Detection device capable of supporting automatic hearing test

Abstract

The utility model relates to the technical field of hearing testing equipment, in particular to testing equipment capable of supporting automatic hearing testing, which comprises a host and earphones. A man-machine interaction module; the data acquisition module is used for generating an audiogram and hearing loss degree data according to the sound signal generated by the audio generation module and the response data of the subject; the storage module is used for storing and uploading the current test data and synchronizing with the cloud to read the historical data of the subject; the processor is used for comparing the historical data read by the storage module with the subject data input by the man-machine interaction module and the audiogram and hearing loss degree data generated by the data acquisition module, and adjusting the sound signal generated by the audio generation module; the data acquisition module supplements the missing part of the current audiogram and the hearing loss degree data or determines different parts again, and then the corresponding type is compared according to the existing case record and a report is generated.

Description

Technical Field

[0001] This utility model relates to the technical field of hearing testing equipment, and specifically to a testing device that can support automatic hearing testing. Background Technology

[0002] Traditional audiometers are mainly used to test a patient's hearing threshold using sound signals of different frequencies and sound pressure levels. However, their function is limited to the data acquisition stage, requiring professionals to manually interpret the results. Therefore, they have the following problems:

[0003] 1. Inefficient data management: The test results rely on paper or scattered electronic records, making it difficult to track changes in the subject's hearing over a long period of time. Moreover, each hearing test requires readjustment, which may not be suitable for some subjects whose hearing changes frequently.

[0004] 2. High subjectivity in analysis: Differences in user experience may lead to misjudgment of results, it is not possible to review case records in a timely manner, and the analysis takes a long time. Changes in the subject's environment, age, and reaction to the test can all affect the results.

[0005] 3. Insufficient remote support: Data cannot be synchronized with the cloud and analyzed remotely, and non-professionals cannot obtain accurate hearing information and problems based on test results.

[0006] While some existing audiometers support electronic storage, they lack intelligent analysis capabilities and fail to address issues such as remote use by non-professionals and adaptability to various scenarios. Therefore, there is an urgent need for an audiometer that integrates data management and automated analysis. Utility Model Content

[0007] To address the problems existing in the prior art, one objective of this invention is to provide a testing device that supports automated hearing testing. This device supports data storage, intelligent analysis, and automatic report generation, integrating testing, storage, and analysis to improve testing efficiency and reduce repetitive work. A second objective of this invention is to provide a method for using the automated hearing testing device. This method further reduces the randomness of changes in a subject's hearing during testing, thereby improving the accuracy of automated hearing testing.

[0008] The detection device supporting automatic hearing testing according to this utility model includes a main unit and an earphone, wherein the main unit is equipped with:

[0009] An audio generation module, connected to the headphones, is used to generate pure tones or speech signals of different frequencies and sound pressure levels.

[0010] The human-computer interaction module, connected to the audio generation module, is used to input subject data, select test modes, and obtain subject response data to sound signals;

[0011] The data acquisition module is connected to the audio generation module and the human-computer interaction module respectively, and generates an audiogram and hearing loss degree data based on the sound signal generated by the audio generation module and the subject's response data;

[0012] A storage module, connected to the data acquisition module, is used to store and upload current test data and synchronize with the cloud to read the subject's historical data;

[0013] The processor is connected to the audio generation module, the human-computer interaction module, the data acquisition module, and the storage module, respectively. It is used to compare the historical data read by the storage module with the subject data input by the human-computer interaction module and the audiogram and hearing loss degree data generated by the data acquisition module. It adjusts the sound signal generated by the audio generation module so that the data acquisition module can supplement the missing parts of the current audiogram and hearing loss degree data or redetermine the different parts. Then, it compares the corresponding type with the existing case records and generates a report.

[0014] In one embodiment, the processor includes:

[0015] The data preprocessing module is connected to the storage module and the audio generation module respectively, and is used to preset the frequency range and amplitude of the sound signal generated by the audio generation module based on the historical data read by the storage module;

[0016] The intelligent analysis module is connected to the human-computer interaction module, the data acquisition module, and the storage module respectively, and is used for data comparison and type analysis to improve the subject's audiogram and hearing loss degree data;

[0017] The report generation module is connected to both the intelligent analysis module and the data acquisition module, and is used to generate a report that includes an audiogram, the degree of hearing loss, and recommended solutions.

[0018] In one embodiment, the intelligent analysis module includes a preset rule base and a machine learning model. The rule base presets hearing loss determination rules, and the machine learning model learns hearing loss patterns through historical data to assist in the analysis of the type.

[0019] In one embodiment, a noise filtering module is further provided between the audio generation module and the processor. The noise filtering module is used to adjust the preset frequency range and amplitude of the processor according to the ambient noise sensed by the sensor.

[0020] In one embodiment, the storage module is connected to a data encryption module for encrypting and anonymizing all data of the subject.

[0021] In one embodiment, the storage module is also connected to a communication module for providing review reports and annotations to professionals via a cloud platform.

[0022] In one embodiment, the headphones are noise-canceling headphones, and the headphones are connected to the audio generation module via wired or wireless means.

[0023] The method of using the detection device that supports automatic hearing testing according to this utility model includes the following steps:

[0024] S1. The subject wears the earphones and inputs personal information and selects the test type through the human-computer interaction module;

[0025] S2. The processor sets the frequency range and amplitude of the hearing test according to the historical data read by the storage module, and causes the audio generation module to output the sound signal of the hearing test to the headphones;

[0026] S3. The subject responds to the human-computer interaction module based on the sound he hears. The data acquisition module generates an audiogram and hearing loss degree data, and compares it with the historical data read by the storage module in the processor. Based on the missing parts of the current audiogram and hearing loss degree data and the parts that are different from the historical data, the frequency range and amplitude of the hearing test are set again, so that the audio generation module outputs the hearing test sound signal with the changed set values ​​to the headphones again.

[0027] S4. After the current audiogram and hearing loss level data are completed and determined, the processor compares the corresponding type with the existing case records and generates a report, which is then recorded in the storage module.

[0028] In one embodiment, prior to step S2, the audio generation module outputs pure tone or speech signals of frequencies and sound pressure levels that the subject can hear, based on historical data, to test and determine the subject's response time.

[0029] In one embodiment, in step S4, the report generated by the processor is uploaded to the cloud through the storage module, where professionals review the report, add annotations, and then send it back to the storage module for recording.

[0030] Compared with the prior art, the beneficial effects of this utility model's technical solution are:

[0031] This invention utilizes an intelligent data management architecture to innovatively construct a hearing test system encompassing detection, storage, and analysis. It supports data storage, intelligent analysis, and automatic report generation. At the data storage level, it can synchronously acquire and compare historical data with cloud data, reflecting changes in the subject's hearing test results. The processor ensures the test results are not accidental by changing preset values, and by comparing these results with existing case records, a general analysis can be obtained. This system is suitable for non-professionals seeking a simple understanding of their own hearing condition and is applicable to clinical hearing tests, health screenings, and remote analysis scenarios, improving testing efficiency and reducing repetitive work.

[0032] This invention, through comparison of historical data and refinement through multiple hearing tests, further reduces accidental errors caused by environmental noise, fluctuations in attention, or changes in physiological state during hearing testing. Furthermore, by analyzing historical data and optimizing the testing process, this method ensures consistent and reliable hearing thresholds across different testing environments, facilitating early detection and intervention of hearing impairments and improving the hearing health management of test takers. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the structure of the detection device that supports automatic hearing testing according to this utility model;

[0034] Figure 2 This is a schematic diagram of the module connection of the detection device that supports automatic hearing testing according to this utility model;

[0035] Figure 3 This is a flowchart illustrating the usage method of the detection device that supports automatic hearing testing according to this utility model.

[0036] Explanation of reference numerals in the attached diagram: 1-Host, 2-Earphone, 3-Audio generation module, 4-Human-computer interaction module, 5-Data acquisition module, 6-Storage module, 7-Processor, 71-Data preprocessing module, 72-Intelligent analysis module, 73-Report generation module, 8-Noise filtering module, 9-Data encryption module, 10-Communication module. Detailed Implementation

[0037] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent. To better illustrate this embodiment, some components in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0038] In the description of this utility model, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can be described as the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this utility model based on the specific circumstances. The technical solution of this utility model will be further described below with reference to the accompanying drawings and embodiments.

[0039] like Figure 1 and Figure 2 As shown, the detection device of this utility model that supports automatic hearing testing includes a main unit 1 and an earphone 2. The main unit 1 is equipped with:

[0040] The audio generation module 3 is connected to the headphones 2 and is used to generate pure tones or speech signals of different frequencies and sound pressure levels.

[0041] The human-computer interaction module 4 is connected to the audio generation module 3 and is used to input subject data, select test modes, and obtain subject response data to sound signals.

[0042] The data acquisition module 5 is connected to the audio generation module 3 and the human-computer interaction module 4 respectively. Based on the sound signal generated by the audio generation module 3 and the subject's response data, it generates an audiogram and hearing loss degree data.

[0043] Storage module 6, connected to data acquisition module 5, is used to store and upload current test data and synchronize with the cloud to read the subject's historical data;

[0044] The processor 7 is connected to the audio generation module 3, the human-computer interaction module 4, the data acquisition module 5, and the storage module 6, respectively. It is used to compare the historical data read by the storage module 6 with the subject data input by the human-computer interaction module 4 and the audiogram and hearing loss degree data generated by the data acquisition module 5. It adjusts the sound signal generated by the audio generation module 3 so that the data acquisition module 5 can supplement the missing parts of the current audiogram and hearing loss degree data or redetermine the different parts. Then, it compares the corresponding type with the existing case records and generates a report.

[0045] This utility model features a compact and rationally connected device structure. Based on an intelligent data management architecture, it innovatively constructs a hearing test system that integrates detection, storage, and analysis, supporting data storage, intelligent analysis, and automatic report generation. At the data storage level, the connection between the storage module 6 and the processor 7 enables the device to synchronously acquire and compare historical data with the cloud. When the audio generation module 3 generates test sound signals, it can reference the subject's individual historical data to reflect changes in the subject's hearing test results. Furthermore, during the comparison process, the processor 7 can adjust preset values ​​to ensure the test results are not accidental. By comparing these results with existing case records, the analysis results of the hearing test can be obtained more accurately. This system is suitable for non-professionals to gain a simple understanding of their own hearing condition and is applicable to clinical hearing tests, health screenings, and remote analysis scenarios, improving testing efficiency and reducing repetitive work.

[0046] In one embodiment, the processor 7 includes:

[0047] The data preprocessing module 71 is connected to the storage module 6 and the audio generation module 3 respectively, and is used to preset the frequency range and amplitude of the sound signal generated by the audio generation module 3 according to the historical data read by the storage module 6.

[0048] The intelligent analysis module 72 is connected to the human-computer interaction module 4, the data acquisition module 5 and the storage module 6 respectively, and is used for data comparison and type analysis to improve the subject's audiogram and hearing loss degree data;

[0049] The report generation module 73 is connected to the intelligent analysis module 72 and the data acquisition module 5, respectively, and is used to generate a report that includes an audiogram, the degree of hearing loss, and a suggested treatment plan.

[0050] After the subject selects a hearing test mode, such as a pure tone test or a test incorporating environmental speech, the data preprocessing module 71 acquires relevant historical data based on the subject's data. Then, based on the historical data, it presets the frequency range and amplitude of the test sound to generate a targeted test sound signal. The report generation module 73 generates an audiogram and hearing loss level data based on the subject's response data. The intelligent analysis module 72 compares this with the most recent historical data. If the audiogram is missing or differs from the historical data, the processor 7 adjusts the frequency range and amplitude again to complete the audiogram and identify the differences. Generally, a difference requires at least two data points to be consistent for a difference to be considered confirmed. This allows for a hearing test that more closely reflects reality, and then a complete hearing threshold curve and hearing loss level are generated accordingly. Furthermore, the intelligent analysis module 72 includes a preset rule base and a machine learning model. The rule base presets hearing loss determination rules, and the machine learning model learns hearing loss patterns from historical data to assist in the analysis of the type. The intelligent analysis module 72 first presets the judgment rules for hearing loss from the rule base, such as comparing common and standardized audiograms with corresponding case situations. Then, combined with historical data, the machine learning model will further improve the display of the judgment results by comparing with other cases, such as the similarity ratio of audiograms in different standard cases. This allows the final test results to be compared with existing case records to identify possible situations, such as noise exposure and age-related hearing loss. The report generation module 73 then generates a report with suggested solutions to assist in the analysis of types such as conductive and sensorineural hearing loss.

[0051] In addition, a noise filtering module 8 is provided between the audio generation module 3 and the processor 7. The noise filtering module 8 adjusts the preset frequency range and amplitude of the processor 7 based on the ambient noise sensed by the sensor. The noise filtering module 8 is equipped with an ambient noise sensor. When the preset values ​​for the frequency range and amplitude of the test sound are set in the data preprocessing module 71 based on the ambient noise, the module adds an assignment to account for the ambient noise, thereby reducing the impact of ambient noise on the hearing test. The storage module 6 is connected to a data encryption module 9, which encrypts and anonymizes all data of the test subjects before uploading it to the professional personnel's information system. Professional personnel must verify the identity of the test subjects to access the relevant information. Furthermore, the storage module 6 is also connected to a communication module 10, which provides review reports and annotations to professionals via a cloud platform. Professional personnel can manually review the test results, especially those of unusual circumstances, such as cases where no similar hearing threshold curves exist in the case records. Additionally, the headphones 2 are noise-canceling headphones, and the headphones 2 can be connected to the audio generation module 3 via wired or wireless connections, such as WiFi or Bluetooth.

[0052] like Figure 3As shown, the method of using the detection device of this utility model that supports automatic hearing testing includes the following steps:

[0053] S1. The subject wears headphones 2 and inputs personal information and selects the test type, such as pure tone test or voice test, through the human-computer interaction module 4.

[0054] S2, the processor 7 sets the frequency range and amplitude of the hearing test based on the historical data read by the storage module 6 and the subject's personal information, such as age and occupation, and causes the audio generation module 3 to output the sound signal of the hearing test to the headphones 2;

[0055] S3. The subject responds to the human-computer interaction module 4 based on the sound he hears. The data acquisition module 5 generates an audiogram and hearing loss degree data, and compares it with the historical data read by the storage module 6 in the processor 7. Based on the missing parts of the current audiogram and hearing loss degree data and the parts that are different from the historical data, the frequency range and amplitude of the hearing test are set again, so that the audio generation module 3 outputs the sound signal of the hearing test after the setting value is changed to the headphones 2 again.

[0056] S4. After the current audiogram and hearing loss level data are completed and confirmed, it is determined that at least two sets of data must be consistent, including test data and the most recent historical data. The processor 7 compares the existing case records to determine the corresponding type and generates a report, which is then recorded in the storage module 6.

[0057] This invention, through comparison of historical data and refinement through multiple hearing tests, further reduces accidental errors caused by environmental noise, fluctuations in attention, or changes in physiological state during hearing testing. Furthermore, by analyzing historical data and optimizing the testing process, this method ensures consistent and reliable hearing thresholds across different testing environments, facilitating early detection and intervention of hearing impairments and improving the hearing health management of test takers.

[0058] Furthermore, prior to step S2, the audio generation module 3 outputs pure tones or speech signals of frequencies and sound pressure levels audible to the subject based on historical data to test and determine the subject's response time. Essentially, before the hearing test, the device records the subject's response time, determining the interval between when the subject hears the sound and when they input the signal to hear the sound into the human-computer interaction module 4. This interval is compensated for when drawing an audiogram or calculating the degree of hearing loss, reducing its impact on the test results. Specifically, in step S4, the report generated by the processor 7 is uploaded to the cloud via the storage module 6. Professionals review the report, add annotations, and then feed it back to the storage module 6 for recording. Especially for special audiograms that cannot match existing case records or have a matching rate below 90%, further manual review by professionals is required. Test results and suggested solutions that have not undergone manual review will be marked on the device for the subject.

[0059] In the description of this application, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is usually based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application.

[0060] The positional relationships described in the figures are for illustrative purposes only and should not be construed as limiting this patent. Clearly, the above embodiments of this utility model are merely examples to clearly illustrate the present utility model, and are not intended to limit the implementation of the present utility model. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this utility model should be included within the scope of protection of the claims of this utility model.

Claims

1. A testing device capable of supporting automatic hearing tests, comprising a main unit (1) and an earphone (2), characterized in that, The host (1) is equipped with: An audio generation module (3) is connected to the headphones (2) and is used to generate pure tones or speech signals of different frequencies and sound pressures; The human-computer interaction module (4) is connected to the audio generation module (3) and is used to input subject data, select test mode and obtain subject response data to sound signals; The data acquisition module (5) is connected to the audio generation module (3) and the human-computer interaction module (4) respectively, and generates an audiogram and hearing loss degree data based on the sound signal generated by the audio generation module (3) and the response data of the subject. The storage module (6) is connected to the data acquisition module (5) and is used to store and upload the current test data and synchronize with the cloud to read the historical data of the subject. The processor (7) is connected to the audio generation module (3), the human-computer interaction module (4), the data acquisition module (5), and the storage module (6), respectively. It is used to compare the historical data read by the storage module (6) with the subject data input by the human-computer interaction module (4) and the audiogram and hearing loss degree data generated by the data acquisition module (5), adjust the sound signal generated by the audio generation module (3), so that the data acquisition module (5) can supplement the missing parts of the current audiogram and hearing loss degree data or redetermine the different parts, and then compare the corresponding type according to the existing case records and generate a report.

2. The detection device supporting automatic hearing testing according to claim 1, characterized in that, The processor (7) includes: The data preprocessing module (71) is connected to the storage module (6) and the audio generation module (3) respectively, and is used to preset the frequency range and amplitude of the audio generation module (3) when generating sound signals based on the historical data read by the storage module (6); The intelligent analysis module (72) is connected to the human-computer interaction module (4), the data acquisition module (5) and the storage module (6) respectively, and is used for data comparison and type analysis to improve the audiogram and hearing loss data of the subject; The report generation module (73) is connected to the intelligent analysis module (72) and the data acquisition module (5) respectively, and is used to generate a report containing audiograms, hearing loss degree and recommended solutions.

3. The detection device supporting automatic hearing testing according to claim 2, characterized in that, The intelligent analysis module (72) includes a preset rule base and a machine learning model. The rule base presets hearing loss determination rules, and the machine learning model learns hearing loss patterns through historical data to assist in the analysis of types.

4. The detection device supporting automatic hearing testing according to claim 3, characterized in that, A noise filtering module (8) is also provided between the audio generation module (3) and the processor (7). The noise filtering module (8) is used to adjust the preset frequency range and amplitude of the processor (7) according to the ambient noise sensed by the sensor.

5. The detection device supporting automatic hearing testing according to claim 4, characterized in that, The storage module (6) is connected to a data encryption module (9) for encrypting and anonymizing all data of the subject.

6. The detection device supporting automatic hearing testing according to claim 5, characterized in that, The storage module (6) is also connected to a communication module (10), which is used to provide professional personnel with review reports and add annotations through a cloud platform.

7. The detection device supporting automatic hearing testing according to claim 6, characterized in that, The earphone (2) is a noise-canceling earphone, and the earphone (2) is connected to the audio generation module (3) via wired or wireless connection.

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