Hearing aid auxiliary fitting system and method
The hearing aid fitting system, which utilizes multi-dimensional detection and data optimization, solves the problems of low fitting efficiency and poor adaptability, achieving efficient fitting and optimized sound quality of personalized hearing aids, and providing long-term support.
Patent Information
- Application Number
- CN202511208750.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-27
- Publication Date
- 2025-12-09
AI Technical Summary
Existing hearing aid fitting methods are inefficient, difficult to adapt to complex acoustic environments, and fail to take into account the user's physiological characteristics, resulting in insufficient personalization accuracy.
It employs a hearing test module, a data acquisition module, an analysis and processing module, a weight allocation module, an auxiliary adjustment module, and a signal optimization module, combined with mathematical algorithms and VR technology, to achieve personalized hearing aid fitting. Hearing aid parameters are optimized through multi-dimensional detection and data processing.
It improves the efficiency and accuracy of hearing aid fitting, enhances the adaptability and sound quality of hearing aids in different scenarios, and provides long-term support services.
Smart Images
Figure CN121099248A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of hearing aids, in particular to a hearing aid auxiliary fitting system and method. BACKGROUND
[0002] Hearing aids are effective tools to help the hearing-impaired improve their hearing, and the hearing test of the patient during the fitting process is the key to fitting the appropriate hearing aid. At present, the hearing test method for the hearing-impaired commonly used by medical institutions is the soundproof room real ear test method, that is, the patient is placed in a soundproof room, and the earphone is connected with the audiometer placed outside the soundproof room and transmits audio signals, the fitter adjusts the sound loudness at a fixed frequency, and the patient presses the button to indicate when hearing the sound, and each frequency is repeated in turn to complete the drawing of the hearing threshold curve; then the fitter fits and debugs the hearing aid according to the audiogram of the patient.
[0003] The fitting of the traditional hearing aid relies on manual debugging, which is not only low in efficiency, but also difficult to adapt to complex acoustic environments. Although modern hearing aids can realize multi-scene switching through preset programs, they have the problem of high transmission delay between devices; at the same time, since the physiological characteristics of the user are not integrated, the adaptability to the mobile scene is weak. In addition, the traditional method relies on a general sound pressure compensation mode and fails to combine the user's subjective auditory spatial perception, further affecting the use effect.
[0004] Therefore, there is an urgent need for a personalized hearing aid auxiliary fitting system that can adapt to the needs of patients to solve the core problems of insufficient cross-scene real-time adaptation and lack of individualization accuracy. SUMMARY
[0005] The present application provides a hearing aid auxiliary fitting system and method to solve the technical problem that the existing hearing aid cannot be individually fitted according to the needs of the patient, resulting in poor hearing aid fitting effect.
[0006] To solve the above technical problems, the present application provides the following technical solutions: A hearing aid auxiliary fitting system, comprising: a hearing detection module: responsible for detecting the hearing of the patient from multiple dimensions, and accurately obtaining the hearing information of the patient; a data acquisition module: accurately acquiring various hearing data of the patient, and providing data basis for individualized fitting of the hearing aid for the patient; an analysis and processing module: used for processing and analyzing the collected data to improve the running efficiency of the data; a weight allocation module: responsible for dynamically allocating weight coefficients to the processed data to balance the importance between the data; an auxiliary debugging module: used for auxiliary debugging of the hearing aid fitted for the patient, so that the hearing aid can adapt to the needs of the patient; Signal optimization module: adopt mathematical algorithm to suppress environmental noise and improve the smoothness of signal, to improve the sound quality of the hearing aid; Follow-up guidance module: used for dynamically adjusting the configuration of the hearing aid according to follow-up data, to provide long-term support service after fitting.
[0007] A hearing aid auxiliary fitting method applied to the hearing aid auxiliary fitting system, comprising the following steps: S1, detecting the hearing of the patient from multiple dimensions, and accurately obtaining the hearing information of the patient; S2, accurately collecting various hearing data of the patient, to provide data basis for personalized hearing aid fitting of the patient; S3, processing and analyzing the collected data to improve the running efficiency of the data; S4, dynamically assigning weight coefficients to the processed data to balance the importance between each data; S5, auxiliary debugging the hearing aid fitted for the patient, to adapt the hearing aid to the personalized needs of the patient; S6, using mathematical algorithm to suppress environmental noise and improve the smoothness of signal, to optimize the sound quality of the hearing aid; S7, dynamically adjusting the configuration parameters of the hearing aid according to follow-up data, to provide long-term support service after fitting.
[0008] The technical scheme provided by the present application has at least the following beneficial effects: The present application can ensure efficient data collection during the fitting process through synchronous data collection and analysis processing, and can improve the running efficiency of the data through data optimization, to ensure the accuracy of the hearing detection data.
[0009] The present application can adjust the parameters of the hearing aid according to the needs of the patient through the cooperation of the weight distribution module and the auxiliary debugging module, to make the hearing aid adapt to the hearing situation of the patient.
[0010] The present application can suppress environmental noise through the signal optimization module, to improve the smoothness of the hearing aid signal and ensure that the patient can clearly hear the external sound.
[0011] The present application can send prompt information to the patient according to the use of the device through the follow-up guidance module, and the patient can choose corresponding measures according to the prompt information, to ensure the integrity of the hearing aid use process. BRIEF DESCRIPTION OF DRAWINGS
[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort based on these drawings.
[0013] Figure 1 is the architecture diagram of the hearing aid auxiliary fitting system provided by the embodiment of the present application. Figure 2 is the flowchart of the hearing aid auxiliary fitting method provided by the embodiment of the present application. DETAILED DESCRIPTION
[0014] In order to make the objects, technical solutions and advantages of the present application more clear, the following will further describe the embodiments of the present application in combination with the drawings.
[0015] The embodiment provides a hearing aid auxiliary fitting system and method.
[0016] Please refer to Figure 1 , Figure 1 is the architecture diagram of the hearing aid auxiliary fitting system provided by the embodiment of the present application.
[0017] A hearing aid auxiliary fitting system comprises: I. Hearing detection module The hearing detection module comprises an environment simulation unit and a hearing test unit, the environment simulation unit is used to simulate high-frequency scenes in the daily life of the patient, and the hearing test unit detects the hearing of the patient by outputting a hearing test signal; The simulation process of the environment simulation unit is: calling a preset high-frequency scene based on the GPS trajectory of the user, and synchronously rendering the corresponding visual scene through VR glasses; The test method adopted by the hearing test unit comprises ascending-descending pure tone audiometry and dynamic scene speech audiometry, the ascending-descending pure tone audiometry step is to generate a detection signal in the order of 1kHz→2kHz→4kHz→0.5kHz→0.25kHz, and the dynamic scene speech audiometry step is to generate a mixed signal of 65dB or less background noise and 5dB target sound.
[0018] It should be noted that the hearing test step is: first, the ascending-descending pure tone audiometry is adopted, the frequency order is 1kHz→2kHz→4kHz→0.5kHz→0.25kHz, the hearing threshold of the patient is determined by testing the minimum sound intensity heard by the patient; then, the high-frequency scene based on the GPS trajectory of the patient is synchronously rendered through the VR glasses, the dynamic scene in which the patient is daily placed is simulated, and the ACT test is adopted to play 65dB or less background noise and 5dB speech sentences at the same time, and the speech audiometry is completed.
[0019] II. Data collection module The collected data includes patient information data, auditory function data, and physiological feedback data, the patient information data includes age, otological history, genetic history; the auditory function data includes hearing threshold and noise environment speech recognition rate; the physiological feedback data includes HRV stress index and patient hearing perception feedback.
[0020] The patient's hearing threshold is calculated from the minimum comfortable level and the maximum uncomfortable level data; the noise environment speech recognition rate is obtained by calculating the noise speech recognition rate and the ACT value, and the weight of the noise speech recognition rate and the ACT value is the same; It should be noted that the determination process of the minimum comfortable level data is: at the frequency point of 1 kHz, the test signal is increased by 10 dB as a step, starting from the hearing threshold + 20 dB, and the patient determines the minimum comfortable level through the Logistic curve according to the perceived hearing loudness; the determination of the maximum uncomfortable level data is to play 1 kHz pure tone, the intensity starts from the minimum comfortable level + 20 dB, and rises at a rate of 2 dB / s, and the patient records immediately when he feels uncomfortable, and if the intensity exceeds 115 dB HL, it is automatically terminated to protect hearing.
[0021] The noise speech recognition rate is calculated by playing standard speech material in a simulated scene with a signal-to-noise ratio of ≥5 dB, and the ACT value is obtained by determining the patient's perception threshold of target speech and background noise.
[0022] The HRV stress index is obtained by PPG sensor, and the patient's hearing perception feedback data is obtained by the patient's intelligibility score of hearing test.
[0023] It should be noted that the HRV stress index is obtained by PPG sensor with a sampling rate of 50 Hz to obtain HRV data, and a sliding window Fourier transform is used to extract the power of 0.04-0.15 Hz low frequency band, and finally normalized to stress index; the intelligibility score ranges from 0 to 10 points, which is given by the patient according to the speech intelligibility in the hearing test process.
[0024] III. Analysis and processing module The analysis and processing module includes data cleaning and feature extraction, data cleaning is used to eliminate interference data to ensure the credibility of the data, and feature extraction is used to focus on key parameters to improve data running efficiency; Data cleaning adopts different processing methods for different types of data: for auditory function data, abnormal value filtering method is used for processing; for physiological feedback data, wavelet transform is used to filter motion artifacts; It should be noted that the outlier is defined as the value of the hearing threshold data > 120 dB HL, and the filtering range of the wavelet transform is the motion artifact with a frequency > 0.5 Hz, which can effectively eliminate transient interference.
[0025] The feature extraction calculates the average value of the hearing threshold data at different frequencies of the audiogram, marks the difference interval of the maximum discomfort level and the minimum comfortable level according to the average value, and obtains the feature data of the hearing threshold; the wavelet transform is used to remove the environmental noise, and then the robust acoustic feature is extracted by combining the mel-frequency wavelet transform coefficient, so as to obtain the feature data of the noise speech recognition rate.
[0026] It should be noted that the average value of the hearing threshold data is calculated in the frequency of 500 Hz, 1 kHz, 2 kHz and 4 kHz in the audiogram.
[0027] Four, weight allocation module The implementation steps of the weight allocation module include: calculating the reliability score of the three types of data sources after collection and processing, calculating the original weight based on the reliability score, and normalizing the original weight; The calculation process of the reliability score is: inputting the noise interference level and the data integrity into the preset exponential function and linear combination, and outputting the reliability score after operation, which is specifically as follows: In the formula, represents the reliability score; represents the noise interference intensity during data collection; represents the data integrity; represents the preset coefficient of the data quality reference weight; represents the preset coefficient of the noise sensitivity attenuation factor; represents the preset coefficient of the data integrity compensation coefficient; It should be noted that the preset coefficient of the data quality reference weight is used to associate the data quality reference weight, and the value range is 0.5-0.8; the preset coefficient of the noise sensitivity attenuation factor is associated with the noise sensitivity attenuation factor, and the value range is 0.15-0.25; the preset coefficient of the data integrity compensation coefficient is associated with the data integrity compensation coefficient, and the value range is 0.3-0.5. In the actual calculation process, the preset coefficient can be adjusted within the value range, and here the default value is used: the preset coefficients of the patient information data are 0.7, 0.22 and 0.3 respectively, the preset coefficients of the auditory function data are 0.16, 0.5 and 0.4 respectively, and the preset coefficients of the physiological feedback data are 0.5, 0.18 and 0.5 respectively.
[0028] The original weight multiplies the reliability score with the base weight, and applies a linear offset based on the reliability score to the product result to obtain the original weight of the data, as follows: In the formula, represents the original weight of the data; represents the base weight of the corresponding data source; It should be noted that the base weight of the patient information data is 0.2, the base weight of the auditory function data is 0.5, and the base weight of the physiological feedback data is 0.3.
[0029] The original weight is scaled by the sum of all weights to obtain the normalized weight result, as follows: In the formula, represents the normalized weight value, represents the sum of the original weights.
[0030] Five, auxiliary debugging module The auxiliary debugging of the auxiliary debugging module includes: hierarchical gain compensation and scene gain fine tuning, the hierarchical gain compensation selects a compensation mode according to the hearing threshold range, and the scene gain fine tuning increases the frequency band compensation according to the environment label; The hierarchical gain compensation calculates the weighted average hearing threshold of the speech frequency, and the hearing threshold is compensated in equal proportion in the range of 20-40 dB, and the hearing threshold is compensated in descending order in the range of 40-70 dB; It should be noted that the process of equal proportion compensation and descending compensation is as follows: In the formula, represents the result of equal proportion compensation; represents the result of descending compensation; represents the average hearing threshold.
[0031] The environment label is divided into indoor scene and outdoor scene, the indoor scene adopts full-band gain +2dB for compensation, and the compensation trigger condition of the outdoor scene is to detect that the noise energy ratio of 1-4kHz is ≥60%, the compensation of the outdoor scene adopts 1-4kHz band gain +3dB, and the low frequency gain-2dB.
[0032] Six, signal optimization module The signal optimization module performs stationary processing on the signal of the hearing aid by using an ensemble empirical mode decomposition algorithm, and adds the Gaussian white noise with uniform distribution into the original signal to be processed, so as to weaken the signal volatility. The original signal processing process is as follows: the Gaussian white noise with uniform distribution is added into the original signal to obtain a processing signal, and the specific formula is as follows: In the formula, represents the processing signal; represents the original signal; represents the Gaussian white noise signal; The original signal is optimized and then subjected to ensemble empirical mode decomposition to obtain IMF components and residual components, and the specific formula is as follows: In the formula, represents the IMF component, represents the residual component; represents the sample number; It should be noted that in the actual processing process, the amplitude of the added Gaussian white noise is set to 0.2, and the maximum number of iterations is 100.
[0033] The original signal is subjected to multiple repeated ensemble empirical mode decomposition to obtain multiple IMF components, and the average value of the multiple IMF components is calculated to output the optimization result, and the specific formula is as follows: In the formula, represents the average value of the IMF components, represents the IMF component of the jth ensemble empirical decomposition.
[0034] It should be noted that the signal optimization process is to perform EEMD processing on the original speech signal to obtain a group of IMF components and one residual component, to perform ensemble empirical mode decomposition on each component, to obtain the best speech signal component after searching for the optimal weight, and to superimpose each speech signal component to obtain the total speech signal.
[0035] The output result is evaluated by using the detuning coefficient, and the specific formula is as follows: In the formula, represents the detuning coefficient; represents the optimal value of the output signal; represents the actual value of the output signal.
[0036] Seven, follow-up guidance module The follow-up guidance module comprises a real-time monitoring unit and an information prompting unit, the real-time monitoring unit is responsible for monitoring device information in real time when the patient wears the hearing aid, and the information prompting unit is responsible for sending prompt guidance to the patient according to the monitoring information of the real-time monitoring unit; The real-time monitoring unit automatically classifies device problems according to a preset threshold, the monitoring information comprises outdoor mode usage rate, manual adjustment frequency and abnormal event, and the device problems comprise scene matching problem, gain mismatch problem and hardware failure problem; It should be noted that the preset threshold is that if the outdoor mode usage rate is <4 hours in a week, the scene matching problem is classified, if the same frequency band adjustment frequency is >=5 times a week, the gain mismatch problem is marked, and if the abnormal event is >=3 times in a week, the hardware failure problem is marked.
[0037] The information prompting unit triggers corresponding prompt information according to the device problems determined by the real-time monitoring unit, for the scene matching problem, issues a strong noise reduction parameter and pushes a noise adaptation training, for the gain mismatch problem, generates a specified frequency band retest instruction and pushes a re-fitting prompt, and for the hardware failure problem, triggers a device self-checking program and pushes a maintenance prompt.
[0038] It should be noted that the real-time monitoring unit is also responsible for monitoring user response behavior, if the user does not respond within 24 hours after the problem occurs, the system will automatically send a prompt information to the administrator.
[0039] Please refer to Figure 2 , Figure 2 A flowchart of a hearing aid auxiliary fitting method provided by the embodiment of the application.
[0040] A hearing aid auxiliary fitting method, comprising the following steps: S1, detecting the hearing of the patient from multiple dimensions, and accurately obtaining the hearing information of the patient; S2, accurately collecting various hearing data of the patient, and providing a data basis for personalized hearing aid fitting of the patient; S3, processing and analyzing the collected data to improve the operation efficiency of the data; S4, dynamically assigning a weight coefficient to the processed data to balance the importance between the various data; S5, auxiliary debugging the hearing aid fitted for the patient, so that the hearing aid adapts to the personalized needs of the patient; S6, using a mathematical algorithm to suppress environmental noise and improve the smoothness of the signal, and optimizing the sound quality of the hearing aid; S7, dynamically adjusting the configuration parameters of the hearing aid according to the follow-up data, and providing long-term support services after fitting.
[0041] Moreover, it is noted that the application can be provided as a method, an apparatus or a computer program product. Therefore, the application embodiments can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the application embodiments can take the form of a computer program product on one or more computer-usable storage media (including disks, diskettes, tapes, optical, solid storage, etc.) embodying computer-readable program code.
[0042] The application embodiments are described with reference to the flowchart illustrations and / or block diagrams of the methods, terminal devices (systems) and computer program products according to the application embodiments. It is understood that each flow and / or block in the flowchart illustrations and / or block diagrams, and combinations of flows and / or blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing terminal devices to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal devices, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the functions specified in the flowchart illustrations and / or block diagrams.
[0043] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal devices to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the functions specified in the flowchart illustrations and / or block diagrams. These computer program instructions can also be loaded onto a computer or other programmable data processing terminal devices to cause a series of operational steps to be performed on the computer or other programmable terminal devices to produce a computer implemented process such that the instructions executed on the computer or other programmable terminal devices provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the functions specified in the flowchart illustrations and / or block diagrams.
[0044] It is also noted that, as used herein, the terms "first", "second", and the like, merely designate a relationship or order of importance and do not necessarily indicate any such actual relationship or order of importance. The terms "comprises", "comprising", or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.
[0045] Finally, it is to be noted that the above-mentioned arrangements merely illustrate preferred embodiments of the application and that, although preferred embodiments of the application have been described, numerous modifications and enhancements can be made thereto by one of ordinary skill in the art without departing from the basic inventive concepts. Accordingly, the attached claims are intended to cover all such modifications and enhancements as fall within the scope of the application.
Claims
1. A hearing aid-assisted fitting system, characterized in that, include: Hearing test module: Responsible for testing the patient's hearing from multiple dimensions and accurately obtaining the patient's hearing information; Data acquisition module: By accurately collecting various hearing data of patients, it provides a data foundation for personalized hearing aid fitting for patients; Analysis and processing module: Used to process and analyze the collected data to improve data processing efficiency; Weighting module: Responsible for dynamically assigning weight coefficients to the processed data to balance the importance of each data point; Assisted adjustment module: Used to assist in adjusting the hearing aid fitted to the patient so that the hearing aid can be adapted to the patient's own needs; Signal optimization module: Employs mathematical algorithms to suppress environmental noise and improve signal stability, thereby enhancing the sound quality of the hearing aid; Follow-up guidance module: used to dynamically adjust the configuration of hearing aids based on follow-up data, providing long-term support services after fitting.
2. The hearing aid fitting system according to claim 1, characterized in that, The hearing detection module includes an environmental simulation unit and a hearing test unit. The environmental simulation unit is used to simulate high-frequency scenarios in the patient's daily life, and the hearing test unit detects the patient's hearing by outputting a hearing test signal. The simulation process of the environment simulation unit is as follows: based on the user's GPS trajectory, a preset high-frequency scene is called, and the corresponding visual scene is rendered synchronously through VR glasses; The hearing test unit employs two test methods: pure tone audiometry using the rising and falling method and dynamic scene speech audiometry. The pure tone audiometry using the rising and falling method involves generating a detection signal in the order of 1kHz→2kHz→4kHz→0.5kHz→0.25kHz. The dynamic scene speech audiometry involves generating a mixed signal of background noise below 65dB and target sound at 5dB.
3. The hearing aid fitting system according to claim 1, characterized in that, The hearing data collected by the data acquisition module includes patient information data, auditory function data, and physiological feedback data. The patient information data includes age, otological history, and genetic history. The auditory function data includes hearing threshold and speech recognition rate in noisy environments. The physiological feedback data includes HRV stress index and patient auditory feedback. The hearing threshold is derived from the patient’s minimum comfort level and maximum discomfort level data. The noise environment speech recognition rate is obtained by calculating the noise speech recognition rate and the ACT value, with the noise speech recognition rate and the ACT value having the same weight. The noise speech recognition rate is calculated by playing standard speech material in a simulated scenario with a signal-to-noise ratio ≥5dB, and the ACT value is obtained by determining the patient perception threshold between the target speech and the background noise. The HRV pressure index is acquired by a PPG sensor, and the patient's auditory feedback data is obtained by the patient's clarity score in the hearing test.
4. The hearing aid fitting system according to claim 1, characterized in that, The analysis and processing module includes data cleaning and feature extraction. Data cleaning is used to remove interfering data, and feature extraction is used to focus on key parameters to improve data processing efficiency. The data cleaning includes outlier filtering for auditory function data and wavelet transform filtering for motion artifacts for physiological feedback data. The feature extraction is achieved by calculating the average value of hearing threshold data at different frequencies of the audiogram, marking the difference range between the maximum discomfort level and the minimum comfort level based on the average value of the hearing threshold data, and obtaining the feature data of hearing threshold. Environmental noise is removed by using wavelet transform, and robust acoustic features are extracted by combining the Merr frequency wavelet transform coefficients to obtain the feature data of noise speech recognition rate.
5. The hearing aid fitting system according to claim 1, characterized in that, The weight allocation steps of the weight allocation module include: calculating reliability scores for the three types of data sources after collection and processing, calculating the original weights based on the reliability scores, and normalizing the original weights. The reliability score is calculated as follows: noise interference level and data integrity are input into a preset exponential function and a linear combination, and the reliability score is output, as shown in the following formula: In the formula, Indicates reliability score; This indicates the intensity of noise interference during data acquisition. This is expressed as the completeness of the collected data; Preset coefficients representing the baseline weights for data quality; The preset coefficient representing the noise sensitivity attenuation factor. The preset coefficient representing the data integrity compensation coefficient; The original weights are obtained by multiplying the reliability score by the base weights and applying a linear offset based on the reliability score to the product, as shown in the following formula: In the formula, Indicates the original weights of the data; This represents the base weight of the corresponding data source; The original weights are scaled proportionally so that the sum of all weights is 1, resulting in a normalized weight result, as shown in the following formula: In the formula, This represents the normalized weight values. This represents the sum of the original weights.
6. The hearing aid fitting system according to claim 1, characterized in that, The auxiliary debugging module includes graded gain compensation and scene gain fine-tuning. The graded gain compensation selects the compensation mode according to the hearing threshold range, and the scene gain fine-tuning adds frequency band compensation according to the environmental label. The graded gain compensation is achieved by calculating the weighted average hearing threshold of speech frequencies. For hearing thresholds between 20-40 dB, proportional compensation is used, while for hearing thresholds between 40-70 dB, decreasing compensation is used. The environmental labels are divided into indoor and outdoor scenes. The indoor scene is compensated with a full-band gain of +2dB. The compensation trigger condition for the outdoor scene is that the noise energy ratio of 1-4kHz is ≥60%. The compensation for the outdoor scene is a gain of +3dB in the 1-4kHz frequency band and a gain of -2dB in the low frequency band.
7. The hearing aid fitting system according to claim 1, characterized in that, The signal optimization module uses an empirical mode decomposition algorithm to stabilize the signal of the hearing aid. By utilizing the uniform distribution of Gaussian white noise, it adds it to the original signal to be processed, decomposing the original signal into a set of stable signal components, thereby reducing signal fluctuations. The original signal processing procedure involves adding uniformly distributed Gaussian white noise to the original signal to obtain the processed signal, as shown in the following formula: In the formula, Indicates the processing of signals; Represents the original signal; This represents a Gaussian white noise signal; The original signal is then optimized and subjected to ensemble empirical mode decomposition to obtain IMF components and residual components, as shown in the following formula: In the formula, Indicates IMF components, Represents the residual components; Indicates the number of samples; The original signal is subjected to repeated ensemble empirical mode decomposition to obtain multiple IMF components. The optimization result is output by calculating the average value of the multiple IMF components, as shown in the following formula: In the formula, The average value of the IMF components. Let represent the IMF component of the j-th set empirical decomposition.
8. The hearing aid fitting system according to claim 7, characterized in that, The original signal is subjected to repeated ensemble empirical mode decomposition to obtain multiple IMF components. The optimization result is output by calculating the average value of the multiple IMF components, wherein: The output result is evaluated using the offset coefficient, as shown in the following formula: In the formula, Indicates the imbalance coefficient; This indicates the optimal value of the output signal; This represents the actual value of the output signal.
9. The hearing aid fitting system according to claim 1, characterized in that, The follow-up guidance module includes a real-time monitoring unit and an information prompting unit. The real-time monitoring unit is responsible for monitoring the device information in real time when the patient wears the hearing aid, and the information prompting unit is responsible for sending prompts and guidance to the patient based on the monitoring information of the real-time monitoring unit. The real-time monitoring unit automatically classifies equipment problems according to preset thresholds. The monitoring information includes outdoor mode usage rate, manual adjustment frequency, and abnormal events. The equipment problems include scene matching problems, gain mismatch, and hardware failure problems. The information prompting unit triggers corresponding prompts based on equipment problems. For example, for scene matching problems, it triggers the issuance of strong noise reduction parameters and pushes noise adaptation training. For gain mismatch problems, it generates a specified frequency band retest command and pushes a refit prompt. For hardware failure problems, it triggers a self-test program and pushes a maintenance prompt.
10. A hearing aid-assisted fitting method, applied to the hearing aid-assisted fitting system according to any one of claims 1-9, characterized in that, Includes the following steps: S1. Detect the patient's hearing from multiple dimensions to accurately obtain the patient's hearing information; S2. Accurately collect various hearing data of patients to provide a data basis for personalized hearing aid fitting; S3. Process and analyze the collected data to improve data processing efficiency; S4. Dynamically assign weight coefficients to the processed data to balance the importance of each data point; S5. To assist in adjusting the hearing aids fitted for the patient, so that the hearing aids can be adapted to the patient's individual needs. S6. Employs mathematical algorithms to suppress environmental noise and improve signal stability, thereby optimizing the sound quality of the hearing aid; S7. Dynamically adjust the configuration parameters of the hearing aid based on follow-up data to provide long-term support services after fitting.