitive behavior activity, while the time-series data characterizes the time and order in which the monitored user completes the current cognitive behavior activity. Then, based on the state data and time-series data, the interaction parameter values of the monitored user for the current cognitive behavior activity are calculated. These interaction parameter values include hit rate and / or average speed.
[0085] As an example, state data can be game state, such as the number of interactions between game objects and the user being monitored, and whether they are right or wrong (e.g., there should be contact, there should be no contact, there should be no contact, there should be no contact, etc.); time series data can include the time and order in which game objects are extracted or clicked. In a game where the current cognitive behavior activity is moving a box, time series data can be the time spent completing the task, as well as the order in which certain objects are clicked or pulled.
[0086] The specific feature calculation and processing process may include first using the Lasso (Least Absolute Shrinkage and Selection Operator) algorithm to verify the pre-set correlation, filtering out irrelevant values in the preprocessed behavioral data. Then, the filtered data is further processed to generate values such as hit rate and average speed. Finally, cross-validation and deletion are performed to obtain the interaction parameter values of the user to be monitored for the current cognitive behavioral activity. The Lasso algorithm is a regularization method used in linear regression models, designed to prevent overfitting by penalizing regression coefficients and to achieve feature selection.
[0087] S204. Dynamically adjust the complexity of the current cognitive behavior activity based on the interaction parameter value to obtain a new current cognitive behavior activity.
[0088] S205. Display new current cognitive behavioral activities and behavioral operation instruction information on the display panel of the terminal device, wherein the behavioral operation instruction information is used to instruct the user to be monitored to perform cognitive behavioral operations based on the new current cognitive behavioral activities.
[0089] This embodiment provides a cognitive behavior processing method based on cognitive impairment. By preprocessing the original behavioral data and performing feature calculation on the preprocessed behavioral data, the interaction parameter values of the user to be monitored for the current cognitive behavior activity are obtained. This provides an accurate data basis for subsequently adjusting the complexity of the current cognitive behavior activity and further ensures the improvement of the user's cognitive ability.
[0090] Figure 3 is a schematic flowchart of a cognitive behavior processing method based on cognitive impairment provided in another embodiment of this application. As shown in Figure 3, the user data collected by the Internet of Things (IoT) device in step 100 and the user data collected by the mobile APP in step 200 can be synchronously uploaded to the cloud. Simultaneously, the cognitive level of the user data collected by the IoT device and the user data collected by the mobile APP can be monitored (i.e., step 300), including preprocessing of the user data (i.e., step 301) and feature processing (i.e., feature extraction in step 3021, feature selection in step 3022, and classification in step 3023). The data preprocessing can include outlier detection in step 3011 and interpolation of missing data in step 3012; the feature extraction in step 3021 can obtain… The process involves obtaining the user's state data and time-series data. The state data can be used to characterize the interaction state between the user and the objects in the current cognitive behavior activity, such as the user's game state in different games. The time-series data can be used to characterize the time and order in which the user completes the current cognitive behavior activity. Step 3022, feature selection, may include calculating the user's interaction parameter values for the current cognitive behavior activity. These interaction parameter values may include hit rate and / or average speed, as well as filtering out data with poor correlation. Step 3023, classification, may include classification based on the interaction parameter values to dynamically adjust the complexity of the current cognitive behavior activity, resulting in a new current cognitive behavior activity, which facilitates the user's cognitive behavior operations based on the new activity.
[0091] More specifically, Figure 4 is a flowchart illustrating a dynamic adjustment of the complexity of current cognitive behavioral activities according to another embodiment of this application. As shown in Figure 4, a baseline activity can first be initiated to understand the current status of the user to be monitored. Based on the current status, a cognitive level classification is performed. The baseline activity corresponding to the complexity of the classification is initiated. Subsequently, user performance data of the user to be monitored for the baseline activity corresponding to the complexity of the classification can be recorded. The user performance data is processed as described above, such as deleting outliers and feature processing, to obtain the interaction parameter value of the user to be monitored. It is determined whether the interaction parameter value exceeds the maximum or minimum threshold set for the level. If the interaction parameter value exceeds the maximum threshold or is less than the minimum threshold, the cognitive level classification needs to be re-performed. The baseline activity corresponding to the complexity of the classification is initiated according to the classification, and the above operation is repeated. If the interaction parameter value is greater than the minimum parameter threshold and less than the maximum parameter threshold, the complexity of the current cognitive behavioral activity can be maintained, the baseline activity corresponding to the complexity of the classification is initiated again, and the above operation is repeated.
[0092] Meanwhile, this embodiment can also be based on multi-sensory observation and participation activities empowered by technology. Figure 5 is a flowchart of a gardening behavior operation provided by another embodiment of this application. As shown in Figure 5, the effect of sensory stimulation can be achieved through gardening behavior operations between the monitored user and the plant, including visual, tactile, auditory, gustatory, olfactory, and cognitive stimulation. For example, when the monitored user needs to stimulate their sense of smell and touch, enhance their visual-spatial skills and executive function, and promote motor skills, the caregiver can encourage the monitored user to smell the leaves in the early stages of plant growth; touch different types of plants (including looking at their texture, such as hard / soft / smooth / rough / solid / liquid); prune the plants; take photos to record and share and compare the plant growth (such as shape, size, or height). Alternatively, when the monitored user needs to stimulate their vision and touch, the caregiver can encourage the monitored user to observe and touch the patterns, shapes, sizes, or heights of different types of vegetables (such as cabbage and tomatoes), and share their sensory information with each other.
[0093] Furthermore, Figure 6 is a schematic diagram of the framework of a cognitive behavioral treatment method based on cognitive impairment provided in another embodiment of this application. As shown in Figure 6, personal well-being can be involved through biopsychosocial caregivers. For example, biological or physiological processes can be used to enrich process theory, and individuals can share their enjoyable and meaningful activities. Cognitive stimulation can also be achieved by using scaffolding support process theory from psychology. Bilateral interpersonal interaction can also be achieved by using the constraint theory of environmental or social factors. At the same time, cognitive well-being of the monitored user can be improved based on multi-sensory cognitive activities and gardening activities.
[0094] As can be seen from the above description, the cognitive behavioral intervention method based on cognitive impairment provided in this embodiment enhances the participation of caregivers in the intervention by utilizing the convenience of mobile applications and the Internet of Things and combining it with the dual approach of horticultural intervention. At the same time, data such as duration and frequency can help caregivers better understand the activities of users with cognitive impairment and make better and more informed decisions. This provides an innovative horticultural intervention experience method for users with cognitive impairment and their caregivers, improving the well-being of users with cognitive impairment and their caregivers.
[0095] Therefore, the cognitive behavioral treatment method based on cognitive impairment provided in this embodiment improves the mental health and quality of life of family caregivers of elderly people with cognitive problems (such as dementia); improves the behavior and / or psychological symptoms of the elderly and slows down the decline in cognitive ability; strengthens the duopoly and harmony between the elderly and caregivers; improves the basic knowledge and ability of healthcare professionals to provide psychological support for the elderly and their family caregivers in using senior technology products; and raises public awareness of supporting senior technology products for the elderly and their caregivers with cognitive problems.
[0096] Corresponding to the cognitive behavior processing method based on cognitive impairment in the above embodiments, Figure 7 is a structural block diagram of a cognitive behavior processing device based on cognitive impairment provided in an embodiment of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.
[0097] Referring to Figure 7, the device includes:
[0098] The acquisition module 501 is used to acquire the raw behavioral data of the user to be monitored. The raw behavioral data includes at least the interaction data of the user to be monitored with respect to the current cognitive behavioral activities configured in the terminal device. The current cognitive behavioral activities are used to promote the cognitive behavioral operations of the user to be monitored.
[0099] Data processing module 502 is used to process raw behavioral data to obtain interaction parameter values of the user to be monitored for the current cognitive behavioral activity;
[0100] The adjustment module 503 is used to dynamically adjust the complexity of the current cognitive behavioral activity based on the interaction parameter value to obtain a new current cognitive behavioral activity.
[0101] The display module 504 is used to display new current cognitive behavioral activities and behavioral operation instruction information on the display panel of the terminal device. The behavioral operation instruction information is used to instruct the user to be monitored to perform cognitive behavioral operations based on the new current cognitive behavioral activities.
[0102] This embodiment provides a cognitive behavior processing device based on cognitive impairment. An acquisition module acquires raw behavioral data of the user to be monitored. This raw behavioral data includes at least interaction data of the user's current cognitive behavioral activities configured within a terminal device, which facilitate cognitive behavior operations. A data processing module processes the raw behavioral data to obtain interaction parameter values for the user's current cognitive behavioral activities. An adjustment module dynamically adjusts the complexity of the current cognitive behavioral activities based on these interaction parameter values, resulting in new current cognitive behavioral activities. A display module displays the new current cognitive behavioral activities and behavioral operation instructions on the terminal device's display panel. The behavioral operation instructions instruct the user to perform cognitive behavior operations based on the new current cognitive behavioral activities. Using this device, by dynamically adjusting the complexity of the current cognitive behavioral activities based on the user's interaction parameter values, the user can better perform cognitive behavior operations based on the new current cognitive behavioral activities, effectively improving the user's cognitive abilities and enhancing their mental health and quality of life.
[0103] Optionally, the complexity of the current cognitive behavioral activity can be dynamically adjusted based on the interaction parameter values to obtain a new current cognitive behavioral activity, including:
[0104] Obtain the preset maximum and minimum parameter thresholds;
[0105] Based on the interaction parameter values, the maximum parameter threshold, and the minimum parameter threshold, the complexity of the current cognitive behavioral activity is dynamically adjusted to obtain a new current cognitive behavioral activity.
[0106] Optionally, the complexity of the current cognitive behavioral activity can be dynamically adjusted based on the interaction parameter values, the maximum parameter threshold, and the minimum parameter threshold to obtain a new current cognitive behavioral activity, including:
[0107] If the interaction parameter value is greater than the maximum parameter threshold, the complexity of the current cognitive behavior activity is increased to obtain a new current cognitive behavior activity;
[0108] If the interaction parameter value is less than the minimum parameter threshold, the complexity of the current cognitive behavior activity is reduced to obtain a new current cognitive behavior activity.
[0109] If the interaction parameter value is greater than the minimum parameter threshold and less than the maximum parameter threshold, then the complexity of the current cognitive behavior activity is maintained, and the current cognitive behavior activity is taken as the new current cognitive behavior activity.
[0110] Optionally, the raw behavioral data can be processed to obtain interaction parameter values of the user to be monitored for the current cognitive behavioral activity, including:
[0111] The original behavioral data is preprocessed to obtain preprocessed behavioral data. The preprocessing includes: anomaly deletion processing and interpolation processing.
[0112] The preprocessed behavioral data is processed by feature calculation to obtain the interaction parameter values of the user to be monitored for the current cognitive behavioral activity.
[0113] Optionally, feature calculation processing is performed on the preprocessed behavioral data to obtain the interaction parameter values of the user to be monitored for the current cognitive behavioral activity, including:
[0114] Feature extraction is performed on the preprocessed behavioral data to obtain the state data and time series data of the user to be monitored. The state data is used to characterize the interaction state between the user to be monitored and the active object in the current cognitive behavior activity, and the time series data is used to characterize the time and order in which the user to be monitored completes the current cognitive behavior activity.
[0115] Based on state data and time series data, calculate the interaction parameter values of the user to be monitored for the current cognitive behavior activity. The interaction parameter values include hit rate and / or average speed.
[0116] Optionally, the raw behavioral data can be preprocessed to obtain preprocessed behavioral data, including:
[0117] The original behavioral data is subjected to anomaly deletion processing to obtain anomaly-processed data.
[0118] Interpolation is performed on the missing data in the data after anomaly handling to obtain preprocessed behavioral data.
[0119] Optionally, the behavioral instruction information includes cognitive outcome information of the user to be monitored, which is used to provide sensory stimulation to the user by performing gardening behavioral actions.
[0120] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.
[0121] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0122] This application also provides a terminal device. FIG8 is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. As shown in FIG8, the terminal device includes: at least one processor 601, a memory 602, an input device 603, an output device 604, and a computer program stored in the memory 602 and executable on at least one processor 601. When the processor 601 executes the computer program, it implements the steps in any of the above method embodiments.
[0123] Input device 603 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the terminal device. Output device 604 may include display devices such as a display screen.
[0124] This application also provides a computer-readable storage medium storing a computer program, which, when executed by processor 601, can implement the steps in the above-described method embodiments.
[0125] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments.
[0126] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by the processor 601, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium can include at least: any entity or device capable of carrying computer program code to a device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, disks, or optical discs. In some jurisdictions, according to legislation and patent practice, computer-readable storage media cannot be electrical carrier signals or telecommunication signals.
[0127] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.
[0128] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0129] In the embodiments provided in this application, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.
[0130] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0131] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.
Claims
1. A cognitive-behavioral processing method based on cognitive impairment, wherein, include: Acquire raw behavioral data of the user to be monitored, wherein the raw behavioral data includes at least the interaction data of the user to be monitored with respect to the current cognitive behavioral activities configured in the terminal device, and the current cognitive behavioral activities are used to promote the cognitive behavioral operations of the user to be monitored. The raw behavioral data is processed to obtain the interaction parameter values of the user to be monitored in response to the current cognitive behavioral activity; The complexity of the current cognitive behavior activity is dynamically adjusted based on the interaction parameter values to obtain a new current cognitive behavior activity. The new current cognitive behavioral activity and behavioral operation instruction information are displayed on the display panel of the terminal device, wherein the behavioral operation instruction information is used to instruct the user to be monitored to perform cognitive behavioral operations based on the new current cognitive behavioral activity.
2. The cognitive behavioral processing method based on cognitive impairment as described in claim 1, wherein, The process of dynamically adjusting the complexity of the current cognitive behavior activity based on the interaction parameter value to obtain a new current cognitive behavior activity includes: Obtain the preset maximum and minimum parameter thresholds; Based on the interaction parameter values, the maximum parameter threshold, and the minimum parameter threshold, the complexity of the current cognitive behavior activity is dynamically adjusted to obtain a new current cognitive behavior activity.
3. The cognitive behavioral processing method based on cognitive impairment as described in claim 2, wherein, The step of dynamically adjusting the complexity of the current cognitive behavioral activity based on the interaction parameter value, the maximum parameter threshold, and the minimum parameter threshold to obtain a new current cognitive behavioral activity includes: If the interaction parameter value is greater than the maximum parameter threshold, the complexity of the current cognitive behavior activity is increased to obtain a new current cognitive behavior activity; If the interaction parameter value is less than the minimum parameter threshold, the complexity of the current cognitive behavior activity is reduced to obtain a new current cognitive behavior activity; If the interaction parameter value is greater than the minimum parameter threshold and less than the maximum parameter threshold, then the complexity of the current cognitive behavior activity is maintained, and the current cognitive behavior activity is taken as the new current cognitive behavior activity.
4. The cognitive behavioral processing method based on cognitive impairment as described in claim 1, wherein, The process of processing the raw behavioral data to obtain the interaction parameter values of the user to be monitored in response to the current cognitive behavioral activity includes: The original behavioral data is preprocessed to obtain preprocessed behavioral data. The preprocessing includes: anomaly deletion processing and interpolation processing. The preprocessed behavioral data is subjected to feature calculation processing to obtain the interaction parameter values of the user to be monitored in relation to the current cognitive behavioral activity.
5. The cognitive behavioral processing method based on cognitive impairment as described in claim 4, wherein, The step of performing feature calculation processing on the preprocessed behavioral data to obtain the interaction parameter values of the user to be monitored in response to the current cognitive behavioral activity includes: Feature extraction is performed on the preprocessed behavioral data to obtain the state data and time series data of the user to be monitored. The state data is used to characterize the interaction state between the user to be monitored and the active object in the current cognitive behavior activity, and the time series data is used to characterize the time and order in which the user to be monitored completes the current cognitive behavior activity. Based on the status data and the time series data, the interaction parameter values of the user to be monitored in relation to the current cognitive behavior activity are calculated, and the interaction parameter values include the hit rate and / or the average speed.
6. The cognitive behavioral processing method based on cognitive impairment as described in claim 4, wherein, The preprocessing of the original behavioral data to obtain preprocessed behavioral data includes: The original behavioral data is subjected to anomaly deletion processing to obtain anomaly-processed data; The missing data in the anomaly-handled data is interpolated to obtain preprocessed behavioral data.
7. The cognitive behavioral processing method based on cognitive impairment as described in claim 1, wherein, The behavioral operation instruction information includes the cognitive result information of the user to be monitored, which is used to provide sensory stimulation to the user to be monitored by performing gardening behavioral operations.
8. A cognitive behavioral processing device based on cognitive impairment, wherein, include: The acquisition module is used to acquire the raw behavioral data of the user to be monitored, wherein the raw behavioral data includes at least the interaction data of the user to be monitored with respect to the current cognitive behavioral activities configured in the terminal device, and the current cognitive behavioral activities are used to promote the cognitive behavioral operations of the user to be monitored. The data processing module is used to process the raw behavioral data to obtain the interaction parameter values of the user to be monitored in response to the current cognitive behavioral activity; The adjustment module is used to dynamically adjust the complexity of the current cognitive behavioral activity based on the interaction parameter value to obtain a new current cognitive behavioral activity. The display module is used to display the new current cognitive behavioral activity and behavioral operation instruction information on the display panel of the terminal device, wherein the behavioral operation instruction information is used to instruct the user to be monitored to perform cognitive behavioral operations based on the new current cognitive behavioral activity.