Hearing aid debugging method and system

Abstract

The invention relates to a hearing-aid debugging method and system, and the method comprises the steps: obtaining a reference parameter of a hearing-aid, applying the reference parameter to the hearing-aid, enabling the hearing-aid to have an initial reference value before the parameter adjustment, guaranteeing that the result is not deviated in the subsequent debugging process, and improving the debugging efficiency. The change conditions of the emotion balance index and the psychological pressure index when the debugger wears the hearing aid to carry out the hearing test are taken as the basis for adjusting the parameters of the hearing aid, so that feedback can be quickly obtained without language communication with the debugger, and the emotion level and the psychological pressure level when the debugger wears the hearing aid can be known in real time; and parameters of the hearing aid can be adjusted in time according to the information, so that the hearing feeling of a debugger is improved. In the hearing aid parameter adjustment process, the hearing aid parameters are subjected to coarse adjustment and fine adjustment in sequence through two different stride adjustment modes, the most comfortable hearing area of a debugged person can be approached at the fastest speed, and the accuracy of the finally obtained hearing aid parameters is improved.

Description

Technical Field

[0001] This application relates to the technical field of hearing aids, and particularly to a hearing aid debugging method and system. Background Art

[0002] The fitting process of digital hearing aids is as follows: First, use methods such as pure tone audiometry, acoustic immittance, and electrophysiology to judge the hearing loss condition of the customer, then preliminarily debug the parameters through intelligent fitting algorithms, and finally verify whether the hearing aid parameters are properly debugged through methods such as speech communication, sound field assessment, speech assessment, and real-ear measurement.

[0003] This detection process has a single scenario, strong immediacy, and poor continuity. Key steps such as pure tone audiometry are too subjective. The above factors will introduce test errors, resulting in inaccurate debugging data; the verification process can calibrate the debugging data, but this feedback mechanism is too single, and most elderly hearing loss customers have difficulty expressing clearly and it is difficult to cooperate with the fitter to complete accurate calibration. Therefore, there are problems of dissatisfaction after multiple and repeated debugging, or the situation that the hearing is good in the fitting institution but the use is poor in daily life.

[0004] In summary, the traditional hearing aid fitting process lacks real-time feedback steps and also lacks steps to adjust the hearing aid parameters in a timely manner according to user feedback, resulting in inaccurate hearing aid parameters.

[0005] To solve the above problems, an adaptive correction process needs to be added to the original fitting and verification processes. The present invention provides an adaptive hearing aid debugging method based on the assessment of the wearer's emotional stress state, which can objectively evaluate the user's usage and autonomously debug and optimize the data. Summary of the Invention

[0006] Based on this, it is necessary to provide a hearing aid debugging method and system for the problem that the traditional hearing aid fitting process lacks real-time feedback steps and also lacks steps to adjust the hearing aid parameters in a timely manner according to user feedback.

[0007] This application provides a hearing aid debugging method, and the hearing aid debugging method includes: Obtain the reference parameters of the hearing aid and apply the reference parameters of the hearing aid to the hearing aid; Adjust the hearing aid parameters by increasing them in the range of the first preset percentage of the current value, apply them to the hearing aid after adjustment, and conduct a hearing test on the sample; Obtain the emotional balance index and psychological stress index of the sample in real time during the hearing test on the sample, and judge whether both the emotional balance index and the psychological stress index increase during the hearing test; If both the emotional balance index and the psychological stress index increase during the hearing test, return to increase the hearing aid parameters by the magnitude of the first preset percentage of the current value. After the adjustment, apply it to the hearing aid and conduct a hearing test on the sample; If both the emotional balance index and the psychological stress index decrease during the hearing test, decrease the hearing aid parameters by the magnitude of the second preset percentage of the current value. After the adjustment, apply it to the hearing aid and conduct a hearing test on the sample; the second preset percentage is less than the first preset percentage; During the hearing test on the sample, obtain the emotional balance index and the psychological stress index of the sample in real time, and determine whether both the emotional balance index and the psychological stress index increase during the hearing test; If both the emotional balance index and the psychological stress index increase during the hearing test, return to decrease the hearing aid parameters by the magnitude of the second preset percentage of the current value. After the adjustment, apply it to the hearing aid and conduct a hearing test on the sample; If both the emotional balance index and the psychological stress index decrease during the hearing test, output the current value of the hearing aid; Write the current value of the hearing aid as the optimal hearing aid parameter into the hearing aid chip of the hearing aid.

[0008] This application also provides a hearing aid debugging system, including: A cloud server for executing the hearing aid debugging method mentioned in the foregoing content; A hearing aid communicatively connected to the cloud server.

[0009] The present application relates to a hearing aid debugging method and system. First, by obtaining the reference parameters of the hearing aid and applying the reference parameters of the hearing aid to the hearing aid, the hearing aid has an initial reference value before starting to adjust the parameters. This reference value can ensure that the result will not deviate during the subsequent debugging process and improve the debugging efficiency. Secondly, the changes in the emotional balance index and psychological stress index of the wearer during the hearing test with the hearing aid are used as the basis for adjusting the parameters of the hearing aid. Without the need for verbal communication with the wearer, the feedback of the wearer can be quickly obtained, and the obtained feedback is the emotional balance index and psychological stress index. Therefore, the solution of the present application can know the emotional level and psychological stress level of the wearer when wearing the hearing aid in real time and quickly based on the emotional balance index and psychological stress index, and can rely on this information to adjust the parameters of the hearing aid in a timely manner to improve the listening experience of the wearer. Finally, during the adjustment process of the hearing aid parameters, the hearing aid parameters are first roughly adjusted and then finely adjusted through two different adjustment amplitudes. Specifically, the hearing aid parameters are increased by a first preset percentage with a larger value until it is monitored that the emotional balance index and psychological stress index of the wearer decrease, and then the hearing aid parameters are decreased by a second preset percentage with a smaller value until the emotional balance index and psychological stress index of the wearer decrease, and the finally obtained optimal hearing aid parameters are output. The hearing aid debugging method provided by the present application can approach the most comfortable area of the wearer's listening experience at the fastest speed, so that the finally obtained optimal hearing aid parameters are highly accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 FIG. is a schematic flowchart of a hearing aid debugging method provided by an embodiment of the present application.

[0011] Figure 2 FIG. is a schematic structural diagram of a hearing aid debugging system provided by an embodiment of the present application.

[0012] REFERENCE SIGNS: 100 - cloud server; 200 - hearing aid. DETAILED DESCRIPTION

[0013] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] The present application provides a hearing aid debugging method. It should be noted that the hearing aid debugging method provided by the present application is applicable to hearing aids of any type, brand, and morphology.

[0015] In addition, the hearing aid debugging method provided in this application does not limit its execution subject. Optionally, the execution subject of the hearing aid debugging method provided in this application can be a hearing aid debugging system. Specifically, the execution subject of the hearing aid debugging method provided in this application can be the cloud server 100 in the hearing aid debugging system.

[0016] As Figure 1 shown, in an embodiment of this application, the hearing aid debugging method includes the following S100 to S800: S100, obtain the reference parameters of the hearing aid and apply the reference parameters of the hearing aid to the hearing aid.

[0017] S200, adjust the hearing aid parameters by increasing them by the magnitude of the first preset percentage of the current value, apply the adjusted parameters to the hearing aid after adjustment, and conduct a hearing test on the sample.

[0018] S300, obtain the emotional balance index and psychological stress index of the sample in real time during the hearing test on the sample, and determine whether both the emotional balance index and the psychological stress index increase during the hearing test.

[0019] S400, if both the emotional balance index and the psychological stress index increase during the hearing test, then return to the step of adjusting the hearing aid parameters by increasing them by the magnitude of the first preset percentage of the current value, apply the adjusted parameters to the hearing aid after adjustment, and conduct a hearing test on the sample.

[0020] S500, if both the emotional balance index and the psychological stress index decrease during the hearing test, then adjust the hearing aid parameters by decreasing them by the magnitude of the second preset percentage of the current value, apply the adjusted parameters to the hearing aid after adjustment, and conduct a hearing test on the sample; the second preset percentage is less than the first preset percentage.

[0021] S600, obtain the emotional balance index and psychological stress index of the sample in real time during the hearing test on the sample, and determine whether both the emotional balance index and the psychological stress index increase during the hearing test.

[0022] S700, if both the emotional balance index and the psychological stress index increase during the hearing test, then return to the step of adjusting the hearing aid parameters by decreasing them by the magnitude of the second preset percentage of the current value, apply the adjusted parameters to the hearing aid after adjustment, and conduct a hearing test on the sample.

[0023] S800, if both the emotional balance index and the psychological stress index decrease during the hearing test, then output the current value of the hearing aid.

[0024] S900, write the current value of the hearing aid as the optimal hearing aid parameters into the hearing aid chip of the hearing aid.

[0025] Specifically, in this application, the sample refers to the person to be debugged.

[0026] In S100, the reference parameter is the benchmark parameter for adjusting the hearing aid parameters. Without the reference parameter, subsequent parameter adjustments would be meaningless, easily deviating far from the user's true hearing comfort zone and reducing the debugging efficiency.

[0027] S200 to S400 are the coarse adjustment processes. It can be understood that during the coarse adjustment processes, we use a first preset percentage that is larger than the second preset percentage in terms of value. Optionally, the first preset percentage can be 10%.

[0028] S500 to S800 are the fine adjustment processes. It can be understood that during the fine adjustment processes, we use a second preset percentage that is smaller than the first preset percentage in terms of value. Optionally, the first preset percentage can be 5%.

[0029] Optionally, the current value of the hearing aid output by S800 can be written into the hearing aid chip of the hearing aid as the optimal hearing aid parameter.

[0030] The following details what the hearing aid parameters for increasing or decreasing the adjustment are exactly.

[0031] The hearing aid parameters are not just one parameter, but are composed of many complex parameters. For different types of hearing aids and different brands of hearing aids, the parameters included in the hearing aid parameters are also different.

[0032] The hearing aid parameters mainly include two major categories of parameters. One category is the hearing gain parameters, and the other category is the noise reduction parameters. The main function of the hearing gain parameters is to amplify the sound signal to make up for the user's hearing loss, while the noise reduction parameters are for the correction of noise.

[0033] Optionally, this application can perform S100 to S800 once on the hearing gain parameters. After determining the optimal parameters of the hearing gain parameters and writing the optimal parameters of the hearing gain parameters into the hearing aid chip, perform S100 to 800 on the noise reduction parameters again, so that the optimal hearing aid parameters can be obtained.

[0034] Optionally, the hearing aid parameters include but are not limited to the hearing gain at each frequency band, the noise reduction at each frequency band, the intensity of feedback suppression, and WDRC.

[0035] The hearing gain at each frequency band (Frequency - Specific Gain) refers to the amplification ability of the hearing aid for sound signals at different frequencies (such as low frequency, middle frequency, high frequency), aiming to compensate for the user's hearing loss at different frequency bands. For example, users with greater high - frequency hearing loss need a higher high - frequency gain. The unit of hearing gain is decibel (dB).

[0036] Noise Reduction by Frequency Band refers to the ability of a hearing aid to suppress background noise in different frequency bands. The hearing aid can identify and reduce noise in non-speech frequency bands (such as the sound of a fan, traffic noise) through algorithms while retaining the speech signal. The unit of noise reduction can be decibels (dB) or percentage (%). When using decibels as the unit, noise reduction represents the absolute value of the reduced noise, such as 15 dB. When using percentage as the unit, noise reduction represents the intensity level of noise suppression. For example, 80% represents the maximum noise suppression ability. Noise reduction can improve the signal-to-noise ratio of the hearing aid and enhance speech clarity when the wearer is in a noisy environment.

[0037] The intensity of feedback suppression refers to the ability to prevent the hearing aid from whistling due to sound feedback. Feedback suppression can cancel the signal oscillation in the feedback path (such as sound leakage from the ear mold). The unit of feedback suppression is level (low, medium, high) or decibels (dB). When the unit of feedback suppression is level, it represents the aggressiveness of the suppression algorithm. When the unit of feedback suppression is decibels, it accurately represents the amount of gain reduction of the system for the feedback frequency.

[0038] WDRC (Wide Dynamic Range Compression) is a non-linear amplification technology that provides high gain for weak sounds and low gain for strong sounds, enabling sounds of different intensities to fall within the audible dynamic range of the wearer.

[0039] Sub-parameters and units of WDRC: Compression Ratio: The ratio of input / output intensity (such as 2:1), without unit.

[0040] Threshold: The sound pressure level that triggers compression, with the unit of dB SPL (such as 40 dB SPL).

[0041] Attack Time: The delay in starting compression after detecting a strong signal, with the unit of milliseconds (ms), such as 5 ms.

[0042] Release Time: The delay in resuming amplification after the signal weakens, with the unit of milliseconds (ms), such as 100 ms).

[0043] WDRC can improve the audibility of weak sounds and avoid discomfort to the wearer caused by strong sounds.

[0044] The parameters of a hearing aid directly affect its hearing aid effect. Appropriate hearing aid parameters can significantly improve the listening experience of the person being adjusted, enhance their emotional balance index and psychological stress index. Conversely, unreasonable hearing aid parameters can cause various discomforts to the person being adjusted, leading to a resistant mentality towards the hearing aid and reducing their emotional balance index and psychological stress index. For example, the person being adjusted cannot hear clearly, cannot communicate with the test sound with interactive nature, feels overly noisy, or feels that the noise is too loud, etc.

[0045] In this embodiment, first, by obtaining the reference parameters of the hearing aid and applying the reference parameters of the hearing aid to the hearing aid, an initial reference value is provided for the hearing aid before starting to adjust the parameters. This reference value can ensure that the result will not deviate during the subsequent debugging process and improve the debugging efficiency. Second, taking the changes in the emotional balance index and psychological stress index of the person being adjusted when wearing the hearing aid during a hearing test as the basis for adjusting the hearing aid parameters, feedback from the person being adjusted can be quickly obtained without verbal communication with the person being adjusted, and the feedback obtained is the emotional balance index and psychological stress index. Therefore, the solution of this application can know the emotional level and psychological stress level of the person being adjusted when wearing the hearing aid in real time and quickly based on the emotional balance index and psychological stress index, and can adjust the hearing aid parameters in a timely manner relying on this information to improve the listening experience of the person being adjusted. Finally, during the adjustment process of the hearing aid parameters, the hearing aid parameters are first roughly adjusted and then finely adjusted through two different adjustment amplitudes. Specifically, first, the hearing aid parameters are increased by adjusting with a step amplitude of a first preset percentage with a larger value until it is monitored that the emotional balance index and psychological stress index of the person being adjusted decrease, and then the hearing aid parameters are decreased by adjusting with an amplitude of a second preset percentage with a smaller value until the emotional balance index and psychological stress index of the person being adjusted decrease, and the final optimal hearing aid parameters are output. The hearing aid debugging method improved in this application can approach the most comfortable area of the listening experience of the person being adjusted at the fastest speed, making the final obtained optimal hearing aid parameters highly accurate.

[0046] In an embodiment of this application, before S100, that is, before obtaining the reference parameters of the hearing aid and applying the reference parameters of the hearing aid to the hearing aid, the hearing aid debugging method further includes the following S010 to S020: S010, determine whether the hearing aid is being debugged for the first time.

[0047] S020, if the hearing aid is being debugged for the first time, retrieve the fitter's debugging parameters and use a preset percentage of the fitter's debugging parameters as the reference parameters of the hearing aid.

[0048] Specifically, optionally, before knowing the hearing aid debugging method described in this application, a fitting specialist debugging parameter library can be established in advance. The fitting specialist debugging parameter library stores user profile files and the corresponding fitting specialist debugging parameters for the user profile files. The fitting specialist debugging parameters are the initial hearing aid parameters generated by a hearing aid fitting specialist after personalized adjustment of various functional parameters of the hearing aid according to the user's hearing loss situation and the user's wearing experience. It is the initial hearing aid parameter generated by traditional fitting methods. There are many traditional fitting methods (see CN114827861A and CN205987370U for reference). The fitting method is not the focus of protection of this application, so it will not be explained in detail here. The user profile file includes the user's age, gender, hearing loss condition data, etc.

[0049] When retrieving the fitting specialist debugging parameters, all user profile files in the fitting specialist debugging parameter library can be traversed to search for the user profile file with the highest similarity to the sample's user profile file as the approximate user profile file, and finally retrieve the fitting specialist debugging parameters corresponding to the approximate user profile file as the sample's fitting specialist debugging parameters. There can be multiple calculation methods for similarity. Optionally, weights can be assigned to each sub-parameter of the user's age, gender, and hearing loss condition data respectively, and then the difference between the ages of the compared users is calculated and normalized to the [0, 1] interval. If the user genders are the same, it is normalized to 0; if the user genders are different, it is normalized to 1. The differences between the sub-parameters of the user's hearing loss condition data are calculated and normalized to the [0, 1] interval. Finally, the weights of each item of data are multiplied by the normalized values, and the sum of the weights of each item of data multiplied by the normalized values is used as the similarity value. The smaller the similarity value, the smaller the difference. The larger the similarity value, the larger the difference. We find the user profile file with the smallest similarity value to the sample as the approximate user profile file, and obtain the fitting specialist debugging parameters corresponding to the approximate user profile file as the sample's fitting specialist debugging parameters. Further, a preset percentage of the sample's fitting specialist debugging parameters is used as the benchmark parameters of the hearing aid.

[0050] Before S100, that is, before obtaining the benchmark parameters of the hearing aid and applying the benchmark parameters of the hearing aid to the hearing aid, the hearing aid debugging method further includes: S030, if the hearing aid is not being debugged for the first time, directly execute S100.

[0051] In this embodiment, by obtaining the benchmark parameters of the hearing aid and applying the benchmark parameters of the hearing aid to the hearing aid, the hearing aid has an initial benchmark value before starting to adjust the parameters. This benchmark value can ensure that the result will not deviate during the subsequent debugging process and improve the debugging efficiency.

[0052] In an embodiment of this application, the preset percentage is 80%.

[0053] Specifically, the preset percentage can also take any value greater than or equal to 50% and less than or equal to 90%. The preset percentage can be 50%. The preset percentage can be 90%. The preset percentage can be 80%. Selecting 80% is a more appropriate value.

[0054] In this embodiment, by setting 80% of the fitter's debugging parameters as the reference parameters, it can provide a certain adjustment space for the subsequent adjustment of the hearing aid parameters, and the adjustment space will not be too large to cause a waste of time. Such a setting can maximize the adjustment efficiency of the hearing aid parameters.

[0055] In an embodiment of the present application, S800 includes, that is, the current value of the output hearing aid includes the following S801: S801, increase and adjust the current value of the hearing aid by the amplitude of the third preset percentage of the current value of the hearing aid, and output the adjusted hearing aid parameters.

[0056] Specifically, the user's feelings are relatively subjective. Although the present application quantifies the user's hearing perception from the emotional balance index and the psychological stress index, in order to avoid fluctuations and errors, after obtaining the current value of the hearing aid in S800, the current parameters are increased by a certain proportion, and this proportion is the third preset percentage of the current value. This can give a certain redundant interval. Use the adjusted hearing aid parameters as the current value of the new hearing aid to execute S900, that is, use the adjusted hearing aid parameters as the optimal hearing aid parameters.

[0057] In this embodiment, before obtaining the adjusted optimal hearing aid parameters, by increasing and adjusting the current value of the hearing aid by the amplitude of the third preset percentage of the current value of the hearing aid, the hearing aid parameters can be further optimized, so that the hearing aid parameters have a high redundancy during subsequent applications, making the debugging method of the hearing aid provided by the present application reproducible. After testing, when the debugging method of the hearing aid provided by the present application is executed multiple times on the same subject to be debugged, the deviation degree of the finally obtained hearing aid parameters does not exceed ±1%, and the stability is high.

[0058] In an embodiment of the present application, after S800 and before S900, that is, after outputting the current value of the hearing aid and before writing the current value of the hearing aid into the hearing aid chip as the optimal hearing aid parameters, the debugging method of the hearing aid further includes the following S810 to S861b: S810, create a monitoring count, and assign an initial value of 0 to the monitoring count.

[0059] S820, use the current value of the hearing aid as the benchmark parameter.

[0060] S830, set the fluctuation range of the benchmark parameter. The fluctuation range of the benchmark parameter is [benchmark parameter - benchmark parameter × fourth preset percentage, benchmark parameter + benchmark parameter × fourth preset percentage].

[0061] S840, perform a volatility test on the current value of the hearing aid, apply the current value of the hearing aid to the hearing aid and conduct a hearing test on the sample.

[0062] S850, obtain the emotional balance index and psychological stress index of the sample in real time during the hearing test on the sample, and determine whether the emotional balance index and psychological stress index of the sample during the hearing test are within the standard parameter fluctuation range.

[0063] S861, if the emotional balance index and psychological stress index of the sample during the hearing test are within the standard parameter fluctuation range, then increase the monitoring count by 1 based on the original value, and determine whether the current value of the monitoring count is greater than or equal to the preset monitoring count.

[0064] S861a, if the current value of the monitoring count is less than the preset monitoring count, then after a preset time period, return to S840, that is, return to performing the volatility test on the current value of the hearing aid, applying the current value of the hearing aid to the hearing aid and conducting a hearing test on the sample.

[0065] S861b, if the value of the monitoring count is greater than or equal to the preset monitoring count, then adjust the value of the monitoring count to 0 and output the current value of the hearing aid.

[0066] Specifically, what is different between this embodiment and the previous embodiments is that the current value of the hearing aid obtained in S800 is not used as the optimal hearing aid parameter, but rather its fluctuation degree needs to be monitored, and it is decided whether to use it as the optimal hearing aid parameter based on the monitoring result. S510 to S861b is a complete process for monitoring the fluctuation degree once.

[0067] In S830, the fourth preset percentage can be 5%. The fluctuation range of the benchmark parameter is [95% of the benchmark parameter, 105% of the benchmark parameter], that is, we need to monitor the fluctuation of the current value of the hearing aid. If it does not exceed the range of ±5%, it indicates that the current value of the hearing aid is qualified, and further determine whether the monitoring count is greater than or equal to the preset monitoring count.

[0068] The preset monitoring count can be 10.

[0069] In one embodiment, after executing S800, execute S810, that is, use the current value of the hearing aid output by S800 as the benchmark parameter in subsequent S820, and then execute subsequent steps S820 to S861b.

[0070] In one embodiment, after executing S801, S810 is executed, that is, the adjusted hearing aid parameters output by S801 are used as the benchmark parameters in subsequent S820, and then subsequent steps S820 to S861b are executed.

[0071] After executing S861b, S900 is executed, that is, the current value of the hearing aid in S861b of this embodiment is used as the optimal hearing aid parameters.

[0072] The preset time period can be 10 seconds.

[0073] In this embodiment, after obtaining the current value of the hearing aid, multiple volatility tests are also performed on it. Only after the results of consecutive multiple volatility tests are qualified can the current value of the hearing aid be used as the optimal hearing aid parameters. Through this kind of test of the degree of volatility, more accurate optimal hearing aid parameters can be obtained, and some interfering factors that are not objective can be excluded, such as the hearing aid being worn improperly and having sound leakage, or the subject being nervous during the first debugging. In addition, a time interval, that is, a preset time period, is set between two adjacent volatility tests to avoid the problem of a large change in the psychological pressure of the subject caused by consecutive multiple tests, and further improve the effectiveness of the volatility test.

[0074] In an embodiment of the present application, after S850, it further includes, that is, when obtaining the emotional balance index and psychological stress index of the sample in real time during the hearing test of the sample, after judging whether the emotional balance index and psychological stress index of the sample during the hearing test are within the standard parameter fluctuation range, the following S862 is further included: S862, if the emotional balance index and psychological stress index of the sample during the hearing test are not within the standard parameter fluctuation range, the value of the monitoring times is adjusted to 0, and the process returns to increasing the hearing aid parameters by a magnitude of the first preset percentage of the current value, and after adjustment, it is applied to the hearing aid and the sample is subjected to a hearing test.

[0075] Specifically, this embodiment requires that it does not exceed the benchmark parameter fluctuation range for consecutive multiple times to complete the output of the optimal hearing aid parameters, and the number of times required is the preset monitoring times. Therefore, in S862, we can see that when the emotional balance index and psychological stress index of the sample during the hearing test are not within the standard parameter fluctuation range, the value of the monitoring times is reset to zero, and the process returns to S200 to start debugging again.

[0076] In this embodiment, by adjusting the value of the monitoring times to 0 when the emotional balance index and psychological stress index of the sample during the hearing test are not within the standard parameter fluctuation range, the continuity of the qualified volatility test is ensured, and the stability of the optimal hearing aid parameters is improved. Only when the hearing aid parameters do not show fluctuations and deviations for consecutive multiple times can they be considered as the optimal hearing aid parameters.

[0077] In an embodiment of the present application, the hearing aid debugging method further includes creating a number of cycles, and the initial value of the number of cycles is 0.

[0078] After S861b, the following S871 to S878b are further included, that is, after determining whether the current value of the monitoring times is greater than or equal to the preset monitoring times, the following S871 to S878b are further included: S871, Take the current hearing aid parameters as the new reference parameters.

[0079] S872, Increase the number of cycles by 1 on the basis of the original value.

[0080] S873, Determine whether the current value of the number of cycles is greater than or equal to the preset number of cycles.

[0081] S874a, If the current value of the number of cycles is less than the preset number of cycles, return to obtaining the reference parameters of the hearing aid, and apply the reference parameters of the hearing aid to the hearing aid.

[0082] S874b, If the current value of the number of cycles is greater than or equal to the preset number of cycles, obtain the reference parameters obtained after each cycle.

[0083] S875, Sort the reference parameters obtained after each cycle in descending order, and select the maximum reference parameter and the minimum reference parameter.

[0084] S876, Calculate the difference between the maximum reference parameter and the minimum reference parameter, and calculate the percentage of the difference to the minimum reference parameter, and define this percentage as the fluctuation percentage.

[0085] S877, Determine whether the fluctuation percentage is less than or equal to the preset fluctuation percentage.

[0086] S878a, If the fluctuation percentage is less than or equal to the preset fluctuation percentage, adjust the value of the number of cycles to 0, obtain the average value of the reference parameters obtained after each cycle, and take this average value as the optimal hearing aid parameters.

[0087] S878b, Write the optimal hearing aid parameters into the hearing aid chip of the hearing aid.

[0088] Specifically, the concept of "cycle" is introduced in this embodiment. Continuing the above embodiments of S810 to S862, if the current value of the hearing aid obtained in S800 does not exceed the fluctuation range of the benchmark parameters for 10 consecutive times, then a complete process of monitoring the fluctuation degree ends, that is, one cycle ends.

[0089] In the foregoing embodiments of S810 to S862, after executing S861b, the current value of the hearing aid is taken as the optimal hearing aid parameter, that is, when the current value of the hearing aid is within the fluctuation range of the benchmark parameter for 10 consecutive times, the current value of the hearing aid is taken as the optimal hearing aid parameter. However, this is not the case in this embodiment, and the current value of the hearing aid needs to be further tested for the degree of fluctuation.

[0090] In this embodiment, after executing S861b, it is considered that one cycle ends. This embodiment also needs to execute the same cycle multiple times, and the specific number of times is the preset number of cycles. Since a relatively optimal hearing aid parameter will be obtained after each cycle, in this embodiment and the foregoing, this relatively optimal hearing aid parameter is used as the benchmark parameter for the next cycle to re-perform the cycle. Due to errors, fluctuations may still occur. After one cycle ends in this embodiment, the next cycle is continued. After all the cycles of the preset number of cycles are executed, the overall monitoring results are summarized. This summary is a summary of the fluctuation conditions of multiple cycles. Similarly to the above embodiments of S810 to S862, the summary of this embodiment also needs to ensure the continuity of qualified data. Therefore, when the fluctuation percentage is less than or equal to the preset fluctuation percentage, the value of the number of cycles is reset to 0, and all the previous cycles are discarded, and the first cycle is restarted until the fluctuation percentages of multiple consecutive (the number of times is the preset number of cycles) cycles are less than or equal to the preset fluctuation percentage, and the average value of the benchmark parameters obtained after each cycle is calculated, and this average value is taken as the optimal hearing aid parameter.

[0091] Optionally, the preset number of cycles can be 5.

[0092] In this embodiment, after one cycle ends, the obtained current hearing aid parameter is used as the benchmark parameter used at the start of the next cycle. After executing the cycle of the predicted number of cycles, by calculating the fluctuation percentage based on the current hearing aid parameter obtained after each cycle and comparing the fluctuation percentage with the preset percentage, the stability of the hearing aid parameters for multiple cycles can be controlled.

[0093] Optionally, the fluctuation percentage can be greater than the fourth preset percentage. Optionally, the fourth preset percentage can be 5%, and the fluctuation percentage can be 10%. It can be understood that the numerical adjustment of the hearing aid parameters is first coarse adjustment and then fine adjustment, while the monitoring of the degree of fluctuation in this embodiment is first fine monitoring and then rough monitoring, that is, the monitoring of the degree of fluctuation in a single cycle uses a smaller fourth preset percentage, and the summary monitoring of the degree of fluctuation after multiple cycles uses a larger fluctuation percentage. In this way, the degree of fluctuation can be minimized in a single cycle, and a relatively looser fluctuation percentage can be used to control the degree of fluctuation in multiple cycles, so that the sensitivity of the entire process of fluctuation monitoring is just right, improving the efficiency of fluctuation monitoring.

[0094] In an embodiment of the present application, after S877, that is, after determining whether the fluctuation percentage is less than or equal to the preset fluctuation percentage, the following S879 is further included: S879, if the fluctuation percentage is greater than the preset fluctuation percentage, adjust the value of the number of cycles to 0, return to obtaining the reference parameters of the hearing aid, and apply the reference parameters of the hearing aid to the hearing aid.

[0095] Specifically, the principle of step S879 is similar to that of S862, which will not be elaborated here.

[0096] In an embodiment of the present application, after S900, that is, after writing the current value of the hearing aid as the optimal hearing aid parameter into the hearing aid chip of the hearing aid, the hearing aid debugging method further includes: S910, generate a personal profile of the sample, and store the optimal hearing aid parameter corresponding to the ID of the sample into the personal profile of the sample.

[0097] Specifically, the optimal hearing aid parameter of each sample ID in the personal profile of the sample is not fixed. With the changes in the age of the person being debugged, the working environment, and the lifespan of the hearing aid, the optimal hearing aid parameter may become invalid.

[0098] Therefore, in an embodiment of the present application, return to S100 every once in a while to execute the hearing aid debugging method mentioned in the present application on the hearing aid again. This period of time can be any time from one month to three years. For example, debug the hearing aid once every three months using the hearing aid debugging method mentioned in the present application, and update the personal profile of the sample after debugging. It should be noted that when executing S100, the reference parameters of the hearing aid can be retrieved by taking the optimal hearing aid parameter of the sample ID in the personal profile of the sample as the reference parameter of the hearing aid.

[0099] In this embodiment, by generating a personal profile of the sample and storing the optimal hearing aid parameter corresponding to the ID of the sample into the personal profile of the sample, it is convenient for subsequent analysis and traceability.

[0100] The optimal hearing aid parameter obtained by the hearing aid debugging method provided by the present application has objectivity and stability. Therefore, the hearing aid debugging method provided by the present application has reproducibility, and the result fluctuation after repeated implementation is extremely small. After testing, when the hearing aid debugging method provided by the present application is repeatedly executed on the same person being debugged, the overall deviation degree of the finally obtained hearing aid parameters does not exceed ±5%. Taking a hearing-impaired user aged 50 and male as the person being debugged, the hearing aid debugging method provided by the present application was repeatedly used for multiple debuggings, and the debugging results are shown in Table 1 (only the first 3 debugging results are shown due to space limitations).

[0101] Table 1 - Debugging Result Table of Hearing Aid Parameters In an embodiment of the present application, the hearing aid debugging method provided by the present application further includes: W100, acquiring the physical sign data of the sample using a test sound, and the facial expression data when acquiring the physical sign data.

[0102] W200, retrieving the hearing assessment data of the sample.

[0103] W300, creating a heart rate variability prediction model.

[0104] W400, inputting the physical sign data and hearing assessment data of the sample as training data into the heart rate variability prediction model to train the heart rate variability prediction model. The output data of the heart rate variability prediction model is the heart rate variability.

[0105] W500, creating an emotion and psychological assessment model.

[0106] W600, inputting the output data of the heart rate variability prediction model, the hearing assessment data, and the facial expression data when acquiring the physical sign data of the sample as training data into the emotion and psychological assessment model to train the emotion and psychological assessment model. The output data of the emotion and psychological assessment model is the emotion balance index and the psychological stress index.

[0107] In an embodiment of the present application, the W100 includes: W110, playing a test sound, and acquiring the heart rate sequence of the sample during the playing of the test sound.

[0108] Specifically, the test sound can be a 5 - minute audio file, and the audio file can be pure music. The heart rate is acquired once every 5 seconds, and 60 heart rates can be obtained. Arranging them in the order of the acquisition time nodes forms a heart rate sequence. There are 60 heart rates in the heart rate sequence. For example, [72, 75, 71,..., 68], unit: bpm, beats per minute.

[0109] In an embodiment of the present application, the W100 further includes: W120, synchronously acquiring the facial expression data of the sample when acquiring the heart rate sequence of the sample.

[0110] Specifically, a camera can be set in front of the sample. The setting position of the camera needs to ensure that the imaging range of the camera completely covers the face of the sample. When collecting the heart rate every 5 seconds, the camera also synchronously takes pictures of the face images of the sample. Subsequently, 60 face images can also be obtained. The analysis module inside the camera can identify and analyze the 60 face images, and obtain the expression data of the sample after identification and analysis. The analysis module inside the camera pre-models the face template of the sample, marks the key points in the face. The key points in the face are the key points in the contours of the five facial features and the key points in the face contour. The key points in the face include but are not limited to 68 key points such as the left eyebrow peak, left eyebrow center, left eyebrow tail, right eyebrow peak, right eyebrow center, right eyebrow tail, left pupil, right pupil, left inner eye corner, left outer eye corner, right inner eye corner, right outer eye corner, nose tip, nose root, left mouth corner, right mouth corner, upper lip center point, left cheekbone and right cheekbone, etc.

[0111] The analysis module can identify the key points of the face and fuse them with the two-dimensional rectangular coordinate system to calculate the coordinates of each key point. The analysis module calculates the expression data based on the coordinates of each key point.

[0112] For example, the expression data includes the vertical offset of the mouth corners, the pupil distance change rate, and the eyebrow distance.

[0113] Method for obtaining the vertical offset of the mouth corners: Obtain the ordinate of the upper lip center point, the ordinate of the left mouth corner, and the ordinate of the right mouth corner.

[0114] Calculate the vertical offset of the mouth corners according to formula 1.

[0115] Formula 1.

[0116] Where, is the vertical offset, is the ordinate of the upper lip center point, is the ordinate of the left mouth corner, is the ordinate of the right mouth corner.

[0117] Method for obtaining the pupil distance change rate: Calculate the pupil distance change rate according to formula 2.

[0118] Formula 2. Where, is the pupil distance change rate, is the baseline pupil distance, is the current pupil distance. The pupil distance is the horizontal distance between the two eyes' pupils, calculated as the absolute value of the difference between the abscissa of the left pupil and the abscissa of the right pupil. The baseline pupil distance is the pupil distance of the user in a calm state.

[0119] The inter - eyebrow distance can be calculated based on the absolute value of the difference between the abscissa of the left eyebrow tail and the abscissa of the right eyebrow tail.

[0120] In an embodiment of the present application, the W210 includes: W210 retrieves the low - frequency threshold, medium - frequency threshold, and high - frequency threshold of the sample.

[0121] Specifically, the hearing assessment data of the sample in this embodiment uses the low - frequency threshold, medium - frequency threshold, and high - frequency threshold. The acquisition method is pure - tone audiometry, that is, the user presses the response button when perceiving the sound. The measured hearing assessment data is stored as an inherent attribute of the sample in the personal file of the sample.

[0122] The low - frequency threshold is the minimum sound intensity that the user can perceive at a frequency of 250 Hz, and the unit is dB HL. The medium - frequency threshold is the minimum sound intensity that the user can perceive at a frequency of 1 kHz, and the unit is dB HL. The high - frequency threshold is the minimum sound intensity that the user can perceive at a frequency of 4 kHz, and the unit is dB HL.

[0123] Before W300, it also includes: W220 cleans the physical sign data of the sample, the facial expression data when collecting the physical sign data, and the hearing assessment data.

[0124] Specifically, the cleaning of the physical sign data can be carried out through several screening conditions. For example, perform heart - rate abnormality judgment. Specifically, traverse each single - point heart - rate data and judge whether each single - point heart - rate data simultaneously meets the following two conditions: Condition 1: The single - point heart - rate data is within the range of [30 beats per minute, 180 beats per minute].

[0125] Condition 2: The change rate of three consecutive single - point heart - rate data is less than 20%.

[0126] If a single - point heart - rate data simultaneously meets the following two conditions, it is determined that the single - point heart - rate data is qualified.

[0127] If a single - point heart - rate data does not meet at least one condition, it is determined that the single - point heart - rate data is unqualified. That is, if Condition 1 is not met, or Condition 2 is not met, or both Condition 1 and Condition 2 are not met, then the single - point heart - rate data is unqualified.

[0128] Optionally, perform linear interpolation processing on the unqualified single - point heart - rate data to replace the unqualified single - point heart - rate data. Optionally, in a specific scheme, take 2 qualified points before and after the unqualified single - point heart - rate data to calculate the average value, and the average value replaces the unqualified single - point heart - rate data.

[0129] The cleaning of the hearing assessment data can be completed in the following ways: Determine whether there is a frequency band threshold greater than 120 dB in any frequency band. If there is a frequency band threshold greater than 120 dB in any frequency band, mark the frequency band threshold greater than 120 dB as an invalid sample and directly eliminate this frequency band threshold.

[0130] W230, perform normalization processing on the physical sign data after cleaning, the expression data when collecting physical sign data after cleaning, and the hearing evaluation data after cleaning.

[0131] Specifically, normalize the physical sign data according to Formula 3. In an embodiment of the present application, normalize the heart rate.

[0132] Formula 3. Wherein, is the normalized heart rate, is the heart rate to be normalized, is the average value of all heart rates in the heart rate sequence, is the standard deviation of all heart rates in the heart rate sequence.

[0133] In an embodiment of the present application, normalize the low-frequency threshold, medium-frequency threshold, and high-frequency threshold according to Formula 4.

[0134] Formula 4. Wherein, is the normalized threshold, is the threshold to be normalized, is the maximum hearing loss.

[0135] For example, the maximum hearing loss is 120 dB, and the low-frequency threshold of the subject to be debugged is 60 dB. Then the normalized low-frequency threshold is 0.5. The normalization of the medium-frequency threshold and high-frequency threshold is the same.

[0136] The following describes the method for normalizing the expression data.

[0137] The normalization of the vertical offset of the corners of the mouth can be performed through Formula 5 and quantified by the corner-of-mouth score.

[0138] Formula 5.

[0139] Wherein, is the corner-of-mouth score. is the vertical offset of the corners of the mouth. is the reference value of the vertical offset, which is determined by a large number of samples.

[0140] When in the natural state, ≈0. When smiling, >0. The larger the absolute value of <0, the larger the absolute value of , the higher the intensity of the downturn of the corners of the mouth. Then, after normalization, is mapped to the interval [-1, 1] to obtain the mouth corner score. The maximum value of the mouth corner score is 1, and the minimum value of the mouth corner score is closer to 1, the closer it is to the smiling state, is closer to -1, the closer it is to the sad or dissatisfied state, and it is in the natural state when it is 0.

[0141] The pupil distance change rate itself is a ratio close to 1 and does not require normalization.

[0142] For the normalization of the eyebrow distance, the ratio of the eyebrow distance to the face width is used for calculation. For example, if the eyebrow distance is 35mm and the face width is 140mm, the ratio of the two is 0.25, and 0.25 is the normalized eyebrow distance. The face width can be set as the straight-line distance between two coordinate points of the left zygomatic bone and the right zygomatic bone, and this straight-line distance is obtained by calculating the difference in the abscissas between the two coordinate points of the left zygomatic bone and the right zygomatic bone.

[0143] W400 includes, that is, inputting the physical sign data and hearing assessment data of the sample as training data into the heart rate variability prediction model to train the heart rate variability prediction model, including: W410, performing data fusion on the training data.

[0144] W410 includes: W411, using the LSTM network to extract heart rate time-dependent features, as shown in formula 6.

[0145] Formula 6.

[0146] Among them, is the hidden state at the current time node t, is the hidden state at the previous time node t - 1, is the normalized heart rate value at the current time node t, and LSTM is the action symbol for using the LSTM network to extract heart rate time-dependent features.

[0147] In W411, the hidden state of the new time node is continuously calculated through formula 6 until the hidden state of the last time node is obtained. For example, if the heart rate data is a heart rate sequence and there are 60 heart rates in the heart rate sequence, then the hidden state of the last time node is . is a 256-dimensional vector.

[0148] W412 splices the heart rate time-dependent features output by the LSTM network and the normalized hearing assessment data, as shown in Equation 7.

[0149] Equation 7.

[0150] Where is the feature spliced by W412, is the heart rate time-dependent feature output by the LSTM network. is the normalized hearing assessment data, and n is the dimension symbol of the hearing assessment data.

[0151] For example, if we use three hearing thresholds as the hearing assessment data, then the hearing assessment data is a three-dimensional vector, and Equation 7 becomes: Then the finally spliced feature is a 259-dimensional vector.

[0152] W420 constructs a hidden layer.

[0153] Specifically, the hidden layer includes a three-layer structure. The first layer structure of the hidden layer includes 1024 neurons, and the calculation formula is Equation 8.

[0154] Equation 8.

[0155] Where is the intermediate result output by the first layer structure, is the feature spliced by W412, is the bias vector, is the weight matrix of the first layer structure, is to The structure processed by the ReLu activation function. ReLU is the action symbol processed by the Relu activation function.

[0156] is a 1024×259-dimensional vector, is a 1024×1-dimensional bias vector, is a 259-dimensional vector, which generates a 1024-dimensional intermediate result through matrix multiplication , and then generates through the Relu activation function. The function of the Relu activation function is to set negative values to zero.

[0157] The second layer structure of the hidden layer includes 512 neurons, and the calculation formula is Equation 9.

[0158] Formula 9.

[0159] Among them, is the intermediate result output by the second-layer structure, is the feature after splicing, is the bias vector, is the weight matrix of the first-layer structure, is to The structure after being processed by the ReLu activation function. ReLU is the action symbol processed by the Relu activation function.

[0160] The principle is the same as that of the first-layer structure and will not be elaborated here.

[0161] The third-layer structure of the hidden layer includes 256 neurons, and the calculation formula is Formula 10.

[0162] Formula 10.

[0163] Among them, is the intermediate result output by the third-layer structure, is the spliced feature, is the bias vector, is the weight matrix of the first-layer structure, is to The structure after being processed by the ReLu activation function.

[0164] The principle is the same as that of the first-layer structure and will not be elaborated here. The hearing assessment data affects the calculation of the first-layer structure through direct splicing to ensure that the model training conforms to the specific pattern of the hearing-impaired population.

[0165] W420, generate the predicted HRV value according to Formula 11, and the predicted HRV value is the output data of the heart rate variability prediction model.

[0166] Formula 11.

[0167] Among them, is the weight vector. is the bias scalar. is to The structure after being processed by the ReLu activation function.

[0168] For example, the input to Formula 11 is a 256-dimensional vector, and the finally generated predicted HRV value is 52.3 milliseconds.

[0169] W430, calculate the loss and update the data related to the weights using the loss.

[0170] Specifically, calculate the loss according to Formula 12.

[0171] Formula 12.

[0172] Wherein, is the hearing impact coefficient, which can be set to a fixed value of 0.1. is the predicted HRV value, is the standard HRV under the same heart rate data. The standard HRV is the HRV value calculated by the normal traditional calculation method (without doping hearing assessment data). is the degree of hearing loss, which is one of the data in the hearing assessment data.

[0173] Of course, Formula 12 is an embodiment, and it is not necessary to use Formula 12 to calculate the loss. In this embodiment, the more severe the hearing loss, the greater the penalty for the prediction result error of the model.

[0174] Next, adjust the data items related to the weight according to Formula 13.

[0175] Formula 13.

[0176] Wherein, is the updated data related to the weight, is the data related to the weight before update. is the learning rate. The initial value of the learning rate can be set to 0.01. G is the cumulative amount of historical gradient squares. W is the data item related to the weight. is the partial derivative of the loss with respect to the data item related to the weight. The data item related to the weight can be , , , .

[0177] W500, create an emotion and psychological assessment model.

[0178] W600, input the output data of the heart rate variability prediction model, the hearing assessment data, and the expression data during the sample collection physical signs data as training data into the emotion and psychological assessment model, and train the emotion and psychological assessment model; the output data of the emotion and psychological assessment model are the emotion balance index and the psychological stress index.

[0179] W600 includes: W610, construct an HRV - hearing feature channel, splice the data output by the heart rate variability prediction model and the hearing assessment data to obtain HRV - hearing features. As shown in Formula 14.

[0180] Formula 14.

[0181] Wherein, is the feature after being spliced by W610, that is, the HRV-auditory feature, is the normalized auditory evaluation data, and n is the dimension symbol of the auditory evaluation data.

[0182] The principles of Formula 14 and Formula 7 are the same, so they will not be repeated here. If n = 3, then is a four-dimensional vector.

[0183] W620 constructs a fully connected layer of HRV-auditory features according to Formula 15.

[0184] Formula 15.

[0185] Among them, is the output result of the fully connected layer of HRV-auditory features, is the weight matrix of the fully connected layer, is the feature after being spliced by W610, is the bias vector of the fully connected layer, which is used to adjust the reference activation value of the fully connected layer. The fully connected layer has 128 neurons, is a 128×4 weight matrix, is a 128×1 bias vector.

[0186] W630 constructs an expression spatio-temporal feature channel to obtain expression pooling features.

[0187] W630 includes: W631 constructs a multi-dimensional expression feature matrix.

[0188] Specifically, following the above embodiments, the expression data includes the vertical offset of the corners of the mouth, the change rate of the interpupillary distance, and the distance between the eyebrows. The time dimension of the extraction is 60 time nodes. Therefore, W631 generates an expression feature matrix at 60 time points. The vertical offset of the corners of the mouth, the change rate of the interpupillary distance, and the distance between the eyebrows are 3 dimensions. Then the expression feature matrix at 60 time points is a 60×3 matrix with 180 elements.

[0189] W631 constructs a convolutional layer and performs 1D convolution on the multi-dimensional expression feature matrix in the convolutional layer according to Formula 16.

[0190] Formula 16.

[0191] Among them, is the output value of the i-th time node and the j-th convolutional kernel. is the convolutional kernel weight matrix, It can be understood as a vector of length 3 for each. It multiplies each element of the multi-dimensional expression feature matrix output by W631 one by one and then sums them up. j is the number of the convolution kernel, and k is the time step offset within the convolution window. are all the features of the (i + k - 1)-th time node in the multi-dimensional expression feature matrix, that is, the vertical offset of the corners of the mouth, the change rate of pupil distance, and the distance between eyebrows at the (i + k - 1)-th time node. is the bias value of the j-th convolution kernel.

[0192] W632 constructs a pooling layer and performs max pooling operation in the pooling layer using Equation 17 to obtain pooling features.

[0193] Equation 17.

[0194] Among them, is the pooling feature of the i-th pooling window and the j-th convolution kernel, that is, the output value of the i-th pooling window and the j-th convolution kernel. i is the number of the pooling window, and j is the number of the convolution kernel. is the output value of the j-th convolution kernel at the (2i - 1)-th time node, is the output value of the j-th convolution kernel at the 2i-th time node.

[0195] W633 performs global average pooling operation in the pooling layer using Equation 18 to obtain global features.

[0196] Equation 18.

[0197] Among them, is the global feature, is the pooling feature of the i-th pooling window and the j-th convolution kernel, i is the number of the pooling window, is the total number of pooling windows.

[0198] W640. Cross-modal fusion of HRV-auditory features and expression pooling features.

[0199] W640 includes: W641 performs cross-modal fusion according to Equation 19.

[0200] Equation 19.

[0201] Among them, is the fusion feature obtained after cross-modal fusion, is the output result of the fully connected layer of HRV-auditory features, is the global feature.

[0202] W642 performs layer normalization according to Equation 20.

[0203] Formula 20.

[0204] Wherein, is the fused feature after standardization, is the mean value of the features of is the standard deviation of is the scaling parameter, and is the translation parameter. The scaling parameter and the translation parameter are similar to the learning step or the learning rate, and are learnable parameters used to adjust the normalized distribution.

[0205] W650 is used to construct the prediction formula of the emotional balance index and the prediction formula of the stress index respectively.

[0206] W650 includes: W651 is used to construct the prediction formula of the emotional balance index, and the prediction formula of the emotional balance index is shown in Formula 21.

[0207] Formula 21.

[0208] Wherein, is the emotional balance index. is the weight vector of the emotional balance index, which determines the contribution provided to the emotional balance index. is the bias scalar of the emotional balance index, which is used to adjust the reference value of. Sigmoid is the Sigmoid function. LayerNorm is the layer normalization function, which is used to unify the dimension. is the fused feature after standardization.

[0209] W652 is used to construct the prediction formula of the psychological stress index, and the prediction formula of the psychological stress index is shown in Formula 22.

[0210] Formula 21.

[0211] Wherein, is the emotional balance index. is the weight vector of the psychological stress index, which determines the contribution provided to the emotional balance index. is the bias scalar of the psychological stress index, which is used to adjust the reference value of. LayerNorm is the layer normalization function, which is used to unify the dimension. It is the fused feature after standardization.

[0212] W652 calculates the loss and updates the data related to the weights using the loss.

[0213] Specifically, W430 in the training process of the heart rate variability prediction model in this step, that is, referring to Formula 12 and Formula 13, can adopt the same principle as Formula 12 and Formula 13 to design the loss calculation method and weight item update of the emotion and psychological assessment model.

[0214] This application also provides a hearing aid debugging system.

[0215] Such as Figure 2 As shown, in an embodiment of this application, the hearing aid debugging system includes a cloud server 100 and a hearing aid 200.

[0216] The cloud server 100 is used to execute the hearing aid debugging method mentioned in any one of the foregoing embodiments. The hearing aid 200 is communicatively connected to the cloud server 100.

[0217] The technical features of the above embodiments can be combined arbitrarily, and there is no limitation on the execution order of the method steps. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as these technical feature combinations do not conflict, they should all be considered as the scope recorded in this specification.

[0218] The above embodiments only represent several implementation manners of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the patent scope of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several deformations and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of this application should be subject to the appended claims.

Claims

1. A method for debugging a hearing aid, characterized in that, The hearing aid debugging method includes: Obtain the reference parameters of the hearing aid and apply the reference parameters of the hearing aid to the hearing aid; Adjust the hearing aid parameters by increasing them by the amplitude of the first preset percentage of the current value, apply the adjusted parameters to the hearing aid after adjustment, and conduct a hearing test on the sample; During the hearing test on the sample, obtain the emotional balance index and psychological stress index of the sample in real time, and determine whether both the emotional balance index and the psychological stress index increase during the hearing test; If both the emotional balance index and the psychological stress index increase during the hearing test, return to the step of adjusting the hearing aid parameters by increasing them by the amplitude of the first preset percentage of the current value, apply the adjusted parameters to the hearing aid after adjustment, and conduct a hearing test on the sample; If both the emotional balance index and the psychological stress index decrease during the hearing test, adjust the hearing aid parameters by decreasing them by the amplitude of the second preset percentage of the current value, apply the adjusted parameters to the hearing aid after adjustment, and conduct a hearing test on the sample; the second preset percentage is less than the first preset percentage; During the hearing test on the sample, obtain the emotional balance index and psychological stress index of the sample in real time, and determine whether both the emotional balance index and the psychological stress index increase during the hearing test; If both the emotional balance index and the psychological stress index increase during the hearing test, return to the step of adjusting the hearing aid parameters by decreasing them by the amplitude of the second preset percentage of the current value, apply the adjusted parameters to the hearing aid after adjustment, and conduct a hearing test on the sample; If both the emotional balance index and the psychological stress index decrease during the hearing test, output the current value of the hearing aid; Write the current value of the hearing aid as the optimal hearing aid parameter into the hearing aid chip of the hearing aid.

2. The hearing aid debugging method according to claim 1, wherein Before the step of obtaining the reference parameters of the hearing aid and applying the reference parameters of the hearing aid to the hearing aid, the hearing aid debugging method further includes: Determine whether the hearing aid is being debugged for the first time; If the hearing aid is being debugged for the first time, retrieve the fitter's debugging parameters and use a preset percentage of the fitter's debugging parameters as the reference parameters of the hearing aid.

3. The hearing aid debugging method according to claim 2, wherein The preset percentage is 80%.

4. The hearing aid debugging method according to claim 1, characterized in that, The step of outputting the current value of the hearing aid includes: Adjust the current value of the hearing aid by increasing it by the amplitude of the third preset percentage of the current value of the hearing aid, and output the adjusted hearing aid parameters.

5. The hearing aid debugging method according to claim 1, characterized in that After the step of outputting the current value of the hearing aid and before the step of writing the current value of the hearing aid as the optimal hearing aid parameter into the hearing aid chip of the hearing aid, the hearing aid debugging method further includes: Create a monitoring count and assign an initial value of 0 to the monitoring count; Use the current value of the hearing aid as a benchmark parameter; Set the benchmark parameter fluctuation range; the benchmark parameter fluctuation range is [benchmark parameter - benchmark parameter × fourth preset percentage, benchmark parameter + benchmark parameter × fourth preset percentage]; Conduct a volatility test on the current value of the hearing aid, apply the current value of the hearing aid to the hearing aid, and conduct a hearing test on the sample; During the hearing test on the sample, obtain the emotional balance index and psychological stress index of the sample in real time, and determine whether the emotional balance index and the psychological stress index of the sample during the hearing test are within the standard parameter fluctuation range; If the emotional balance index and psychological stress index of the sample during the hearing test are within the standard parameter fluctuation range, increase the monitoring count by 1 based on the original value, and determine whether the current value of the monitoring count is greater than or equal to the preset monitoring count; If the current value of the monitoring count is less than the preset monitoring count, after a preset time period, return to perform a volatility test on the current value of the hearing aid, apply the current value of the hearing aid to the hearing aid and conduct a hearing test on the sample; If the value of the monitoring count is greater than or equal to the preset monitoring count, adjust the value of the monitoring count to 0 and output the current value of the hearing aid.

6. The hearing aid debugging method according to claim 5, wherein After obtaining the emotional balance index and psychological stress index of the sample in real time during the hearing test on the sample and determining whether the emotional balance index and psychological stress index of the sample during the hearing test are within the standard parameter fluctuation range, it further includes: If the emotional balance index and psychological stress index of the sample during the hearing test are not within the standard parameter fluctuation range, adjust the value of the monitoring count to 0, return to increase the hearing aid parameters by a magnitude of the first preset percentage of the current value, apply the adjusted parameters to the hearing aid and conduct a hearing test on the sample.

7. The hearing aid debugging method according to claim 6, characterized in that, The hearing aid debugging method further includes creating a loop count, the initial value of the loop count being 0. After adjusting the value of the monitoring count to 0 and outputting the current value of the hearing aid, it further includes: Taking the current value of the hearing aid as the new reference parameter; Increasing the loop count by 1 based on the original value; Determining whether the current value of the loop count is greater than or equal to the preset loop count; If the current value of the loop count is less than the preset loop count, return to obtain the reference parameter of the hearing aid and apply the reference parameter of the hearing aid to the hearing aid; If the current value of the loop count is greater than or equal to the preset loop count, obtain the new reference parameters obtained after each loop; Sort the new reference parameters obtained after each loop in descending order, and select the maximum reference parameter and the minimum reference parameter; Calculate the difference between the maximum reference parameter and the minimum reference parameter, and calculate the percentage of this difference to the minimum reference parameter, and define this percentage as the fluctuation percentage; Determine whether the fluctuation percentage is less than or equal to the preset fluctuation percentage; If the fluctuation percentage is less than or equal to the preset fluctuation percentage, adjust the value of the loop count to 0, calculate the average value of the new reference parameters obtained after each loop, and output this average value as the current value of the hearing aid.

8. The hearing aid debugging method according to claim 7, characterized in that After determining whether the fluctuation percentage is less than or equal to the preset fluctuation percentage, it further includes: If the fluctuation percentage is greater than the preset fluctuation percentage, adjust the value of the loop count to 0, return to obtain the reference parameter of the hearing aid and apply the reference parameter of the hearing aid to the hearing aid.

9. The hearing aid debugging method according to claim 8, characterized in that, After writing the current value of the hearing aid as the optimal hearing aid parameter into the hearing aid chip of the hearing aid, the hearing aid debugging method further includes: Generating a personal profile of the sample, and storing the optimal hearing aid parameter corresponding to the ID of the sample into the personal profile of the sample.

10. A hearing aid debugging system, characterized in that, Including: A cloud server for executing the hearing aid debugging system according to any one of claims 1 to 9; A hearing aid communicatively connected to the cloud server.

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