A binaural hearing aid fitting method using interaural time difference cues
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BOYIN HEARING TECH (SHANGHAI) CO LTD
- Filing Date
- 2025-11-11
- Publication Date
- 2026-05-29
Smart Images
Figure CN121462965B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of hearing aid testing technology, and in particular to a method for testing the tightness of hearing aid wearing using dual-channel ultrasonic phase difference. Background Technology
[0002] The tightness of a hearing aid's fit is crucial to its acoustic performance and user experience. Wearing it too loosely can lead to:
[0003] Feedback howling: Sound leaks from the gap between the ear canal and the shell, is picked up again by the microphone, forming positive feedback and producing an uncomfortable howling sound.
[0004] Degraded sound quality: Excessive leakage of low-frequency sounds causes the sound to become thinner and distorted.
[0005] Unstable fit: It is easy to fall off when the user moves.
[0006] Wearing them too tightly can cause ear pain and a feeling of pressure. Wearing them for a long time may cause skin damage and affect the user's willingness to wear them.
[0007] Existing technological explorations largely focus on the detection of single physical quantities. For example, schemes based on the transit time of a single ultrasonic wave, while capable of measuring absolute distance, are significantly affected by the ambient temperature and humidity, resulting in limited accuracy in the complex environment of the ear canal. Schemes based on contact sensors, as mentioned earlier, have inherent limitations. Furthermore, these schemes generally share a common problem: they cannot effectively distinguish between "overall looseness" and "unilateral tilting," two states that have different impacts on acoustic performance but both require correction, and they lack effective criteria for judging the "overly tight" state.
[0008] Therefore, a dual-channel ultrasonic phase difference method for detecting hearing aid wearing tightness is proposed. Summary of the Invention
[0009] This manual provides a dual-channel ultrasonic phase difference method for detecting the tightness of hearing aid wearing, which achieves high-precision, multi-dimensional, real-time online monitoring and intelligent feedback of hearing aid wearing status.
[0010] This manual provides a method for detecting the tightness of hearing aid wearing using a dual-channel ultrasonic phase difference method, including:
[0011] The first and second ultrasonic transducers, which are set at different positions on the hearing aid shell, are controlled to emit ultrasonic signals that have been orthogonally encoded.
[0012] The ultrasonic receiving transducer receives the mixed echo signal, and an adaptive filter matching the encoding method is used to separate the signal to obtain the first echo signal corresponding to the first transmitting transducer and the second echo signal corresponding to the second transmitting transducer.
[0013] The first echo signal and the second echo signal are processed in parallel to extract phase information including the first phase difference and the second phase difference, time information including the first transit time and the second transit time, and amplitude information including the first signal attenuation coefficient and the second signal attenuation coefficient.
[0014] The extracted phase information, time information, and amplitude information are input into a pre-trained wearing state classification model, and the classification result of the current wearing state is output.
[0015] Based on the classification results, an adaptive prompt action matching the state level is triggered.
[0016] Optionally, before inputting the extracted phase information, time information, and amplitude information into the pre-trained wearing state classification model and outputting the classification result of the current wearing state, the following steps are included:
[0017] Based on the first phase difference and the second phase difference, the main gap evaluation parameters are calculated using a joint solution model.
[0018] Based on the first transit time and the second transit time, calculate the auxiliary distance verification parameters;
[0019] When the deviation between the main gap assessment parameter and the auxiliary distance verification parameter exceeds the tolerance, the data credibility flag is activated, and a remeasurement is triggered.
[0020] Optionally, when the deviation between the main gap assessment parameter and the auxiliary distance verification parameter exceeds the tolerance, activating the data confidence flag and triggering a remeasurement includes:
[0021] Δd phase =[c / (4πf)]*|Δφ1-Δφ2|
[0022] Where, Δd phase The main gap evaluation parameters are: c is the actual propagation speed of ultrasound in a specific medium, f is the center frequency of the ultrasound signal emitted by the ultrasound transducer, Δφ1 is the first phase difference, and Δφ2 is the second phase difference.
[0023] Δd tof =(c / 2)*|Δt1-Δt2|
[0024] Where, Δd tof As auxiliary distance verification parameters, Δt1 is the first transit time, and Δt2 is the second transit time;
[0025] The trigger condition for the data credibility flag is: |Δd phase -Δd tof |>δ, where δ is the preset credibility threshold.
[0026] Optionally, the pre-trained wearing state classification model is obtained by collecting a large amount of sample data and their corresponding state labels under different known wearing states, and then training the machine learning algorithm; the machine learning algorithm is one of support vector machine, random forest or neural network.
[0027] Optionally, triggering an adaptive cue action matching the state level based on the classification result includes:
[0028] When the status is "wearing well", no prompt will be given or only a green light will be displayed;
[0029] When the condition is locally slightly loose, a single gentle vibration is given as a warning.
[0030] When the overall condition is too loose, intermittent audio-visual prompts will be given;
[0031] When the condition is that the garment is too tight, a continuous low-frequency vibration will be emitted as a warning.
[0032] Optionally, the first ultrasonic transmitting transducer, the second ultrasonic transmitting transducer, and the receiving transducer are all manufactured using micro-MEMS technology and are conformally packaged with the hearing aid housing.
[0033] Optionally, the hearing aid's main control DSP or dedicated ASIC has a monitoring algorithm embedded in it, which runs as a background task.
[0034] Optional, also includes:
[0035] Monitor hearing aid wearing status data;
[0036] The data is uploaded to the cloud or mobile terminal via a wireless communication module for long-term wearing habit analysis and remote fitting assistance.
[0037] This specification also provides an electronic device, wherein the electronic device includes:
[0038] A processor; and a memory storing computer-executable instructions, which, when executed, cause the processor to perform any of the methods described above.
[0039] This specification also provides a computer-readable storage medium that stores one or more programs that, when executed by a processor, implement any of the methods described above.
[0040] In this invention, phase information provides extremely high sensitivity, transit time provides an absolute distance reference and performs temperature drift compensation and verification for phase measurements, and the signal attenuation coefficient reflects the propagation characteristics of sound waves in narrow gaps. The fusion of these three factors greatly improves the accuracy and robustness of the system in complex ear canal environments. Through machine learning models, the deep mapping relationship between multimodal features and complex wearing states can be understood, enabling intelligent judgment of ergonomics, which is impossible with traditional threshold comparison methods. Adaptive prompts form a closed loop of "detection-judgment-personalized feedback," significantly improving the intelligence level of the hearing aid and user stickiness. Through hardware and software co-design (such as utilizing existing DSP computing power and adopting MEMS technology), a balance between high performance, miniaturization, and low cost is achieved. Attached Figure Description
[0041] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0042] Figure 1 A schematic diagram illustrating the principle of a dual-channel ultrasonic phase difference method for detecting the tightness of hearing aid wearing, as provided in the embodiments of this specification;
[0043] Figure 2 This is a schematic diagram of a dual-channel ultrasonic phase difference hearing aid fitting tightness detection device provided in the embodiments of this specification;
[0044] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification;
[0045] Figure 4 This is a schematic diagram of a computer-readable medium provided for embodiments of this specification. Detailed Implementation
[0046] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.
[0047] The following is in conjunction with the appendix Figure 1-4Exemplary embodiments of the invention will be described more fully here. However, exemplary embodiments can be implemented in many forms and should not be construed as limiting the invention to the embodiments set forth herein. Rather, these exemplary embodiments are provided to make the invention more comprehensive and complete, and to facilitate a full communication of the inventive concept to those skilled in the art. The same reference numerals in the figures denote the same or similar elements, components, or parts, and therefore repeated descriptions of them are omitted.
[0048] Subject to the technical concept of this invention, the features, structures, characteristics or other details described in a particular embodiment may be combined in one or more other embodiments in a suitable manner.
[0049] In the description of specific embodiments, the features, structures, characteristics, or other details described in this invention are intended to enable those skilled in the art to fully understand the embodiments. However, it is not excluded that those skilled in the art can practice the technical solutions of this invention without one or more of the specific features, structures, characteristics, or other details.
[0050] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.
[0051] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.
[0052] The terms “and / or” or “and / or” include all combinations of any one or more of the listed items.
[0053] Figure 1 This is a schematic diagram illustrating the principle of a dual-channel ultrasonic phase difference method for detecting the tightness of hearing aid wear, as provided in an embodiment of this specification. The method may include:
[0054] S110: Controls the first and second ultrasonic transducers, which are set at different positions on the hearing aid housing, to emit ultrasonic signals that have been orthogonally encoded.
[0055] S120: The ultrasonic receiving transducer receives the mixed echo signal and uses an adaptive filter that matches the encoding method to separate the signal, thereby obtaining the first echo signal corresponding to the first transmitting transducer and the second echo signal corresponding to the second transmitting transducer.
[0056] S130: Process the first echo signal and the second echo signal in parallel, and extract phase information including the first phase difference and the second phase difference, time information including the first transit time and the second transit time, and amplitude information including the first signal attenuation coefficient and the second signal attenuation coefficient.
[0057] S140: Input the extracted phase information, time information and amplitude information into the pre-trained wearing state classification model, and output the classification result of the current wearing state;
[0058] S150: Based on the classification result, trigger an adaptive prompt action that matches the state level.
[0059] In the specific embodiments described in this specification, orthogonal coding preferably uses Gold code sequences because their autocorrelation peaks are sharp and their cross-correlation is low, which is beneficial for high-precision signal separation at the receiving end. The first and second ultrasonic transmitting transducers (T1, T2) transmit their respective coded ultrasonic pulse trains in a time-division multiplexing (TDMA) manner under the precise timing control of a microcontroller (MCU), rather than continuous waves, to reduce power consumption and avoid continuous interference between channels. After the receiving transducer captures the mixed echo signal, it is fed into an adaptive filter based on the least mean square (LMS) algorithm for separation. The reference input of this filter is the locally generated original coded sequences of T1 and T2. By continuously adjusting the filter weights, the mean square error between the output signal and the echo signal of the target channel is minimized, thereby cleanly extracting the first echo signal S1(t) and the second echo signal S2(t) from the mixed signal. Subsequently, three parallel digital signal processing (DSP) threads are initiated: Thread 1 uses a digital phase-locked loop (DPLL) to accurately measure the instantaneous phases of S1(t) and S2(t) relative to the transmitted signal, and calculates the first phase difference Δφ1 and the second phase difference Δφ2; Thread 2 uses cross-correlation operations to find the peak position of the cross-correlation function between the transmitted code and the echo signal, thereby accurately calculating the first transit time Δt1 and the second transit time Δt2; Thread 3 calculates the energy integral of the echo signal and, combined with the known transmitted energy and propagation model, estimates the first signal attenuation coefficient α1 and the second signal attenuation coefficient α2. These three types of information together constitute a six-dimensional feature vector [Δφ1, Δφ2, Δt1, Δt2, α1, α2], which is fed into a pre-trained wear state classification model in real time for inference.
[0060] Optionally, prior to S140, the following steps are included:
[0061] Based on the first phase difference and the second phase difference, the main gap evaluation parameters are calculated using a joint solution model.
[0062] Based on the first transit time and the second transit time, calculate the auxiliary distance verification parameters;
[0063] When the deviation between the main gap assessment parameter and the auxiliary distance verification parameter exceeds the tolerance, the data credibility flag is activated, and a remeasurement is triggered.
[0064] In the specific implementation of this specification, before inputting the feature vector into the classification model, the system performs a data reliability self-verification process. The core of this process is the joint solution model, which utilizes the complementary characteristics of phase difference measurement (high precision but with ambiguity) and transit time measurement (absolute but with relatively low precision). The main gap evaluation parameter Δd... phase Calculated from phase difference information, it is extremely sensitive to distance changes at the micrometer level. Auxiliary distance verification parameter Δd tof This distance is calculated from the transit time information, providing an unambiguous absolute distance reference. Ideally, Δd... phase With Δd tof The data should be highly consistent. The system has a built-in reliability judge that continuously compares the absolute value of the deviation between the two data points. The tolerance δ is not a fixed value, but a threshold that is dynamically adjusted according to the current signal-to-noise ratio (SNR); when the SNR is low, the tolerance δ is appropriately relaxed. Once the deviation exceeds the tolerance, the data reliability flag is immediately set, the current measurement cycle is interrupted, the MCU schedules the task, discards the unreliable data, and triggers a new round of measurement sequence. This effectively avoids misjudgments caused by signal distortion, multipath effects, or sudden environmental noise, greatly improving the robustness and reliability of the system.
[0065] Optionally, when the deviation between the main gap assessment parameter and the auxiliary distance verification parameter exceeds the tolerance, activating the data confidence flag and triggering a remeasurement includes:
[0066] Δd phase =[c / (4πf)]*|Δφ1-Δφ2|
[0067] Where, Δd phase The main gap evaluation parameters are: c is the actual propagation speed of ultrasound in a specific medium, f is the center frequency of the ultrasound signal emitted by the ultrasound transducer, Δφ1 is the first phase difference, and Δφ2 is the second phase difference.
[0068] Δd tof =(c / 2)*|Δt1-Δt2|
[0069] Where, Δd tof As auxiliary distance verification parameters, Δt1 is the first transit time, and Δt2 is the second transit time;
[0070] The trigger condition for the data credibility flag is: |Δdphase -Δd tof |>δ, where δ is the preset credibility threshold.
[0071] In the specific implementation of this specification, the actual propagation speed *c* of ultrasound in a specific medium is a key variable. To improve accuracy, it is not treated as a constant (e.g., 340 m / s), but is dynamically compensated for using one of the following methods: 1) Integrating a miniature temperature and humidity sensor inside the hearing aid to monitor the environment inside the ear canal in real time, and dynamically calculating the current sound speed *c* based on the empirical formula *c ≈ 331.4 + 0.6 * T* (where T is the temperature in Celsius); 2) During the production calibration process, testing is conducted under different temperature and humidity combinations to generate a lookup table (LUT) of sound speed *c* and ambient temperature and humidity, which is then burned into the device's memory. *c* is obtained by interpolation through the lookup table during use. The center frequency *f* of the ultrasound signal is a preset known quantity, preferably within the range of 2 MHz to 5 MHz. This range is chosen because a frequency that is too low will result in insufficient phase sensitivity to tiny gaps, while a frequency that is too high will cause excessive attenuation of ultrasound in the air, affecting the effective detection distance. The reliability threshold *δ*, after extensive experimental calibration, is typically set to an equivalent physical distance of approximately 50 micrometers. This model cleverly utilizes the measurement results from two different physical principles for cross-validation, which is the cornerstone of achieving high precision and high reliability in this scheme.
[0072] Optionally, the pre-trained wearing state classification model is obtained by collecting a large amount of sample data and their corresponding state labels under different known wearing states, and then training the machine learning algorithm; the machine learning algorithm is one of support vector machine, random forest or neural network.
[0073] In the specific implementation of this specification, firstly, a multi-state wearing dataset needs to be constructed. In a laboratory environment, a specially designed hearing aid clamp with a precision micro-adjustment mechanism is used to simulate various known states such as "well-fitting," "slightly loose in certain areas," "overall too loose," and "too tight." A 3D laser scanner is used to precisely measure the physical dimensions of the calibrated gaps, serving as the physical ground truth for the state labels. Then, for each state, tens of thousands of sets of six-dimensional feature vectors are collected using thousands of prototype machines, and each data point is accurately labeled. Next, this dataset is used to train the RandomForest algorithm, which automatically evaluates the importance of each feature (feature importance ranking). For example, it might find that (Δφ1-Δφ2) is the most important feature for distinguishing tightness, while (α1, α2) contributes significantly in determining whether it is too tight. The training process includes ten-fold cross-validation to evaluate the model's generalization ability and grid search for hyperparameter tuning. The finally trained model will be lightweighted and converted into a C code library that can run efficiently on embedded MCUs or DSPs, and then integrated into the firmware.
[0074] Optionally, S150 includes:
[0075] When the status is "wearing well", no prompt will be given or only a green light will be displayed;
[0076] When the condition is locally slightly loose, a single gentle vibration is given as a warning.
[0077] When the overall condition is too loose, intermittent audio-visual prompts will be given;
[0078] When the condition is that the garment is too tight, a continuous low-frequency vibration will be emitted as a warning.
[0079] In the specific implementation described in this specification, the adaptive prompting action is a multi-layered, multi-modal human-computer interaction system. Its hardware foundation includes a miniature linear resonant actuator (LRA) for providing precise tactile feedback, a tri-color LED (red, yellow, green) for visual cues, and the hearing aid's own speaker for generating prompting sounds. When the classification result is "partially loose," the system drives the LRA to perform a gentle vibration at 60% intensity for 100ms, intended to alert the user without causing disturbance. For "overall too loose," the system enters a composite prompting mode: the red LED flashes at 1Hz, while the speaker plays a short "beep" sound at 800Hz, synchronized with the LED's rhythm, creating a strong visual and auditory warning. For "too tight," continuous low-frequency (e.g., 50Hz) vibration is initiated. This low-frequency vibration pattern is designed to simulate a "throbbing" sensation, allowing the user to intuitively associate it with physical discomfort and thus realize the need for adjustment. All prompting strategies are stored in a prompting strategy lookup table, executed by the MCU based on the classification result index.
[0080] Optionally, the first ultrasonic transmitting transducer, the second ultrasonic transmitting transducer, and the receiving transducer are all manufactured using micro-MEMS technology and are conformally packaged with the hearing aid housing.
[0081] In the specific embodiments described in this specification, a capacitive micromachining ultrasonic transducer (CMUT) array is preferably used. The CMUT chip can be directly integrated onto the inner wall of the hearing aid shell using semiconductor micromachining technology, achieving true conformal packaging. This means its shape perfectly conforms to the curved surface of the hearing aid shell without any abrupt protrusions, ensuring wearing comfort. Compared to traditional piezoelectric ceramic transducers (PZT), CMUTs offer advantages such as wide bandwidth, high sensitivity, and ease of integration with CMOS circuits. The first and second transmitting transducers and the receiving transducer can be three independent elements on the CMUT array, or they can be a shared element that switches between transmitting and receiving modes via electronic switching, further saving space and cost. The packaging material needs to have high acoustic impedance matching characteristics to ensure efficient propagation of ultrasonic waves through the shell material and air.
[0082] Optionally, the hearing aid's main control DSP or dedicated ASIC has a monitoring algorithm embedded in it, which runs as a background task.
[0083] In the specific implementation described in this specification, running as a foreground / background task means that tasks with high real-time requirements and high computational load, such as signal acquisition, preprocessing, and phase / time / amplitude information extraction, are executed as high-priority interrupt service routines (ISRs) or placed in the background loop of the DSP to ensure that data is not lost. Meanwhile, logic with lower real-time requirements, such as feature vector organization, classification model inference, and joint solution, are executed as foreground tasks, scheduled by the MCU's main loop. Where resources permit, the machine learning classification model can be deployed on the hearing aid's main control chip's dedicated neural network accelerator (NPU) or high-performance DSP core to achieve high-speed inference with low power consumption. The system also features a low-power management mode that automatically pauses monitoring tasks to save power when the user removes their hearing aid.
[0084] Optional, also includes:
[0085] Monitor hearing aid wearing status data;
[0086] The data is uploaded to the cloud or mobile terminal via a wireless communication module for long-term wearing habit analysis and remote fitting assistance.
[0087] In the specific implementation of this specification, the uploading of status data and cloud analysis constitute a closed loop of "end-cloud collaboration" for intelligent health management. At the device side (hearing aid), the system periodically (e.g., hourly) or packages data for each abnormal status event (e.g., detecting too loose / too tight). The data packet includes: timestamp, classification result, original values of the six-dimensional feature vector, data credibility flag, and device ID. The packaged data is wirelessly transmitted to the user's smartphone app via a Bluetooth Low Energy (BLE) module integrated into the hearing aid. The mobile app then encrypts and uploads the data to a cloud server via the internet. In the cloud, big data analytics can be used to analyze the user's long-term wearing habits, such as generating weekly / monthly fit reports and identifying issues like habitually wearing one side too tightly. For audiologists, this data can be accessed remotely through a secure web portal, allowing them to identify wearing problems before the user notices them and optimize the hearing aid's gain parameters or suggest remaking the shell during the next remote or in-person adjustment, thus shifting from passive repair to proactive health management.
[0088] In this invention, phase information provides extremely high sensitivity, transit time provides an absolute distance reference and performs temperature drift compensation and verification for phase measurements, and the signal attenuation coefficient reflects the propagation characteristics of sound waves in narrow gaps. The fusion of these three factors greatly improves the accuracy and robustness of the system in complex ear canal environments. Through machine learning models, the deep mapping relationship between multimodal features and complex wearing states can be understood, enabling intelligent judgment of ergonomics, which is impossible with traditional threshold comparison methods. Adaptive prompts form a closed loop of "detection-judgment-personalized feedback," significantly improving the intelligence level of the hearing aid and user stickiness. Through hardware and software co-design (such as utilizing existing DSP computing power and adopting MEMS technology), a balance between high performance, miniaturization, and low cost is achieved.
[0089] Figure 2 This is a schematic diagram of a dual-channel ultrasonic phase difference hearing aid fitting tightness detection device provided in an embodiment of this specification. The device may include:
[0090] Control module 10 is used to control the first and second ultrasonic transducers, which are set at different positions on the hearing aid shell, to emit ultrasonic signals that have been orthogonally encoded.
[0091] The separation module 20 is used to receive the mixed echo signal through the ultrasonic receiving transducer and perform signal separation using an adaptive filter that matches the encoding method to obtain the first echo signal corresponding to the first transmitting transducer and the second echo signal corresponding to the second transmitting transducer.
[0092] Processing module 30 is used to process the first echo signal and the second echo signal in parallel, and extract phase information including the first phase difference and the second phase difference, time information including the first transit time and the second transit time, and amplitude information including the first signal attenuation coefficient and the second signal attenuation coefficient.
[0093] The classification module 40 is used to input the extracted phase information, time information and amplitude information into the pre-trained wearing state classification model and output the classification result of the current wearing state;
[0094] The response module 50 is used to trigger an adaptive prompt action that matches the state level based on the classification result.
[0095] Optionally, prior to the classification module, the following is included:
[0096] Based on the first phase difference and the second phase difference, the main gap evaluation parameters are calculated using a joint solution model.
[0097] Based on the first transit time and the second transit time, calculate the auxiliary distance verification parameters;
[0098] When the deviation between the main gap assessment parameter and the auxiliary distance verification parameter exceeds the tolerance, the data credibility flag is activated, and a remeasurement is triggered.
[0099] Optionally, when the deviation between the main gap assessment parameter and the auxiliary distance verification parameter exceeds the tolerance, activating the data confidence flag and triggering a remeasurement includes:
[0100] Δd phase =[c / (4πf)]*|Δφ1-Δφ2|
[0101] Where, Δd phase The main gap evaluation parameters are: c is the actual propagation speed of ultrasound in a specific medium, f is the center frequency of the ultrasound signal emitted by the ultrasound transducer, Δφ1 is the first phase difference, and Δφ2 is the second phase difference.
[0102] Δd tof =(c / 2)*|Δt1-Δt2|
[0103] Where, Δd tof As auxiliary distance verification parameters, Δt1 is the first transit time, and Δt2 is the second transit time;
[0104] The trigger condition for the data credibility flag is: |Δd phase -Δd tof |>δ, where δ is the preset credibility threshold.
[0105] Optionally, the pre-trained wearing state classification model is obtained by collecting a large amount of sample data and their corresponding state labels under different known wearing states, and then training the machine learning algorithm; the machine learning algorithm is one of support vector machine, random forest or neural network.
[0106] Optionally, the response module includes:
[0107] When the status is "wearing well", no prompt will be given or only a green light will be displayed;
[0108] When the condition is locally slightly loose, a single gentle vibration is given as a warning.
[0109] When the overall condition is too loose, intermittent audio-visual prompts will be given;
[0110] When the condition is that the garment is too tight, a continuous low-frequency vibration will be emitted as a warning.
[0111] Optionally, the first ultrasonic transmitting transducer, the second ultrasonic transmitting transducer, and the receiving transducer are all manufactured using micro-MEMS technology and are conformally packaged with the hearing aid housing.
[0112] Optionally, the hearing aid's main control DSP or dedicated ASIC has a monitoring algorithm embedded in it, which runs as a background task.
[0113] Optional, also includes:
[0114] Monitor hearing aid wearing status data;
[0115] The data is uploaded to the cloud or mobile terminal via a wireless communication module for long-term wearing habit analysis and remote fitting assistance.
[0116] The functions of the apparatus in this embodiment have been described in the above method embodiments. Therefore, for any parts not detailed in this embodiment, please refer to the relevant descriptions in the foregoing embodiments, which will not be repeated here.
[0117] Based on the same inventive concept, embodiments of this specification also provide an electronic device.
[0118] The following describes embodiments of the electronic device of the present invention, which can be considered as specific implementations of the methods and apparatus embodiments of the present invention described above. Details described in the embodiments of the electronic device of the present invention should be considered as supplements to the methods or apparatus embodiments described above; details not disclosed in the embodiments of the electronic device of the present invention can be implemented with reference to the methods or apparatus embodiments described above.
[0119] Figure 3 This is a schematic diagram of an electronic device provided as an embodiment of this specification. Refer to the following... Figure 3 The electronic device 300 according to this embodiment of the present invention will be described. Figure 3 The electronic device 300 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.
[0120] like Figure 3 As shown, the electronic device 300 is presented in the form of a general-purpose computing device. The components of the electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one storage unit 320, a bus 330 connecting different system components (including storage unit 320 and processing unit 310), a display unit 340, etc.
[0121] The storage unit stores program code that can be executed by the processing unit 310, causing the processing unit 310 to perform the steps described in the processing method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 310 can perform, for example... Figure 1 The steps are shown.
[0122] The storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 3201 and / or a cache storage unit 3202, and may further include a read-only memory unit (ROM) 3203.
[0123] The storage unit 320 may also include a program / utility 3204 having a set (at least one) program module 3205, such program module 3205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.
[0124] Bus 330 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.
[0125] Electronic device 300 can also communicate with one or more external devices 400 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable viewers to interact with electronic device 300, and / or with any device that enables electronic device 300 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 350. Furthermore, electronic device 300 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 360. Network adapter 360 can communicate with other modules of electronic device 300 via bus 330. It should be understood that, although... Figure 3 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0126] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described in this invention can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the method described above according to this invention. When the computer program is executed by a data processing device, it enables the computer-readable medium to implement the method described above, i.e.: as... Figure 1 The method shown.
[0127] Figure 4 This is a schematic diagram of a computer-readable medium provided for embodiments of this specification.
[0128] accomplish Figure 1 The computer program of the method shown can be stored on one or more computer-readable media. A computer-readable medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.
[0129] The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.
[0130] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java and C++, and conventional procedural programming languages such as C or similar languages. The program code can execute entirely on the audience's computing device, partially on the audience's device, as a standalone software package, partially on the audience's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the audience's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).
[0131] In summary, the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that in practice, general-purpose data processing devices such as microprocessors or digital signal processors (DSPs) can be used to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.
[0132] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the present invention is not inherently related to any specific computer, virtual device, or electronic device, and various general-purpose devices can also implement the present invention. The above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. 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.
[0133] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
[0134] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A method for detecting the tightness of hearing aid wearing using dual-channel ultrasonic phase difference, characterized in that, include: The first and second ultrasonic transducers, which are set at different positions on the hearing aid shell, are controlled to emit ultrasonic signals that have been orthogonally encoded. The ultrasonic receiving transducer receives the mixed echo signal, and an adaptive filter matching the encoding method is used to separate the signal to obtain the first echo signal corresponding to the first transmitting transducer and the second echo signal corresponding to the second transmitting transducer. The first echo signal and the second echo signal are processed in parallel to extract phase information including the first phase difference and the second phase difference, time information including the first transit time and the second transit time, and amplitude information including the first signal attenuation coefficient and the second signal attenuation coefficient. The extracted phase information, time information, and amplitude information are input into a pre-trained wearing state classification model, and the classification result of the current wearing state is output. Based on the classification results, an adaptive prompt action matching the state level is triggered.
2. The method for detecting hearing aid wearing tightness using dual-channel ultrasonic phase difference as described in claim 1, characterized in that, Before inputting the extracted phase information, time information, and amplitude information into the pre-trained wearing state classification model and outputting the classification result of the current wearing state, the process includes: Based on the first phase difference and the second phase difference, the main gap evaluation parameters are calculated using a joint solution model. Based on the first transit time and the second transit time, calculate the auxiliary distance verification parameters; When the deviation between the main gap assessment parameter and the auxiliary distance verification parameter exceeds the tolerance, the data credibility flag is activated, and a remeasurement is triggered.
3. The method for detecting hearing aid wearing tightness using dual-channel ultrasonic phase difference as described in claim 2, characterized in that, When the deviation between the main gap assessment parameter and the auxiliary distance verification parameter exceeds the tolerance, the data reliability flag is activated and a remeasurement is triggered, including: Δd phase =[c / (4πf)]*|Δφ1-Δφ2| Where, Δd phase The main gap evaluation parameters are: c is the actual propagation speed of ultrasound in a specific medium, f is the center frequency of the ultrasound signal emitted by the ultrasound transducer, Δφ1 is the first phase difference, and Δφ2 is the second phase difference. Δd tof =(c / 2)*|Δt1-Δt2| Where, Δd tof As auxiliary distance verification parameters, Δt1 is the first transit time, and Δt2 is the second transit time; The trigger condition for the data credibility flag is: |Δd phase -Δd tof |>δ, where δ is the preset credibility threshold.
4. The method for detecting hearing aid wearing tightness using dual-channel ultrasonic phase difference as described in claim 1, characterized in that, The pre-trained wearing state classification model is obtained by collecting a large amount of sample data and their corresponding state labels under different known wearing states, and then training the machine learning algorithm; the machine learning algorithm is one of support vector machine, random forest or neural network.
5. The method for detecting hearing aid wearing tightness using dual-channel ultrasonic phase difference as described in claim 1, characterized in that, The step of triggering an adaptive prompt action matching the state level based on the classification result includes: When the status is "wearing well", no prompt will be given or only a green light will be displayed; When the condition is locally slightly loose, a single gentle vibration is given as a warning. When the overall condition is too loose, intermittent audio-visual prompts will be given; When the condition is that the garment is too tight, a continuous low-frequency vibration will be emitted as a warning.
6. The method for detecting hearing aid wearing tightness using dual-channel ultrasonic phase difference as described in claim 1, characterized in that, The first ultrasonic transmitting transducer, the second ultrasonic transmitting transducer, and the receiving transducer are all manufactured using micro-MEMS technology and are conformally packaged with the hearing aid housing.
7. The method for detecting hearing aid wearing tightness using dual-channel ultrasonic phase difference as described in claim 1, characterized in that, The hearing aid's main control DSP or dedicated ASIC has a built-in monitoring algorithm that runs as a background task.
8. The method for detecting hearing aid wearing tightness using dual-channel ultrasonic phase difference as described in claim 1, characterized in that, Also includes: Monitor hearing aid wearing status data; The data is uploaded to the cloud or mobile terminal via a wireless communication module for long-term wearing habit analysis and remote fitting assistance.
9. An electronic device, wherein, The electronic device includes: A processor; and a memory storing computer-executable instructions, which, when executed, cause the processor to perform the method as described in any one of claims 1-8.
10. A computer-readable storage medium, wherein, The computer-readable storage medium stores one or more programs that, when executed by a processor, implement the method of any one of claims 1-8.