Concentration degree evaluation method and device, readable storage medium and terminal equipment

Abstract

The invention belongs to the technical field of intelligent terminals, and particularly relates to a concentration evaluation method and device, a computer readable storage medium and terminal equipment. The method comprises the following steps: acquiring a to-be-detected target image frame; detecting a preset target behavior in the target image frame to obtain a concentration condition detection result; and according to the concentration condition detection result, performing concentration evaluation from a preset concentration evaluation dimension to obtain a concentration evaluation result. Through the method, the concentration degree can be evaluated from the preset concentration degree evaluation dimension according to the concentration condition detection result, the concentration degree of the user can be evaluated more comprehensively, and the accuracy of concentration degree evaluation is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of intelligent terminals, and in particular, relates to a concentration evaluation method, apparatus, computer-readable storage medium, and terminal device. Background Art

[0002] With the development of smart technology, more and more smart terminals are entering the user's field of vision. During the user's work or study, smart terminals can be used for supervision and companionship, and can timely detect the user's inattention behavior and conduct concentration assessment to help users better understand the status of work or study.

[0003] However, it is difficult to accurately evaluate the user's concentration using existing concentration evaluation methods, resulting in a poor user experience. Summary of the invention

[0004] In view of this, the embodiments of the present application provide a concentration evaluation method, apparatus, computer-readable storage medium and terminal device to solve the problem that existing concentration evaluation methods are difficult to accurately evaluate the user's concentration, resulting in a poor user experience.

[0005] A first aspect of an embodiment of the present application provides a concentration evaluation method, which may include:

[0006] Acquire the target image frame to be detected;

[0007] Detecting a preset target behavior in the target image frame to obtain a focus detection result;

[0008] According to the concentration detection result, a concentration evaluation is performed from a preset concentration evaluation dimension to obtain a concentration evaluation result.

[0009] In a specific implementation of the first aspect, before performing concentration evaluation from preset concentration evaluation dimensions according to the concentration detection result to obtain the concentration evaluation result, the method may further include:

[0010] Acquire learning data corresponding to the target image frame;

[0011] The step of performing concentration evaluation from a preset concentration evaluation dimension according to the concentration detection result to obtain a concentration evaluation result may include:

[0012] According to the concentration detection result and the learning data, a concentration assessment is performed from a concentration assessment dimension to obtain the concentration assessment result.

[0013] In a specific implementation of the first aspect, performing concentration evaluation from a concentration evaluation dimension according to the concentration detection result and the learning data to obtain the concentration evaluation result may include:

[0014] Determining a dimension assessment result of a concentration assessment dimension according to the concentration detection result and the learning data;

[0015] The concentration evaluation result is determined according to the dimension evaluation results of each concentration evaluation dimension.

[0016] In a specific implementation of the first aspect, the concentration evaluation dimension includes at least one of persistence, stability, concentration, accuracy, and efficiency;

[0017] Among them, the dimension evaluation result of the sustainability is calculated by the longest continuous concentration time, the dimension evaluation result of the stability is calculated by the number and duration of interruptions, the dimension evaluation result of the concentration is calculated by the number and duration of occurrences of the target behavior, the dimension evaluation result of the accuracy is calculated by the accuracy in the learning data, and the dimension evaluation result of the efficiency is calculated by the efficiency in the learning data.

[0018] In a specific implementation of the first aspect, determining the concentration evaluation result according to the dimension evaluation results of each concentration evaluation dimension may include:

[0019] The dimensional evaluation results of the concentration evaluation dimension are weighted averaged according to a preset weight coefficient to obtain the concentration evaluation result.

[0020] In a specific implementation of the first aspect, acquiring the target image frame to be detected may include:

[0021] Detect whether there is an object in the preset target area;

[0022] If there is an object in the target area, acquiring an image of the target area;

[0023] Detecting whether there is a human face in the image of the target area;

[0024] If a human face exists in the image of the target area, the target image frame corresponding to the target area is acquired.

[0025] In a specific implementation of the first aspect, after performing concentration evaluation from a preset concentration evaluation dimension according to the concentration detection result and obtaining the concentration evaluation result, the method may further include:

[0026] Generating an assessment report corresponding to the concentration assessment result;

[0027] The evaluation report is displayed through a preset display device.

[0028] A second aspect of an embodiment of the present application provides a concentration evaluation device, which may include:

[0029] An image frame acquisition module, used to acquire a target image frame to be detected;

[0030] A behavior detection module, used to detect a preset target behavior in the target image frame to obtain a focus detection result;

[0031] The concentration evaluation module is used to perform concentration evaluation from a preset concentration evaluation dimension according to the concentration detection result to obtain a concentration evaluation result.

[0032] In a specific implementation of the second aspect, the concentration evaluation device may further include:

[0033] A data acquisition module, used to acquire learning data corresponding to the target image frame;

[0034] The concentration assessment module may include:

[0035] A concentration evaluation unit is used to perform concentration evaluation from a concentration evaluation dimension according to the concentration detection result and the learning data to obtain the concentration evaluation result.

[0036] In a specific implementation of the second aspect, the concentration evaluation unit may include:

[0037] A dimension evaluation subunit, used to determine a dimension evaluation result of a concentration evaluation dimension according to the concentration detection result and the learning data;

[0038] The result determination subunit is used to determine the concentration evaluation result according to the dimension evaluation result of the concentration evaluation dimension.

[0039] In a specific implementation of the second aspect, the concentration evaluation dimension includes at least one of persistence, stability, concentration, accuracy, and efficiency;

[0040] Among them, the dimension evaluation result of the sustainability is calculated by the longest continuous concentration time, the dimension evaluation result of the stability is calculated by the number and duration of interruptions, the dimension evaluation result of the concentration is calculated by the number and duration of occurrences of the target behavior, the dimension evaluation result of the accuracy is calculated by the accuracy in the learning data, and the dimension evaluation result of the efficiency is calculated by the efficiency in the learning data.

[0041] In a specific implementation of the second aspect, the result determination subunit may include:

[0042] The weighted averaging subunit is used to perform weighted averaging on the dimension evaluation results of the concentration evaluation dimension according to a preset weight coefficient to obtain the concentration evaluation result.

[0043] In a specific implementation of the second aspect, the image frame acquisition module may include:

[0044] An object detection unit, used to detect whether there is an object in a preset target area;

[0045] an image acquisition unit, configured to acquire an image of the target area if there is an object in the target area;

[0046] A face detection unit, used to detect whether there is a face in the image of the target area;

[0047] The image frame acquisition unit is used to acquire the target image frame corresponding to the target area if there is a face in the image of the target area.

[0048] In a specific implementation of the second aspect, the concentration evaluation device may further include:

[0049] A report generation module, used to generate an evaluation report corresponding to the concentration evaluation result;

[0050] The report display module is used to display the evaluation report through a preset display device.

[0051] A third aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any one of the above-mentioned concentration assessment methods are implemented.

[0052] A fourth aspect of an embodiment of the present application provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the above-mentioned concentration assessment methods when executing the computer program.

[0053] A fifth aspect of an embodiment of the present application provides a computer program product. When the computer program product is run on a terminal device, the terminal device executes the steps of any one of the above-mentioned concentration evaluation methods.

[0054] Compared with the prior art, the embodiments of the present application have the following beneficial effects: the embodiments of the present application obtain a target image frame to be detected; a preset target behavior is detected in the target image frame to obtain a concentration detection result; and a concentration evaluation is performed from a preset concentration evaluation dimension based on the concentration detection result to obtain a concentration evaluation result. Through the embodiments of the present application, a concentration evaluation can be performed from a preset concentration evaluation dimension based on the concentration detection result, so that a more comprehensive evaluation of the user's concentration can be achieved, and the accuracy of the concentration evaluation can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 This is a flow chart of an embodiment of a concentration evaluation method in an embodiment of the present application;

[0057] Figure 2 A schematic flow chart for conducting concentration assessment from the concentration assessment dimension;

[0058] Figure 3 Schematic diagram of the time period for concentration assessment;

[0059] Figure 4 This is a structural diagram of an embodiment of a concentration evaluation device in an embodiment of the present application;

[0060] Figure 5 This is a schematic block diagram of a terminal device in an embodiment of the present application. DETAILED DESCRIPTION

[0061] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described below are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0062] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0063] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0064] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0065] As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0066] In addition, in the description of the present application, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0067] With the development of smart technology, more and more smart terminals are entering the user's field of vision. During the user's work or study, smart terminals can be used for supervision and companionship, and can timely detect the user's inattention behavior and conduct concentration assessment to help users better understand the status of work or study.

[0068] However, it is difficult to accurately evaluate the user's concentration using existing concentration evaluation methods, resulting in a poor user experience.

[0069] In view of this, the embodiments of the present application provide a concentration evaluation method, device, computer-readable storage medium and terminal device to solve the problem that the existing concentration evaluation method is difficult to accurately evaluate the user's concentration, resulting in a poor user experience. Through the embodiments of the present application, the concentration can be evaluated from the preset concentration evaluation dimension according to the concentration detection result, which can achieve a more comprehensive evaluation of the user's concentration and improve the accuracy of the concentration evaluation.

[0070] It should be noted that the executor of the method of the present application is a terminal device, specifically, it can be a common computing device such as a learning machine, a learning tablet, a smart desk lamp, a desktop computer, a notebook, a PDA, or other computing devices.

[0071] It is understandable that in actual application scenarios, users can study or work through terminal devices, and when users study or work, the terminal devices can perform concentration assessment. For example, the user can enable the concentration assessment function on the client installed on the terminal device, and perform concentration assessment through the method provided in the embodiment of the present application. For another example, when the terminal device detects that the user is studying or working, the concentration assessment function can be enabled autonomously.

[0072] Specifically, see Figure 1 , an embodiment of a concentration evaluation method in the embodiments of the present application may include:

[0073] Step S101: Acquire a target image frame to be detected.

[0074] In the embodiment of the present application, an image frame containing a target area and containing a human face may be used as a target image frame to be detected, and the target image frame may be acquired using a preset camera acquisition device.

[0075] It is understandable that the video acquisition device can be a camera installed on the terminal device, or a camera connected to the terminal device for communication. In the embodiment of the present application, the front camera of the terminal device can be preferably used to acquire the target image frame.

[0076] In the embodiment of the present application, the target area can be the area to be detected, and the scope of the target area can be set according to actual needs, and the present application does not specifically limit this. For example, a circular area with a radius of 1 meter and a camera acquisition device as the center can be used as the target area; for another example, a learning and working area divided by the user can be used as the target area.

[0077] It is understandable that the user usually studies or works in the target area. Therefore, the embodiment of the present application can evaluate the user's concentration when a face is detected in the target area.

[0078] Specifically, a preset distance sensor can be used to detect whether there is an object in the target area. When the distance between the object and the distance sensor is less than or equal to a preset distance threshold, it can be considered that there is an object in the target area; if there is an object in the target area, an image of the target area can be captured by a camera capture device, and according to any face detection method in the prior art, it can be detected whether there is a face in the image of the target area; if there is a face in the image of the target area, a target image frame corresponding to the target area is obtained.

[0079] It is understandable that if it is detected that there is no object in the target area, or it is detected that there is no face in the target area, it can be considered that the user is not in the target area, and there is no need to perform concentration evaluation at this time.

[0080] Step S102: Detect a preset target behavior in the target image frame to obtain a focus detection result.

[0081] In the embodiment of the present application, a preset target behavior detection model can be used to detect in the target image frame to obtain a focus detection result. The target behavior detection model is an artificial intelligence model that can be used for target behavior detection, which may include but is not limited to any common artificial intelligence model in the prior art such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), YOLO, Fast R-CNN, Faster R-CNN, etc.

[0082] It should be noted that the embodiments of the present application can detect a variety of target behaviors, including but not limited to inattentive behaviors such as looking around, leaving the seat, playing with the phone, eating, etc., and the detected concentration detection results may include whether the target behavior appears in the target image frame and the number and duration of the target behavior, which can be used to evaluate the concentration situation. In order to improve the detection accuracy of the target behavior, a specific target behavior detection model can be used to detect a specific target behavior. For example, the looking detection model can be used to detect the looking behavior. For another example, the leaving seat detection model can be used to detect the leaving seat behavior. For another example, the leaving seat detection model can be used to detect the leaving seat behavior. For another example, the playing phone detection model is used to detect the playing phone behavior, and the eating detection model is used to detect the eating behavior.

[0083] In an embodiment of the present application, before using the target behavior detection model to perform target behavior detection, the target behavior detection model can be trained. Specifically, a preset number of target behavior detection training sample sets can be obtained, each of which includes a sample image and a corresponding state label; the sample image of each training sample in the target behavior detection training sample set is used as input, and the state label of each training sample in the target behavior detection training sample set is used as the expected output, and the initial artificial intelligence model is trained to obtain the target behavior detection model.

[0084] Since there can be multiple target behavior detection models, in order to ensure the training effect of the target behavior detection model, each target behavior detection model can be trained separately. For example, a preset looking detection training sample set can be obtained to train the looking detection model. For another example, a preset leaving seat detection training sample set can be obtained to train the leaving seat detection model. For another example, a preset playing mobile phone detection training sample set can be obtained to train the playing mobile phone detection model. For another example, a preset eating detection training sample set can be obtained to train the eating detection model.

[0085] In a possible embodiment, when training the target behavior detection model, the target behavior detection training sample set can be divided into a training set and a test set according to a preset ratio, and the training set is used for model training, and the test set is used for model testing.

[0086] It is understandable that, in order to make the target behavior detection model more robust, for a specific target behavior detection model, the corresponding target behavior detection training sample set may include a preset number of positive sample images and negative sample images in various application scenarios; wherein the positive sample image is a sample image with a specific target behavior; and the negative sample image is a sample image without a specific target behavior. For example, for the look-around detection model, the look-around detection training sample set may include a preset number of positive sample images and negative sample images in various application scenarios, wherein the positive sample image is a sample image with a look-around behavior; and the negative sample image is a sample image without a look-around behavior. For another example, for the seat-leaving detection model, the seat-leaving detection training sample set may include a preset number of positive sample images and negative sample images in various application scenarios, wherein the positive sample image is a sample image with a seat-leaving behavior; and the negative sample image is a sample image without a seat-leaving behavior. For another example, for the mobile phone playing detection model, the mobile phone playing detection training sample set may include a preset number of positive sample images and negative sample images in various application scenarios, wherein the positive sample image is a sample image with a mobile phone playing behavior; and the negative sample image is a sample image without a mobile phone playing behavior. For another example, for an eating detection model, the eating detection training sample set may include a preset number of positive sample images and negative sample images in various application scenarios, wherein the positive sample images are sample images with eating behavior; and the negative sample images are sample images without eating behavior.

[0087] In a possible embodiment, since there may be multiple image frames to be detected, the multiple image frames can be detected frame by frame, and the specific target behavior detection model can be updated according to the detection results of the target behavior detection model to improve the detection accuracy of the target behavior detection model. Specifically, if a specific target behavior is detected in an image frame, the image frame can be used as a positive sample image in a specific target behavior training sample set; if the specific target behavior is not detected in the image frame, the image frame can be used as a negative sample image in a specific target behavior training sample set to update the specific target behavior detection model. For example, if the image frame is detected to have a mobile phone playing behavior, the image frame can be used as a positive sample image in a mobile phone playing detection training sample set to update the mobile phone playing detection model; if the image frame is detected to have no mobile phone playing behavior, the image frame can be used as a negative pressure sample image in a mobile phone playing detection training sample set to update the mobile phone playing detection model.

[0088] It is understandable that if the target behavior is detected in the target image frame, a corresponding reminder can be made on the terminal device, and a specific terminal device screenshot, an image captured by the front camera of the terminal device, and an image captured by the smart camera connected to the terminal device can be displayed. Among them, the screenshot of the terminal device can be used to determine whether the user stays in the expected learning content; the image captured by the front camera can be used to show the specific state of the user when the target behavior exists; the image captured by the smart camera can be used to show the current user's work or learning environment to assist in determining whether there are more interferences. For example, when it is detected that the user has left his seat, a message reminder can be made on the client installed on the terminal device after the user returns to his seat, and the terminal device screenshot, the image captured by the front camera, and the image captured by the smart camera can be displayed. For another example, when it is detected that the user has played with the mobile phone, and the mobile phone playing behavior lasts for more than 10 seconds, a message reminder can be made on the client, and the terminal device screenshot, the image captured by the front camera, and the image captured by the smart camera can be displayed.

[0089] Step S103: According to the concentration detection result, a concentration evaluation is performed from a preset concentration evaluation dimension to obtain a concentration evaluation result.

[0090] In the embodiments of the present application, the concentration assessment dimensions can be determined according to different application scenarios, and the concentration assessment can be performed according to the concentration assessment dimensions, wherein the concentration assessment dimensions can be specific and situationalized according to actual needs, and the present application does not limit this.

[0091] In the embodiment of the present application, the concentration evaluation can be performed from at least one of the five concentration evaluation dimensions of continuity, stability, concentration, accuracy and efficiency. For example, when a user is learning online videos, the concentration of the user's online video learning process can be evaluated from the three concentration evaluation dimensions of continuity, stability and concentration.

[0092] In a specific implementation of an embodiment of the present application, concentration can be evaluated based on the concentration detection result and the learning data, wherein the learning data may include any one of homework data, reading data, and online video learning data. For example, if the user's learning or work content is doing homework, concentration can be evaluated based on the concentration detection result and the homework data, wherein the homework data can be data on the user's homework efficiency and homework accuracy during the homework process. For another example, if the user's learning or work content is reading comprehension, concentration can be evaluated based on the concentration detection result and the reading data, wherein the reading data can be data on the reading speed and comprehension accuracy during the user's reading process. For another example, if the user's learning or work content is online video learning, concentration can be evaluated based on the concentration detection result and the online video learning data, wherein the online video learning data can be data on the video bounce rate during the user's video learning process.

[0093] For the sake of convenience, the calculation of the dimension evaluation results of the concentration evaluation dimension in the embodiment of the present application will be described here in combination with the specific scenario of doing homework. Figure 2 When a user is doing homework, the specific process of evaluating concentration from the five concentration evaluation dimensions of continuity, stability, concentration, accuracy and efficiency can include the following steps:

[0094] Step S201: Determine the dimension evaluation results of each concentration evaluation dimension according to the target behavior detection result and the learning data.

[0095] The learning data may be operation data, that is, data on operation efficiency and operation accuracy of the user during the operation.

[0096] In the embodiment of the present application, the dimension evaluation result of persistence can be calculated based on the user's concentration time, wherein the user's concentration time is the time when there is no target behavior (non-concentrated behavior). Figure 3At this time, it is necessary to evaluate the concentration from T0 to T3. There is no inattention behavior from T0 to T1, inattention behavior is detected from T1 to T2, and there is no inattention behavior from T2 to T3. The user's single concentration duration is from T0 to T1 and from T2 to T3 respectively. The total concentration duration of the user from T0 to T3 is the sum of the single concentration durations, that is, the sum of T0 to T1 and T2 to T3.

[0097] Specifically, if the single concentration duration is greater than or equal to the preset first duration threshold, the single dimension evaluation score of this continuity can be the preset full score of this dimension; if the single concentration duration is less than the first duration threshold, the single dimension evaluation score of this continuity = (single concentration duration / first duration threshold)*full score of dimension; if the total concentration duration is greater than or equal to the preset second duration threshold, the continuity dimension evaluation score can be the maximum single dimension evaluation score; if the total concentration duration is less than the second duration threshold, the continuity dimension evaluation score = the maximum single dimension evaluation score*min(total concentration duration / second duration threshold).

[0098] It can be understood that the first duration threshold, the second duration threshold and the full score of the continuity dimension can be specified and situationally set according to actual needs. The present application does not limit this. Preferably, the first duration threshold can be set to half an hour, the second duration threshold can be set to 1 hour, and the full score of the continuity dimension can be set to 5 points.

[0099] In the embodiment of the present application, the dimensional evaluation result of stability can also be calculated based on the total concentration time of the user and the number and duration of the seat-leaving behavior; wherein the number of seat-leaving behaviors can be called the number of unstable times, and the duration of the seat-leaving behavior can be called the unstable duration. Here, the dimensional evaluation result of stability can be determined based on the average half-hour unstable number of times and the average half-hour unstable duration.

[0100] Specifically, the average number of instability times in half an hour = number of instability times * half an hour / total concentration time; the average instability time in half an hour = instability time * half an hour / total concentration time.

[0101] If the average half-hour instability times is less than or equal to the preset first number threshold, the instability times score of stability can be the preset full score of the dimension; if the average half-hour instability times is greater than the first number threshold and less than or equal to the preset second number threshold, the instability times score of stability = full score of dimension - (average half-hour instability times / second number threshold) * full score of dimension; if the average half-hour instability times is greater than the second number threshold, the instability times score of stability is 0.

[0102] If the average half-hour unstable duration is less than or equal to the preset third duration threshold, the unstable duration score of stability can be the full score of the dimension; if the average half-hour unstable duration is greater than the third duration threshold and less than or equal to the preset fourth duration threshold, the unstable duration score of stability = full score of dimension - (average half-hour unstable duration / fourth duration threshold) * full score of dimension; if the average half-hour unstable duration is greater than the fourth duration threshold, the unstable duration score of stability is 0.

[0103] Afterwards, the unstable frequency score of stability and the unstable duration score of stability are combined to obtain the final stability score = first coefficient * unstable frequency score + second coefficient * unstable duration score, where the first coefficient and the second coefficient can be preset values, and the first coefficient + the second coefficient = 1. Based on this, the dimensional evaluation score of stability can be obtained = final stability score * min (1, total concentration time / 1 hour).

[0104] It is understandable that the first number threshold, the second number threshold, the third duration threshold, the fourth duration threshold, the full score of the stability dimension, the first coefficient and the second coefficient can all be specific and situationalized according to actual needs, and this application does not limit this. Here, it is preferred to set the first number threshold to 2, the second number threshold to 10, the third duration threshold to 5 minutes, the fourth duration threshold to 15 minutes, the full score of the stability dimension to 5 points, the first coefficient to 0.2, and the second coefficient to 0.8.

[0105] In the embodiment of the present application, the concentration dimension evaluation result can also be calculated based on the number and duration of inattention behaviors other than leaving the seat (looking around, playing with mobile phones, and eating). Specifically, the average number of inattentions in half an hour = number of inattentions * half an hour / total concentration duration; the average inattention duration in half an hour = inattention duration * half an hour / total concentration duration.

[0106] If the average number of inattentions in half an hour is less than or equal to the preset third number threshold, the score for inattentions in concentration can be the full score of the preset dimension; if the average number of inattentions in half an hour is greater than the third number threshold and less than or equal to the preset fourth number threshold, the score for inattentions in concentration = full score of dimension - (average number of inattentions in half an hour / fourth number threshold) * full score of dimension; if the average number of inattentions in half an hour is greater than the fourth number threshold, the score for inattentions in concentration is 0.

[0107] If the average half-hour inconcentration duration is less than or equal to the preset fifth duration threshold, the concentration inconcentration duration score can be the full score of the dimension; if the average half-hour inconcentration duration is greater than the fifth duration threshold and less than or equal to the preset sixth duration threshold, the concentration inconcentration duration score = the full score of the dimension - (average half-hour inconcentration duration / sixth duration threshold) * the full score of the dimension; if the average half-hour inconcentration duration is greater than the sixth duration threshold, the concentration inconcentration duration score is 0.

[0108] After that, the concentration dimensional evaluation score = third coefficient * concentration dimensional evaluation score + fourth coefficient * concentration dimensional evaluation score can be obtained by combining the concentration frequency score and concentration duration score; wherein the third coefficient and the fourth coefficient can be preset values, and the third coefficient + the fourth coefficient = 1. Based on this, the final concentration dimensional evaluation score can be obtained = the final concentration score * min (1, total concentration duration / 1 hour).

[0109] It is understandable that the third number threshold, the fourth number threshold, the fifth duration threshold, the sixth duration threshold, the full score of the concentration dimension, the third coefficient and the fourth coefficient can all be specific and situationalized according to actual needs, and this application does not limit this. Here, it is preferred to set the third number threshold to 2, the fourth number threshold to 10, the fifth duration threshold to 5 minutes, the sixth duration threshold to 15 minutes, the full score of the concentration dimension to 5 points, the third coefficient to 0.2, and the fourth coefficient to 0.8.

[0110] In the embodiment of the present application, the dimension evaluation score of the accuracy rate can also be calculated according to the accuracy rate of the homework, wherein the accuracy rate of the homework = the number of questions answered correctly / the total number of questions answered. Accordingly, the dimension evaluation score of the accuracy rate can be obtained = homework accuracy rate * dimension full score.

[0111] It is understandable that the full score of the accuracy dimension can be set according to actual needs, and can be preferably set to 5 points here.

[0112] In the embodiment of the present application, the dimension evaluation score of efficiency can also be calculated based on the average time of doing a single question of the homework, where the average time of doing a single question of the homework = the total time of doing the question / the total number of questions. And the dimension evaluation score of efficiency = dimension full score * min (1, average time of a single question / 4).

[0113] It is understandable that the full score of the efficiency dimension can be set according to actual needs, and can be preferably set to 5 points here.

[0114] Step S202: Determine a concentration evaluation result according to the dimension evaluation result of the concentration evaluation dimension.

[0115] In the embodiment of the present application, the dimensional evaluation results of the concentration evaluation dimension can be weighted averaged according to a preset weight coefficient to obtain the concentration evaluation result.

[0116] It is understandable that the weight coefficient can be set in a specific and situational manner according to the number of focus assessment dimensions and actual needs, and this application does not limit this.

[0117] In a possible embodiment, the same weight coefficient can be set for each concentration evaluation result of the evaluation dimension. For example, when the user is doing homework, the concentration can be evaluated from the five concentration evaluation dimensions of continuity, stability, concentration, homework accuracy, and homework efficiency. The weight coefficients corresponding to the five concentration evaluation dimensions can all be 0.2, and the concentration evaluation result = 0.2*continuity dimension evaluation score + 0.2*stability dimension evaluation score + 0.2*concentration dimension evaluation score + 0.2*accuracy dimension evaluation score + 0.2*efficiency dimension evaluation score. For another example, when the user is learning online videos, the concentration can be evaluated from the three concentration evaluation dimensions of continuity, stability, and concentration. The weight coefficients corresponding to the three concentration evaluation dimensions can all be 1 / 3, and the concentration evaluation result = 1 / 3*continuity dimension evaluation score + 1 / 3*stability dimension evaluation score + 1 / 3*concentration dimension evaluation score.

[0118] In another possible embodiment, when the concentration evaluation is performed only from one concentration evaluation dimension, the weight coefficient corresponding to the concentration evaluation dimension may be set to 1. For example, when the concentration evaluation is performed only from the dimension of persistence, the concentration evaluation result = the dimension evaluation score of persistence.

[0119] In an embodiment of the present application, after determining the concentration evaluation result, an evaluation report corresponding to the concentration evaluation result can also be generated, and the evaluation report can be displayed through a preset display device. Specifically, the evaluation corresponding to the concentration evaluation result can be determined based on the relationship between the concentration evaluation result and the gear threshold. If the concentration evaluation result is less than the preset first gear threshold, the evaluation corresponding to the concentration evaluation result can be determined to be "poor"; if the concentration evaluation result is greater than or equal to the first gear threshold and less than the preset second gear threshold, the evaluation corresponding to the concentration evaluation result can be determined to be "good"; if the concentration evaluation result is greater than or equal to the second gear threshold and less than the preset third gear threshold, the evaluation corresponding to the concentration evaluation result can be determined to be "excellent"; if the concentration evaluation result is greater than the third gear threshold, the evaluation corresponding to the concentration evaluation result can be determined to be "excellent+".

[0120] It is understandable that the first gear threshold, the second gear threshold and the third gear threshold can be specifically and situationally set according to the actual situation, and the present application does not limit this. Here, the first gear threshold can be preferably set to 3, the second gear threshold can be set to 4, and the third gear threshold can be set to 4.5.

[0121] After obtaining the evaluation report corresponding to the concentration evaluation result, the evaluation report can be displayed on a display device. Specifically, the display device can be a terminal device that executes the method of the present application, or it can be another terminal device that is communicatively connected to the terminal device. For example, if the terminal device that executes the method of the present application is a learning machine, the display device can be the learning machine itself, or it can be a smart phone that is communicatively connected to the learning machine.

[0122] In summary, the embodiment of the present application obtains a target image frame to be detected; detects a preset target behavior in the target image frame to obtain a concentration detection result; and performs a concentration evaluation from a preset concentration evaluation dimension based on the concentration detection result to obtain a concentration evaluation result. Through the embodiment of the present application, a concentration evaluation can be performed from a preset concentration evaluation dimension based on the concentration detection result, so as to achieve a more comprehensive evaluation of the user's concentration and improve the accuracy of the concentration evaluation.

[0123] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0124] Corresponding to a concentration evaluation method described in the above embodiment, Figure 4 A structural diagram of an embodiment of a concentration assessment device provided in an embodiment of the present application is shown.

[0125] In an embodiment of the present application, a concentration evaluation device may include:

[0126] The image frame acquisition module 401 is used to acquire the target image frame to be detected;

[0127] A behavior detection module 402 is used to detect a preset target behavior in the target image frame to obtain a focus detection result;

[0128] The concentration evaluation module 403 is used to perform concentration evaluation from a preset concentration evaluation dimension according to the concentration detection result to obtain a concentration evaluation result.

[0129] In a specific implementation of the embodiment of the present application, the concentration evaluation device may further include:

[0130] A data acquisition module, used to acquire learning data corresponding to the target image frame;

[0131] The concentration assessment module may include:

[0132] A concentration evaluation unit is used to perform concentration evaluation from a concentration evaluation dimension according to the target behavior detection result and the learning data to obtain the concentration evaluation result.

[0133] In a specific implementation of the embodiment of the present application, the concentration evaluation unit may include:

[0134] A dimension evaluation subunit, used to determine a dimension evaluation result of a concentration evaluation dimension according to the concentration detection result and the learning data;

[0135] The result determination subunit is used to determine the concentration evaluation result according to the dimension evaluation result of the concentration evaluation dimension.

[0136] In a specific implementation of the embodiment of the present application, the focus assessment dimension includes at least one of persistence, stability, concentration, accuracy and efficiency;

[0137] Among them, the dimension evaluation result of the sustainability is calculated by the longest continuous concentration time, the dimension evaluation result of the stability is calculated by the number and duration of interruptions, the dimension evaluation result of the concentration is calculated by the number and duration of occurrences of the target behavior, the dimension evaluation result of the accuracy is calculated by the accuracy in the learning data, and the dimension evaluation result of the efficiency is calculated by the efficiency in the learning data.

[0138] In a specific implementation of the embodiment of the present application, the result determination subunit may include:

[0139] The weighted averaging subunit is used to perform weighted averaging on the dimension evaluation results of the concentration evaluation dimension according to a preset weight coefficient to obtain the concentration evaluation result.

[0140] In a specific implementation of the embodiment of the present application, the image frame acquisition module may include:

[0141] An object detection unit, used to detect whether there is an object in a preset target area;

[0142] an image acquisition unit, configured to acquire an image of the target area if there is an object in the target area;

[0143] A face detection unit, used to detect whether there is a face in the image of the target area;

[0144] The image frame acquisition unit is used to acquire the target image frame corresponding to the target area if there is a face in the image of the target area.

[0145] In a specific implementation of the embodiment of the present application, the concentration evaluation device may further include:

[0146] A report generation module, used to generate an evaluation report corresponding to the concentration evaluation result;

[0147] The report display module is used to display the evaluation report through a preset display device.

[0148] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, modules and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0149] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0150] Figure 5 A schematic block diagram of a terminal device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0151] like Figure 5 As shown, the terminal device 5 of this embodiment includes: a processor 50, a memory 51, and a computer program 52 stored in the memory 51 and executable on the processor 50. When the processor 50 executes the computer program 52, the steps in the above-mentioned various concentration evaluation method embodiments are implemented, for example Figure 1 Alternatively, when the processor 50 executes the computer program 52, the functions of each module / unit in the above-mentioned device embodiments are realized, for example Figure 4 The functions of modules 401 to 403 are shown.

[0152] Exemplarily, the computer program 52 may be divided into one or more modules / units, which are stored in the memory 51 and executed by the processor 50 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 52 in the terminal device 5.

[0153] The terminal device 5 can be a computing device such as a learning machine, a learning tablet, a smart desk lamp, a desktop computer, a notebook, a PDA, etc. It can be understood by those skilled in the art that Figure 5It is only an example of the terminal device 5 and does not constitute a limitation of the terminal device 5. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device 5 may also include input and output devices, network access devices, buses, etc.

[0154] The processor 50 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0155] The memory 51 may be an internal storage unit of the terminal device 5, such as a hard disk or memory of the terminal device 5. The memory 51 may also be an external storage device of the terminal device 5, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 5. Further, the memory 51 may also include both an internal storage unit of the terminal device 5 and an external storage device. The memory 51 is used to store the computer program and other programs and data required by the terminal device 5. The memory 51 may also be used to temporarily store data that has been output or is to be output.

[0156] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0157] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0158] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0159] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0160] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0161] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0162] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0163] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A concentration evaluation method, characterized in that: include: Acquire the target image frame to be detected; Detecting a preset target behavior in the target image frame to obtain a focus detection result; According to the concentration detection result, a concentration evaluation is performed from a preset concentration evaluation dimension to obtain a concentration evaluation result.

2. The concentration evaluation method according to claim 1, characterized in that: Before performing concentration evaluation from a preset concentration evaluation dimension according to the concentration detection result and obtaining the concentration evaluation result, the method further includes: Acquire learning data corresponding to the target image frame; The step of performing concentration evaluation from a preset concentration evaluation dimension according to the concentration detection result to obtain a concentration evaluation result includes: According to the concentration detection result and the learning data, a concentration assessment is performed from a concentration assessment dimension to obtain the concentration assessment result.

3. The concentration evaluation method according to claim 2, characterized in that: The step of performing concentration evaluation from a concentration evaluation dimension according to the concentration detection result and the learning data to obtain the concentration evaluation result includes: Determining a dimension assessment result of a concentration assessment dimension according to the concentration detection result and the learning data; The concentration evaluation result is determined according to the dimension evaluation result of the concentration evaluation dimension.

4. The concentration evaluation method according to claim 3, characterized in that: The concentration evaluation dimension includes at least one of persistence, stability, concentration, accuracy and efficiency; Among them, the dimension evaluation result of the sustainability is calculated by the longest continuous concentration time, the dimension evaluation result of the stability is calculated by the number and duration of interruptions, the dimension evaluation result of the concentration is calculated by the number and duration of occurrences of the target behavior, the dimension evaluation result of the accuracy is calculated by the accuracy in the learning data, and the dimension evaluation result of the efficiency is calculated by the efficiency in the learning data.

5. The concentration evaluation method according to claim 4, characterized in that: The step of determining the concentration evaluation result according to the dimension evaluation result of the concentration evaluation dimension includes: The dimensional evaluation results of the concentration evaluation dimension are weighted averaged according to a preset weight coefficient to obtain the concentration evaluation result.

6. The concentration evaluation method according to claim 1, characterized in that: The step of acquiring a target image frame to be detected comprises: Detect whether there is an object in the preset target area; If there is an object in the target area, acquiring an image of the target area; Detecting whether there is a human face in the image of the target area; If a human face exists in the image of the target area, the target image frame corresponding to the target area is acquired.

7. The concentration evaluation method according to any one of claims 1 to 6, characterized in that: After performing concentration evaluation from a preset concentration evaluation dimension according to the concentration detection result and obtaining the concentration evaluation result, the method further includes: Generating an assessment report corresponding to the concentration assessment result; The evaluation report is displayed through a preset display device.

8. A concentration assessment device, characterized in that: include: An image frame acquisition module, used to acquire a target image frame to be detected; A behavior detection module, used to detect a preset target behavior in the target image frame to obtain a focus detection result; The concentration evaluation module is used to perform concentration evaluation from a preset concentration evaluation dimension according to the concentration detection result to obtain a concentration evaluation result.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the concentration evaluation method according to any one of claims 1 to 7 are implemented.

10. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the concentration evaluation method according to any one of claims 1 to 7 are implemented.

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