Method and system for interaction of elderly groups' intelligent activities based on virtual reality

Abstract

The present invention discloses a method and system for intelligent activity interaction of elderly groups based on virtual reality, comprising: obtaining basic health data and activity intention information of elderly users for embedded representation, reading health status representation and activity intention representation of elderly users; generating activity project recommendation constraints based on activity intention representation, obtaining recommended activity projects and recommended activity durations for elderly users based on activity recommendation constraints and health status representation; generating user needs based on motion feature flow, recommended activity duration, and activity intention information, converting them into virtual reality design elements to construct a virtual scene; obtaining interactive feedback based on interactive element analysis of the virtual scene, evaluating the activity effects of elderly users, and making adaptive activity project adjustments. The present invention utilizes virtual reality technology to improve the motivation of elderly people to participate in activities and increase the accessibility of activities within institutions to elderly people, thereby promoting activities of elderly people within institutions and reducing sedentary behavior.

Description

Technical Field

[0001] The present invention relates to the field of virtual reality technology, and more specifically, to a method and system for elderly group intelligent activity interaction based on virtual reality. Background Art

[0002] Virtual reality technology can simulate various scenarios and activities, such as travel, museum visits, and coursework, providing a diverse range of entertainment options for seniors. It can also offer gaming and entertainment services, helping to enhance their physical and mental health. Therefore, how to leverage virtual reality to diversify activities for seniors and reduce the risks of prolonged sedentary behavior is an urgent issue. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention proposes a method and system for intelligent activity interaction for the elderly based on virtual reality. The purpose is to solve the problem of lack of diversity in entertainment activities in existing nursing homes by constructing virtual reality scenes to simulate scenes for physical activities and group activities of the elderly in nursing homes.

[0004] The first aspect of the present invention provides a method for elderly group intelligent activity interaction based on virtual reality, comprising:

[0005] Obtaining basic health data and activity intention information of the elderly user, preprocessing the basic health data and activity intention information, and reading the health status representation and activity intention representation of the elderly user;

[0006] generating activity item recommendation constraints according to the activity intention expression, obtaining recommended activity items for elderly users based on the activity recommendation constraints and the health status expression, and setting a recommended activity duration for the recommended activity items;

[0007] Extracting motion features from the recommended activity items to construct a motion feature stream, generating user needs based on activity selection feedback, converting the user needs into virtual reality design elements, and constructing a virtual scene based on the design elements;

[0008] Read the interactive elements in the virtual scene, obtain interactive feedback based on the interactive element analysis, evaluate the activity effect of the elderly user through the interactive feedback, and make adaptive activity item adjustments.

[0009] In this solution, the basic health data and activity intention information are pre-processed to read the health status representation and activity intention representation of the elderly user, specifically:

[0010] Read the elderly user's physical examination data and monitoring data of wearable health monitoring devices as the elderly user's basic health data, obtain the elderly user's questionnaire data and interactive activity items within a preset time period as the elderly user's activity intention information;

[0011] Preprocessing the basic health data, performing multidimensional feature extraction on the preprocessed basic health data using two parallel data processing branches, extracting low-dimensional features of the basic health data in the main data processing branch, importing the low-dimensional features into the convolutional neural network channel, and introducing channel attention to extract high-dimensional features, and obtaining the output vector of the main data processing branch;

[0012] In the side data processing branch, the pooling window is used to fill the average value of the low-dimensional features in the window into the corresponding position of the output feature vector according to the pooling step size to obtain the output vector of the side data processing branch;

[0013] Fusing the output vector of the main data processing branch and the output vector of the side data processing branch to obtain multi-dimensional features and generate a health status representation of the elderly user;

[0014] The questionnaire data and interactive activity item lists of elderly users are preprocessed to extract the corresponding word vectors. The obtained word vectors are used for bidirectional feature extraction using the Bi-GRU model, and a multi-head attention mechanism is introduced to weight the hidden states to obtain the activity intention representation containing the activity interests of elderly users.

[0015] In this solution, activity recommendation constraints are generated according to the activity intention expression, and recommended activity items for elderly users are obtained based on the activity recommendation constraints and the health status expression, specifically:

[0016] Based on big data and data analysis methods, a preset number of activity categories of interest to elderly groups are obtained, activity recommendation constraints are generated according to the activity intention expressions, activity categories are preliminarily selected using the activity recommendation constraints, and primary weights are set for the preliminarily selected activity categories;

[0017] Retrieving and acquiring motion data from rehabilitation training instances and daily care instances based on the health status representation of the elderly user, calculating Pearson correlation parameters for each activity item category based on the motion data, and screening relevant activity item categories that meet a preset threshold using the Pearson correlation parameters;

[0018] Aggregating the pre-selected activity item categories and the related activity item categories to generate a set of activity items to be selected, representing the elderly user's health status representation and the set of activity items to be selected in a graph, and performing a random walk based on the graph representation with the elderly user's health status node as the starting point;

[0019] Obtain a meta-path containing several activity items obtained through random walks, use a graph attention network to learn and represent the meta-path, and calculate the weight of each activity item node in the meta-path to represent the importance of the node to the health status of the elderly user;

[0020] The meta-path with the highest total weight is screened, and the activity project nodes therein are extracted as recommended activity projects for elderly users.

[0021] In this plan, the recommended activity duration of the recommended activity project is set as follows:

[0022] Obtaining rehabilitation training examples and daily care examples corresponding to the recommended activity items, analyzing the corresponding health status intervals and corresponding activity duration intervals in the rehabilitation training examples, and constructing a mapping relationship between the health status intervals and the activity duration intervals;

[0023] Obtain a health status representation of the elderly user, locate the corresponding activity duration sub-interval based on the mapping relationship, introduce a nonlinear adaptive factor based on the activity duration sub-interval to obtain a recommended activity duration function for the elderly user, so as to determine the recommended activity duration of the recommended activity item.

[0024] In this solution, the motion features of the recommended activities are extracted to construct a motion feature stream. User needs are generated based on the activity selection feedback. The user needs are converted into virtual reality design elements, and a virtual scene is constructed based on the design elements. Specifically:

[0025] Decompose motion features based on the rehabilitation training instances and daily care instances corresponding to the recommended activity items, add timestamps to the motion features based on the activity duration in the instances, concatenate the motion features based on the timestamps, and construct a global motion feature stream by aggregating the concatenation order of different instances;

[0026] Obtaining activity selection feedback from elderly users for each recommended activity item and its global motion feature stream, reading the activity item category and selected motion features in the activity selection feedback, analyzing the richness of the motion features selected by the elderly users, adjusting the selected motion features based on the richness, and constructing the motion feature stream in combination with the recommended activity duration;

[0027] Generating user needs based on the recommended activity item category selected by the elderly user and the motion feature stream, and extracting visual elements, auditory elements, human-computer elements, and interactive elements of the activity virtual scene based on the user needs;

[0028] The virtual scene is initialized by the obtained design elements, and the feedback from elderly users is used to optimize the virtual scene.

[0029] In this solution, the interactive elements in the virtual scene are read, interactive feedback is obtained based on the interactive element analysis, and the activity effect of the elderly user is evaluated through the interactive feedback, specifically:

[0030] Obtain interactive feedback from elderly users when they perform interactive actions in virtual scenes, and obtain indicator parameters of each motion feature in the motion feature stream from four aspects: motion speed, motion completion, motion smoothness, and motion accuracy;

[0031] Constructing an indicator parameter matrix according to the indicator parameters, and constructing a threshold matrix according to a preset evaluation indicator threshold, and obtaining a residual vector between the indicator parameter matrix and the threshold matrix;

[0032] The weight is set by the feature step length occupied by each motion feature in the motion feature stream, and the residual vector of the motion is weighted and summed to obtain the evaluation result of the activity effect of the elderly user.

[0033] In this plan, adaptive activity project adjustments are carried out, specifically:

[0034] Based on the interaction feedback, the interaction elements that the elderly user fails to meet the target are marked, the position deviation corresponding to the marked interaction elements is read, and the position deviation is divided according to the preset deviation threshold. If it is not greater than, the target position of the interaction element is reset according to the current reachability of the elderly user's action;

[0035] If it is greater than, the motion characteristics of the interaction elements corresponding to the position deviation are used as dependent variables, and the interaction elements that meet the action requirements are used as the retained motion feature set. The motion characteristics are decomposed according to the rehabilitation training examples and daily care examples corresponding to the recommended activity items to construct the original motion feature set;

[0036] Extracting the reachable motion features of the elderly users from the retained motion feature set, constructing retrieval tags based on the reachable motion features, and screening the motion features that meet the similarity criteria in the original motion feature set to generate a set of motion features to be introduced;

[0037] Using the Pearson correlation coefficient to expand the mRMR algorithm, the motion feature with the greatest correlation with the dependent variable is obtained from the set of motion features to be introduced, and the fitness ranking of the introduced motion features is obtained based on the principle of maximizing the correlation between the motion feature and the dependent variable and minimizing the redundancy in the set of retained motion features;

[0038] The motion features with the highest adaptability are introduced to replace the motion features of the interactive elements corresponding to the position deviation to achieve the adjustment of the activity items.

[0039] The second aspect of the present invention provides a virtual reality-based elderly group intelligent activity interaction system, including a data acquisition module, an activity recommendation module, a virtual scene module, an activity evaluation module and an evaluation result output module;

[0040] The data collection module is used to collect basic health data and activity intention information of elderly users and perform preprocessing to obtain health status representation and activity intention representation of elderly users;

[0041] The activity recommendation module obtains recommended activity items for the elderly user based on the activity interest constraints and health status of the elderly user, and obtains recommended activity durations for the recommended activity items;

[0042] The virtual scene module is used to determine user needs based on motion characteristics and activity intention information, extract virtual reality design elements based on user needs, and construct a virtual scene using the design elements;

[0043] The activity evaluation module reads the interactive feedback of the interactive elements in the virtual scene and evaluates the activity effect of the elderly user through the interactive feedback;

[0044] The evaluation result output module is used to output the evaluation data of the elderly user's activity process and perform data visualization.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention uses virtual reality technology to provide virtual scenes for physical activities of the elderly in nursing homes, including virtual scenes for group activities. Starting from the health status of the elderly users, activity projects with a high degree of adaptability to the health status and activity interests of the elderly users are generated, thereby increasing the motivation of the elderly to participate in activities and increasing the accessibility of activities within the institution to the elderly, thereby promoting activities of the elderly in the institution and reducing sedentary behavior. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments or exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or exemplary descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained according to these drawings without paying any creative work.

[0048] Figure 1 A flowchart of the elderly group intelligent activity interaction method based on virtual reality of the present invention is shown;

[0049] Figure 2 A flowchart of obtaining recommended activity items for elderly users according to an embodiment of the present invention is shown;

[0050] Figure 3 A flowchart illustrating an embodiment of the present invention for evaluating the activity effects of elderly users is shown;

[0051] Figure 4 The block diagram of the elderly group intelligent activity interaction system based on virtual reality of the present invention is shown. DETAILED DESCRIPTION

[0052] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0054] Figure 1 The flowchart of the virtual reality-based elderly group intelligent activity interaction method of the present invention is shown.

[0055] like Figure 1 As shown, the first embodiment of the present invention provides a method for elderly group intelligent activity interaction based on virtual reality, including:

[0056] S102, obtaining basic health data and activity intention information of the elderly user, preprocessing the basic health data and activity intention information, and reading the health status representation and activity intention representation of the elderly user;

[0057] S104, generating activity recommendation constraints according to the activity intention expression, obtaining recommended activity items for the elderly user based on the activity recommendation constraints and the health status expression, and setting a recommended activity duration for the recommended activity items;

[0058] S106, extracting motion features from the recommended activity items to construct a motion feature stream, generating user needs based on activity selection feedback, converting the user needs into virtual reality design elements, and constructing a virtual scene based on the design elements;

[0059] S108, reading the interactive elements in the virtual scene, obtaining interactive feedback according to the interactive element analysis, evaluating the activity effect of the elderly user through the interactive feedback, and making adaptive activity item adjustments.

[0060] It should be noted that different preset medical data sources are accessed, and the physical examination data of the elderly user within the preset time and the monitoring data of the wearable health monitoring device are read as the basic health data of the elderly user. The questionnaire survey data of the elderly user and the interactive activity items within the preset time period are obtained as the activity intention information of the elderly user; the basic health data are preprocessed, and the preprocessed basic health data are subjected to multidimensional feature extraction using two parallel data processing branches. In the main data processing branch, the low-dimensional features of the basic health data are extracted and the low-dimensional features are introduced into the convolutional neural network channel, and the channel attention is introduced to extract high-dimensional features to obtain the output vector of the main data processing branch; in the side data processing branch, the average value of the low-dimensional features in the window is filled into the corresponding position of the output feature vector according to the pooling step size using the pooling window to obtain the output vector of the side data processing branch; the features of the basic health data extracted by the two branches include both deep high-dimensional complex features and low-dimensional feature information closely related to the original data, which are represented by the features of the rich basic health data. The output vector of the main data processing branch and the output vector of the side data processing branch are fused to obtain multidimensional features and generate a representation of the health status of the elderly user.

[0061] The questionnaire data and interactive activity item lists of elderly users are preprocessed to extract the corresponding word vectors. The obtained word vectors are used for bidirectional feature extraction using the Bi-GRU model. The time-related features of the activity items of interest to elderly users are learned through the GRU network. A multi-head attention mechanism is introduced to weight the hidden state, and the fully connected layer is used to obtain the activity intention representation containing the activity interests of elderly users.

[0062] Figure 2 A flow chart of obtaining recommended activity items for elderly users according to an embodiment of the present invention is shown.

[0063] According to an embodiment of the present invention, activity recommendation constraints are generated based on the activity intention expression, and recommended activity items for elderly users are obtained based on the activity recommendation constraints and the health status expression, specifically:

[0064] S202, based on big data and data analysis methods, obtaining a preset number of activity categories that are of interest to the elderly group, generating activity recommendation constraints based on the activity intention expressions, using the activity recommendation constraints to preliminarily select activity categories, and setting primary weights for the preliminarily selected activity categories;

[0065] S204, retrieving motion data from rehabilitation training instances and daily care instances based on the health status representation of the elderly user, calculating Pearson correlation parameters for each activity item category based on the motion data, and screening relevant activity item categories that meet a preset threshold using the Pearson correlation parameters;

[0066] S206, aggregating the pre-selected activity item categories and the related activity item categories to generate a set of activity items to be selected, creating a graph representation of the elderly user's health status and the set of activity items to be selected, and performing a random walk based on the graph representation with the elderly user's health status node as a starting point;

[0067] S208, obtaining a meta-path containing several activity items obtained by random walk, using a graph attention network to learn and represent the meta-path, and calculating the weight of each activity item node in the meta-path to represent the importance of the node to the health status of the elderly user;

[0068] S210 , screening the meta-path with the highest total weight, and extracting the activity item nodes therein as recommended activity items for elderly users.

[0069] It should be noted that the meta-path is obtained through methods such as Metapath random walk, and the meta-path is learned and represented using the graph attention network. The importance of each activity item node in the meta-path to the head node (the health status node of the elderly user) is learned based on the graph convolution and attention mechanism. The weight of each activity item node is obtained according to the importance. If each activity item node in the meta-path has a primary weight, the obtained weight and the primary weight are combined to obtain the total weight of all activity item nodes in the meta-path. After sorting the meta-path using the total weight, the activity item node in the meta-path with the highest total weight is obtained as the recommended activity item for the elderly user.

[0070] It should be noted that the rehabilitation training examples and daily care examples corresponding to the recommended activity items are obtained, the corresponding health status intervals and the activity duration intervals of single activities in the rehabilitation training examples are analyzed, and a mapping relationship between the health status intervals and the activity duration intervals is constructed. Each health status is mapped to several single activity durations. The health status representation of the elderly user is obtained, and the corresponding activity duration sub-interval is located based on the mapping relationship. A nonlinear adaptive factor is introduced based on the activity duration sub-interval to obtain the recommended activity duration function of the elderly user, so as to determine the recommended activity duration of the recommended activity items. The recommended activity duration function λ is expressed as: λ c ,λ z The upper and lower limits of the activity duration subinterval of the distribution, a represents a fixed constant, gammainv represents the incomplete function used to update the activity duration function, and k represents the health status.

[0071] It should be noted that the motion features are decomposed according to the rehabilitation training examples and daily care examples corresponding to the recommended activity items, timestamps are added to the motion features according to the activity duration in the examples, the motion features are connected in series according to the timestamps, and a global motion feature stream is constructed by aggregating the series order of different instances; the activity selection feedback of the elderly users for each recommended activity item and its global motion feature stream is obtained, the activity item category and the selected motion features in the activity selection feedback are read, and the richness of the motion features selected by the elderly users is analyzed. The richness is obtained through the correlation of the motion features selected by the elderly users, and the selected motion features are adjusted based on the richness. When the action features selected by the elderly users are relatively single, the action feature categories are adaptively enriched. In addition, when the difficulty coefficient of the action features selected by the elderly users is analyzed and the difficulty coefficient is low, the high-level difficulty action features are appropriately increased to increase the activity amount of the elderly users, and the motion feature stream is constructed in combination with the recommended activity duration.

[0072] User requirements are generated based on the recommended activity categories selected by the elderly user and the motion feature stream. Based on these user requirements, the visual, auditory, human-machine, and interactive elements of the activity virtual scene are extracted. Visual elements in the virtual scene must be clearly visible and recognizable. This prevents elderly users from misclassifying and potentially leading to erroneous activity assessments. The visibility and recognizability of elements in the virtual scene can be improved by increasing the brightness and contrast of the virtual scene. Auditory elements in the virtual scene must have clear audio cues to ensure clarity. In addition, upbeat background sound effects can be set based on the elderly user's musical preferences. Ergonomic considerations related to size, position, and angle of human-machine elements in the virtual scene are considered to ensure the safety and comfort of elderly users and ensure accessibility of their movements. Interactive elements in the virtual scene must be tailored to the elderly user's comprehension capabilities. More spatial guidance and prompts should be added to reduce interaction complexity and ensure clear and quick understanding.

[0073] Figure 3 A flow chart of evaluating the activity effects of elderly users according to an embodiment of the present invention is shown.

[0074] According to an embodiment of the present invention, the interactive elements in the virtual scene are read, interactive feedback is obtained based on the interactive element analysis, and the activity effect of the elderly user is evaluated based on the interactive feedback, specifically:

[0075] S302, obtaining interactive feedback corresponding to the interactive action of the elderly user when performing interactive elements in the virtual scene, and obtaining indicator parameters of each motion feature in the motion feature stream from four aspects: motion speed, motion completion, motion smoothness, and motion accuracy;

[0076] S304, constructing an indicator parameter matrix according to the indicator parameters, and constructing a threshold matrix according to a preset evaluation indicator threshold, and obtaining a residual vector between the indicator parameter matrix and the threshold matrix;

[0077] S306 , setting weights according to the feature step lengths occupied by each motion feature in the motion feature stream, performing weighted summation on the motion residual vectors, and obtaining an evaluation result of the activity effect of the elderly user.

[0078] It should be noted that the motion speed index includes the average speed and maximum speed of elderly users in the movement of interactive elements; the motion completion index obtains the number of times the user completes the interactive elements in the activity and the difficulty coefficient of the interactive elements. When the difficulty coefficient is large, the threshold of the motion completion index is appropriately lowered; the motion smoothness is obtained by calculating the rate of change of the acceleration of the elderly user in the movement of interactive elements; the motion accuracy includes the motion trajectory deviation and the target position deviation, and the user measures the activity quality of the elderly user.

[0079] It should be noted that, based on the interaction feedback, the interaction elements that do not meet the standards of elderly users are obtained and marked, and the position deviations corresponding to the marked interaction elements are read. The position deviations are divided according to preset deviation thresholds of different levels, and the primary deviation threshold is set according to the adaptive interaction difficulty adjustment. Action features that are not greater than the primary deviation threshold are considered to meet the standards. An advanced threshold is set based on the primary threshold, and the adjustment of the motion features is judged based on the advanced threshold. If the value of the primary threshold is not greater than the advanced threshold, the target position of the interactive element is reset according to the current motion reachability of the elderly user. If the value of the primary threshold is greater than the advanced threshold, the motion feature of the interactive element corresponding to the position deviation is used as the dependent variable, and the interactive elements that meet the motion requirements are used as the retained motion feature set. The motion features are decomposed according to the rehabilitation training examples and daily care examples corresponding to the recommended activity items to construct an original motion feature set. The motion reachability features of the elderly user in the retained motion feature set are extracted, and a retrieval tag is constructed based on the motion reachability features. The motion features that meet the similarity criteria in the original motion feature set are screened to generate a set of motion features to be introduced. The Pearson correlation coefficient is used to expand the mRMR algorithm to obtain the motion feature with the greatest correlation with the dependent variable in the set of motion features to be introduced. Based on the principle that the correlation between the motion feature and the dependent variable is the greatest and the redundancy in the retained motion feature set is the least, the fitness ranking of the introduced motion features is obtained. The motion feature with the highest fitness is introduced to replace the motion feature of the interactive element corresponding to the position deviation to achieve activity item adjustment.

[0080] In addition, in the simulation scenario, substandard interactive elements are obtained based on position deviations, and spatial position distribution characteristics of the substandard interactive elements are generated. A substandard interaction heat map is generated based on the spatial position distribution characteristics. Based on the substandard interaction heat map, the location areas with weak control capabilities during the activities of elderly users are obtained. The location areas with weak control capabilities are used to provide feedback to the virtual scenes of elderly users, increase the number and difficulty of interactive element tasks in the location areas with weak control capabilities, and realize targeted activity training for elderly users.

[0081] Figure 4 The block diagram of the elderly group intelligent activity interaction system based on virtual reality of the present invention is shown.

[0082] The second embodiment of the present invention provides a virtual reality-based elderly group intelligent activity interaction system 4, which includes a data acquisition module 41, an activity recommendation module 42, a virtual scene module 43, an activity evaluation module 44 and an evaluation result output module 45;

[0083] The data collection module is used to collect basic health data and activity intention information of elderly users and perform preprocessing to obtain health status representation and activity intention representation of elderly users;

[0084] The activity recommendation module obtains recommended activity items for the elderly user based on the activity interest constraints and health status of the elderly user, and obtains recommended activity durations for the recommended activity items;

[0085] The virtual scene module is used to determine user needs based on motion characteristics and activity intention information, extract virtual reality design elements based on user needs, and construct a virtual scene using the design elements;

[0086] The activity evaluation module reads the interactive feedback of the interactive elements in the virtual scene and evaluates the activity effect of the elderly user through the interactive feedback;

[0087] The evaluation result output module is used to output the evaluation data of the elderly user's activity process and perform data visualization.

[0088] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0089] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0090] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0091] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0092] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0093] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for elderly group intelligent activity interaction based on virtual reality, characterized in that: The following steps are involved: Obtaining basic health data and activity intention information of the elderly user, preprocessing the basic health data and activity intention information, and reading the health status representation and activity intention representation of the elderly user; generating activity item recommendation constraints according to the activity intention expression, obtaining recommended activity items for elderly users based on the activity item recommendation constraints and the health status expression, and setting a recommended activity duration for the recommended activity items; Obtaining rehabilitation training examples and daily care examples corresponding to the recommended activity items, analyzing the corresponding health status intervals and corresponding activity duration intervals in the rehabilitation training examples, and constructing a mapping relationship between the health status intervals and the activity duration intervals; Obtaining a health status representation of the elderly user, locating a corresponding activity duration subinterval based on the mapping relationship, introducing a nonlinear adaptive factor based on the activity duration subinterval to obtain a recommended activity duration function for the elderly user, so as to determine a recommended activity duration for the recommended activity item; The recommended activity duration function , expressed as: , Respectively represent the upper and lower limits of the activity duration sub-interval, represents a fixed constant, Indicates that the incomplete function is used to update the activity duration function. Indicates health status; Extracting motion features from the recommended activity items to construct a motion feature stream, generating user needs based on activity selection feedback, converting the user needs into virtual reality design elements, and constructing a virtual scene based on the design elements; Reading interactive elements in the virtual scene, obtaining interactive feedback based on interactive element analysis, evaluating the activity effect of the elderly user through the interactive feedback, and making adaptive activity item adjustments; In a virtual scene, substandard interactive elements are obtained based on position deviations, and spatial position distribution characteristics of the substandard interactive elements are generated. A substandard interaction heat map is generated based on the spatial position distribution characteristics. Based on the substandard interaction heat map, location areas with weak control capabilities during the elderly user's activities are obtained. The weak control capability location areas are used to provide feedback to the elderly user's virtual scene, increase the number and difficulty of interactive element tasks in the weak control capability location areas, and achieve targeted activity training for elderly users.

2. The method for elderly group intelligent activity interaction based on virtual reality according to claim 1, characterized in that: Pre-process the basic health data and activity intention information to read the health status and activity intention of the elderly user, specifically: Read the elderly user's physical examination data and monitoring data of wearable health monitoring devices as the elderly user's basic health data, obtain the elderly user's questionnaire data and interactive activity items within a preset time period as the elderly user's activity intention information; Preprocessing the basic health data, performing multidimensional feature extraction on the preprocessed basic health data using two parallel data processing branches, extracting low-dimensional features of the basic health data in the main data processing branch and importing the low-dimensional features into the convolutional neural network channel, introducing channel attention to extract high-dimensional features, and obtaining an output vector of the main data processing branch; In the side data processing branch, the pooling window is used to fill the average value of the low-dimensional features in the window into the corresponding position of the output feature vector according to the pooling step size to obtain the output vector of the side data processing branch; Fusing the output vector of the main data processing branch and the output vector of the side data processing branch to obtain multi-dimensional features and generate a health status representation of the elderly user; The questionnaire data and interactive activity item lists of elderly users are preprocessed to extract the corresponding word vectors. The obtained word vectors are used for bidirectional feature extraction using the Bi-GRU model, and a multi-head attention mechanism is introduced to weight the hidden states to obtain the activity intention representation containing the activity interests of elderly users.

3. The method for elderly group intelligent activity interaction based on virtual reality according to claim 1, characterized in that: Generate activity item recommendation constraints according to the activity intention expression, and obtain recommended activity items for elderly users based on the activity item recommendation constraints and the health status expression, specifically: Based on big data and data analysis methods, a preset number of activity categories of interest to elderly groups are obtained, activity recommendation constraints are generated according to the activity intention expressions, activity categories are preliminarily selected using the activity recommendation constraints, and primary weights are set for the preliminarily selected activity categories; Retrieving and acquiring motion data from rehabilitation training instances and daily care instances based on the health status representation of the elderly user, calculating Pearson correlation parameters for each activity item category based on the motion data, and screening relevant activity item categories that meet a preset threshold using the Pearson correlation parameters; Aggregating the pre-selected activity item categories and the related activity item categories to generate a set of activity items to be selected, representing the elderly user's health status representation and the set of activity items to be selected in a graph, and performing a random walk based on the graph representation with the elderly user's health status node as the starting point; Obtain a meta-path containing several activity items obtained through random walks, use a graph attention network to learn and represent the meta-path, and calculate the weight of each activity item node in the meta-path to represent the importance of the node to the health status of the elderly user; The meta-path with the highest total weight is screened, and the activity project nodes therein are extracted as recommended activity projects for elderly users.

4. The method for elderly group intelligent activity interaction based on virtual reality according to claim 1, characterized in that: Extract the motion features from the recommended activity items to construct a motion feature stream, combine the activity selection feedback to generate user needs, convert the user needs into virtual reality design elements, and construct a virtual scene based on the design elements, specifically: Decompose motion features based on the rehabilitation training instances and daily care instances corresponding to the recommended activity items, add timestamps to the motion features based on the activity duration in the instances, concatenate the motion features based on the timestamps, and construct a global motion feature stream by aggregating the concatenation order of different instances; Obtaining activity selection feedback from elderly users for each recommended activity item and its global motion feature stream, reading the activity item category and selected motion features in the activity selection feedback, analyzing the richness of the motion features selected by the elderly users, adjusting the selected motion features based on the richness, and constructing the motion feature stream in combination with the recommended activity duration; Generating user needs based on the recommended activity item category selected by the elderly user and the motion feature stream, and extracting visual elements, auditory elements, human-computer elements, and interactive elements of the activity virtual scene based on the user needs; The virtual scene is initialized by the obtained design elements, and the feedback from elderly users is used to optimize the virtual scene.

5. The method for elderly group intelligent activity interaction based on virtual reality according to claim 1, characterized in that: Reading interactive elements in the virtual scene, obtaining interactive feedback based on interactive element analysis, and evaluating the activity effect of the elderly user through the interactive feedback, specifically: Obtain interactive feedback from elderly users when they perform interactive actions in virtual scenes, and obtain indicator parameters of each motion feature in the motion feature stream from four aspects: motion speed, motion completion, motion smoothness, and motion accuracy; Constructing an indicator parameter matrix according to the indicator parameters, and constructing a threshold matrix according to a preset evaluation indicator threshold, and obtaining a residual vector between the indicator parameter matrix and the threshold matrix; The weight is set by the feature step length occupied by each motion feature in the motion feature stream, and the residual vector of the motion is weighted and summed to obtain the evaluation result of the activity effect of the elderly user.

6. The method for elderly group intelligent activity interaction based on virtual reality according to claim 1, characterized in that: Conduct adaptive activity adjustments, specifically: Based on the interaction feedback, the interaction elements that the elderly user fails to meet the target are marked, the position deviation corresponding to the marked interaction elements is read, and the position deviation is divided according to the preset deviation threshold. If it is not greater than, the target position of the interaction element is reset according to the current reachability of the elderly user's action; If it is greater than, the motion characteristics of the interaction elements corresponding to the position deviation are used as dependent variables, and the interaction elements that meet the action requirements are used as the retained motion feature set. The motion characteristics are decomposed according to the rehabilitation training examples and daily care examples corresponding to the recommended activity items to construct the original motion feature set; Extracting the reachable motion features of the elderly users from the retained motion feature set, constructing retrieval tags based on the reachable motion features, and screening the motion features that meet the similarity criteria in the original motion feature set to generate a set of motion features to be introduced; Using the Pearson correlation coefficient to expand the mRMR algorithm, the motion feature with the greatest correlation with the dependent variable is obtained from the set of motion features to be introduced, and the fitness ranking of the introduced motion features is obtained based on the principle of maximizing the correlation between the motion feature and the dependent variable and minimizing the redundancy in the retained motion feature set; The motion features with the highest adaptability are introduced to replace the motion features of the interactive elements corresponding to the position deviation to achieve the adjustment of the activity items.

7. A virtual reality-based elderly group intelligent activity interaction system, characterized by: Implementing the elderly group intelligent activity interaction method based on virtual reality as described in any one of claims 1 to 6, comprising a data acquisition module, an activity recommendation module, a virtual scene module, an activity evaluation module and an evaluation result output module; The data collection module is used to collect basic health data and activity intention information of elderly users and perform preprocessing to obtain health status representation and activity intention representation of elderly users; The activity recommendation module obtains recommended activity items for the elderly user based on the activity interest constraints and health status of the elderly user, and obtains recommended activity durations for the recommended activity items; The virtual scene module is used to determine user needs based on motion characteristics and activity selection feedback, extract virtual reality design elements based on user needs, and construct a virtual scene using the design elements; The activity evaluation module reads the interactive feedback of the interactive elements in the virtual scene and evaluates the activity effect of the elderly user through the interactive feedback; The evaluation result output module is used to output the evaluation data of the elderly user's activity process and perform data visualization.

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