CN114420294A

CN114420294A - Psychological development level assessment method, device, equipment, storage medium and system

Info

Publication number: CN114420294A
Application number: CN202210296158.2A
Authority: CN
Inventors: 黄超; 张跃曦
Original assignee: Beijing Wujiang Naozhi Technology Co ltd
Current assignee: Beijing Wujiang Naozhi Technology Co ltd
Priority date: 2022-03-24
Filing date: 2022-03-24
Publication date: 2022-04-29

Abstract

The invention provides a psychological development level assessment method, a device, equipment, storage medium and a system, wherein theories of Kaiser berg action side writing, Raban/Ba Tuneff action analysis and the like are introduced, so that human body actions can reflect the psychological development level of a person more truly relative to answer questionnaires, and further, by providing prompt information corresponding to a psychological development item to be tested, and shooting a depth image in the action process of the tested user according to the prompt information, a corresponding target depth image sequence is obtained, then characteristic extraction is carried out, finally the action characteristics of the tested user are input into a pre-trained psychological development level assessment model corresponding to the psychological development item to be tested, and the development level information of the psychological development item to be tested corresponding to the tested user is obtained. The whole process does not need manual intervention of a psychological consultant, does not need special scenes and fields, and can reduce labor cost, economic cost and time cost for evaluating the psychological development level of the user.

Description

Psychological development level assessment method, device, equipment, storage medium and system

Technical Field

The embodiment of the disclosure relates to the technical field of psychological development level assessment, in particular to a psychological development level assessment method, device, equipment, storage medium and system.

Background

Mental development refers to a series of mental changes that occur throughout the life of an individual. Psychological development is a continuous and uninterrupted process, and each psychological process and personality trait gradually and continuously develop. The psychological development is directional, and the psychological development direction is different for people of different ages and different regions. The mental development level can be used to measure different directions of mental development. Therefore, whether a human mental development level can be accurately evaluated or not can play a crucial role in the career planning and the life style of the human.

Currently, in the aspect of assessment of mental development level, a psychological counselor firstly makes psychological counseling with the tested user and gives a psychological development level result of the corresponding user according to the process of the psychological counseling. Alternatively, a psychological test questionnaire is provided for the user, and the answer result of the tested user is automatically analyzed through an algorithm to give a corresponding psychological development level result.

Disclosure of Invention

The embodiment of the disclosure provides a mental development level assessment method, a mental development level assessment device, equipment, a storage medium and a mental development level assessment system.

In a first aspect, embodiments of the present disclosure provide a mental development level assessment method, including:

acquiring a target depth image sequence, wherein the target depth image sequence is at least two frames of continuous depth images obtained by shooting the detected user to act according to prompt information corresponding to the psychological development project to be detected;

performing feature extraction based on the target depth image sequence to obtain action features of the tested user;

and inputting the action characteristics of the tested user into a pre-trained psychological development level assessment model corresponding to the psychological development project to be tested to obtain development level information of the tested user corresponding to the psychological development project to be tested, wherein the psychological development level assessment model is used for representing the corresponding relation between the action characteristics of the human body and the psychological development level information.

In some optional embodiments, the method further comprises: and presenting the development level information of the tested user corresponding to the mental development project to be tested.

In some optional embodiments, before the acquiring the target depth image sequence, the method further comprises: and presenting prompt information corresponding to the mental development project to be tested.

In some optional embodiments, before the presenting the prompt information corresponding to the mental development item to be tested, the method further includes: presenting a preset candidate psychological development item identification set; in response to detecting the selection operation of the target candidate mental development item identifier in the preset candidate mental development item identifier set, determining the mental development item indicated by the target candidate mental development item identifier as the mental development item to be detected.

In some optional embodiments, before inputting the motion characteristics of the tested user into the pre-trained mental development level assessment model corresponding to the mental development item to obtain development level information of the tested user corresponding to the mental development item to be tested, the method further includes: acquiring a target physiological characteristic data sequence, wherein the target physiological characteristic data sequence is a corresponding physiological characteristic data sequence obtained by collecting at least one item of physiological characteristic data of the detected user in the process of shooting and obtaining the target depth image sequence; and performing feature extraction based on the target depth image sequence to obtain the action features of the tested user, including: and performing feature extraction based on the target depth image sequence and the target physiological feature data sequence to obtain the action features of the detected user.

In some alternative embodiments, the at least one physiological characteristic data comprises at least one of: heart rate parameters, electrodermal parameters.

In some optional embodiments, the prompt information corresponding to the mental development item to be tested includes video and/or audio; the presenting of the prompt information corresponding to the mental development project to be tested and the obtaining of the target depth image sequence include: and playing prompt information corresponding to the psychological development project to be tested and acquiring at least two frames of continuous depth images obtained by shooting the action of the tested user by the depth camera in real time in the playing process.

In some optional embodiments, the prompt information corresponding to the mental development item to be tested includes at least one of the following items: images, text, and audio; and the acquiring a sequence of target depth images comprises: and acquiring at least two frames of continuous depth images obtained by shooting the action of the tested user by the depth camera within a preset action duration corresponding to the psychological development project to be tested.

In some optional embodiments, the performing feature extraction based on the target depth image sequence to obtain motion features of the tested user includes: performing characteristic space conversion on the target depth image sequence to obtain a target point cloud data sequence; and extracting features based on the target point cloud data sequence to obtain the action features of the detected user.

In some optional embodiments, the performing feature extraction based on the target point cloud data sequence to obtain the motion feature of the tested user includes: performing action segmentation based on the target point cloud data sequence to obtain at least one target point cloud data subsequence ordered according to time; extracting the characteristics of each target point cloud data subsequence to obtain corresponding characteristics; and determining the motion characteristics of the tested user based on the characteristics of each target point cloud data subsequence.

In some optional embodiments, the mental development level assessment model corresponding to the mental development project to be tested is obtained through the following training steps: acquiring a training sample set, wherein the training sample comprises a sample depth image sequence obtained by shooting a sample user to perform actions according to prompt information corresponding to the mental development item to be detected and labeled mental development level information used for representing the mental development level corresponding to the user action corresponding to the sample depth image sequence; for the training samples in the training sample set, performing the following parameter adjustment operations until a preset training end condition is met: extracting features based on the sample depth image sequence in the training sample to obtain corresponding action features; inputting the obtained action characteristics into an initial psychological development level evaluation model to obtain corresponding psychological development level information; adjusting model parameters of the initial mental development level assessment model based on the difference between the obtained mental development level information and the labeled mental development level information in the training sample; and determining the initial psychological development level evaluation model obtained by training as a pre-trained psychological development level evaluation model corresponding to the psychological development project to be tested.

In a second aspect, embodiments of the present disclosure provide a mental development level assessment apparatus, including: the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is configured to acquire a target depth image sequence, and the target depth image sequence is at least two frames of continuous depth images obtained by shooting a tested user to act according to prompt information corresponding to a psychological development project to be tested; a feature extraction unit configured to perform feature extraction based on the target depth image sequence to obtain motion features of the tested user;

and the level evaluation unit is configured to input the action characteristics of the tested user into a pre-trained psychological development level evaluation model corresponding to the psychological development project to be tested, so as to obtain development level information of the psychological development project to be tested corresponding to the tested user, wherein the psychological development level evaluation model is used for representing the corresponding relation between the human action characteristics and the psychological development level information.

In some optional embodiments, the apparatus further comprises: the first presentation unit is configured to present development level information of the tested user corresponding to the mental development item to be tested.

In some optional embodiments, the apparatus further comprises: and the second presentation unit is configured to present prompt information corresponding to the mental development item to be tested before the target depth image sequence is acquired.

In some optional embodiments, the apparatus further comprises: the third presenting unit is configured to present a preset candidate mental development item identification set before presenting the prompt information corresponding to the mental development item to be tested; a first determining unit, configured to determine, in response to detecting a selection operation for a target candidate mental development item identifier in the preset candidate mental development item identifier set, a mental development item indicated by the target candidate mental development item identifier as the mental development item to be tested.

In some optional embodiments, the apparatus further comprises: a second obtaining unit, configured to obtain a target physiological characteristic data sequence before inputting the motion characteristics of the detected user into a pre-trained psychological development level evaluation model corresponding to the psychological development project to be detected to obtain development level information of the psychological development project to be detected corresponding to the detected user, where the target physiological characteristic data sequence is a corresponding physiological characteristic data sequence obtained by collecting at least one item of physiological characteristic data of the detected user during shooting and obtaining the target depth image sequence; and the feature extraction unit is further configured to: and performing feature extraction based on the target depth image sequence and the target physiological feature data sequence to obtain the action features of the detected user.

In some optional embodiments, the prompt information corresponding to the mental development item to be tested includes video and/or audio; the second presentation unit and the first acquisition unit are further configured to: and playing prompt information corresponding to the psychological development project to be tested and acquiring at least two frames of continuous depth images obtained by shooting the action of the tested user by the depth camera in real time in the playing process.

In some optional embodiments, the prompt information corresponding to the mental development item to be tested includes at least one of the following items: images, text, and audio; and the first obtaining unit is further configured to: and acquiring at least two frames of continuous depth images obtained by shooting the action of the tested user by the depth camera within a preset action duration corresponding to the psychological development project to be tested.

In some optional embodiments, the feature extraction unit is further configured to: performing characteristic space conversion on the target depth image sequence to obtain a target point cloud data sequence; and inputting the target point cloud data sequence into the feature extraction model to obtain the action features of the tested user.

In some optional embodiments, the performing feature extraction based on the target point cloud data sequence obtains an action feature of the tested user: performing action segmentation based on the target point cloud data sequence to obtain at least one target point cloud data subsequence ordered according to time; extracting the characteristics of each target point cloud data subsequence to obtain corresponding characteristics; and determining the motion characteristics of the tested user based on the characteristics of each target point cloud data subsequence.

In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device, on which one or more programs are stored, which, when executed by the one or more processors, cause the one or more processors to implement the method as described in any implementation manner of the first aspect.

In a fourth aspect, embodiments of the present disclosure provide a computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by one or more processors, implements the method as described in any of the implementations of the first aspect.

In a fifth aspect, embodiments of the present disclosure provide a mental development level assessment system, including: a depth camera; and an electronic device communicatively connected with the depth camera, the electronic device configured to perform the method as described in any implementation manner of the first aspect.

In some optional embodiments, the system further comprises: and the wearable equipment is in communication connection with the electronic equipment and is used for acquiring human physiological characteristic data.

In the prior art, according to the method for providing the psychological development level information by the psychological consultant manually according to the psychological consultation process, different psychological consultants may provide different psychological development level results depending on the personal professional experience of the psychological consultant, the cognition and the decision of the user are influenced, the user to be tested needs to go to a psychological consultation center to evaluate the field and pay corresponding expenses, and the economic cost and the time cost for obtaining the psychological development level information by the user are increased for the user. The method for automatically analyzing the answer results of the psychological test questionnaire of the tested user by adopting the algorithm has indirection and relativity, and has the problem of inaccurate analysis caused by measurement errors and variation factors. There may be a problem in that the tested user gets psychometric contents in other channels in advance and the tendency selection causes inaccurate analysis. In addition, the existing mental development level assessment tool combined with action information focuses more on assessment and diagnosis of diseases, such as prediction of depression by using gait recognition, and does not pay attention to multi-dimensional information of individual mental development levels, such as emotional expression, attention, development of mental dynamics within flexibility, individual development and reaction in relationship and the like.

In order to solve the above problems in the prior art of assessing the psychological development level of a user, the psychological development level assessment method, apparatus, device, storage medium and system provided by the embodiments of the present disclosure introduce theories such as kest bang action side writing, lapban/barternef action analysis, and the like, collect information of human action postures for more directly assessing the individual psychological development level, further obtain a corresponding target depth image sequence by providing prompt information corresponding to a psychological development item to be tested, and shooting a depth image during the tested user acts according to the prompt information, further obtain a corresponding target depth image sequence, perform feature extraction based on the target depth image sequence, obtain an action feature of the tested user, and input the action feature of the tested user into a pre-trained psychological development level assessment model corresponding to the psychological development item to be tested, so as to obtain the development level information of the psychological development project to be tested corresponding to the tested user. Therefore, the whole process does not need manual intervention of a psychological consultant, does not need special scenes and fields, can reduce the labor cost, the economic cost and the time cost for evaluating the psychological development level of the user, does not depend on the personal experience of the psychological consultant, and further unifies the evaluation standard. In addition, through paying attention to non-verbal expression modes such as actions and the like, the expression mode and the interaction mode of the individual are known, the history and style of unique social, emotional, cognitive and social development of the individual can be better known by combining with the information of body actions, the body posture of the individual, the development of the individual and the development and reaction of the individual in the relationship are evaluated, so that the psychological development level of the user is more comprehensively and accurately evaluated, and important guide information is provided for subsequent personalized intervention.

Drawings

Other features, objects, and advantages of the disclosure will become apparent from a reading of the following detailed description of non-limiting embodiments which proceeds with reference to the accompanying drawings. The drawings are only for purposes of illustrating the particular embodiments and are not to be construed as limiting the disclosure. In the drawings:

FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present disclosure may be applied;

FIG. 2 is a flow chart of one embodiment of a mental development level assessment method according to the present disclosure;

FIG. 3 is a flow chart of one embodiment of training steps according to the present disclosure;

fig. 4 is a schematic structural view of an embodiment of a psychological development level evaluating apparatus according to the present disclosure;

FIG. 5 is a schematic block diagram of a computer system suitable for use with an electronic device implementing embodiments of the present disclosure.

Detailed Description

The present disclosure is described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.

It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 illustrates an exemplary system architecture 100 of a mental development level assessment system according to the present disclosure.

As shown in FIG. 1, the system architecture 100 may include a depth camera 101 and an electronic device 102, wherein the depth camera 101 is communicatively coupled with the electronic device 102. That is, the depth image captured by the depth camera 101 may be transmitted to the electronic device 102. A user may interact with the depth camera 101 through the electronic device 102 to control the depth image device to capture images or send images, etc. The electronic device 102 may have various client applications installed thereon, such as a mental development level assessment application, a video playing application, an audio playing application, a text or image display application, a voice recognition application, a web browser application, and the like.

The electronic device 102 may be hardware or software. When the electronic device 102 is hardware, it may include, but is not limited to, a smart phone, a tablet computer, a laptop portable computer, a desktop computer, and the like. The electronic device 102 may have a display means (e.g., a display) and/or a sound playing means (e.g., a speaker, an earphone, etc.). Alternatively, the system 100 may also include a display device and/or a sound playing device communicatively coupled to the electronic device 102. Furthermore, the electronic device 102 may control the display device and/or the sound playing device to present or play the prompt information corresponding to the mental development item to be tested. Still alternatively, the system 100 may further include a display device and/or a sound playing device that is not in communication connection with the electronic device 102, and further, the display device and/or the sound playing device may be controlled by other devices or apparatuses to present the prompt information corresponding to the mental development item to be tested to the tested user. Namely, the display device and/or the sound playing device are used for presenting prompt information corresponding to the mental development item to be tested to the tested user. Here, for example, the prompt information corresponding to the mental development item to be measured is presented by playing audio and video or presenting text and/or images. The depth camera 101, the display device and/or the sound playing device may be located in the same scene, so that the tested user may obtain the prompt information corresponding to the mental development item to be tested, and the depth camera 101 may acquire the depth image of the tested user.

When the electronic device 102 is software, it can be installed in the electronic devices listed above. It may be implemented as a plurality of software or software modules (for example, for providing mental development level assessment services) or as a single software or software module. And is not particularly limited herein.

In some optional embodiments, the system architecture 100 may further comprise

wearable devices

1031, 1032, 1033 communicatively connected to the electronic device 102 for collecting human physiological characteristic data.

The psychological development level assessment method provided by the present disclosure may be executed by the electronic device 102, and accordingly, a psychological development level assessment apparatus may be provided in the electronic device 102.

It should be understood that the number of depth cameras, electronic devices, and wearable devices in fig. 1 is merely illustrative. There may be any number of depth cameras, electronic devices, and wearable devices, as desired for implementation.

With continuing reference to fig. 2, a flow 200 of one embodiment of a mental development level assessment method according to the present disclosure is shown, the mental development level assessment method including the steps of:

step 201, a target depth image sequence is obtained.

In this embodiment, the target depth image sequence may be at least two continuous depth images obtained by capturing the motion of the detected user according to the prompt information corresponding to the mental development item to be detected by using the depth camera.

Here, the depth image is an image in which the distance (also referred to as depth) from the depth camera to each point in the image capturing scene is taken as a pixel value. The depth camera usually performs image acquisition at a certain frame rate, and after one frame of image is acquired, one frame of depth image may be generated, and consecutive depth images of more than one frame may be acquired frame by frame according to the acquisition time sequence of the depth camera.

Here, the user under test may be various types of users. The tested user can be a common user, and can also be various users suspected or diagnosed to have certain cognitive disorder or psychological disease, such as users suspected or diagnosed to have autism or hyperactivity disorder. The user to be tested may also be a user of various ages, which is not specifically limited by this disclosure.

Here, the mental development item to be tested may be various motion-based observation elements, and the mental functional development of the individual may be evaluated by integrating the elements in the analysis of the shift motion, such as emotion, attention, self-feeling, communication, decision making, and the like, for example, the mental development item to be tested may be a cognitive level, a social level, an emotion level, and the like, or a space (developmental space, kinesthetic space, public space, interpersonal space, spatial sequence), a body and configuration (position of limbs and trunk, relationship between body parts, axis positioning of body in space, spatial tension of body, body shape, body configuration relationship), an action period (rhythm, duration, transition, flow), a force effect (time related to decision, force related to intention, flow related to how accuracy is performed, space related to intention), and the like.

The prompt information corresponding to the mental development project to be tested can be various information which is obtained by carrying out statistical analysis and induction in advance by technical experts in the related field according to professional knowledge and is used for evaluating the development level of the user in the mental development project to be tested and guiding the user to carry out corresponding actions. The prompt message can be used to guide the user to make a designated action, such as squatting, jumping, opening arms, etc. As an example, in practice, a psychological counselor (or referred to as a dance therapist, i.e. a psychological therapist mainly using dance and movement as an intervention mode) may ask a user to make an action using a joint between the spine or the head and the coccyx when testing head-tail coordination in a psychological development project, and judge the psychological development level of the user in terms of attention, emotional expression, sense of agility, etc. according to the magnitude of the action made by the user. For example, the spine of an individual is more upright and is difficult to freely bend and stretch according to the environment, the range of motion from the head to the upper half of the spine is larger than that of the lower half, which means that the individual is less actively involved in communication and interaction, and the interpersonal position is less flexible and more alert.

The information form of the prompt message is not particularly limited in this disclosure. For example, the reminder information may include at least one of: images, text, audio, and video. For example, when the prompt information is an image, the prompt information may be at least one image, and the prompt information in the form of the image may be at least one action key image corresponding to the specified action, which is presented in time sequence, and each action key image may show the key action by using a cartoon image or show the key action by using a human being demonstration. When the prompt information is text or audio, the action may be specified by text or audio description. When the reminder information is a video, the video may be a video recording an exemplary corresponding designated action by the designated person.

In some alternative embodiments, the subject of execution of the mental development level assessment method (e.g., electronic device 102 shown in fig. 1) may obtain the target depth image sequence on a local volatile or non-volatile storage medium. Optionally, the depth camera may be in wired communication connection with the execution main body or in wireless communication connection within the same local area network, and then the depth image collected by the depth camera may be sent to the execution main body in real time and stored in a volatile storage medium or a non-volatile storage medium local to the execution main body.

In some optional embodiments, the execution body may also remotely acquire the target depth image sequence from another electronic device communicatively connected to the execution body. Accordingly, the execution main body and the depth camera can be located at different physical positions, and therefore remote psychological development level assessment of the tested user is achieved.

In some optional embodiments, the execution body may also acquire the target depth image sequence from a depth camera communicatively coupled to the execution body. Based on this, optionally, when the prompt information corresponding to the mental development item to be tested includes video and/or audio, step 201 may be performed as follows: and acquiring at least two continuous depth images obtained by shooting the action of the tested user by the depth camera in real time in the process of playing the prompt information corresponding to the psychological development project to be tested.

And step 202, performing feature extraction based on the target depth image sequence to obtain the action features of the tested user.

In this embodiment, the execution subject may adopt various human motion feature extraction methods, which are now known or developed in the future, to perform feature extraction based on the target depth image sequence, so as to obtain motion features of the tested user. The purpose of human action feature extraction is to enable a computer to reasonably describe human actions so as to realize automatic judgment and understanding of human action behaviors. For example, the following human motion feature extraction method may be adopted: the method comprises the steps of extracting bottom layer local space-time interest points based on video streaming, describing the motion characteristic attributes based on middle-layer semantic learning or tracking and limb deformable templates based on high-layer semantic characteristic points.

The method based on the underlying local spatio-temporal interest points needs to extract the local spatio-temporal interest points of a target object (such as a human body), and combines certain optical flow motion estimation to obtain modeling of the motion of the target object and express limb actions with various description operators.

The middle-layer semantic learning-based method generally performs higher-level semantic feature modeling on bottom-layer action features through methods such as foreground salient regions, moving target detection, object contour segmentation, judgment dictionary learning, multi-channel feature fusion, convolutional neural networks and the like on the basis of extracting bottom-layer local action features, and obtains global or local space-time feature expression of target object movement in multi-frame video streams.

The method based on the high-level semantic feature points relies on manual labeling, skeletal joint points of a human body are calibrated to track in real time, a limb tree structure model or a deformable template is constructed, and motion characteristics of the human body are represented by combining joint point motion history and common description operators.

In some alternative embodiments, step 202 may include

steps

2021 and 2022 as follows:

step 2021, performing feature space conversion on the target depth image sequence to obtain a target point cloud data sequence.

In practice, the pixel value of a pixel point with a coordinate of pixel position (x, y) in a depth image in a target depth image sequence is the distance (or called depth) between an object corresponding to the pixel point and a depth camera. The conversion relationship between the depth image in the sequence of target depth images and the sequence of target point cloud data is related to the parameters of the depth camera. That is, the execution subject may perform feature space conversion on the target depth image sequence through parameters of the depth camera to obtain a target point cloud data sequence. Depth images in the target depth image sequence before feature space conversion are two-dimensional data under an image coordinate system, point cloud data in the converted target point cloud data sequence are three-dimensional coordinate points under a world coordinate system, and the three-dimensional coordinate points can correspond to position points of a detected human body.

And step 2022, extracting features based on the target point cloud data sequence to obtain the action features of the detected user.

Here, various implementation manners may be adopted to perform feature extraction based on the target point cloud data sequence, so as to obtain the motion features of the detected user.

Alternatively, step 2022 may be performed as follows:

firstly, motion segmentation is carried out based on a target point cloud data sequence to obtain at least one target point cloud data subsequence ordered according to time.

Here, various now known or future developed motion segmentation algorithms may be used to perform motion segmentation based on the target point cloud data sequence to determine the boundary of each motion during the motion of the tested user, and at least one time-ordered subsequence of target point cloud data may be obtained according to the determined boundary. Each resulting target point cloud data subsequence may correspond to a sub-action after action segmentation, and the sub-action may be considered an indivisible action unit. For example, methods including, but not limited to, Principal Component Analysis (PCA), sliding window mahalanobis distance calculation, clustering-based methods, and deep learning-based motion segmentation methods may be employed.

And secondly, extracting the characteristics of each target point cloud data subsequence to obtain corresponding characteristics.

And finally, determining the action characteristics of the tested user based on the characteristics of each target point cloud data subsequence.

For example, the corresponding features of the target point cloud data subsequences may be spliced according to the time sequence of each target point cloud data subsequence in the target point cloud data sequence to obtain the motion features of the detected user.

For another example, a mean feature of corresponding features of each target point cloud data subsequence may also be calculated to obtain an action feature of the tested user.

The data included in the target point cloud data sequence after spatial conversion is converted into a world coordinate system, the coordinates of the position points of the human body are correspondingly detected, background coordinate points are removed, feature extraction is carried out based on the target point cloud data sequence, the calculation amount can be reduced, and the feature extraction effect can be improved.

Step 203, inputting the action characteristics of the tested user into a pre-trained psychological development level evaluation model corresponding to the psychological development item to be tested, and obtaining the development level information of the psychological development item to be tested corresponding to the tested user.

Here, the mental development level evaluation model of the mental development item to be tested is used for representing the correspondence between the human body motion characteristics and the development level information of the mental development item to be tested. Therefore, the action characteristics of the tested user are input into the pre-trained psychological development level evaluation model corresponding to the psychological development project to be tested, and the development level information of the psychological development project to be tested corresponding to the tested user can be obtained.

Here, the development level information of the mental development item to be tested corresponding to the tested user is used for representing the development level of the mental development item to be tested corresponding to the tested user. For example, for a horizontal spatial application in the developing spatial dimension, which is the mental development project to be tested, the mental development level information can be centralized or decentralized.

The development level information of the tested user corresponding to the mental development project to be tested can be information in various forms. For example, the development level information of the mental development item to be tested corresponding to the tested user may be text information, or may be an image, audio, or video generated based on the text information, which is not specifically limited in this disclosure.

As an example, the mental development level assessment model of the mental development project to be tested may be a correspondence table which is pre-made by technicians based on statistical analysis of a large number of user action characteristics and development level information of the mental development project to be tested of corresponding users and stores correspondence of a plurality of user action characteristics and development level information of the mental development project to be tested; or a calculation formula which is preset by a technician based on statistics of a large number of user action characteristics and is stored in the execution main body, and is used for performing numerical calculation on one or more characteristic components in the user action characteristics to obtain an information identifier, wherein the information identifier is used for indicating development level information of the mental development project to be tested.

In some optional implementations, the mental development level assessment model corresponding to the mental development item to be tested may be various classification models, which is not specifically limited by the present disclosure. For example, the classification model may be a linear classifier or a non-linear classifier. The linear classifier may be, for example, a logistic regression classifier, a bayesian classifier, a single-layer perceptron, a linear regression classifier, a linear kernel support vector machine, and the nonlinear classifier may be, for example, a decision tree, a random forest, a gradient boosting decision tree, a nonlinear kernel support vector machine, a multi-layer perceptron, etc.

In some alternative embodiments, the mental development level assessment model corresponding to the mental development project to be tested can also be pre-established through the training step 300 shown in fig. 3. The training step 300 may include the following steps 301 to 303:

step 301, a training sample set is obtained.

Here, the training samples in the training sample set may include a sample depth image sequence obtained by shooting a sample user to perform an action according to the prompt information corresponding to the mental development item to be tested, and labeled mental development level information used for representing the development level of the mental development item to be tested corresponding to the user action corresponding to the sample depth image sequence.

Here, the labeled mental development level information corresponding to the behavior of the sample user according to the prompt information corresponding to the mental development project to be tested may be obtained by analyzing, evaluating and labeling the actual behavior of the sample user and the professional knowledge in the field of the mental development project to be tested by professional technicians (e.g., a psychological consultant, a psychological therapist, a dance therapist, etc.).

The training sample set can be considered as recording a sample depth image sequence obtained by a plurality of sample users acting according to the prompt information corresponding to the mental development item to be detected and the labeled mental development level information of the corresponding mental development level item to be detected.

It should be noted that, in practice, a plurality of sample users may be composed of a representative population. The representative population is a population that simulates the composition of a large sample population with a small number of sample users and has a composition that is substantially identical or similar to the large sample population. For example, when a large sample group includes 0.5% of autism patients, the plurality of sample users may also include 0.5% of autism patients. For example, if a large group of individuals includes 5% of alzheimer patients and 5% of children with difficulty in memory development, the plurality of sample users may include 5% of alzheimer patients and 5% of children with difficulty in memory development. For example, when a large sample group includes 10% of the patients with hyperactivity, 10% of the patients with hyperactivity may be included in the plurality of sample users.

Step 302, for the training samples in the training sample set, performing parameter adjustment operation until a preset training end condition is satisfied.

Here, the parameter adjusting operation may include:

firstly, feature extraction is carried out on the basis of a sample depth image sequence in the training sample to obtain corresponding action features.

It should be noted that, the same method as the method for extracting the features in step 202 may be adopted here, and the feature extraction is performed based on the sample depth image sequence in the training sample, which is not described herein again.

Secondly, inputting the obtained action characteristics into an initial psychological development level evaluation model to obtain corresponding psychological development level information.

And finally, adjusting model parameters of the initial mental development level evaluation model based on the difference between the obtained mental development level information and the labeled mental development level information in the training sample.

Here, the difference between the obtained mental development level information and the labeled mental development level information in the training sample may be calculated using various loss functions (e.g., L1 norm, L2 norm, cross entropy loss function, etc.).

Here, various implementations may be employed to adjust model parameters of the initial mental development level evaluation model based on a difference between the obtained mental development level information and the labeled mental development level information in the training sample. For example, a BP (Back Propagation) algorithm, a Stochastic Gradient Descent (SGD), Newton's Method, Quasi-Newton Method, Conjugate Gradient Method, heuristic optimization Method, and various other optimization algorithms now known or developed in the future may be used.

Here, the training end condition may include, for example, at least one of: the time for executing the parameter adjustment operation reaches a preset duration, the times for executing the parameter adjustment operation reaches preset times, and the difference between the obtained psychological development level information and the labeled psychological development level information in the training sample is smaller than a preset difference threshold value.

Optionally, the parameter adjusting operation may further include: after adjusting model parameters of the initial mental development level assessment model each time based on the difference between the obtained mental development level information and the labeled mental development level information in the training sample, testing the test samples in the test sample set by using the current initial mental development level assessment model, and determining the corresponding testing accuracy. The training end condition may further include: the increase of the test accuracy in n consecutive parameter adjustment operations (n is a positive integer) is smaller than a preset accuracy increase threshold, for example, the increase is 0. That is, if the increase of the test accuracy in n (n is a positive integer) consecutive times of the parameter adjustment operation is smaller than the preset accuracy increase threshold, the execution of the parameter adjustment operation may be stopped. The test samples in the test sample set can comprise a sample depth image sequence obtained by shooting actions of a sample user according to prompt information corresponding to the psychological development item to be tested and labeled psychological development level information used for representing the development level of the psychological development item to be tested corresponding to the user actions corresponding to the sample depth image sequence; correspondingly, the current initial psychological development level evaluation model is used for testing the test samples in the test sample set, namely for each test sample, the depth image sequence in the test sample is subjected to feature extraction according to corresponding parameters of the current feature extraction process to obtain corresponding test sample features, and then the obtained test sample features are input into the current initial psychological development level evaluation model to obtain a test result; if the obtained test result is the same as the marked psychological development level information in the test sample, the test is determined to be correct for the test sample; otherwise, if the difference is not the same, the test is considered to be wrong. By testing the ratio of the correct number of the test samples to the number of the test samples in the test sample set, the corresponding test accuracy can be calculated. It should be noted that the test sample set may not intersect with the training sample set, or may partially intersect with the training sample set, which is not specifically limited by the present disclosure.

It should be noted that, while the model parameters of the initial mental development level assessment model are adjusted, parameters used in the feature extraction process (i.e., parameters used in the process of extracting features from the sample depth image sequence in the training sample to obtain corresponding motion features) may be adjusted synchronously, so as to improve the accuracy of the mental development level assessment model assessment.

Step 303, determining the trained initial mental development level assessment model as a pre-trained mental development level assessment model corresponding to the mental development project to be tested.

The development level determination model of the mental development project to be tested, which is pre-established in the training step 300 as shown in fig. 3, is established based on the sample depth image sequence obtained by a plurality of sample users acting according to the prompt information corresponding to the mental development project to be tested and the marked mental development level information of the corresponding mental development level project to be tested, that is, the mental development level evaluation model of the mental development project to be tested is trained in a supervised manner, so that the accuracy of the mental development level evaluation model of the mental development level of the mental development project to be tested in inputting the action characteristics of the tested user to output the development level information of the mental development project to be tested corresponding to the tested user can be improved.

In some optional embodiments, the executing body may further perform the following step 204 before performing step 202:

step 204, acquiring a target physiological characteristic data sequence.

Here, the target physiological characteristic data sequence may be a corresponding physiological characteristic data sequence obtained by acquiring at least one item of physiological characteristic data of the detected user in the process of shooting the detected user to perform an action according to the prompt information corresponding to the psychological development item to be detected and obtaining the target depth image sequence.

Here, the at least one physiological characteristic data includes at least one of: heart rate parameters, electrodermal parameters. The wearable device for acquiring physiological characteristic data of the tested user may be a wearable device for acquiring heart rate and/or skin electrical parameters of the user, for example, a bracelet.

It should be noted that, the executing main body may execute step 201 first and then execute step 204, or may execute step 204 first and then execute step 201, or execute step 201 and step 204 synchronously, which is not specifically limited in this disclosure.

Based on the above optional implementation manner of step 204, in step 202, feature extraction is performed based on the target depth image sequence to obtain the motion feature of the measured user, which may also be performed as follows: and performing feature extraction based on the target depth image sequence and the target physiological feature data sequence to obtain the action features of the detected user. The method comprises the steps that a tested user wears at least one wearable device for collecting human body physiological characteristic data, and in the process that the tested user acts according to prompt information corresponding to a psychological development project to be tested, the tested user is shot by a depth camera to obtain a target depth image sequence and at least one wearable device is used for collecting the physiological characteristic data of the tested user to obtain a target physiological characteristic data sequence. Furthermore, the motion characteristics of the detected user obtained after the characteristic extraction include the depth image data of the detected user and the physiological characteristic data of the detected user, the motion characteristics of the detected user are subsequently input into a psychological development level evaluation model corresponding to the psychological development project to be detected, the obtained development level information of the psychological development project corresponding to the detected user is obtained, the depth image data and the physiological characteristic data of the detected user are referred to, the reference factors are more comprehensive, and the development level of the psychological development project corresponding to the detected user can be more comprehensively reflected.

In some optional embodiments, after step 203 is executed, that is, after obtaining the development level information of the mental development item to be tested corresponding to the tested user, the executing main body may further execute the following step 205:

and step 205, presenting the development level information of the mental development project to be tested corresponding to the tested user.

Here, the tested user may be in various data forms corresponding to the mental development item to be tested, for example, text information, images, audio or video. And further presenting the development level information of the psychological development project to be tested corresponding to the tested user, and also presenting texts, images, audio or video and the like. Furthermore, the tested user can know the development level information of the psychological development project to be tested in time, and decision guidance is provided for work, study and life.

In some optional embodiments, the executing main body may further perform the following step 206 after performing step 201:

and step 206, presenting prompt information corresponding to the mental development item to be tested.

Here, the corresponding presentation method may be adopted according to a specific data form of the presentation information corresponding to the mental development item to be measured. For example, the prompt may be text, i.e., by presenting text to describe to the tested user the action required to be performed. For another example, the prompt message may be an image, that is, a cartoon or a human action image in the image is presented to describe the action subject to be performed to the tested user. Also for example, the prompt may be audio, i.e., a voice telling that the user to be tested is to be described the action to be performed. For another example, the prompt message may be a video, that is, by playing the video, a cartoon image or an action process shown by a real person may be shown in the video to describe the action requirement to be performed to the tested user.

Optionally, when the prompt information corresponding to the mental development item to be tested includes video and/or audio, step 206 and step 201 may be performed as follows:

and playing prompt information corresponding to the psychological development project to be tested and acquiring at least two continuous depth images obtained by shooting the action of the tested user by the depth camera in real time in the playing process.

Optionally, when the prompt information corresponding to the mental development item to be tested includes at least one of the following items: image, text and audio, steps 206 and 201 may also be performed as follows:

presenting prompt information corresponding to the psychological development project to be tested; and acquiring at least two frames of continuous depth images obtained by the depth camera shooting the motion of the tested user in real time within a preset motion duration corresponding to the psychological development project to be tested. Here, the preset action duration corresponding to the mental development item to be measured may be a duration parameter value that is preset by a technician and stored in the execution main body. And the preset action duration corresponding to the psychological development item to be detected is used for representing that the user needs to complete corresponding actions within the preset action duration.

In some optional embodiments, the executing body may further perform the following

steps

207 and 208 after performing step 206:

and step 207, presenting a preset candidate mental development item identification set.

Here, each candidate mental development item identifier in the preset candidate mental development item identifier set may be used to uniquely indicate a different mental development item. Mental development item identification may also be presented in various forms, which may include text, images, audio, video, and the like, for example.

Step 208, in response to detecting the selection operation for the target candidate mental development item identifier in the preset candidate mental development item identifier set, determining the mental development item indicated by the target candidate mental development item identifier as the mental development item to be tested.

Through

steps

207 and 208, the user can select a psychological development item desired to be tested from the psychological development item set indicated by the preset candidate psychological development item identification set, then prompt information (for example, a real-person action display video) corresponding to the psychological development item selected by the user is presented through step 206, the user can act according to the presented prompt information, a corresponding user action depth image sequence is collected through a depth camera, feature extraction is carried out on the basis of the collected user action depth image sequence to obtain user action features, the user action features are input to a psychological development level evaluation model corresponding to the psychological development item selected by the user to determine development level information of the psychological development item to be tested corresponding to the user, the obtained development level information is presented to the user, and the user can select, test, and test, and test, And performing a closed-loop psychological development project evaluation process of feedback of the development level information result so that the user can know the psychological development level of the user and make a next decision based on the psychological development level.

According to the psychological development level assessment method provided by the embodiment of the disclosure, theories such as Kaiser berg action side writing and Laban/Muttneff action analysis are introduced, it is considered that human body actions can reflect the psychological development level of a human body more truly than answer questionnaires, then the prompt information corresponding to the psychological development item to be tested is provided, a depth image is shot in the process that the tested user acts according to the prompt information, a corresponding target depth image sequence is obtained, the action characteristics of the tested user are obtained by performing characteristic extraction based on the target depth image sequence, and then the action characteristics of the tested user are input into a pre-trained psychological development level assessment model corresponding to the psychological development item to be tested, so that the development level information of the psychological development item to be tested corresponding to the tested user is obtained. Therefore, the whole process does not need manual intervention of a psychological consultant, does not need special scenes and fields, can reduce the labor cost, the economic cost and the time cost for evaluating the psychological development level of the user, does not depend on the personal experience of the psychological consultant, and further unifies the evaluation standard. In addition, through paying attention to non-verbal expression modes such as actions and the like, the expression mode and the interaction mode of the individual are known, the history and style of unique social, emotional, cognitive and social development of the individual can be better known by combining with the information of body actions, the body posture of the individual, the development of the individual and the development and reaction of the individual in the relationship are evaluated, so that the psychological development level of the user is evaluated more comprehensively and accurately, and important guide information is provided for subsequent personalized intervention.

With further reference to fig. 4, as an implementation of the method shown in the above figures, the present disclosure provides an embodiment of a mental development level assessment apparatus, which corresponds to the embodiment of the method shown in fig. 2, and which is particularly applicable to various electronic devices.

As shown in fig. 4, the psychological development level evaluating apparatus 400 of the present embodiment includes: a first obtaining unit 401, configured to obtain a target depth image sequence, where the target depth image sequence is at least two continuous depth images obtained by shooting a detected user to perform an action according to prompt information corresponding to a psychological development item to be detected; a feature extraction unit 402 configured to perform feature extraction based on the target depth image sequence to obtain motion features of the tested user; and the level evaluation unit 403 is configured to input the motion characteristics of the tested user into a pre-trained mental development level evaluation model corresponding to the mental development item to obtain development level information of the tested user corresponding to the mental development item to be tested, wherein the mental development level evaluation model is used for representing a corresponding relationship between the human motion characteristics and the mental development level information.

In this embodiment, the detailed processing of the first obtaining unit 401, the feature extracting unit 402, and the level evaluating unit 403 of the mental development level evaluating apparatus 400 and the technical effects thereof can refer to the related descriptions of step 201, step 202, and step 203 in the corresponding embodiment of fig. 2, which are not repeated herein.

In some optional embodiments, the apparatus 400 may further include: a first presentation unit (not shown in fig. 4) configured to present the development level information of the tested user corresponding to the mental development item to be tested.

In some optional embodiments, the apparatus 400 may further include: a second presentation unit (not shown in fig. 4) configured to present prompt information corresponding to the mental development item to be tested before the acquiring of the target depth image sequence.

In some optional embodiments, the apparatus 400 may further include: a third presenting unit (not shown in fig. 4) is configured to present a preset set of candidate mental development item identifications before said presenting the prompt information corresponding to the mental development item to be tested; a first determining unit (not shown in fig. 4) configured to determine, in response to detecting a selection operation for a target candidate mental development item identifier in the preset candidate mental development item identifier set, a mental development item indicated by the target candidate mental development item identifier as the mental development item to be tested.

In some optional embodiments, the apparatus 400 may further include: a second obtaining unit (not shown in fig. 4) configured to obtain a target physiological characteristic data sequence before inputting the motion characteristics of the tested user into a pre-trained psychological development level assessment model corresponding to the psychological development project to be tested to obtain development level information of the psychological development project to be tested corresponding to the tested user, wherein the target physiological characteristic data sequence is a corresponding physiological characteristic data sequence obtained by collecting at least one item of physiological characteristic data of the tested user in the process of shooting and obtaining the target depth image sequence; and the feature extraction unit 402 may be further configured to: and performing feature extraction based on the target depth image sequence and the target physiological feature data sequence to obtain the action features of the detected user.

In some alternative embodiments, the at least one physiological characteristic data may include at least one of: heart rate parameters, electrodermal parameters.

In some optional embodiments, the prompt information corresponding to the mental development item to be tested may include video and/or audio; the second rendering unit (not shown in fig. 4) and the first obtaining unit 401 may be further configured to: and playing prompt information corresponding to the psychological development project to be tested and acquiring at least two frames of continuous depth images obtained by shooting the action of the tested user by the depth camera in real time in the playing process.

In some optional embodiments, the prompt information corresponding to the mental development item to be tested may include at least one of the following: images, text, and audio; and the first obtaining unit 401 may be further configured to: and acquiring at least two frames of continuous depth images obtained by shooting the action of the tested user by the depth camera within a preset action duration corresponding to the psychological development project to be tested.

In some optional embodiments, the feature extraction unit 402 may be further configured to: performing characteristic space conversion on the target depth image sequence to obtain a target point cloud data sequence; and inputting the target point cloud data sequence into the feature extraction model to obtain the action features of the tested user.

In some optional embodiments, the performing feature extraction based on the target point cloud data sequence to obtain the motion feature of the tested user may include: performing action segmentation based on the target point cloud data sequence to obtain at least one target point cloud data subsequence ordered according to time; extracting the characteristics of each target point cloud data subsequence to obtain corresponding characteristics; and determining the motion characteristics of the tested user based on the characteristics of each target point cloud data subsequence.

In some alternative embodiments, the mental development level assessment model corresponding to the mental development item to be tested may be obtained through the following training steps: acquiring a training sample set, wherein the training sample comprises a sample depth image sequence obtained by shooting a sample user to perform actions according to prompt information corresponding to the mental development item to be detected and labeled mental development level information used for representing the mental development level corresponding to the user action corresponding to the sample depth image sequence; for the training samples in the training sample set, performing the following parameter adjustment operations until a preset training end condition is met: extracting features based on the sample depth image sequence in the training sample to obtain corresponding action features; inputting the obtained action characteristics into an initial psychological development level evaluation model to obtain corresponding psychological development level information; adjusting model parameters of the initial mental development level assessment model based on the difference between the obtained mental development level information and the labeled mental development level information in the training sample; and determining the initial psychological development level evaluation model obtained by training as a pre-trained psychological development level evaluation model corresponding to the psychological development project to be tested.

It should be noted that, for details of implementation and technical effects of each unit in the mental development level assessment apparatus provided by the embodiments of the present disclosure, reference may be made to descriptions of other embodiments in the present disclosure, and details are not described herein again.

Referring now to FIG. 5, a block diagram of a computer system 500 suitable for use in implementing the electronic device of the present disclosure is shown. The computer system 500 shown in fig. 5 is only an example and should not bring any limitations to the functionality or scope of use of the embodiments of the present disclosure.

As shown in fig. 5, computer system 500 may include a processing device (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage device 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the computer system 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, and the like; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the computer system 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates a computer system 500 having various means of electronic equipment, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of embodiments of the present disclosure.

It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the mental development level assessment method as shown in the embodiment shown in fig. 2 and its alternative embodiments.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a cell does not in some cases constitute a limitation of the cell itself, for example, the first acquisition cell may also be described as a "cell acquiring a target depth image sequence".

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.

Claims

1. A mental development level assessment method comprising:

2. The method of claim 1, wherein the method further comprises:

and presenting the development level information of the tested user corresponding to the mental development project to be tested.

3. The method of claim 1, wherein prior to the acquiring a sequence of target depth images, the method further comprises:

and presenting prompt information corresponding to the mental development project to be tested.

4. The method of claim 3, wherein prior to said presenting the reminder information corresponding to the mental development item under test, the method further comprises:

presenting a preset candidate psychological development item identification set;

in response to detecting the selection operation of the target candidate mental development item identifier in the preset candidate mental development item identifier set, determining the mental development item indicated by the target candidate mental development item identifier as the mental development item to be detected.

5. The method according to claim 1, wherein before inputting the motion characteristics of the tested user into a pre-trained mental development level assessment model corresponding to the mental development item to be tested, and obtaining development level information of the tested user corresponding to the mental development item to be tested, the method further comprises:

acquiring a target physiological characteristic data sequence, wherein the target physiological characteristic data sequence is a corresponding physiological characteristic data sequence obtained by collecting at least one item of physiological characteristic data of the detected user in the process of shooting and obtaining the target depth image sequence; and

the feature extraction based on the target depth image sequence to obtain the action features of the tested user comprises the following steps:

and performing feature extraction based on the target depth image sequence and the target physiological feature data sequence to obtain the action features of the detected user.

6. The method of claim 5, wherein the at least one physiological characteristic data comprises at least one of: heart rate parameters, electrodermal parameters.

7. The method according to claim 3, wherein the prompt information corresponding to the mental development item to be tested comprises video and/or audio; and

the presenting of the prompt information corresponding to the mental development project to be tested and the obtaining of the target depth image sequence include:

and playing prompt information corresponding to the psychological development project to be tested and acquiring at least two frames of continuous depth images obtained by shooting the action of the tested user by the depth camera in real time in the playing process.

8. The method according to claim 3, wherein the prompt information corresponding to the mental development item to be tested comprises at least one of the following items: images, text, and audio; and

the acquiring of the target depth image sequence includes:

and acquiring at least two frames of continuous depth images obtained by shooting the action of the tested user by the depth camera within a preset action duration corresponding to the psychological development project to be tested.

9. The method of claim 1, wherein the performing feature extraction based on the target depth image sequence to obtain motion features of the tested user comprises:

performing characteristic space conversion on the target depth image sequence to obtain a target point cloud data sequence;

and extracting features based on the target point cloud data sequence to obtain the action features of the detected user.

10. The method of claim 9, wherein the performing feature extraction based on the target point cloud data sequence to obtain motion features of the tested user comprises:

performing action segmentation based on the target point cloud data sequence to obtain at least one target point cloud data subsequence ordered according to time;

extracting the characteristics of each target point cloud data subsequence to obtain corresponding characteristics;

and determining the motion characteristics of the tested user based on the characteristics of each target point cloud data subsequence.

11. The method according to claim 1, wherein the mental development level assessment model corresponding to the mental development item to be tested is obtained by the following training steps:

acquiring a training sample set, wherein the training sample comprises a sample depth image sequence obtained by shooting a sample user to perform actions according to prompt information corresponding to the mental development item to be detected and labeled mental development level information used for representing the mental development level corresponding to the user action corresponding to the sample depth image sequence;

for the training samples in the training sample set, performing the following parameter adjustment operations until a preset training end condition is met: extracting features based on the sample depth image sequence in the training sample to obtain corresponding action features; inputting the obtained action characteristics into an initial psychological development level evaluation model to obtain corresponding psychological development level information; adjusting model parameters of the initial mental development level assessment model based on the difference between the obtained mental development level information and the labeled mental development level information in the training sample;

and determining the initial psychological development level evaluation model obtained by training as a pre-trained psychological development level evaluation model corresponding to the psychological development project to be tested.

12. A psychological development level assessing device comprising:

the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is configured to acquire a target depth image sequence, and the target depth image sequence is at least two frames of continuous depth images obtained by shooting a tested user to act according to prompt information corresponding to a psychological development project to be tested;

a feature extraction unit configured to perform feature extraction based on the target depth image sequence to obtain motion features of the tested user;

13. An electronic device, comprising:

one or more processors;

a storage device having one or more programs stored thereon,

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-11.

14. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by one or more processors, implements the method of any one of claims 1-11.

15. A mental development level assessment system comprising:

a depth camera; and

an electronic device communicatively connected with the depth camera, the electronic device configured to perform the method of any of claims 1-11.

16. The system of claim 15, wherein the system further comprises:

and the wearable equipment is in communication connection with the electronic equipment and is used for acquiring human physiological characteristic data.