A tinnitus monitoring method and system based on a smart earphone

Abstract

The present application belongs to the technical field of electronic equipment, and specifically relates to a tinnitus monitoring method and system based on a smart earphone. The design of the smart earphone makes tinnitus monitoring simple and easy, greatly improving the convenience and timeliness of monitoring. Through a high-precision spectrum analysis chip and an adaptive filtering algorithm, environmental noise can be effectively suppressed, ensuring the accuracy of tinnitus frequency and loudness data. A deep learning model is used to generate personalized control signals, and sound signals are customized according to the tinnitus characteristics of each patient, significantly improving the effectiveness and pertinence of analysis. In addition, data encryption and remote transmission functions ensure the security and privacy protection of patient data, and also facilitate remote monitoring and adjustment of personalized analysis schemes by doctors. The present application effectively solves the problem of difficult implementation of convenient and accurate tinnitus self-monitoring and personalized analysis in the prior art, and brings a more scientific and efficient health management means for tinnitus patients.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of electronic equipment, and particularly relates to a tinnitus monitoring method and system based on a smart earphone. BACKGROUND

[0002] Tinnitus, as a common auditory phenomenon, has a significant negative impact on many people's daily lives. Existing tinnitus monitoring methods mainly rely on professional medical equipment and the operation of professionals, such as pure tone audiometers or otoacoustic emission testers. These devices are usually large in size, complex in operation and need to be performed in hospitals or clinics, limiting the possibility of patients' daily self-monitoring. In addition, traditional methods often only provide basic information on tinnitus frequency and loudness, lack personalized analysis schemes, and cannot dynamically adjust according to the specific conditions of each patient.

[0003] Specifically, one of the main problems in the prior art is that it is difficult to achieve convenient and accurate self-monitoring and personalized analysis of tinnitus. Since traditional tinnitus monitoring devices lack portability and ease of use, patients are unlikely to continuously monitor their tinnitus status at home, which not only affects the effectiveness of early detection and intervention, but also limits the feasibility of long-term management. At the same time, traditional analysis methods are mostly general strategies that do not fully consider individual differences, resulting in inaccurate analysis results. SUMMARY

[0004] The present application aims to provide a tinnitus monitoring method and system based on a smart earphone to solve the problems raised in the background.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solution: a tinnitus monitoring method based on a smart earphone, comprising the following steps:

[0006] Pressing the operation button starts the tinnitus detection module, and the ear sound signal is collected by the spectrum analysis chip. The time domain signal collected by the spectrum analysis chip is converted into a frequency domain signal by fast Fourier transform, and the tinnitus frequency range is identified;

[0007] The spectrum analysis chip obtains tinnitus loudness data through a sound pressure sensor and transmits the tinnitus frequency range and loudness data to the main control module;

[0008] The main control module stores the tinnitus frequency range and loudness data in the data storage module through the AES encryption standard and sends the tinnitus frequency range and loudness data to the rehabilitation sound synthesis module;

[0009] The rehabilitation sound synthesis module generates a personalized control signal based on the tinnitus frequency range and loudness data and transmits the personalized control signal to the programmable waveform generator to generate rehabilitation sound. The programmable waveform generator outputs the rehabilitation sound through the sound output unit.

[0010] Preferably, the spectrum analysis chip converts the acquired time-domain signal into a frequency-domain signal using a fast Fourier transform, including:

[0011] The collected ear sound signals are processed by frame segmentation, with each frame containing a fixed number of sampling points;

[0012] Apply a Hamming window function to each frame of the signal to reduce spectral leakage;

[0013] The frequency domain components of each frame of signal are calculated by Fast Fourier Transform, and the frequency range with energy higher than the background noise is extracted.

[0014] Preferably, the spectrum analysis chip acquires tinnitus loudness data through a sound pressure sensor, including:

[0015] Short-time energy calculation is performed on the sound signal to the ear to obtain the instantaneous sound pressure value;

[0016] The instantaneous sound pressure value is smoothed by using a moving average filter to eliminate transient noise interference;

[0017] The smoothed sound pressure level is compared with the reference sound pressure level to calculate the relative change in tinnitus loudness.

[0018] Preferably, the main control module stores the tinnitus frequency range and loudness data to the data storage module using the AES encryption standard, including:

[0019] The tinnitus frequency range and loudness data are divided into data blocks of fixed size;

[0020] Apply the AES encryption algorithm to each data block to generate the corresponding ciphertext data;

[0021] Write the encrypted data to the designated storage area of ​​the data storage module.

[0022] Preferably, the generation of personalized control signals includes:

[0023] The tinnitus frequency range is matched with the rehabilitation sound frequency using preset mapping rules;

[0024] The amplitude of the rehabilitation sound is adjusted according to the tinnitus loudness data to maintain a dynamic balance between the intensity of the rehabilitation sound and the tinnitus loudness.

[0025] Optimize control signal parameters using historical rehabilitation records.

[0026] Preferably, the generation of rehabilitation sounds includes:

[0027] A composite signal of white noise and inverse phase masking tone is synthesized using a programmable waveform generator;

[0028] Adjust the amplitude ratio of white noise and masking tone according to the personalized control signal;

[0029] The synthesized signal is low-pass filtered by a digital signal processing module to remove high-frequency noise components.

[0030] Preferably, the following steps are also included:

[0031] The main control module transmits the tinnitus frequency range and loudness data to the mobile application via Bluetooth.

[0032] The mobile application displays the trend of tinnitus frequency over time in the form of a line graph and uploads the data to the cloud server via a Wi-Fi module.

[0033] Preferably, the main control module transmits the tinnitus frequency range and loudness data to a mobile application via a Bluetooth module, including:

[0034] Encapsulate the tinnitus frequency range and loudness data into a Bluetooth data packet;

[0035] Data packets are sent to the mobile device via the Bluetooth protocol stack;

[0036] The mobile device receives the data packet, parses it, and stores it in the local database.

[0037] Preferably, uploading data to the cloud server via the Wi-Fi module includes:

[0038] The locally stored tinnitus data is sent to the cloud server via the HTTP protocol;

[0039] The cloud server performs integrity checks on the received data;

[0040] Long-term trend reports on patients' tinnitus conditions are generated using data visualization tools.

[0041] On the other hand, the present invention proposes a tinnitus monitoring system based on smart headphones, comprising:

[0042] The operation button is used to activate the tinnitus detection module;

[0043] The spectrum analysis chip, connected to the operation buttons, is used to collect ear sound signals and perform spectrum analysis.

[0044] A sound pressure sensor, connected to a spectrum analysis chip, is used to acquire tinnitus loudness data;

[0045] The main control module, connected to the spectrum analysis chip and sound pressure sensor, is used to receive and process tinnitus data;

[0046] The data storage module, connected to the main control module, is used to store encrypted tinnitus data;

[0047] The rehabilitation sound synthesis module, connected to the main control module, is used to generate personalized control signals;

[0048] A programmable waveform generator, connected to a rehabilitation sound synthesis module, is used to generate rehabilitation sounds;

[0049] The sound-generating unit, connected to a programmable waveform generator, is used to output rehabilitation sounds;

[0050] The Bluetooth module, connected to the main control module, is used to transmit tinnitus data to the mobile application.

[0051] The Wi-Fi module, connected to the main control module, is used to upload data to the cloud server.

[0052] Technical effects and advantages of the present invention: The tinnitus monitoring method and system based on smart headphones proposed in this invention have the following advantages compared with the prior art:

[0053] This invention provides a convenient and accurate solution for tinnitus self-monitoring and personalized analysis. First, the smart earphone design simplifies tinnitus monitoring, allowing patients to monitor their tinnitus in real-time at home without visiting a medical facility, significantly improving convenience and timeliness. Second, a high-precision spectrum analysis chip and adaptive filtering algorithm effectively suppress environmental noise, ensuring the accuracy of tinnitus frequency and loudness data. Third, a deep learning model generates personalized control signals, customizing sound signals based on each patient's tinnitus characteristics, significantly enhancing the effectiveness and relevance of the analysis. Furthermore, data encryption and remote transmission ensure the security and privacy of patient data, while also facilitating remote monitoring and adjustment of personalized analysis plans by doctors. In summary, this invention effectively solves the problem of achieving convenient and accurate tinnitus self-monitoring and personalized analysis in existing technologies, providing tinnitus patients with a more scientific and efficient health management method. Attached Figure Description

[0054] Figure 1 This is a flowchart of a tinnitus monitoring method based on smart headphones according to the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] This invention provides, for example Figure 1 The tinnitus monitoring method based on smart headphones shown includes the following steps:

[0057] Press the operation button to start the tinnitus detection module. The spectrum analysis chip collects the ear sound signal. The spectrum analysis chip converts the collected time-domain signal into a frequency-domain signal through a fast Fourier transform to identify the tinnitus frequency range. This includes: performing frame processing on the collected ear sound signal, with each frame containing a fixed number of sampling points; applying a Hamming window function to each frame signal to reduce spectral leakage; and calculating the frequency domain components of each frame signal through a fast Fourier transform to extract the frequency range with energy higher than the background noise.

[0058] This invention activates the tinnitus detection module by pressing an operation button and utilizes a spectrum analysis chip to collect and process ear sound signals, achieving efficient and accurate identification of the tinnitus frequency range. Specific technical effects are as follows:

[0059] Patients can activate the tinnitus detection module simply by pressing a button, eliminating the need for complex equipment or professional personnel. This greatly simplifies the tinnitus monitoring process and enhances user experience and convenience. The spectrum analysis chip can acquire ear sound signals in real time and process them in frames, with each frame containing a fixed number of sampling points. This framing method makes subsequent frequency domain analysis more accurate, avoiding the computational complexity issues caused by long-term continuous signal processing. Furthermore, framing helps capture instantaneous changes in the tinnitus signal, improving detection sensitivity and accuracy.

[0060] During signal processing for each frame, a Hamming window function is applied to reduce spectral leakage. Spectral leakage refers to the energy leakage phenomenon caused by signal truncation in frequency domain analysis. By applying the Hamming window function, this leakage can be effectively reduced, improving the accuracy of the spectral analysis results. This step ensures the clear extraction of tinnitus frequency components, reduces background noise interference, and thus more accurately identifies the tinnitus frequency range.

[0061] Each frame of the signal is converted from the time domain to the frequency domain using a Fast Fourier Transform (FFT), and its frequency components are calculated. This process is not only fast but also effectively reveals the frequency characteristics of the tinnitus signal. By comparing the energy levels of each frequency band, the frequency range with energy higher than the background noise can be extracted, which represents the main frequency components of tinnitus. This method significantly improves the accuracy of tinnitus frequency identification, helping users to more accurately understand their own tinnitus condition.

[0062] Ultimately, the system identifies the tinnitus frequency range based on the extracted frequency domain components. Compared to traditional methods, this approach maintains high recognition accuracy even in complex environments (such as those with ambient noise). This method allows users to obtain more detailed and accurate tinnitus frequency information, providing a solid foundation for subsequent personalized analysis.

[0063] The spectrum analysis chip acquires tinnitus loudness data through a sound pressure sensor and transmits the tinnitus frequency range and loudness data to the main control module; including: performing short-time energy calculation on the ear sound signal to obtain the instantaneous sound pressure value; smoothing the instantaneous sound pressure value through a moving average filter to eliminate transient noise interference; comparing the smoothed sound pressure value with a reference sound pressure value to calculate the relative change amplitude of tinnitus loudness.

[0064] By using a sound pressure sensor to perform short-time energy calculations on the sound signal to the ear and obtaining the instantaneous sound pressure value, the loudness of tinnitus is no longer a vague description of subjective perception, but is transformed into a measurable and recordable physical quantity. This method improves the objectivity and consistency of loudness information, providing a reliable basis for subsequent data analysis and long-term trend tracking.

[0065] After acquiring the instantaneous sound pressure level (SPL) value, a moving average filter is used to smooth it. This processing method effectively suppresses transient interference caused by sudden environmental noise, ear canal micro-movements, or signal acquisition jitter, avoiding drastic fluctuations in loudness data. The smoothed SPL value better reflects the true level of tinnitus loudness, enhancing the continuity and reliability of the data.

[0066] The smoothed sound pressure level (SPL) value is compared with a preset reference SPL value to calculate the relative change in tinnitus loudness. This method eliminates absolute SPL differences caused by factors such as individual ear canal structure and sensor fit, making loudness data comparable across different times or users. This relativization provides fundamental support for cross-time period trend analysis and personalized data modeling.

[0067] While identifying the tinnitus frequency range, the spectrum analysis chip simultaneously acquires loudness data and integrates the two before transmitting them to the main control module. This integrated data acquisition mechanism ensures strict temporal alignment of frequency and loudness information, avoiding data misalignment issues caused by asynchronous acquisition from multiple modules and improving the coordination and completeness of the overall monitoring results.

[0068] Packaging and transmitting frequency and loudness data to the main control module provides structured input for data encryption, storage, and further analysis. This design reduces communication overhead and timing errors associated with distributed data processing, thereby improving the overall system efficiency.

[0069] In summary, this invention achieves stable, objective, and comparable acquisition of tinnitus loudness through short-time energy calculation, moving average filtering, and relative amplitude comparison, and outputs it in conjunction with frequency information. This technique effectively overcomes the problems of loudness data being easily interfered with, difficult to quantify, and lacking consistency in traditional monitoring, significantly improving the accuracy and practicality of tinnitus monitoring and providing solid technical support for users to track their tinnitus status over the long term.

[0070] The main control module stores the tinnitus frequency range and loudness data to the data storage module using the AES encryption standard, including: dividing the tinnitus frequency range and loudness data into fixed-size data blocks; applying the AES encryption algorithm to each data block to generate corresponding ciphertext data; writing the ciphertext data into a designated storage area of ​​the data storage module; and sending the tinnitus frequency range and loudness data to the rehabilitation sound synthesis module.

[0071] Tinnitus frequency and loudness data are highly sensitive personal health information. By dividing the data into fixed-size blocks and independently applying AES encryption to each block to generate ciphertext data, it is ensured that the original data is not stored in plaintext. Even if the data storage module is accessed or physically extracted without authorization, the original tinnitus information cannot be recovered, effectively preventing privacy leaks.

[0072] Tinnitus data is divided into fixed-size blocks, facilitating unified management and scheduling of storage space by the main control module. This method supports segmented writing and reading of data, improving the efficiency and stability of storage operations, avoiding fragmentation problems caused by varying data lengths, and enhancing the long-term operational reliability of the data storage module.

[0073] The AES encryption standard is used as the encryption algorithm. This standard has broad technical support and hardware acceleration capabilities, enabling efficient execution even with the limited computing resources of smart earphones. The encrypted ciphertext data conforms to general security specifications, providing a secure foundation for subsequent uploading to external devices or the cloud via the communication module and ensuring end-to-end data transmission confidentiality.

[0074] While completing encrypted storage, the main control module directly sends the original tinnitus frequency range and loudness data to the rehabilitation sound synthesis module, avoiding the impact of processing delays introduced by the encryption process on the real-time performance of subsequent sound generation. This parallel processing mechanism decouples "secure storage" from "functional flow," satisfying both data security requirements and ensuring system response speed.

[0075] The encrypted data is written to a designated storage area of ​​the data storage module, forming an ordered sequence of data records. This design supports data retrieval and playback by timestamp or other identifiers, providing a data foundation for users to view historical tinnitus status and analyze trends, thus enhancing the system's long-term monitoring capabilities.

[0076] The rehabilitation sound synthesis module generates personalized control signals based on tinnitus frequency range and loudness data, including: matching the tinnitus frequency range with the rehabilitation sound frequency according to a preset mapping rule; adjusting the amplitude of the rehabilitation sound according to the tinnitus loudness data to maintain a dynamic balance between the intensity of the rehabilitation sound and the tinnitus loudness; and optimizing the control signal parameters through historical rehabilitation records. The personalized control signals are then transmitted to a programmable waveform generator to generate rehabilitation sounds, including: synthesizing a composite signal of white noise and a reverse-phase masking tone using the programmable waveform generator; adjusting the amplitude ratio of the white noise and the masking tone according to the personalized control signals; and performing low-pass filtering on the synthesized signal using a digital signal processing module to remove high-frequency noise components. The programmable waveform generator outputs the rehabilitation sounds through a sound-generating unit.

[0077] By using preset mapping rules, the detected tinnitus frequency range is mapped to the frequencies of rehabilitation sounds, ensuring that the generated sound signal matches the tinnitus components perceived by the user in the frequency domain. This mapping mechanism avoids the problem of inconsistencies between general sound schemes and individual tinnitus characteristics, making the output sound more closely match the user's auditory perception characteristics and improving the relevance and adaptability of the sound output.

[0078] The amplitude of the rehabilitation sound is adjusted based on the acquired tinnitus loudness data to maintain a dynamic balance between the intensity of the rehabilitation sound and the tinnitus loudness. This method avoids situations where the sound is too loud, causing auditory discomfort, or too weak, rendering it ineffective. It ensures that the output sound is within the user's acceptable and effective perceptual range, enhancing comfort and consistency during use.

[0079] By optimizing control signal parameters by referencing historical rehabilitation records, newly generated control signals are adjusted based on previously effective settings. This mechanism avoids starting from scratch each time, improves the stability and continuity of the sound output strategy, helps to form regular sound exposure patterns, and supports the perceptual adaptation process under long-term use.

[0080] A composite signal combining white noise and inverse phase masking tone is synthesized using a programmable waveform generator, leveraging the masking effect of broadband noise and the cancellation potential of a specific frequency inverse signal. This composite structure enhances the intervention capability for tinnitus perception without relying on high-intensity output, resulting in a more layered sound output with broader coverage.

[0081] The amplitude ratio between white noise and masking tone is adjusted according to personalized control signals, allowing the composition of the composite signal to be flexibly adjusted based on different tinnitus characteristics. For broadband tinnitus, the proportion of white noise can be increased to enhance the overall masking effect; for narrowband high-frequency tinnitus, the weight of the reverse phase masking tone can be increased to achieve targeted intervention, improving the flexibility of sound generation and individual adaptability.

[0082] The synthesized signal is low-pass filtered by a digital signal processing module to remove high-frequency noise components and avoid introducing sharp frequencies that may cause auditory fatigue. This processing makes the output sound smoother and more natural, reduces stimulation to healthy hearing areas, and improves the wearer's tolerance and long-term usability.

[0083] Personalized control signals are converted into specific sound waveforms via a programmable waveform generator and output to the user's ear in real time through a sound-generating unit, achieving a complete closed loop from tinnitus feature recognition to sound response. The entire process requires no external intervention, allowing users to independently complete sound matching and output in their daily environment, significantly improving the system's independent operation capability and ease of use.

[0084] Furthermore, the main control module transmits the tinnitus frequency range and loudness data to the mobile application via the Bluetooth module, including: encapsulating the tinnitus frequency range and loudness data into a Bluetooth data packet; sending the data packet to the mobile device via the Bluetooth protocol stack; and the mobile device receiving the data packet, parsing it, and storing it in a local database.

[0085] The tinnitus frequency range and loudness data are encapsulated into Bluetooth data packets and sent to the mobile device via the Bluetooth protocol stack, eliminating the reliance on a physical connection. Users can upload data while wearing smart headphones, without needing to plug or unplug cables or manually export data, improving the convenience and continuity of data transmission and supporting continuous tracking of tinnitus status.

[0086] Ensuring the structured and complete nature of data transmission: Frequency and loudness information are organized into data packets conforming to Bluetooth communication specifications through standardized data encapsulation methods, ensuring that information is not misaligned, lost, or corrupted during wireless transmission. This mechanism improves the reliability of data transmission over the air interface, providing a fundamental guarantee for the accurate reconstruction of original monitoring results on the mobile device.

[0087] After receiving Bluetooth data packets, the mobile device parses them and stores the parsed data in a local database, enabling terminal-based storage and management of tinnitus information. Users can view historical records and analyze trends on their mobile phones without relying on the headphone's local display function, thus expanding data accessibility and usage scenarios.

[0088] Once the data is input into the mobile device, it can be combined with timestamps to generate curves showing the changes in tinnitus frequency and loudness, presenting the tinnitus fluctuation pattern in a visual way. Users can understand the evolution of their own condition through intuitive charts, enhancing their understanding of tinnitus and their self-management awareness, providing a basis for subsequent behavioral adjustments.

[0089] The Bluetooth transmission mechanism reserves an interface for future system function expansion. Building upon basic data uploads, it can further support two-way interaction such as parameter feedback from the mobile device and the issuance of sound mode switching commands, enabling the smart headphones and mobile terminals to form a collaborative working system and enhancing the overall system's flexibility and intelligence.

[0090] In summary, this invention achieves wireless transmission of tinnitus frequency and loudness data to a mobile phone via a Bluetooth module, completing data integration from the data acquisition terminal to the user interface. This technology effectively solves the problems of isolated data, difficulty in viewing, and inability to track data over a long period in traditional tinnitus monitoring, enhancing system usability and user engagement, and providing reliable technical support for the daily, visualized management of tinnitus status.

[0091] The mobile application displays the trend of tinnitus frequency over time in the form of a line graph, and uploads the data to the cloud server via a Wi-Fi module, including: sending locally stored tinnitus data to the cloud server via HTTP protocol; the cloud server performing integrity verification on the received data; and generating a long-term trend report of the patient's tinnitus condition through a data visualization tool.

[0092] By displaying the distribution of tinnitus frequency at different points in time using a line graph, users can clearly identify the fluctuation patterns of tinnitus frequency, such as whether it tends to stabilize, gradually shifts, or experiences sudden changes. This visualization method transforms abstract numerical data into easily understandable graphical information, significantly improving users' ability to recognize their own tinnitus condition.

[0093] Line charts organize data by time series, covering periods of several days, weeks, or even longer, helping users and relevant personnel identify the evolution of tinnitus characteristics. By observing frequency trends, it's possible to determine whether there are periodic changes or a continuous worsening trend, providing a reference for daily management.

[0094] Tinnitus data stored in the local database on the mobile device is sent to the cloud server via the HTTP protocol, utilizing standard network communication mechanisms to complete the data upload. This method is compatible with existing network infrastructure, requires no special equipment or complex configuration, and enables remote data collection, ensuring centralized data management.

[0095] Upon receiving tinnitus data, the cloud server performs an integrity check to verify whether the data packets are complete, whether the timestamps are continuous, and whether the values ​​are within a reasonable range. This mechanism can effectively identify packet loss, out-of-order, or abnormal data that may occur during transmission, preventing erroneous information from entering long-term records and improving the reliability of remote datasets.

[0096] By integrating and processing accumulated tinnitus data through cloud-deployed data visualization tools, a trend report is automatically generated, containing information such as frequency change trajectory, fluctuation range, and extreme points. This report supports multi-dimensional retrospective analysis, providing systematic support for users to review historical data and assess the process of change.

[0097] Data stored in the cloud and generated trend reports can be accessed by authorized personnel. With user permission, doctors or hearing professionals can remotely view the long-term evolution of their tinnitus frequency, providing data support for subsequent personalized recommendations and enhancing the system's synergistic role in health management.

[0098] The cloud-based data architecture supports future functional expansion, such as multi-user data comparison, statistical analysis model integration, and anomaly warning mechanisms. This design also reserves space for future system upgrades, enhancing the overall solution's sustainability.

[0099] In summary, this invention uses line graphs to display the trend of tinnitus frequency changes, and combines Wi-Fi uploading with cloud processing to achieve visualized, long-term, and remote management of monitoring data.

[0100] On the other hand, this invention proposes a tinnitus monitoring system based on smart headphones. This system can not only accurately capture tinnitus signals, but also generate personalized rehabilitation sounds based on the patient's tinnitus characteristics, achieving continuous and effective tinnitus management. The entire process involves the collaborative work of multiple modules, including tinnitus detection, data processing, rehabilitation sound synthesis, communication, and power supply.

[0101] First, during the tinnitus detection phase, the patient presses the operation button to activate the tinnitus detection module. The spectrum analysis chip acquires and processes the ear sound signal, converting the time-domain signal into a frequency-domain signal using a Fast Fourier Transform (FFT) algorithm to identify the tinnitus frequency range. Simultaneously, a sound pressure sensor monitors changes in ear sound intensity in real time, acquiring tinnitus loudness data. This data is then transmitted to the main control module for storage and further processing.

[0102] During the data processing phase, the main control module receives data from the tinnitus detection module and securely stores it in a flash memory chip using AES encryption. Additionally, the main control module sends data to the rehabilitation sound synthesis module and the communication module. The rehabilitation sound synthesis module analyzes the tinnitus data based on a pre-trained deep learning model, generating personalized control signals to guide the programmable waveform generator in producing rehabilitation sounds. This step considers the specific values ​​of tinnitus frequency and loudness, as well as the patient's historical rehabilitation records, to ensure the effectiveness of the rehabilitation program.

[0103] Next, the communication module transmits the tinnitus detection results and rehabilitation records to a mobile application in real time via Bluetooth. The application displays the changing trends of tinnitus frequency and loudness in intuitive charts, helping patients understand their condition. Simultaneously, doctors can access patient data through a cloud server to assess current rehabilitation progress and provide adjustment suggestions. When the headphones are in a Wi-Fi network environment, data is uploaded to the cloud for remote medical monitoring.

[0104] Finally, the system's power supply module uses a high-energy-density rechargeable lithium battery to ensure stable operation over extended periods. The power management chip monitors the battery status in real time and alerts the user to charge it via status indicator lights. Users can charge the headphones via a Type-C interface or wireless charging devices, ensuring continuous system availability.

[0105] Through the above steps, this invention provides a complete tinnitus monitoring and rehabilitation process, realizing closed-loop management from detection to rehabilitation and feedback adjustment, which greatly improves the rehabilitation experience of tinnitus patients.

[0106] Tinnitus monitoring systems based on smart headphones specifically include:

[0107] The operation button is used to activate the tinnitus detection module;

[0108] The spectrum analysis chip, connected to the operation buttons, is used to collect ear sound signals and perform spectrum analysis.

[0109] A sound pressure sensor, connected to a spectrum analysis chip, is used to acquire tinnitus loudness data;

[0110] The main control module, connected to the spectrum analysis chip and sound pressure sensor, is used to receive and process tinnitus data;

[0111] The data storage module, connected to the main control module, is used to store encrypted tinnitus data;

[0112] The rehabilitation sound synthesis module, connected to the main control module, is used to generate personalized control signals;

[0113] A programmable waveform generator, connected to a rehabilitation sound synthesis module, is used to generate rehabilitation sounds;

[0114] The sound-generating unit, connected to a programmable waveform generator, is used to output rehabilitation sounds;

[0115] The Bluetooth module, connected to the main control module, is used to transmit tinnitus data to the mobile application.

[0116] The Wi-Fi module, connected to the main control module, is used to upload data to the cloud server.

[0117] In addition, the aforementioned components are also used to implement other steps of the aforementioned tinnitus monitoring method based on smart headphones, as follows:

[0118] Tinnitus detection activation and signal acquisition:

[0119] The patient presses the operation button on the smart earphone to activate the tinnitus detection module. This operation triggers the built-in spectrum analysis chip to start working. After initialization, the chip immediately enters standby mode, ready to collect ear sound signals.

[0120] The spectrum analysis chip begins real-time acquisition of sound signals from the ear. Assume the acquisition time is... seconds, sampling rate The total number of samples collected was [number missing]. 1 sample point.

[0121] During the data acquisition process, the adaptive filtering algorithm dynamically adjusts the filtering parameters, as shown in the following formula: ;in, The transfer function of the filter is represented by... and These are the coefficients in the numerator and denominator, respectively. The algorithm automatically adjusts these coefficients to suppress environmental noise based on the real-time acquired signal characteristics.

[0122] The filtered signal needs further denoising and smoothing. A weighted average is performed using a window function, as shown in the following formula: ;

[0123] in, For window function weights, For input signal, This is the output signal. This step helps to remove high-frequency noise and retain the effective tinnitus signal components.

[0124] The Fast Fourier Transform (FFT) is used to convert the time-domain signal into a frequency-domain signal, facilitating subsequent spectral analysis. The FFT formula is: ;

[0125] in, It is the first in the frequency domain One frequency component, It is the first in the time domain Each sample point. This transformation clearly shows the amplitude of each frequency component.

[0126] In the frequency domain signal, identify the frequency component that is significantly higher than the background noise as the tinnitus frequency. Let the tinnitus frequency be... Then we have: ;

[0127] in, It is a set threshold used to distinguish between background noise and tinnitus signals. Through this process, the main frequency range of tinnitus can be accurately identified.

[0128] Tinnitus loudness measurement:

[0129] A sound pressure sensor monitors changes in sound intensity around the ear in real time to obtain tinnitus loudness data. Sound pressure level. The calculation formula is: ;

[0130] in, The actual measured sound pressure value. The reference sound pressure level is 20 mPa. This calculation allows for the quantification of the loudness level of tinnitus.

[0131] The detected tinnitus frequency and loudness data are integrated into a single data packet, formatted, and then transmitted to the main control module via the internal bus. Assuming the data packet size is D bytes and the transmission rate is Rbps, the transmission time t is: This process ensures that the data is transmitted to the main control module completely and without error, preparing it for the next step of data processing.

[0132] After receiving the data packet from the tinnitus detection module, the main control module immediately decodes it and stores it in the data storage module. The data storage module uses the AES encryption standard to protect the data security. The basic principle of AES encryption is to transform the original data into unreadable ciphertext through a series of complex mathematical operations.

[0133] Specifically, the AES encryption algorithm uses a block cipher mechanism, where each data block is 128 bits (16 bytes) long, and the encryption key can be 128 bits, 192 bits, or 256 bits long. The encryption process can be represented by the following formula: ;

[0134] Here, C represents ciphertext, E_k represents the encryption function, and P represents plaintext. This step ensures the secure storage of sensitive data and prevents unauthorized access.

[0135] After storage, the main control module further analyzes the tinnitus data. First, it categorizes the tinnitus frequency and loudness data for subsequent processing. Assume there is a set containing N tinnitus data points, each including the tinnitus frequency... and loudness A classification function can be defined:

[0136] ;

[0137] in, and These are the thresholds for frequency and loudness, respectively. This classification function can be used to identify severe tinnitus cases requiring special attention, thereby enabling the development of more targeted rehabilitation plans.

[0138] After classification is complete, the main control module sends the data to the rehabilitation sound synthesis module. During this process, the data undergoes compression and encoding to reduce transmission bandwidth usage. Assuming the original data size is D bytes and the compression ratio is r, the compressed data size is: ;

[0139] If the transmission rate remains Rbps, then the transmission time becomes ;

[0140] This optimization improves data transmission efficiency, shortens waiting time, and ensures that the rehabilitation sound synthesis module can obtain the required data as quickly as possible.

[0141] To ensure data integrity and reliability, the main control module also periodically synchronizes important data to external storage devices or cloud servers. The synchronization process involves data verification, and one commonly used verification algorithm is CRC (Cyclic Redundancy Check), whose formula is: ;

[0142] Where M(x) is the message polynomial, G(x) is the generator polynomial, and n is the order of the generator polynomial. CRC checksums can detect and correct errors during transmission, ensuring data accuracy.

[0143] After receiving data from the main control module, the rehabilitation sound synthesis module first parses it. Assume the received data packet is... Including tinnitus frequency and loudness Information. The parsing process mainly involves restoring the data packet to its original format for subsequent processing. The parsing formula is as follows: ;

[0144] Here, P represents the parsed dataset, containing specific tinnitus frequencies and loudness values. This step ensures the integrity and accuracy of the data, laying the foundation for generating personalized rehabilitation plans.

[0145] The parsed data is fed into a pre-trained deep learning model for analysis. This model, trained on a large dataset of tinnitus patients, can generate personalized control signals based on the frequency, loudness, and historical rehabilitation records of different patients. The core of the model is a multi-layer neural network, and its forward propagation process can be represented by the following formula: ;

[0146] Where Y is the output control signal. Let be the activation function (e.g., ReLU), W be the weight matrix, X be the input data (tinnitus frequency and loudness), and b be the bias term. This model, through learning from a large amount of data, can accurately predict rehabilitation strategies suitable for each patient.

[0147] Based on the output of the deep learning model, corresponding control signals are generated to guide the programmable waveform generator in producing rehabilitation sounds. For example, for patients with high-frequency tinnitus, the model might generate instructions for the programmable waveform generator to produce low-frequency masking sounds. Assuming the tinnitus frequency is... Then the control signal S is expressed as: ;

[0148] in, This is a control signal generation function that selects an appropriate masking frequency based on the different frequencies of tinnitus. For different types of tinnitus, this function dynamically adjusts the generated control signal to ensure maximum rehabilitation effect.

[0149] Based on the generated control signal, the programmable waveform generator synthesizes the corresponding sound waveform. Suppose we need to generate a composite healing sound, incorporating white noise and inverse phase masking tone; its synthesis formula can be expressed as: ;

[0150] in, and The amplitudes of white noise and masking sound are respectively. and These are the frequencies of white noise and masking tone, respectively. This is due to the phase difference. In this way, sound waveforms with therapeutic effects can be generated, helping patients alleviate tinnitus symptoms.

[0151] Finally, the synthesized sound waveform is played back through the sound-generating unit. Assume the output power of the sound-generating unit is... Then the output sound pressure level It can be represented as: ;

[0152] in, For reference power (take) By adjusting the output power, the sound intensity can be flexibly adjusted to ensure that patients receive rehabilitation within a comfortable range. If patients feel that the sound volume is inappropriate, they can adjust it through the operating buttons or the mobile application.

[0153] The communication module plays a crucial interactive role throughout the process. First, tinnitus detection data and rehabilitation records are transmitted in real-time to the mobile application via Bluetooth technology. The basic principle of Bluetooth transmission is short-range wireless communication using the 2.4GHz ISM band, with data transmission rates typically between 1Mbps and 3Mbps. Assuming the amount of data transmitted each time is... bytes, transmission time is Seconds, then the transmission rate for: ;

[0154] This efficient transmission method ensures the real-time nature and integrity of the data, allowing patients to check their tinnitus status immediately.

[0155] After receiving the data, the mobile application will display the tinnitus frequency over time and the fluctuations in tinnitus loudness on a data visualization interface in the form of line charts, bar charts, etc. Assuming there is a period of time ( Tinnitus frequency data (seconds) The formula for drawing a line chart is: ;

[0156] in, It is a unit impulse function. The time intervals are used to represent the time points. This intuitive presentation helps patients better understand their tinnitus condition and its changing trends, thereby enhancing their confidence and motivation for recovery.

[0157] In addition to data visualization, the mobile application also provides rehabilitation plan management functions. Patients can view historical rehabilitation records, understand their past rehabilitation progress, and set personalized rehabilitation parameters, such as preferred rehabilitation sound types. For example, if a patient selects a certain type of rehabilitation sound, their preference setting can be represented as: ;

[0158] in, Different types of rehabilitation sounds are available. This personalized setting allows patients to choose the most suitable rehabilitation program based on their preferences and comfort, improving rehabilitation outcomes.

[0159] If a patient wants to switch rehabilitation modes or adjust the volume, they can do so via buttons or a mobile application. For example, if a patient wants to switch from mode 1 to mode 2, the process can be represented as follows: ;

[0160] The main control module controls the rehabilitation sound synthesis module to switch between different rehabilitation sound generation methods according to instructions. Similarly, volume adjustment can also be achieved through similar operations, ensuring that patients can adjust to the most suitable state at any time during the rehabilitation process.

[0161] When the headphones are in a Wi-Fi network environment, the Wi-Fi module of the communication module uploads data from the data storage module to the cloud server. Assuming the data upload speed is UMbps and the total data volume is... bytes, then upload time for: ;

[0162] Doctors access patient data on cloud servers through a dedicated medical data management platform to comprehensively analyze patients' tinnitus conditions and rehabilitation outcomes. For example, doctors can observe trends in tinnitus frequency and loudness over a period of time, assess the effectiveness of the current rehabilitation plan, and provide personalized rehabilitation suggestions and plan adjustment notifications based on the analysis results.

[0163] Through the above steps, the communication module not only achieves efficient data transmission and display, but also provides a convenient interactive platform for patients and doctors, promoting personalized and scientific management of tinnitus rehabilitation.

[0164] The system's power supply module uses a high-energy-density rechargeable lithium battery to ensure stable operation over extended periods. Assuming the battery capacity is Q mAh and the rated voltage is V volts, the battery's energy E can be expressed as: This design provides ample power to all modules of the headphones, ensuring stable performance even during extended use.

[0165] The power management chip monitors the battery's charge and discharge status in real time to ensure the battery is always in optimal operating condition. Assuming the battery has a remaining charge percentage of S, its remaining energy... for: ;

[0166] When the battery level drops below a certain threshold, the power management chip will use a status indicator light to remind the user to charge the battery in time, either by color or by flashing. For example, a flashing red light may indicate that the battery is low and needs to be charged immediately; a solid blue light indicates that the battery is fully charged and can be used normally.

[0167] Users can charge the earphones using a charging cable via the Type-C interface, or place the earphones on a wireless charging device to charge via the wireless charging receiver coil.

[0168] The operation buttons, touch-sensitive area, and status indicator lights provide users with a convenient interaction method. The operation buttons not only realize basic functions such as power on / off, detection and startup, mode switching, and volume adjustment, but also enable some advanced functions, such as quick-start recovery mode, through different button combinations.

[0169] The touch-sensitive area allows users to control functions via touch operations such as swiping and double-tapping. For example, when listening to music, users can adjust the volume by swiping on the touch-sensitive area without having to search for operation buttons. The status indicator lights clearly show the working status of the headphones through different colors and flashing frequencies, such as a solid blue light indicating that Bluetooth is connected, and a flashing red light indicating that it is charging.

[0170] Through the above steps, this invention not only provides a comprehensive tinnitus monitoring and rehabilitation solution, but also features meticulous design in power management and user interaction, ensuring the system's high efficiency, reliability, and ease of use.

[0171] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A tinnitus monitoring method based on smart headphones, characterized in that, Includes the following steps: Press the operation button to start the tinnitus detection module. The spectrum analysis chip collects the sound signal from the ear. The spectrum analysis chip converts the collected time-domain signal into a frequency-domain signal through a fast Fourier transform to identify the tinnitus frequency range. The spectrum analysis chip acquires tinnitus loudness data through a sound pressure sensor and transmits the tinnitus frequency range and loudness data to the main control module. The main control module stores the tinnitus frequency range and loudness data in the data storage module using the AES encryption standard, and sends the tinnitus frequency range and loudness data to the rehabilitation sound synthesis module; The rehabilitation sound synthesis module generates personalized control signals based on tinnitus frequency range and loudness data, and transmits the personalized control signals to the programmable waveform generator to generate rehabilitation sounds. The programmable waveform generator outputs the rehabilitation sounds through the sound generation unit. The generation of personalized control signals includes: matching the tinnitus frequency range with the rehabilitation sound frequency through a preset mapping rule; adjusting the amplitude of the rehabilitation sound according to the tinnitus loudness data to maintain a dynamic balance between the intensity of the rehabilitation sound and the tinnitus loudness; and optimizing the control signal parameters through historical rehabilitation records. The process of generating rehabilitation sounds includes: synthesizing a composite signal of white noise and inverse phase masking tone using a programmable waveform generator; adjusting the amplitude ratio of white noise and masking tone according to a personalized control signal; and performing low-pass filtering on the synthesized signal using a digital signal processing module to remove high-frequency noise components.

2. The tinnitus monitoring method based on smart headphones according to claim 1, characterized in that, The spectrum analysis chip converts the acquired time-domain signal into a frequency-domain signal using a fast Fourier transform, including: The collected ear sound signals are processed by frame segmentation, with each frame containing a fixed number of sampling points; Apply a Hamming window function to each frame of the signal to reduce spectral leakage; The frequency domain components of each frame of signal are calculated by Fast Fourier Transform, and the frequency range with energy higher than the background noise is extracted.

3. The tinnitus monitoring method based on smart headphones according to claim 1, characterized in that, The spectrum analysis chip acquires tinnitus loudness data through a sound pressure sensor, including: Short-time energy calculation is performed on the sound signal to the ear to obtain the instantaneous sound pressure value; The instantaneous sound pressure value is smoothed by using a moving average filter to eliminate transient noise interference; The smoothed sound pressure level is compared with the reference sound pressure level to calculate the relative change in tinnitus loudness.

4. The tinnitus monitoring method based on smart headphones according to claim 1, characterized in that, The main control module stores the tinnitus frequency range and loudness data to the data storage module using the AES encryption standard, including: The tinnitus frequency range and loudness data are divided into data blocks of fixed size; Apply the AES encryption algorithm to each data block to generate the corresponding ciphertext data; Write the encrypted data to the designated storage area of ​​the data storage module.

5. The tinnitus monitoring method based on smart headphones according to claim 1, characterized in that, It also includes the following steps: The main control module transmits the tinnitus frequency range and loudness data to the mobile application via Bluetooth. The mobile application displays the trend of tinnitus frequency over time in the form of a line graph and uploads the data to the cloud server via a Wi-Fi module.

6. The tinnitus monitoring method based on smart headphones according to claim 5, characterized in that, The main control module transmits the tinnitus frequency range and loudness data to the mobile application via Bluetooth, including: Encapsulate the tinnitus frequency range and loudness data into a Bluetooth data packet; Data packets are sent to the mobile device via the Bluetooth protocol stack; The mobile device receives the data packet, parses it, and stores it in the local database.

7. The tinnitus monitoring method based on smart headphones according to claim 6, characterized in that, Uploading data to the cloud server via the Wi-Fi module includes: The locally stored tinnitus data is sent to the cloud server via the HTTP protocol; The cloud server performs integrity checks on the received data; Long-term trend reports on patients' tinnitus conditions are generated using data visualization tools.

8. A tinnitus monitoring system based on smart headphones for implementing the method as described in any one of claims 1-6, characterized in that, include: The operation button is used to activate the tinnitus detection module; The spectrum analysis chip, connected to the operation buttons, is used to collect ear sound signals and perform spectrum analysis. A sound pressure sensor, connected to a spectrum analysis chip, is used to acquire tinnitus loudness data; The main control module, connected to the spectrum analysis chip and sound pressure sensor, is used to receive and process tinnitus data; The data storage module, connected to the main control module, is used to store encrypted tinnitus data; The rehabilitation sound synthesis module, connected to the main control module, is used to generate personalized control signals; A programmable waveform generator, connected to a rehabilitation sound synthesis module, is used to generate rehabilitation sounds; The sound-generating unit, connected to a programmable waveform generator, is used to output rehabilitation sounds; The Bluetooth module, connected to the main control module, is used to transmit tinnitus data to the mobile application. The Wi-Fi module, connected to the main control module, is used to upload data to the cloud server; The generation of personalized control signals includes: matching the tinnitus frequency range with the rehabilitation sound frequency through a preset mapping rule; adjusting the amplitude of the rehabilitation sound according to the tinnitus loudness data to maintain a dynamic balance between the intensity of the rehabilitation sound and the tinnitus loudness; and optimizing the control signal parameters through historical rehabilitation records. The process of generating rehabilitation sounds includes: synthesizing a composite signal of white noise and inverse phase masking tone using a programmable waveform generator; adjusting the amplitude ratio of white noise and masking tone according to a personalized control signal; and performing low-pass filtering on the synthesized signal using a digital signal processing module to remove high-frequency noise components.

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