Hearing aid operation self-checking method and use method thereof
By integrating multiple detection modules and intelligent algorithms into the hearing aid, real-time self-testing and automatic diagnosis of the hearing aid are realized, solving the problem of cumbersome testing in existing technologies and improving the reliability of the device and user experience.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-13
- Publication Date
- 2026-03-13
AI Technical Summary
Current hearing aid testing requires specialized instruments to monitor internal hardware and software functions, making testing cumbersome and potentially affecting the user's auditory experience, or even causing hearing damage.
The system employs a microphone detection module, an amplifier detection module, a speaker detection module, a battery detection module, a noise suppression detection module, a feedback suppression detection module, a data acquisition and processing module, a filter module, an algorithm module, and an alarm display module that are electrically connected to each other. It performs self-testing through a recursive minimum mean square error adaptive filter and intelligent algorithms, thereby monitoring the operating status of the hearing aid in real time and diagnosing faults.
It enables real-time self-testing and automatic diagnosis of hearing aids, allowing users to promptly identify potential problems, ensure proper device operation, reduce failure rates, extend lifespan, and increase user confidence and satisfaction.
Smart Images
Figure CN121665168A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of hearing aid self-testing technology, specifically a hearing aid operation self-testing method. Background Technology
[0002] Hearing aids are assistive devices used by people with hearing impairments to compensate for the deficiencies caused by hearing loss, thereby improving their ability to communicate with others. A hearing aid consists of a microphone, amplifier, receiver, and power supply. Hearing aids are categorized by sound amplification level: low-power, medium-power, and high-power. When wearing a hearing aid, the appropriate product should be selected based on its electroacoustic characteristics and the relationship between it and the patient's hearing characteristics and needs. After wearing the hearing aid, it is best to familiarize yourself with various background sounds in a quiet environment and try to distinguish each sound. Hearing aids should be tested during use to detect potential problems early and extend their lifespan.
[0003] Hearing aids are common hearing assistive devices that amplify ambient sounds, helping people with hearing impairments hear clearer sounds. With technological advancements, modern hearing aids possess various intelligent functions, such as noise suppression, echo cancellation, and speech recognition. These functions enable hearing aids to better adapt to different hearing needs and environments. However, despite the increasingly powerful functions of hearing aids, users may still encounter various problems during use, such as device malfunctions and poor sound quality. Currently, hearing aid testing involves manual checks of the hearing aid's sound, battery, functional safety, and wireless connectivity. However, monitoring the internal hardware and software functions requires specialized equipment, making hearing aid testing cumbersome. These problems may affect the user's auditory experience and may even cause further hearing damage. Therefore, to ensure the normal operation of hearing aids, it is necessary to perform regular self-tests. This paper proposes a self-testing method for hearing aids. Summary of the Invention
[0004] To address the problems in the prior art, this invention provides a self-testing method for hearing aid operation.
[0005] The technical solution adopted by this invention to solve its technical problem is: a hearing aid operation self-test method, including a microphone detection module, an amplifier detection module, a speaker detection module, a battery detection module, a noise suppression detection module, a feedback suppression detection module, an acquisition and processing module, a filter module, an algorithm module, and an alarm display module. The microphone detection module, amplifier detection module, speaker detection module, battery detection module, noise suppression detection module, feedback suppression detection module, acquisition and processing module, filter module, algorithm module, and alarm display module are electrically connected to each other and can operate independently. The microphone detection module checks whether the microphone can receive sound signals normally; the amplifier detection module checks whether the amplifier can amplify sound signals normally; the speaker detection module checks whether the speaker can output sound signals normally; the battery detection module checks whether the battery power is sufficient; the noise suppression detection module checks whether the noise suppression function is normal; and the feedback suppression detection module checks whether the feedback suppression function is normal.
[0006] The data acquisition and processing module will collect and detect data from the microphone detection module, amplifier detection module, speaker detection module, battery detection module, noise suppression detection module, and feedback suppression detection module respectively. The data acquisition and processing module is responsible for collecting various data from each module of the device in real time through its internal sensors and monitoring circuits; these data include, but are not limited to, audio signals, power supply voltage, temperature, humidity, etc. The collected data is first preprocessed, including noise reduction and normalization, to ensure the accuracy and consistency of the data.
[0007] The filter module uses a recursive minimum mean square error adaptive filter to filter the data; The recursive minimum mean square error adaptive filter is a filter that can adaptively adjust the filter parameters according to the input signal. It continuously adjusts the filter parameters by estimating and comparing the input signal to minimize the error between the output signal and the input signal. In hearing aids, adaptive filters can be used to process the input audio signal to improve signal quality and eliminate noise. By adjusting the filter coefficients, the RLS algorithm can adapt to different signal characteristics, thereby providing the best audio enhancement effect in hearing aids.
[0008] The intelligent algorithm includes a decision tree algorithm and a neural network algorithm. The decision tree algorithm constructs a tree structure to make decisions based on the data collected and processed by the hearing aid, and at the same time, it branches according to the data characteristics to determine whether different modules are wrong. Simultaneously, it is combined with neural network algorithms for calculation, which are assisted by data input, forward propagation, loss calculation, back propagation, and repeated training; hearing aid detection data is input into the neural network algorithm; The data is processed layer by layer along the neural network to obtain the output structure; By comparing the output results with the true values, the value of the loss function is calculated. A database is built using the function value and the data, and the data is continuously calculated and trained. Based on the value of the loss function, the parameters of the neural network are adjusted using the backpropagation algorithm; The process of forward propagation, calculation, and back propagation is repeated until satisfactory performance or convergence is achieved. Then, data comparison is performed to check for errors, which are then sent to the alarm display module.
[0009] The alarm display module performs self-tests through each module and diagnoses the hearing aid based on the results of the fault diagnosis technology. If a fault is detected, it promptly issues an alarm signal and takes corresponding measures, such as turning off the hearing aid or prompting the user to replace the battery.
[0010] The filter module and algorithm module are used to monitor the key components of the hearing aid in real time, including hardware circuits, software programs and sensors. By monitoring the operating status of these components, potential faults can be detected in a timely manner. Data processing is performed using decision tree and neural network algorithms, and a model-based fault diagnosis technique is adopted. This technique first establishes a normal working model of the hearing aid, and then determines whether there is a fault by comparing the difference between the actual working data and the model data. The accuracy and efficiency of fault diagnosis are improved by continuously learning and calculating through intelligent algorithm modules.
[0011] The data processed by the filter module is sent to the fault diagnosis module; this module first analyzes the data to determine whether there are any anomalies. If an anomaly is detected, the module will perform fault diagnosis based on the nature and severity of the anomaly; if audio signal distortion is detected, the fault diagnosis module will determine whether it is caused by a hardware failure or a software configuration problem, and take appropriate measures. Artificial intelligence technology can help improve the accuracy and efficiency of fault diagnosis during the diagnostic process.
[0012] A method for using a hearing aid self-test includes the following steps: S1: Power-on self-test: When the user turns on the hearing aid, the device will automatically perform a basic hardware check, including whether the microphone, amplifier, speaker, etc. are responding. During the self-test, the hearing aid may prompt the user on the current test progress through the display screen or voice. Hearing test: The hearing aid will play a series of sounds of different frequencies and intensities through the headphones or bone conduction device. The user needs to adjust the settings according to what they hear. This step is intended to verify whether the hearing amplification effect of the hearing aid meets the user's hearing needs. S2: Audio Test: The hearing aid plays a specific audio signal to test whether the microphone can capture the sound normally and to determine whether the amplifier can process the signal correctly. Users can check the audio quality and clarity through the hearing aid's control panel or application. Feedback test: The hearing aid will simulate feedback in real use, such as when the user speaks and the sound is fed back to the microphone to test whether the feedback suppression function is working properly. If the hearing aid detects feedback, it should be able to effectively suppress this sound to avoid howling. S3: Calibration Test: The hearing aid uses a built-in calibration program to verify that all settings are accurate, including volume, frequency response, and balance. If inaccurate settings are found, the hearing aid will automatically adjust them to ensure the best hearing experience. The results show that after the hearing aid completes all self-test steps, it will display the test results on the screen or synchronize the results with the user's smartphone application via Bluetooth technology.
[0013] The beneficial effects of this invention are: This invention discloses a hearing aid self-testing method and its usage. By setting up a filtering module and intelligent algorithm for fault diagnosis, it realizes real-time monitoring and automatic diagnosis of the hearing aid during operation. This effectively improves the self-testing capability, allowing users to promptly identify potential problems with the hearing aid, such as hardware failures or setting errors, thereby ensuring that the device can work normally when needed. The self-test results allow users to intuitively understand the working status of the hearing aid, increasing their confidence and satisfaction during use. Through regular self-testing and timely maintenance, the failure rate of the hearing aid can be reduced, extending its service life. Self-testing can help users make adjustments when problems are minor, thereby reducing maintenance costs caused by malfunctions. Attached Figure Description
[0014] The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0015] Figure 1 This is a schematic diagram of the process structure of a hearing aid self-testing method provided by the present invention. Detailed Implementation
[0016] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.
[0017] like Figure 1 As shown, the hearing aid self-test method of the present invention includes a microphone detection module, an amplifier detection module, a speaker detection module, a battery detection module, a noise suppression detection module, a feedback suppression detection module, a data acquisition and processing module, a filter module, an algorithm module, and an alarm display module. The microphone detection module, amplifier detection module, speaker detection module, battery detection module, noise suppression detection module, feedback suppression detection module, data acquisition and processing module, filter module, algorithm module, and alarm display module are electrically connected to each other and can operate independently. The microphone detection module checks whether the microphone can receive sound signals normally; the amplifier detection module checks whether the amplifier can amplify sound signals normally; the speaker detection module checks whether the speaker can output sound signals normally; the battery detection module checks whether the battery power is sufficient; the noise suppression detection module checks whether the noise suppression function is normal; and the feedback suppression detection module checks whether the feedback suppression function is normal.
[0018] The data acquisition and processing module will collect and detect data from the microphone detection module, amplifier detection module, speaker detection module, battery detection module, noise suppression detection module, and feedback suppression detection module respectively. The data acquisition and processing module is responsible for collecting various data from each module of the device in real time through its internal sensors and monitoring circuits; these data include, but are not limited to, audio signals, power supply voltage, temperature, humidity, etc. The collected data is first preprocessed, including noise reduction and normalization, to ensure the accuracy and consistency of the data.
[0019] The filter module uses a recursive minimum mean square error adaptive filter to filter the data; The recursive minimum mean square error adaptive filter is a filter that can adaptively adjust the filter parameters according to the input signal. It continuously adjusts the filter parameters by estimating and comparing the input signal to minimize the error between the output signal and the input signal. In hearing aids, adaptive filters can be used to process the input audio signal to improve signal quality and eliminate noise. By adjusting the filter coefficients, the RLS algorithm can adapt to different signal characteristics, thereby providing the best audio enhancement effect in hearing aids.
[0020] The intelligent algorithm includes a decision tree algorithm and a neural network algorithm. The decision tree algorithm constructs a tree structure to make decisions based on the data collected and processed by the hearing aid, and at the same time, it branches according to the data characteristics to determine whether different modules are wrong. Simultaneously, it is combined with neural network algorithms for calculation, which are assisted by data input, forward propagation, loss calculation, back propagation, and repeated training; hearing aid detection data is input into the neural network algorithm; The data is processed layer by layer along the neural network to obtain the output structure; By comparing the output results with the true values, the value of the loss function is calculated. A database is built using the function value and the data, and the data is continuously calculated and trained. Based on the value of the loss function, the parameters of the neural network are adjusted using the backpropagation algorithm; The process of forward propagation, calculation, and back propagation is repeated until satisfactory performance or convergence is achieved. Then, data comparison is performed to check for errors, which are then sent to the alarm display module.
[0021] The alarm display module performs self-tests through each module and diagnoses the hearing aid based on the results of the fault diagnosis technology. If a fault is detected, it promptly issues an alarm signal and takes corresponding measures, such as turning off the hearing aid or prompting the user to replace the battery.
[0022] The filter module and algorithm module are used to monitor the key components of the hearing aid in real time, including hardware circuits, software programs and sensors. By monitoring the operating status of these components, potential faults can be detected in a timely manner. Data processing is performed using decision tree and neural network algorithms, and a model-based fault diagnosis technique is adopted. This technique first establishes a normal working model of the hearing aid, and then determines whether there is a fault by comparing the difference between the actual working data and the model data. The accuracy and efficiency of fault diagnosis are improved by continuously learning and calculating through intelligent algorithm modules.
[0023] The data processed by the filter module is sent to the fault diagnosis module; this module first analyzes the data to determine whether there are any anomalies. If an anomaly is detected, the module will perform fault diagnosis based on the nature and severity of the anomaly; if audio signal distortion is detected, the fault diagnosis module will determine whether it is caused by a hardware failure or a software configuration problem, and take appropriate measures. Artificial intelligence technology can help improve the accuracy and efficiency of fault diagnosis during the diagnostic process.
[0024] In practical use: When the user turns on the hearing aid, the device will automatically perform a basic hardware check, including whether the microphone, amplifier, and speaker are responding. During the self-test, the hearing aid may provide the user with the current progress through the display screen or voice prompts. The hearing aid will then play a series of sounds of different frequencies and intensities through the headphones or bone conduction unit. The user needs to adjust the settings according to what they hear. This step aims to verify whether the hearing amplification effect of the hearing aid meets the user's hearing needs. The hearing aid will then play specific audio signals to test whether the microphone can capture the sound normally and whether the amplifier can process these signals correctly. The user can do this through the hearing aid's control panel. The hearing aid uses an app to verify the quality and clarity of the audio; it simulates real-world feedback, such as sound being fed back to the microphone when the user speaks, to check if the feedback suppression function is working properly. If the hearing aid detects feedback, it should be able to effectively suppress the sound to avoid whistling; the hearing aid uses a built-in calibration program to verify the accuracy of all settings, including volume, frequency response, and balance. If inaccurate settings are found, the hearing aid will automatically adjust to ensure the best hearing experience; after completing all self-test steps, the hearing aid will display the test results on the screen or synchronize the results with the user's smartphone app via Bluetooth.
[0025] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of protection claimed by the present invention. The scope of protection of the present invention is defined by the appended claims and their equivalents.
Claims
1. A self-test method for hearing aid operation, characterized in that, It includes a microphone detection module, an amplifier detection module, a speaker detection module, a battery detection module, a noise suppression detection module, a feedback suppression detection module, a data acquisition and processing module, a filter module, an algorithm module, and an alarm display module. These modules are electrically connected to each other and can operate independently. The microphone detection module checks whether the microphone can receive sound signals normally; the amplifier detection module checks whether the amplifier can amplify sound signals normally; the speaker detection module checks whether the speaker can output sound signals normally; the battery detection module checks whether the battery power is sufficient; and the noise suppression detection module checks whether the noise suppression function is normal. The feedback suppression detection module checks whether the feedback suppression function is normal.
2. The hearing aid self-test method according to claim 1, characterized in that: The data acquisition and processing module will collect and detect data from the microphone detection module, amplifier detection module, speaker detection module, battery detection module, noise suppression detection module, and feedback suppression detection module respectively. The data acquisition and processing module is responsible for collecting various data from each module of the device in real time through its internal sensors and monitoring circuits; these data include, but are not limited to, audio signals, power supply voltage, temperature, humidity, etc. The collected data is first preprocessed, including noise reduction and normalization, to ensure the accuracy and consistency of the data.
3. The hearing aid self-test method according to claim 1, characterized in that: The filter module uses a recursive minimum mean square error adaptive filter to filter the data; The recursive minimum mean square error adaptive filter is a filter that can adaptively adjust the filter parameters according to the input signal. It continuously adjusts the filter parameters by estimating and comparing the input signal to minimize the error between the output signal and the input signal. In hearing aids, adaptive filters can be used to process the input audio signal to improve signal quality and eliminate noise. By adjusting the filter coefficients, the RLS algorithm can adapt to different signal characteristics, thereby providing the best audio enhancement effect in hearing aids.
4. The hearing aid self-test method according to claim 1, characterized in that: The intelligent algorithm includes a decision tree algorithm and a neural network algorithm. The decision tree algorithm constructs a tree structure to make decisions based on the data collected and processed by the hearing aid, and at the same time, it branches according to the data characteristics to determine whether different modules are wrong. Simultaneously, it is used in conjunction with neural network algorithms to assist in computation, through data input, forward propagation, loss calculation, back propagation, and repeated training. Input the hearing aid test data into the neural network algorithm; The data is processed layer by layer along the neural network to obtain the output structure; By comparing the output results with the true values, the value of the loss function is calculated. A database is built using the function value and the data, and the data is continuously calculated and trained. Based on the value of the loss function, the parameters of the neural network are adjusted using the backpropagation algorithm; The process of forward propagation, calculation, and back propagation is repeated until satisfactory performance or convergence is achieved. Then, data comparison is performed to check for errors, which are then sent to the alarm display module.
5. The hearing aid self-test method according to claim 1, characterized in that: The alarm display module performs self-tests through each module and diagnoses the hearing aid based on the results of the fault diagnosis technology. If a fault is detected, it promptly issues an alarm signal and takes corresponding measures, such as turning off the hearing aid or prompting the user to replace the battery.
6. The hearing aid self-test method according to claim 1, characterized in that: The filter module and algorithm module are used to monitor the key components of the hearing aid in real time, including hardware circuits, software programs and sensors. By monitoring the operating status of these components, potential faults can be detected in a timely manner. Data processing is performed using decision tree and neural network algorithms, and a model-based fault diagnosis technique is adopted. This technique first establishes a normal working model of the hearing aid, and then determines whether there is a fault by comparing the difference between the actual working data and the model data. The accuracy and efficiency of fault diagnosis are improved by continuously learning and calculating through intelligent algorithm modules.
7. The hearing aid self-test method according to claim 1, characterized in that: The data processed by the filter module is sent to the fault diagnosis module; this module first analyzes the data to determine whether there are any anomalies. If an anomaly is detected, the module will perform fault diagnosis based on the nature and severity of the anomaly; if audio signal distortion is detected, the fault diagnosis module will determine whether it is caused by a hardware failure or a software configuration problem, and take appropriate measures. Artificial intelligence technology can help improve the accuracy and efficiency of fault diagnosis during the diagnostic process.
8. A method of using the hearing aid operation self-test method as described in any one of claims 1-7, characterized in that: Includes the following steps: first step: Power-on self-test: When the user turns on the hearing aid, the device will automatically perform a basic hardware check, including whether the microphone, amplifier, speaker, etc. are responding. During the self-test, the hearing aid may prompt the user on the current test progress through the display screen or voice. Hearing test: The hearing aid will play a series of sounds of different frequencies and intensities through the headphones or bone conduction device. The user needs to adjust the settings according to what they hear. This step is intended to verify whether the hearing amplification effect of the hearing aid meets the user's hearing needs. Step Two: Audio test: The hearing aid plays a specific audio signal to test whether the microphone can capture the sound normally and to determine whether the amplifier can process the signal correctly. Users can check the audio quality and clarity through the hearing aid's control panel or application. Feedback test: The hearing aid will simulate feedback in real use, such as when the user speaks and the sound is fed back to the microphone to test whether the feedback suppression function is working properly. If the hearing aid detects feedback, it should be able to effectively suppress this sound to avoid howling. Step 3: Calibration test: The hearing aid uses a built-in calibration program to verify that all settings are accurate, including volume, frequency response and balance. If inaccurate settings are found, the hearing aid will automatically adjust to ensure the best hearing experience. The results show that after the hearing aid completes all self-test steps, it will display the test results on the screen or synchronize the results with the user's smartphone application via Bluetooth technology.