An analytical method for auxiliary diagnosis and treatment of children with autism

Abstract

The present invention relates to the field of auxiliary diagnosis and treatment, and in particular to an analysis method for auxiliary diagnosis and treatment of autistic children. The present invention performs pre-detection on a target object and stores detection data to analyze the target object's motion feature representation, determine the target object's motion feature feedback degree, and perform auxiliary diagnosis and treatment analysis, including: adjusting the image acquisition accuracy of the image acquisition device for the target object, calling the video record in the target object's review case, identifying emotional gaze features, limb movement amplitude, and the change ratio of feature matching parameters relative to the pre-detection based on the video record, and comparing them with expected control data determined based on a pre-stored intervention diagnosis and treatment plan to determine whether the feature matching standard is met; generating a recommended number of re-examinations based on the motion feature representation, and outputting the results of the auxiliary diagnosis and treatment analysis through an output end. The data analysis results are accurate and reliable, can provide doctors with auxiliary diagnosis and treatment data support, and improve diagnosis and treatment efficiency.

Description

Technical Field

[0001] The present invention relates to the field of auxiliary diagnosis and treatment, and in particular to an analysis method for auxiliary diagnosis and treatment of autistic children. Background Art

[0002] With the development of computer vision and artificial intelligence technology, the use of visual behavioral analysis to assist in the diagnosis of autism has attracted more and more attention. By analyzing the patient's behavioral video data and extracting features for identification and diagnosis, it has the advantages of being non-interventional and concealed. At the same time, studies have shown that early intervention can significantly improve the social and communication skills of children with autism and improve their quality of life. Therefore, timely diagnosis and intervention of children with autism in the early stages of discovery have become an important part of clinical practice.

[0003] Chinese patent publication number: CN115578670A, discloses a method, device and diagnosis and treatment machine-assisted system for behavior recognition and motion prediction of autistic children, the method comprising: (1) acquiring original skeleton video data of the child from different angles; (2) inputting the acquired original skeleton video data into a view adaptive conversion unit to adaptively convert it into a consistent coordinate system to obtain an optimal representation of the human skeleton; (3) inputting the optimal representation of the human skeleton into a multi-scale feature extraction unit to extract high-performance spatiotemporal features of the human skeleton; (4) inputting the high-performance spatiotemporal features into a multi-task learning unit to perform behavior recognition and motion prediction on the human body, and obtaining behavior classification results and motion prediction results.

[0004] However, the existing technology still has the following problems: it only considers motion characteristics and does not consider the gaze characteristics of the target object. In addition, it does not adaptively adjust the data evaluation method according to the diagnosis and treatment plan to provide data support for doctors, resulting in poor auxiliary diagnosis and treatment effects. Summary of the Invention

[0005] To this end, the present invention provides an analysis method for auxiliary diagnosis and treatment of autistic children, which is used to overcome the problems in the prior art that only movement characteristics are considered, but the gaze characteristics of the target object are not considered, and the data evaluation method is not adaptively adjusted according to the diagnosis and treatment plan to provide data support for doctors, resulting in poor auxiliary diagnosis and treatment effects.

[0006] To achieve the above objectives, the present invention provides an analysis method for auxiliary diagnosis and treatment of autistic children, comprising:

[0007] Step S1, performing a pre-detection on a target object and storing detection data for the target object, wherein the pre-detection includes setting an image acquisition device to detect movement characteristics of the target object when performing a communication movement, wherein the movement characteristics include body movement amplitude and reaction time;

[0008] Step S2, analyzing the motion feature representation of the target object based on the motion feature to determine the motion feature feedback degree of the target object;

[0009] Step S3, based on the feedback degree of the target object's motion characteristics, performs auxiliary diagnosis and treatment analysis, including:

[0010] Adjusting the image acquisition accuracy of the image acquisition device for the target subject, calling the video record in the target subject's review case, identifying the emotional gaze characteristics, body movement amplitude, and the change ratio of the feature matching parameters relative to the pre-detection based on the video record, and comparing them with the expected control data determined based on the pre-stored intervention diagnosis and treatment plan to determine whether the feature matching criteria are met;

[0011] Or, generating a recommended number of review times based on the action feature representation;

[0012] The feature matching parameter is determined by combining the ratio of the emotional gaze sub-features with the amplitude of the body movement, wherein the emotional gaze features include the gaze duration of the eye area and the gaze duration of the mouth area;

[0013] Step S4: outputting the auxiliary diagnosis and treatment analysis result through the output terminal.

[0014] Furthermore, in step S2, the action feature representation of the target object is parsed according to formula (1) based on the action feature, including:

[0015]

[0016] In formula (1), S represents the motion feature representation, F represents the limb motion amplitude, F0 represents the limb motion amplitude threshold, T represents the reaction time, T0 represents the reaction time threshold, α represents the limb motion amplitude weight coefficient, and β represents the reaction time weight coefficient.

[0017] Furthermore, in step S2, determining the motion feature feedback degree of the target object includes:

[0018] If the motion feature representation value is greater than or equal to the motion feature representation value threshold, the motion feature of the target object is determined to be at a weak feedback level;

[0019] If the motion feature representation amount is less than the motion feature representation amount threshold, it is determined that the motion feature of the target object is at a strong feedback level.

[0020] Furthermore, in step S3, auxiliary diagnosis and treatment analysis is performed based on the feedback degree of the target object's motion characteristics, including:

[0021] If the target subject's motion characteristics are at a weak feedback level, the image acquisition accuracy of the image acquisition device for the target subject is adjusted, and the video recording of the target subject's review case is called. Based on the video recording, the emotional gaze characteristics, the amplitude of the body movement, and the change ratio of the feature matching parameters relative to the pre-detection are identified, and compared with the expected control data determined based on the pre-stored intervention diagnosis and treatment plan to determine whether the feature matching criteria are met;

[0022] If the action feature of the target object is a strong feedback level, a recommended number of reviews is generated based on the action feature representation.

[0023] Furthermore, in step S3, adjusting the image acquisition accuracy of the image acquisition device for the target object includes:

[0024] Improve the resolution and maximum frame rate of the image acquisition device.

[0025] Furthermore, in step S3, the feature matching parameters are determined by combining the ratio of the emotional gaze sub-features with the body movement amplitude, including:

[0026] obtaining a video record captured by an image acquisition device to determine emotional gaze sub-features and body movement amplitudes;

[0027] The ratio of the eye area gaze duration to the mouth area gaze duration is used as the ratio of the emotional gaze sub-feature and as the first feature;

[0028] Calculating the ratio of the limb movement amplitude to the expected threshold of the limb movement amplitude as the second feature;

[0029] The sum of the first feature and the second feature is determined as a feature matching parameter.

[0030] Furthermore, in step S3, the process of identifying the emotional gaze features, body movement amplitude, and the change ratio of the feature matching parameters relative to the pre-detection based on the video record and comparing them with the expected control data determined based on the pre-stored intervention diagnosis and treatment plan includes:

[0031] Determine the change ratio of emotional gaze features, the change ratio of body movement amplitude, and the change ratio of feature matching parameters;

[0032] Determining the expected emotional gaze feature change ratio, expected limb movement amplitude change ratio, and expected feature matching parameter change ratio corresponding to the intervention diagnosis and treatment plan;

[0033] comparing the emotion gaze feature change ratio with an expected emotion gaze feature change ratio;

[0034] comparing the limb movement amplitude change ratio with the expected limb movement amplitude change ratio;

[0035] The feature matching parameter change ratio is compared with an expected feature matching parameter change ratio.

[0036] Furthermore, in step S3, the determination of whether the feature matching standard is met includes:

[0037] If the expected matching conditions are met, it is determined that the feature matching criteria are met;

[0038] The expected matching conditions include that the feature matching parameter change ratio is less than the expected feature matching parameter change ratio, the emotional gaze feature change ratio is less than the expected emotional gaze feature change ratio, and the limb movement amplitude change ratio is less than the expected limb movement amplitude change ratio.

[0039] Furthermore, in step S3, generating a recommended number of review times based on the action feature representation includes:

[0040] The number of recommended reviews is positively correlated with the action feature representation quantity.

[0041] Furthermore, the step S3 also includes pre-storing expected control data for each intervention treatment plan, including:

[0042] Expected feature matching parameter change ratio, expected emotional gaze feature change ratio, and expected limb movement amplitude change ratio.

[0043] Compared with the existing technology, the present invention performs pre-detection on the target object and stores the detection data to analyze the target object's motion feature representation, determine the target object's motion feature feedback degree, and perform auxiliary diagnosis and treatment analysis, including: adjusting the image acquisition accuracy of the image acquisition device for the target object, calling the video records in the target object's review case, identifying the emotional gaze characteristics, limb movement amplitude and the change ratio of the feature matching parameters relative to the pre-detection based on the video records, and comparing them with the expected control data determined based on the pre-stored intervention diagnosis and treatment plan to determine whether the feature matching standards are met; generating a recommended number of re-examinations based on the motion feature representation, and outputting the results of the auxiliary diagnosis and treatment analysis through the output end. The data analysis results are accurate and reliable, can provide doctors with auxiliary diagnosis and treatment data support, and improve diagnosis and treatment efficiency.

[0044] In particular, the present invention analyzes the motion feature representation of the target object through motion feature analysis. In actual situations, the basic motion development speed of the target object is relatively slow, and there will be problems of limb movement incoordination and movement disorders. By observing the limb movement amplitude of the target object when it produces communication movements, for example, the limb amplitude presented by the subject who needs to receive diagnosis and treatment will be relatively small. This situation may be related to motor skill disorders, which are usually manifested as early motor lag, coordination disorders, physical decline and visual-motor integration disorders. At the same time, the target object requires a longer reaction time when communicating or needing to make a corresponding response. This situation may be related to its social interaction and communication barriers. They need more time to process and respond to social signals. Therefore, the present application analyzes the motion feature representation of the target object through the limb movement amplitude and reaction time when the target object produces communication movements, so as to characterize the degree of feedback that the target object can give when dealing with interaction and communication, and provide data support for the subsequent determination of the target object's motion feature feedback degree, so as to adaptively perform auxiliary diagnosis and treatment analysis, thereby ensuring the accuracy and reliability of the data analysis results, providing auxiliary diagnosis and treatment data support for doctors, and improving the efficiency of diagnosis and treatment.

[0045] In particular, the present invention selects different auxiliary diagnosis and treatment analysis methods according to the different feedback levels of the action characteristics presented by the target object, including: when the action characteristics of the target object are at a weak feedback level, the present invention pre-adjusts the accuracy of the video recording of the target object's review diagnosis and treatment process, improves the resolution and maximum frame rate of image acquisition, and conducts a detailed observation of the relevant key features of the target object, and then extracts the emotional gaze sub-features and limb movement amplitude presented by the target object during interaction and communication in the high-precision video image to determine the feature matching parameters. Therefore, the present application uses feature matching parameters to characterize the changes in the situation and the degree of improvement of the target object after a certain period of time after the pre-detection adopts the intervention diagnosis and treatment plan, and performs an overall analysis based on the feature matching parameters while considering the analysis of the various detailed features targeted by each sub-feature, not only judging the use of intervention diagnosis and treatment as a whole Whether the changes in the situation after a certain period of time under the treatment plan match the expected degree of change in diagnosis and treatment, and the degree of matching, and by specifically analyzing the changes in each sub-feature and the matching of the sub-feature data corresponding to the expected changes in diagnosis and treatment, the degree of matching of specific features can be relatively accurately identified, and auxiliary support can be provided for the subsequent modification of targeted treatment plans. The gaze characteristics of the target object are considered in combination with further analysis of the amplitude characteristics of limb movements to improve the accuracy and reliability of auxiliary diagnosis and treatment; when the movement characteristics of the target object are at a strong feedback level, the abnormal tendency of the movement characteristics of the target object is slight. By observing the movement characteristics of the target object and analyzing the data provided by the movement characteristic representation, the data evaluation method is comprehensively and adaptively adjusted. The data analysis results are accurate and reliable, which can provide doctors with auxiliary diagnosis and treatment data support and improve the efficiency of diagnosis and treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of the steps of an analysis method for assisting diagnosis and treatment of autistic children according to an embodiment of the invention;

[0047] Figure 2 A logic decision diagram for determining the degree of feedback of the action characteristics of a target object according to an embodiment of the invention;

[0048] Figure 3 A logical decision diagram for selecting an auxiliary diagnosis and treatment analysis method for an embodiment of the invention;

[0049] Figure 4 This is a logical decision diagram for determining whether a feature matching criterion is met according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0051] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0052] See also Figures 1 to 4 As shown, Figure 1 Schematic diagram of the steps of the analysis method for auxiliary diagnosis and treatment of autistic children according to an embodiment of the present invention. Figure 2 A logical decision diagram for determining the degree of feedback of the action characteristics of the target object according to an embodiment of the invention. Figure 3 A logical decision diagram for selecting an auxiliary diagnosis and treatment analysis method for an embodiment of the invention, Figure 4 This is a logical decision diagram for determining whether a feature matching criterion is met according to an embodiment of the invention. The analysis method for auxiliary diagnosis and treatment of autistic children according to an embodiment of the invention includes:

[0053] Step S1, performing a pre-detection on a target object and storing detection data for the target object, wherein the pre-detection includes setting an image acquisition device to detect movement characteristics of the target object when performing a communication movement, wherein the movement characteristics include body movement amplitude and reaction time;

[0054] Step S2, analyzing the motion feature representation of the target object based on the motion feature to determine the motion feature feedback degree of the target object;

[0055] Step S3, based on the feedback degree of the target object's motion characteristics, performs auxiliary diagnosis and treatment analysis, including:

[0056] Adjusting the image acquisition accuracy of the image acquisition device for the target subject, calling the video record in the target subject's review case, identifying the emotional gaze characteristics, body movement amplitude, and the change ratio of the feature matching parameters relative to the pre-detection based on the video record, and comparing them with the expected control data determined based on the pre-stored intervention diagnosis and treatment plan to determine whether the feature matching criteria are met;

[0057] Or, generating a recommended number of review times based on the action feature representation;

[0058] The feature matching parameter is determined by combining the ratio of the emotional gaze sub-features with the amplitude of the body movement, wherein the emotional gaze features include the gaze duration of the eye area and the gaze duration of the mouth area;

[0059] Step S4: outputting the auxiliary diagnosis and treatment analysis result through the output terminal.

[0060] Specifically, there is no limitation on the image acquisition device, which only needs to have the function of acquiring the image of the target object, for example, a photographic device. Of course, authorization is required to obtain the image of the target object, which will not be elaborated here.

[0061] Specifically, there is no specific limitation on the method of storing the detection data for the target object. A database specifically for storing detection data and treatment plans can be established at the output end, and the detection data and treatment plans can be stored in the database. At the same time, the type of database is not limited and will not be elaborated here.

[0062] Specifically, a preliminary test is performed when the target object is interviewed. There is no limitation on the method of determining the amplitude of body movements. The amplitude of movement can be determined by video analysis methods. For example, the amplitude of movement can be represented by obtaining the average moving distance of each joint point of the target object within a predetermined time during the interview. Of course, other methods can also be used, which will not be repeated here.

[0063] It is understandable that the reaction time is the time duration from the moment the communication object sends a communication to the target object to the moment the target object responds, which will not be elaborated here.

[0064] It is understandable that the time of communication and the time of response can be determined by analyzing mouth features. For example, an algorithm or model that can realize the corresponding function is pre-trained, and a logical component is imported to realize the corresponding function. This will not be repeated here.

[0065] It can be understood that the eye area fixation duration refers to the duration that the target object fixates on the eye area, and the mouth area fixation duration refers to the duration that the target object fixates on the mouth area;

[0066] In clinical manifestations, subjects with typical autism development have significantly shortened gaze durations on key emotional feature areas such as the eyes and mouth, and prefer to gaze at the mouth area and avoid gazing at the eye area.

[0067] Specifically, in step S2, the action feature representation of the target object is analyzed based on the action feature according to formula (1), including:

[0068]

[0069] In formula (1), S represents the motion feature representation, F represents the limb motion amplitude, F0 represents the limb motion amplitude threshold, T represents the reaction time, T0 represents the reaction time threshold, α represents the limb motion amplitude weight coefficient, and β represents the reaction time weight coefficient.

[0070] In this embodiment, the limb movement amplitude threshold F0 and the reaction time threshold T0 are obtained in advance. The relevant video data of several target subjects diagnosed with autism performing communication movements are collected through historical data, the limb movement amplitude data and the reaction time data are extracted, and the average limb movement amplitude ΔF and the average reaction time ΔT presented by the several target subjects are solved. It is set that F0 = r1×ΔF, T0 = r2×ΔT, r1 is the first precision coefficient, r2 is the second precision coefficient, 1.1<r1<1.15, 1.05<r1<1.1, α is 0.55, and β is 0.45.

[0071] The present invention analyzes the motion feature representation of the target object through motion feature analysis. In actual situations, the basic motion development speed of the target object is relatively slow, and there will be problems of limb movement incoordination and movement disorders. By observing the limb movement amplitude of the target object when it produces communication movements, for example, the limb amplitude presented by the subject who needs to receive diagnosis and treatment will be relatively small. This situation may be related to motor skill disorders. These disorders usually manifest as early motor lag, coordination disorders, physical decline and visual-motor integration disorders. At the same time, the target object requires a longer reaction time when communicating or needing to make a corresponding response. This situation may be related to its social interaction and communication barriers. They need more time to process and respond to social signals. Therefore, the present application analyzes the motion feature representation of the target object through the limb movement amplitude and reaction time when the target object produces communication movements to characterize the degree of feedback that the target object can give when dealing with interaction and communication, and provide data support for the subsequent determination of the target object's motion feature feedback degree, so as to adaptively perform auxiliary diagnosis and treatment analysis, thereby ensuring the accuracy and reliability of the data analysis results, providing auxiliary diagnosis and treatment data support for doctors, and improving the efficiency of diagnosis and treatment.

[0072] Specifically, in step S2, determining the target object's motion feature feedback degree includes:

[0073] If the motion feature representation value is greater than or equal to the motion feature representation value threshold, the motion feature of the target object is determined to be at a weak feedback level;

[0074] If the motion feature representation amount is less than the motion feature representation amount threshold, it is determined that the motion feature of the target object is at a strong feedback level.

[0075] The action feature characterization threshold S0 is selected in the interval [1.12, 1.23].

[0076] Specifically, in step S3, auxiliary diagnosis and treatment analysis is performed based on the feedback degree of the target object's motion characteristics, including:

[0077] If the target subject's motion characteristics are at a weak feedback level, the image acquisition accuracy of the image acquisition device for the target subject is adjusted, and the video recording of the target subject's review case is called. Based on the video recording, the emotional gaze characteristics, the amplitude of the body movement, and the change ratio of the feature matching parameters relative to the pre-detection are identified, and compared with the expected control data determined based on the pre-stored intervention diagnosis and treatment plan to determine whether the feature matching criteria are met;

[0078] If the action feature of the target object is a strong feedback level, a recommended number of reviews is generated based on the action feature representation.

[0079] Specifically, in step S3, adjusting the image acquisition accuracy of the image acquisition device for the target object includes:

[0080] Improve the resolution and maximum frame rate of the image acquisition device.

[0081] It is understandable that during the pre-detection process, conventional high-definition resolution and conventional maximum frame rate can be used for image acquisition, where the conventional high-definition resolution is 720p (1280×720 pixels) and the conventional maximum frame rate is 30Fps. During the review of target objects with weak feedback characteristics, the resolution and maximum frame rate of the image acquisition device can be increased to 1440p (2560×1440 pixels) and the maximum frame rate can be increased to 60~120Fps in order to observe the reactions and movements of the target objects more clearly and meticulously.

[0082] Specifically, in step S3, the feature matching parameters are determined by combining the ratio of the emotional gaze sub-features with the body movement amplitude, including:

[0083] obtaining a video record captured by an image acquisition device to determine emotional gaze sub-features and body movement amplitudes;

[0084] The ratio of the eye area gaze duration to the mouth area gaze duration is used as the ratio of the emotional gaze sub-feature and as the first feature;

[0085] Calculating the ratio of the limb movement amplitude to the expected threshold of the limb movement amplitude as the second feature;

[0086] The sum of the first feature and the second feature is determined as a feature matching parameter.

[0087] In this embodiment, the feature matching parameters are determined according to the following formula:

[0088]

[0089] Where P represents the feature matching parameter, E represents the gaze duration of the eye area, Y represents the gaze duration of the mouth area, F represents the limb movement amplitude, and Fe represents the expected threshold of the limb movement amplitude.

[0090] Among them, the eye area gaze duration E and the mouth area gaze duration Y are obtained based on the video information collected by the image acquisition device during the target object review, and Fe is pre-set, and Fe=F0×1.15 is set.

[0091] Specifically, in step S3, the process of identifying the emotional gaze features, body movement amplitude, and the change ratio of the feature matching parameters relative to the pre-detection based on the video record, and comparing them with the expected control data determined based on the pre-stored intervention diagnosis and treatment plan includes:

[0092] Determine the change ratio of emotional gaze features, the change ratio of body movement amplitude, and the change ratio of feature matching parameters;

[0093] Determining the expected emotional gaze feature change ratio, expected limb movement amplitude change ratio, and expected feature matching parameter change ratio corresponding to the intervention diagnosis and treatment plan;

[0094] comparing the emotion gaze feature change ratio with an expected emotion gaze feature change ratio;

[0095] comparing the limb movement amplitude change ratio with the expected limb movement amplitude change ratio;

[0096] comparing the feature matching parameter change ratio with an expected feature matching parameter change ratio;

[0097] It can be understood that the change ratio is the ratio of the change amount to the initial amount;

[0098] The emotional gaze feature change ratio is the average of the eye gaze feature change ratio and the mouth gaze feature change ratio.

[0099] Specifically, in step S3, the determination of whether the feature matching standard is met includes:

[0100] If the expected matching conditions are met, it is determined that the feature matching criteria are met;

[0101] The expected matching conditions include that the feature matching parameter change ratio is less than the expected feature matching parameter change ratio, the emotional gaze feature change ratio is less than the expected emotional gaze feature change ratio, and the limb movement amplitude change ratio is less than the expected limb movement amplitude change ratio.

[0102] Specifically, in step S3, generating the recommended number of review times based on the action feature representation includes:

[0103] The number of recommended reviews is positively correlated with the action feature representation quantity.

[0104] In this embodiment, optionally,

[0105] Compare the motion feature characterization value S with the first motion feature characterization value comparison threshold S1 and the second motion feature characterization value comparison threshold S2,

[0106] If S>S2, the first recommended number of re-examinations is determined to be c1, and c1=4c0;

[0107] If S1≤S≤S2, the second recommended number of re-examinations is determined to be c2, and c2=2c0 is set;

[0108] If S<S1, the accuracy improvement value is determined to be the third accuracy improvement value c3, and c3 is set to [1.5c0];

[0109] Wherein, c0 represents the number of baseline review, S1=1.2S0, S2=1.4S0.

[0110] It is understandable that the number of baseline reviews is determined based on historical prior data and will not be further elaborated here.

[0111] The present invention selects different auxiliary diagnosis and treatment analysis methods according to the different feedback levels of the action characteristics presented by the target object, including: when the action characteristics of the target object are at a weak feedback level, the present invention pre-adjusts the accuracy of the video recording of the target object's review and treatment process, improves the resolution and maximum frame rate of image acquisition, and conducts a detailed observation of the relevant key features of the target object, and then extracts the emotional gaze sub-features and limb movement amplitude presented by the target object during interaction and communication in the high-precision video image to determine the feature matching parameters. Therefore, the present application uses feature matching parameters to characterize the changes in the situation and the degree of improvement of the target object after a certain period of time after the pre-detection adopts the intervention diagnosis and treatment plan, and performs an overall analysis based on the feature matching parameters while considering the analysis of the various detailed features targeted by each sub-feature, not only judging the intervention diagnosis and treatment plan as a whole Whether the changes in the case after a certain period of time match the expected degree of change in diagnosis and treatment, and the degree of matching, and by specifically analyzing the changes in each sub-feature and the matching of the sub-feature data corresponding to the expected changes in diagnosis and treatment, the degree of matching of specific features can be relatively accurately identified, and auxiliary support can be provided for the subsequent modification of targeted diagnosis and treatment plans. The gaze characteristics of the target object are considered in combination with further analysis of the amplitude characteristics of limb movements to improve the accuracy and reliability of auxiliary diagnosis and treatment; when the movement characteristics of the target object are at a strong feedback level, the abnormal tendency of the movement characteristics of the target object is slight. By observing the movement characteristics of the target object and analyzing the data provided by the movement characteristic representation, the data evaluation method is comprehensively and adaptively adjusted. The data analysis results are accurate and reliable, which can provide doctors with auxiliary diagnosis and treatment data support and improve the efficiency of diagnosis and treatment.

[0112] Specifically, the step S3 also includes pre-storing the expected control data of each intervention treatment plan, including:

[0113] Expected feature matching parameter change ratio, expected emotional gaze feature change ratio, and expected limb movement amplitude change ratio.

[0114] It is understood that the expected control data for each intervention treatment plan are determined based on historical prior data, including:

[0115] Record the test data of several target subjects after treatment with different types of intervention treatment plans, and classify the test data corresponding to the same intervention treatment plan.

[0116] Calculate the mean change ratio of the feature matching parameters from the implementation of the intervention treatment plan to the review in the test data as the expected feature matching parameter change ratio;

[0117] Calculate the mean change ratio of emotional gaze features from the implementation of the intervention treatment plan to the review period in the test data as the expected change ratio of emotional gaze features;

[0118] Calculate the mean change ratio of limb movement amplitude from the implementation of the intervention treatment plan to the reexamination in the test data as the expected change ratio of limb movement amplitude;

[0119] The expected feature matching parameter change ratio, the expected emotional gaze feature change ratio, and the expected limb movement amplitude change ratio are determined as the expected control data corresponding to the intervention treatment plan.

[0120] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. An analysis method for auxiliary diagnosis and treatment of autistic children, characterized in that: include: Step S1, performing a pre-detection on a target object and storing detection data for the target object, wherein the pre-detection includes setting an image acquisition device to detect movement characteristics of the target object when performing a communication movement, wherein the movement characteristics include body movement amplitude and reaction time; Step S2, analyzing the motion feature representation of the target object based on the motion feature to determine the motion feature feedback degree of the target object; Step S3, based on the feedback degree of the target object's motion characteristics, performs auxiliary diagnosis and treatment analysis, including: If the target subject's motion characteristics are at a weak feedback level, the image acquisition accuracy of the image acquisition device for the target subject is adjusted, and the video recording of the target subject's review case is called. Based on the video recording, the emotional gaze characteristics, the amplitude of the body movement, and the change ratio of the feature matching parameters relative to the pre-detection are identified, and compared with the expected control data determined based on the pre-stored intervention diagnosis and treatment plan to determine whether the feature matching criteria are met; If the target object's action feature is a strong feedback level, generating a recommended number of reviews based on the action feature representation; The feature matching parameter is determined by combining the ratio of the emotional gaze sub-features with the amplitude of the body movement, wherein the emotional gaze features include the gaze duration of the eye area and the gaze duration of the mouth area; Step S4, outputting the auxiliary diagnosis and treatment analysis results through the output terminal; In the step S2, the motion feature representation of the target object is analyzed based on the motion feature according to formula (1), including: In formula (1), S represents the motion feature representation, F represents the limb movement amplitude, F0 represents the limb movement amplitude threshold, T represents the reaction time, T0 represents the reaction time threshold, α represents the limb movement amplitude weight coefficient, and β represents the reaction time weight coefficient; In step S2, determining the target object's motion feature feedback degree includes: If the motion feature representation value is greater than or equal to the motion feature representation value threshold, the motion feature of the target object is determined to be at a weak feedback level; If the motion feature representation amount is less than the motion feature representation amount threshold, it is determined that the motion feature of the target object is at a strong feedback level.

2. The analysis method for auxiliary diagnosis and treatment of autistic children according to claim 1, characterized in that: In step S3, the image acquisition accuracy of the image acquisition device for the target object is adjusted. include, Improve the resolution and maximum frame rate of the image acquisition device.

3. The analysis method for auxiliary diagnosis and treatment of autistic children according to claim 1, characterized in that: In step S3, the feature matching parameters are determined by combining the ratio of the emotional gaze sub-features with the body movement amplitude, including: obtaining a video record captured by an image acquisition device to determine emotional gaze sub-features and body movement amplitudes; The ratio of the eye area gaze duration to the mouth area gaze duration is used as the ratio of the emotional gaze sub-feature and as the first feature; Calculating the ratio of the limb movement amplitude to the expected threshold of the limb movement amplitude as the second feature; The sum of the first feature and the second feature is determined as a feature matching parameter.

4. The analysis method for auxiliary diagnosis and treatment of autistic children according to claim 3, characterized in that: In step S3, the process of identifying the emotional gaze features, body movement amplitude, and the change ratio of the feature matching parameters relative to the pre-detection based on the video record and comparing them with the expected control data determined based on the pre-stored intervention diagnosis and treatment plan includes: Determine the change ratio of emotional gaze features, the change ratio of body movement amplitude, and the change ratio of feature matching parameters; Determining the expected emotional gaze feature change ratio, expected limb movement amplitude change ratio, and expected feature matching parameter change ratio corresponding to the intervention diagnosis and treatment plan; comparing the emotion gaze feature change ratio with an expected emotion gaze feature change ratio; comparing the limb movement amplitude change ratio with the expected limb movement amplitude change ratio; The feature matching parameter change ratio is compared with an expected feature matching parameter change ratio.

5. The analysis method for auxiliary diagnosis and treatment of autistic children according to claim 4, characterized in that: In step S3, the determination of whether the feature matching standard is met includes: If the expected matching conditions are met, it is determined that the feature matching criteria are met; The expected matching conditions include that the feature matching parameter change ratio is less than the expected feature matching parameter change ratio, the emotional gaze feature change ratio is less than the expected emotional gaze feature change ratio, and the limb movement amplitude change ratio is less than the expected limb movement amplitude change ratio.

6. The analysis method for auxiliary diagnosis and treatment of autistic children according to claim 1, characterized in that: In step S3, generating a recommended number of review times based on the action feature representation includes: The number of recommended reviews is positively correlated with the action feature representation quantity.

7. The analysis method for auxiliary diagnosis and treatment of autistic children according to claim 1, characterized in that: The step S3 also includes pre-storing the expected control data of each intervention treatment plan, including: Expected feature matching parameter change ratio, expected emotional gaze feature change ratio, and expected limb movement amplitude change ratio.

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