A method for cough training in children

Abstract

The present invention relates to the field of artificial intelligence technology, and in particular to a method for children's cough training, comprising: obtaining audio information and displacement change characteristics, determining a displacement characteristic time domain segment and a sound characteristic time domain segment, screening audio information, and analyzing whether a target cough is qualified; in the present invention, the chest cavity fluctuation condition is determined based on the displacement change amplitude and displacement change rate of a target chest cavity part, the time interval of a suspected patient's cough is determined based on the acquired sound signal frequency and sound signal amplitude, and the time period of the target chest cavity fluctuation condition and the sound for the target cough is further determined. Considering that the sound collection environment is difficult to unify in actual situations and that environmental noise will affect the subsequent analysis of target cough information, the present invention comprehensively analyzes the target cough condition based on the collected sound signal and the chest cavity fluctuation condition, thereby improving the accuracy of the analysis of the target cough condition.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method for children's cough training. Background Art

[0002] Cough training during pulmonary rehabilitation helps clear the airway and can help with expectoration. This is especially true for pediatric patients, as different pediatric patients tend to have inconsistent descriptions of their coughing conditions, which affects the progress of cough training. On the other hand, it is difficult to control the coughing strength of pediatric patients, making it difficult to determine the quality of cough training. The existing technology extracts samples from sound sample signals and electromyographic sample signals, and compares the extracted sample signals with the valid sample signals to determine the target cough detection model. However, it does not consider the influence of ambient noise during the target coughing process, resulting in inaccurate target cough data. The target cough strength is not analyzed, and insufficient cough strength leads to unqualified target coughs, which will affect cough training.

[0003] Chinese Patent Publication No. CN114446319A discloses a cough detection model training method, a cough detection method, and a cough detection device. The method includes: obtaining a user's cough sample signal and a signal label corresponding to the cough sample signal, wherein the cough sample signal includes a sound sample signal and an electromyographic sample signal, and the signal label is used to indicate the cough state corresponding to the cough sample signal; performing effective sample extraction on the sound sample signal and the electromyographic sample signal to obtain an effective cough sample signal corresponding to the cough sample signal; performing feature extraction on the effective cough sample signal to obtain a peak feature, a Mel-frequency cepstral coefficient feature, and an energy feature corresponding to the cough sample signal; and training a cough detection model based on the peak feature, the Mel-frequency cepstral coefficient feature, the energy feature, and the signal label to obtain a target cough detection model.

[0004] However, the prior art still has the following problems:

[0005] The influence of surrounding noise during the target coughing process was not considered, resulting in inaccurate target cough data. The target cough strength was not analyzed. Insufficient cough strength resulted in unqualified target coughs, which would affect cough training. Summary of the Invention

[0006] To this end, the present invention provides a method for children's cough training to overcome the problems in the prior art that the influence of surrounding noise during the target cough process is not considered, resulting in inaccurate target cough data being obtained, and the target cough strength is not analyzed. The insufficient cough strength leads to unqualified target coughs, which affects cough training.

[0007] To achieve the above objectives, the present invention provides a method for children's cough training, comprising:

[0008] Step S1: The target wears a monitoring module equipped with an acoustic sensor and an inertial sensor, and obtains audio information during a predetermined period of time while the target wears the monitoring module and displacement change characteristics of the location where the monitoring module is set, wherein the monitoring module is set at the target's chest area;

[0009] Step S2: determining a displacement change characterization parameter based on the displacement change amplitude and displacement change rate of the displacement change feature in different time domain segments, determining a displacement feature time domain segment in the displacement change feature based on the displacement change characterization parameter, determining a sound characterization parameter based on the sound signal frequency and sound signal amplitude of the audio information, and determining a sound feature time domain segment in the audio information based on the sound characterization parameter;

[0010] Step S3, filtering audio information during the target wearing process during a predetermined time period based on the overlapping time domain segments of the displacement feature time domain segment and the sound feature time domain segment, and verifying the filtered audio information;

[0011] Step S4: determining a qualified characterization parameter based on the displacement change characterization parameter and the sound characterization parameter, and analyzing the verified audio information according to the qualified characterization parameter to determine whether the target cough is qualified.

[0012] Furthermore, determining the displacement change characterization parameter includes:

[0013] Solve the ratio of the displacement change amplitude of the current time domain segment to the preset displacement change amplitude standard threshold to obtain the first displacement change feature,

[0014] Solve the ratio of the displacement change rate of the current time domain segment to the preset displacement change rate standard threshold to obtain the second displacement change feature,

[0015] The first displacement change feature and the second displacement change feature are weighted and summed to obtain a displacement change characterization parameter.

[0016] Furthermore, determining the displacement feature time domain segment in the displacement change feature according to the displacement change characterization parameter includes:

[0017] If the displacement change characterization parameter is greater than or equal to the displacement change characterization parameter standard threshold, determining that the current time domain segment is a displacement characteristic time domain segment;

[0018] If the displacement change characterization parameter is less than the displacement change characterization parameter standard threshold, the current time domain segment is determined to be a non-displacement feature time domain segment.

[0019] Furthermore, determining the sound characterization parameters includes:

[0020] Solve the ratio of the sound signal frequency of the current time domain segment of the audio information to the preset sound signal frequency standard threshold to obtain the first sound feature,

[0021] Solve the ratio of the sound signal amplitude of the current time domain segment of the audio information to a preset sound signal amplitude standard threshold to obtain a second sound feature,

[0022] The first sound feature and the second sound feature are weighted and summed to obtain a sound characterization parameter.

[0023] Furthermore, determining the sound feature time domain segment in the audio information based on the sound characterization parameter includes:

[0024] If the sound characterization parameter is greater than or equal to the sound characterization parameter standard threshold, determining that the current time domain segment is a sound feature time domain segment;

[0025] If the sound characterization parameter is less than the sound characterization parameter standard threshold, the current time domain segment is determined to be a non-sound feature time domain segment.

[0026] Furthermore, the filtering of audio information during the target wearing process in a predetermined time period based on the overlapping time domain segments of the displacement feature time domain segments and the sound feature time domain segments includes:

[0027] Determine the overlapping time domain segment of the displacement feature time domain segment and the sound feature time domain segment,

[0028] Filter out the overlapping time domain segments from the audio information during the target wearing process.

[0029] Furthermore, the verification of the filtered audio information includes:

[0030] If the sound signal strength of a single overlapping time domain segment of the filtered audio information is greater than or equal to a preset sound signal strength standard threshold, the overlapping time domain segment is determined to be a non-target cough time domain segment;

[0031] If the sound signal strength of a single overlapping time domain segment of the filtered audio information is less than the preset sound signal strength standard threshold, the overlapping time domain segment is determined to be a target cough time domain segment.

[0032] Furthermore, determining the qualified characterization parameter based on the displacement change characterization parameter and the sound characterization parameter includes:

[0033] Solve the ratio of the displacement change characterization parameter of the target cough time domain segment to a preset displacement change characterization parameter standard threshold to obtain a first characterization feature,

[0034] Solve the ratio of the sound characterization parameter of the target cough time domain segment to the preset sound characterization parameter standard threshold to obtain a second characterization feature,

[0035] The qualified characterization parameter is obtained by weighted summing the first characterization feature and the second characterization feature.

[0036] Furthermore, analyzing the verified audio information according to the qualified characterization parameters to determine whether the target cough is qualified includes:

[0037] If the qualified characterization parameter of the audio information that has passed the verification is greater than or equal to the preset qualified characterization parameter standard threshold, then the target cough is determined to be qualified;

[0038] If the qualified characterization parameter of the verified audio information is less than the preset qualified characterization parameter standard threshold, the target cough is determined to be unqualified.

[0039] Furthermore, a qualified signal is issued when it is determined that the target cough is qualified.

[0040] Compared with the prior art, the present invention has the advantage of comprehensively determining the chest rise and fall condition based on the displacement change amplitude and displacement change rate of the target chest region, thereby preliminarily determining the suspected coughing time period of the patient based on the chest rise and fall condition. In actual situations, the suspected coughing time period determined based on the target chest rise and fall condition may contain other conditions, such as rapid breathing. The preliminarily determined suspected coughing time period is based on a comprehensive analysis of the patient and surrounding environmental sounds based on the acquired sound signal frequency and amplitude. In actual situations, the suspected coughing time period determined based on the sound signal may contain external noise, such as the coughing sounds of other targets. Given that external conditions such as the sound collection environment are difficult to uniform in actual situations, environmental noise can affect the subsequent analysis of the target cough information. If noise is present during the target coughing time period, it can affect the determination of the target cough condition. Therefore, the present invention further determines the suspected coughing time period based on the target chest rise and fall condition and the collected sound. The overlapping portion of the time period determined based on the chest rise and fall condition and the sound is determined as the time period containing the patient's coughing sound, thereby improving the accuracy of determining the target coughing time period.

[0041] In particular, the present invention preliminarily determines the time domain segment of the suspected target cough based on the overlapping part of the displacement feature time domain segment and the sound feature time domain segment. The sound signal intensity of the patient's cough in the absence of noise is different from the sound signal intensity in the presence of ambient noise. The present invention obtains the sound signal intensity of the determined time domain segment of the suspected target cough, and compares the sound signal intensity of the time domain segment with the sound signal intensity of a single person coughing in a quiet environment in historical records, so as to screen out the time period containing ambient noise and screen out the time domain segment of the target cough in the collected audio information, further narrowing the scope of the time period to be analyzed and improving the work efficiency of subsequent analysis of the patient's coughing condition.

[0042] In particular, the present invention uses the sound data parameters of the patient's coughing time period that are screened out to screen the collected audio information, so as to avoid analyzing each time period of the audio information, thereby improving the screening efficiency of the patient's coughing time period in the audio information. In actual situations, if the patient's coughing strength is too low, it will affect the expected effect of cough training. The present invention calculates the data characterization parameters of the patient's coughing time period, and compares the characterization parameters with the data characterization parameters of qualified cough data in historical data to ensure the qualification of the patient's cough, thereby further ensuring the effect of the patient's cough training. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of the method for children's cough training of the present invention;

[0044] Figure 2 A flow chart for determining a time domain segment of a displacement feature in a displacement change feature according to the present invention;

[0045] Figure 3 A flow chart for determining a time domain segment of a sound feature in audio information according to the present invention;

[0046] Figure 4 This is a flow chart of the determination of verifying the filtered audio information according to the present invention. DETAILED DESCRIPTION

[0047] 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.

[0048] It should be pointed out that the data in this embodiment are obtained by comprehensive analysis and evaluation of the historical data of the six months before this judgment and the corresponding historical judgment results of the system of the present invention. Before this test, the system of the present invention comprehensively determines the values ​​of the various preset parameter standards for this judgment based on the evaluation values ​​of the 37,383 retrieval results detected cumulatively in the first three months. It can be understood by those skilled in the art that the system of the present invention can determine the single parameter mentioned above by selecting the value with the highest proportion as the preset standard parameter based on the data distribution, using weighted summation to use the obtained value as the preset standard parameter, substituting each historical data into a specific formula and using the value obtained by the formula as the preset standard parameter or other selection methods, as long as the system of the present invention can clearly define the different specific situations in the single judgment process through the obtained values.

[0049] 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.

[0050] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0051] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0052] See also Figure 1 As shown, it is a flow chart of the method for children's cough training of the present invention.

[0053] An embodiment of the present invention provides a method for cough training for children, comprising:

[0054] Step S1: The target wears a monitoring module equipped with an acoustic sensor and an inertial sensor, and obtains audio information during a predetermined period of time while the target wears the monitoring module and displacement change characteristics of the location where the monitoring module is set, wherein the monitoring module is set at the target's chest area;

[0055] Step S2: determining a displacement change characterization parameter based on the displacement change amplitude and displacement change rate of the displacement change feature in different time domain segments, determining a displacement feature time domain segment in the displacement change feature based on the displacement change characterization parameter, determining a sound characterization parameter based on the sound signal frequency and sound signal amplitude of the audio information, and determining a sound feature time domain segment in the audio information based on the sound characterization parameter;

[0056] Step S3, filtering audio information during the target wearing process during a predetermined time period based on the overlapping time domain segments of the displacement feature time domain segment and the sound feature time domain segment, and verifying the filtered audio information;

[0057] Step S4: determining a qualified characterization parameter based on the displacement change characterization parameter and the sound characterization parameter, and analyzing the verified audio information according to the qualified characterization parameter to determine whether the target cough is qualified.

[0058] Specifically, determining the displacement change characterization parameter includes:

[0059] Solve the ratio of the displacement change amplitude of the current time domain segment to the preset displacement change amplitude standard threshold to obtain the first displacement change feature,

[0060] Solve the ratio of the displacement change rate of the current time domain segment to the preset displacement change rate standard threshold to obtain the second displacement change feature,

[0061] The first displacement change feature and the second displacement change feature are weighted and summed to obtain a displacement change characterization parameter.

[0062] Specifically, in this embodiment, the preset displacement change amplitude standard threshold W0 is obtained in advance, and the displacement change amplitudes of the chest cavity when several different target coughs are qualified are recorded, and the average displacement change amplitude △W is solved, and W0 is set to α×△W, where α represents the first accuracy coefficient, 0.75<α<0.85.

[0063] Specifically, in this embodiment, the preset displacement change rate standard threshold B0 is obtained in advance, and the displacement change rates of the chest cavity when several different target coughs are qualified are recorded to solve the average displacement change rate △B, and set B0 = β×△B, β represents the second accuracy coefficient, 0.7<β<0.8.

[0064] Specifically, the weight coefficient w of the first displacement change feature is selected in the interval [0.3, 0.4], the weight coefficient b of the second displacement change feature is selected in the interval [0.6, 0.7], and w+b=1.

[0065] See also Figure 2 As shown in FIG, it is a flow chart for determining the time domain segment of the displacement feature in the displacement change feature.

[0066] Specifically, determining the displacement feature time domain segment in the displacement change feature according to the displacement change characterization parameter includes:

[0067] If the displacement change characterization parameter is greater than or equal to the displacement change characterization parameter standard threshold, determining that the current time domain segment is a displacement characteristic time domain segment;

[0068] If the displacement change characterization parameter is less than the displacement change characterization parameter standard threshold, the current time domain segment is determined to be a non-displacement feature time domain segment.

[0069] Specifically, in this embodiment, the standard threshold value of the displacement change characterization parameter is selected within the interval [0.8, 1.2].

[0070] Specifically, determining the sound characterization parameters includes:

[0071] Solve the ratio of the sound signal frequency of the current time domain segment of the audio information to the preset sound signal frequency standard threshold to obtain the first sound feature,

[0072] Solve the ratio of the sound signal amplitude of the current time domain segment of the audio information to a preset sound signal amplitude standard threshold to obtain a second sound feature,

[0073] The first sound feature and the second sound feature are weighted and summed to obtain a sound characterization parameter.

[0074] Specifically, in this embodiment, the preset sound signal frequency standard threshold Q0 is obtained by pre-measurement, and the sound signal frequencies of several different target coughs are recorded when they are qualified. The mean value △Q of the sound signal frequencies is solved, and Q0 is set to γ×△Q, where γ represents the third accuracy coefficient, 0.7<γ<0.85.

[0075] Specifically, in this embodiment, the preset sound signal amplitude standard threshold Z0 is obtained by pre-measurement, and the sound signal amplitudes of several different target coughs are recorded when they are qualified. The mean value △Z of the sound signal amplitude is solved, and Z0 is set to z×△Z, where z represents the fourth accuracy coefficient, 0.75<z<0.8.

[0076] See also Figure 3 As shown, it is a flow chart for determining the time domain segment of sound features in audio information.

[0077] Specifically, determining the sound feature time domain segment in the audio information based on the sound characterization parameter includes:

[0078] If the sound characterization parameter is greater than or equal to the sound characterization parameter standard threshold, determining that the current time domain segment is a sound feature time domain segment;

[0079] If the sound characterization parameter is less than the sound characterization parameter standard threshold, the current time domain segment is determined to be a non-sound feature time domain segment.

[0080] Specifically, in this embodiment, the standard threshold of the sound characterization parameter is selected in the interval [0.85, 1.1].

[0081] Specifically, the filtering of audio information during the target wearing process in a predetermined time period based on the overlapping time domain segment of the displacement feature time domain segment and the sound feature time domain segment includes:

[0082] Determine the overlapping time domain segment of the displacement feature time domain segment and the sound feature time domain segment,

[0083] The audio information in each overlapping time domain segment is filtered out from the audio information during the target wearing process.

[0084] See also Figure 4As shown, it is a decision flow chart for verifying the filtered audio information.

[0085] Specifically, the verification of the filtered audio information includes:

[0086] If the sound signal strength of a single overlapping time domain segment of the filtered audio information is greater than or equal to a preset sound signal strength standard threshold, the overlapping time domain segment is determined to be a non-target cough time domain segment;

[0087] If the sound signal strength of a single overlapping time domain segment of the filtered audio information is less than the preset sound signal strength standard threshold, the overlapping time domain segment is determined to be a target cough time domain segment.

[0088] Specifically, in this embodiment, the preset sound signal strength standard threshold value P0 is obtained in advance, and the sound signal frequencies of several different target coughs are recorded when they are qualified. The average sound signal frequency △P is solved, and P0 is set to p×△P, where p represents the fifth precision coefficient, 0.75<p<0.8.

[0089] Specifically, determining the qualified characterization parameter based on the displacement change characterization parameter and the sound characterization parameter includes:

[0090] Solve the ratio of the displacement change characterization parameter of the target cough time domain segment to a preset displacement change characterization parameter standard threshold to obtain a first characterization feature,

[0091] Solve the ratio of the sound characterization parameter of the target cough time domain segment to the preset sound characterization parameter standard threshold to obtain a second characterization feature,

[0092] The qualified characterization parameter is obtained by weighted summing the first characterization feature and the second characterization feature.

[0093] Specifically, in this embodiment, the preset displacement change characterization parameter standard threshold value A0 is obtained in advance, and the displacement change characterization parameters when several different target coughs are qualified are calculated respectively, and the mean value △A of the displacement change characterization parameter is solved, and A0 is set to a×△A, where a represents the sixth accuracy coefficient, 0.75<a<0.8.

[0094] Specifically, in this embodiment, the preset sound characterization parameter standard threshold C0 is obtained in advance, and the sound characterization parameters of several different target coughs are calculated respectively when they are qualified, and the mean value △C of the sound characterization parameters is solved. It is set that C0=c×△C, where c represents the seventh precision coefficient, 0.7<c<0.8.

[0095] Specifically, in this embodiment, the weight coefficient x of the first characterizing feature is selected in the interval [0.5, 0.6], the weight coefficient y of the second characterizing feature is selected in the interval [0.4, 0.5], and x+y=1.

[0096] Specifically, analyzing the verified audio information according to the qualified characterization parameters to determine whether the target cough is qualified includes:

[0097] If the qualified characterization parameter of the audio information that has passed the verification is greater than or equal to the preset qualified characterization parameter standard threshold, then the target cough is determined to be qualified;

[0098] If the qualified characterization parameter of the verified audio information is less than the preset qualified characterization parameter standard threshold, the target cough is determined to be unqualified.

[0099] Specifically, in this embodiment, the preset qualified characterization parameter standard threshold is selected between the interval [1, 1.2].

[0100] Specifically, a qualified signal is issued when it is determined that the target cough is qualified.

[0101] 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.

[0102] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for cough training for children, characterized in that: include: Step S1: The target wears a monitoring module equipped with an acoustic sensor and an inertial sensor, and obtains audio information during a predetermined period of time while the target wears the monitoring module and displacement change characteristics of the location where the monitoring module is set, wherein the monitoring module is set at the target's chest area; Step S2: determining a displacement change characterization parameter based on the displacement change amplitude and displacement change rate of the displacement change feature in different time domain segments, determining a displacement feature time domain segment in the displacement change feature based on the displacement change characterization parameter, determining a sound characterization parameter based on the sound signal frequency and sound signal amplitude of the audio information, and determining a sound feature time domain segment in the audio information based on the sound characterization parameter; Step S3, filtering audio information during the target wearing process during a predetermined time period based on the overlapping time domain segments of the displacement feature time domain segment and the sound feature time domain segment, and verifying the filtered audio information; Step S4, determining a qualified characterization parameter based on the displacement change characterization parameter and the sound characterization parameter, and analyzing the verified audio information according to the qualified characterization parameter to determine whether the target cough is qualified; The verifying of the filtered audio information includes: If the sound signal strength of a single overlapping time domain segment of the filtered audio information is greater than or equal to a preset sound signal strength standard threshold, the overlapping time domain segment is determined to be a non-target cough time domain segment; If the sound signal strength of a single overlapping time domain segment of the filtered audio information is less than the preset sound signal strength standard threshold, the overlapping time domain segment is determined to be a target cough time domain segment; The determining of the qualified characterization parameter based on the displacement change characterization parameter and the sound characterization parameter includes: Solve the ratio of the displacement change characterization parameter of the target cough time domain segment to a preset displacement change characterization parameter standard threshold to obtain a first characterization feature, Solve the ratio of the sound characterization parameter of the target cough time domain segment to the preset sound characterization parameter standard threshold to obtain a second characterization feature, Obtaining a qualified characterization parameter by weighted summing of the first characterization feature and the second characterization feature; Determining the displacement change characterization parameter includes: Calculating the ratio of the displacement change amplitude in the current time domain segment to a preset displacement change amplitude standard threshold to obtain a first displacement change feature; Calculating the ratio of the displacement change rate in the current time domain segment to a preset displacement change rate standard threshold to obtain a second displacement change feature; A displacement change characterization parameter is obtained by weighted summing the first displacement change feature and the second displacement change feature; The step of determining the displacement feature time domain segment in the displacement change feature according to the displacement change characterization parameter includes: If the displacement change characterization parameter is greater than or equal to the displacement change characterization parameter standard threshold, determining that the current time domain segment is a displacement characteristic time domain segment; If the displacement change characterization parameter is less than the displacement change characterization parameter standard threshold, the current time domain segment is determined to be a non-displacement feature time domain segment.

2. The method for children's cough training according to claim 1, characterized in that: The determining of the sound characterization parameters includes: Solve the ratio of the sound signal frequency of the current time domain segment of the audio information to the preset sound signal frequency standard threshold to obtain the first sound feature, Solve the ratio of the sound signal amplitude of the current time domain segment of the audio information to a preset sound signal amplitude standard threshold to obtain a second sound feature, The first sound feature and the second sound feature are weighted and summed to obtain a sound characterization parameter.

3. The method for children's cough training according to claim 1, characterized in that: The determining of the sound feature time domain segment in the audio information based on the sound characterization parameter includes: If the sound characterization parameter is greater than or equal to the sound characterization parameter standard threshold, determining that the current time domain segment is a sound feature time domain segment; If the sound characterization parameter is less than the sound characterization parameter standard threshold, the current time domain segment is determined to be a non-sound feature time domain segment.

4. The method for children's cough training according to claim 1, characterized in that: The filtering of audio information during the wearing process of the target in a predetermined time period based on the overlapping time domain segment of the displacement feature time domain segment and the sound feature time domain segment includes: Determine the overlapping time domain segment of the displacement feature time domain segment and the sound feature time domain segment, The audio information in each overlapping time domain segment is filtered out from the audio information during the target wearing process.

5. The method for children's cough training according to claim 1, characterized in that: Analyzing the verified audio information according to the qualified characterization parameters to determine whether the target cough is qualified includes: If the qualified characterization parameter of the audio information that has passed the verification is greater than or equal to the preset qualified characterization parameter standard threshold, then the target cough is determined to be qualified; If the qualified characterization parameter of the verified audio information is less than the preset qualified characterization parameter standard threshold, the target cough is determined to be unqualified.

6. The method for children's cough training according to claim 5, characterized in that: When the target cough is judged to be qualified, a qualified signal is sent.

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