783results about "Audiometering" patented technology

Techniques and apparatuses are described that perform audioplethysmography and motion-sensing data fusion. Fusing motion-sensing data with audioplethysmography expands the situations in which audioplethysmography can operate. In one aspect, audioplethysmography and motion-sensing data fusion can be used for motion-artifact filtering. With motion-artifact filtering, noise caused by motion of a user can be attenuated to improve sensitivity and accuracy for audioplethysmography. This performance improvement expands the ability of audioplethysmography to support use cases during situations in which the user engages in an activity. In another aspect, audioplethysmography and motion-sensing data fusion can expand a functionality of a hearable to include activity detection and / or activity classification. While activity detection and / or activity classification can provide additional contextual information for other use cases associated with audioplethysmography, either can also be used to control an operation of the hearable and / or a computing device.

The invention belongs to the technical field of hearing testing, and relates to a self-adaptive hearing screening optimization method based on acoustic feature analysis, which comprises the following steps: generating audio data with different signal-to-noise ratios through a constructed audio library, measuring an acoustic transfer function of a user ear canal by using a probe microphone, and generating a personalized acoustic transfer function in combination with a head-related transfer function. Time domain and time frequency features are extracted by means of EEG data, and the auditory reaction ability and the spatial auditory ability of the user are quantified in combination with the spatial positioning features of the binaural simulation microphones; the method comprises the following steps: constructing a multi-modal auditory feature vector, weighting features in combination with a multi-head attention mechanism, predicting the hearing age of a user through a time sequence Transform encoder in combination with the age of the user and a standard threshold value, and evaluating hearing loss according to a prediction result; the individual difference of each user can be accurately adapted, the defect that individual difference and complex environmental noise are neglected in a traditional hearing screening method is overcome, and therefore the accuracy of a screening result is improved.

The invention provides a sound coordination method and system based on a tinnitus condition, and relates to the technical field of tinnitus treatment.The method comprises the steps that a tinnitus feature vector of a patient is established through broadband hearing testing and three-dimensional sound field modeling, and a dual-mode treatment audio is generated based on the tinnitus feature vector; the dual-mode treatment audio comprises offset sound waves with opposite phases and masking sound waves with matched frequency bands; establishing a dynamic mapping relationship between a game interaction event and the audio parameters of the dual-mode treatment audio; and dynamically adjusting treatment parameters according to the dynamic mapping relation and the real-time physiological feedback data. The tinnitus characteristic vector of the patient is accurately established through comprehensive application of broadband hearing testing, three-dimensional sound field modeling and binaural sound field matching, and the treatment audio is dynamically adjusted according to personalized characteristics of tinnitus. According to the personalized sound coordination method, the tinnitus symptom can be effectively relieved according to the tinnitus symptom of each patient, and the treatment compliance of the patient is improved.

The invention discloses an auditory cognitive impairment evaluation and screening system, which belongs to the technical field of data analysis, and specifically comprises the following steps: setting basic test parameters and initial stimulation parameters, and synchronously acquiring voice, eye movement trajectory data and brain wave signals by combining space-time anchoring marks to form a multi-modal data set with a timestamp; a multi-stage cognitive test is carried out in an adaptive test engine, and stimulation parameters are dynamically adjusted by using a forgetting curve prediction algorithm according to real-time accuracy and response time; based on the adjusted stimulation parameters, performing time domain alignment on the multi-modal data set by adopting a dynamic time warping algorithm and taking a space-time anchoring mark as a benchmark, extracting features and combining the features into a cross-modal feature vector; and the cross-modal feature vectors are input into a pre-trained LSTM-decision tree fusion diagnosis model, and a quantitative evaluation report of obstacle type and degree grading is output, so that the accuracy of cognitive disorder diagnosis is improved.

The invention provides a hearing screening system suitable for rural old people, and belongs to the technical field of data processing, and the system comprises a data collection module which collects voice and determines a comprehensive data set; the equipment calibration module is used for determining an audio output parameter of the hardware equipment based on the hardware equipment characteristics in combination with the hardware equipment characteristics, and calibrating the hardware equipment; the parameter determination module is used for determining voice interaction parameters; the hearing test module is used for generating an audiogram; the preliminary judgment module is used for analyzing the audiogram and determining a preliminary judgment result; and the result determination module is used for selecting vocabularies from a preset vocabulary database and a dialect voice database, playing high-frequency audios corresponding to the vocabularies on the calibrated hardware equipment, obtaining user feedback and determining an optimization judgment result in combination with the vocabularies and the dialect voice database. The method has the advantages of individuation, accuracy and high efficiency, is low in cost, is especially suitable for hearing screening requirements of rural old people, and improves the universality and accuracy of screening.

An optical coherence tomography (OCT) imaging tool including an optical waveguide having a proximal end and a distal end, at least a portion of the optical waveguide disposed at the distal end of the optical waveguide having an increased elasticity than a proximal portion of the optical waveguide; a sheath surrounding at least a portion of the optical waveguide; and an optical probe coupled to the optical waveguide, the optical probe including: an optical element coaxially aligned with a central axis of the distal end of the optical waveguide, the optical element being configured to rotate about the central axis and redirect light emitted by the optical waveguide toward a circumference of the optical probe from the central axis, and the focusing element being disposed within a housing.

The invention relates to an apparatus (1) and method for automatically determining the blood flow direction (42) in a blood vessel (14) using the fluorescence light from a fluorophore (16). Blood flow direction (42) is determined by first identifying a blood vessel structure (38) in an input frame (6) from a camera assembly (2) using a pattern recognition module (26). Blood flow direction (42) is determined from the spatial gradient (dl / dx) of the fluorescence intensity (I) along the identified blood vessel structure (38) and the temporal gradient (dl / dt). An output frame (48) is displayed on a display (36) with time-varying marker data (52) overlaid on the identified blood vessels structure (38) and representative of the blood flow direction (42).

The invention discloses a Eustachian tube blowing VR training method and system based on an image sensor, and relates to the technical field of biological feature recognition, the method comprises the steps that training tasks are set, corresponding VR scenes are constructed, and each task corresponds to a tympanic membrane standard motion model; a user wears VR equipment to select a training task, the system collects tympanic membrane image data, extracts dynamic characteristic parameters such as tympanic membrane displacement, deformation curvature and response frequency, compares the dynamic characteristic parameters with a standard model, generates a training score and displays the training score in a VR environment. The technical problems that the training effect is difficult to guarantee and the user experience is poor due to the fact that an existing Eustachian tube blowing training method lacks an objective quantitative evaluation and real-time feedback mechanism are solved, objective quantitative evaluation and real-time feedback of the tympanic membrane dynamic characteristics are achieved by combining the VR technology and the image sensor technology, and the training efficiency is improved. And the technical effects of training effect and user experience are obviously improved.

The invention relates to a hearing threshold prediction method based on frequency sweep OAEs and a deep learning model, and the method comprises the steps: employing a frequency sweep stimulation signal normal form with a unified mode to extract an SFOAEs signal and a DPOAEs signal, the stimulation sound normal form of the SFOAEs signal is a four-segment frequency sweep signal, and the stimulation sound normal form of the DPOAEs signal is a single-segment frequency sweep signal. And carrying out interference signal filtering on a residual signal of the single DPOAEs signal or the SFOAEs signal through a tracking filter with a dynamically changed zero pole so as to extract frequency sweep OAEs spectrum information. And constructing a data set of a deep learning model based on the sweep frequency OAEs spectrum information, and training and evaluating the deep learning model to obtain a hearing threshold prediction model. And calling the hearing threshold prediction model to perform hearing threshold prediction on the SFOAEs signals and the DPOAEs signals under different test intensities, and outputting hearing thresholds corresponding to the frequencies, thereby ensuring the accuracy of diagnosing the hearing loss degree.

The invention relates to the technical field of medical data processing, in particular to a data analysis and processing system based on clinical hearing detection, and aims to solve the problems that dynamic characteristics of auditory nerve response cannot be systematically described, high-dimensional multi-modal feature vectors cannot be constructed, and the data analysis and processing efficiency is low in the prior art. A stable and reliable input basis cannot be provided for intelligent classification, anomaly detection and individualized analysis, and the sensitivity and specificity of hearing state recognition are reduced; the multi-dimensional hearing feature extraction module fuses time domain, frequency domain and non-linear complexity analysis methods, systematically depicts dynamic features of auditory nerve response, constructs a high-dimensional multi-modal feature vector by integrating three-dimensional features, remarkably improves sensitivity and specificity of hearing state recognition, and improves the accuracy of hearing state recognition. The feature representation method provides a stable and reliable input basis for subsequent intelligent classification, anomaly detection and individualized analysis.

The embodiment of the invention discloses a hearing loss compensation method based on hearing aid equipment and a terminal, which are used for simplifying a hearing loss test process and improving the accuracy of hearing loss compensation. The hearing loss compensation method based on the hearing aid device is executed by a terminal, communication connection is established between the terminal and the hearing aid device to be fitted, and the method comprises the following steps: performing a hearing loss test on a hearing aid object to obtain a hearing loss test result; performing hearing loss compensation according to the hearing loss test result to obtain hearing loss compensation gain; the hearing loss compensation gain is sent to the hearing aid device, and the hearing aid device is used for conducting hearing loss compensation on a first voice signal according to the hearing loss compensation gain to obtain a second voice signal.

The invention discloses a tympanic membrane repair postoperative wound assessment method based on artificial intelligence, and relates to the technical field of medical assessment, and the method comprises the steps: obtaining multi-modal image data of a reference tympanic membrane and a target tympanic membrane through a multi-modal dynamic imaging fusion technology, determining a target biomechanical feature ratio through a biomechanical feature fusion technology, and obtaining a target tympanic membrane target biomechanical feature ratio; the method further comprises the steps of obtaining a physiological parameter set of the reference tympanic membrane and the target tympanic membrane, training an associated parameter prediction model to construct a metabolism-mechanical associated model, and achieving accurate evaluation of tympanic membrane wound healing conditions. The invention also covers related electronic equipment, a computer readable storage medium and a computer program product.

The invention relates to the technical field of ophthalmology informatization, in particular to an intelligent optic nerve function evaluation method and system based on visual evoked potential, and the method comprises the steps: obtaining an electroencephalogram signal, an eye movement track and an auditory reaction signal of a subject; obtaining visual stimulation parameters, wherein the visual stimulation parameters comprise stimulation frequency, stimulation intensity and stimulation duration; on the basis of the electroencephalogram signals, the eye movement tracks and the auditory reaction signals, signal preprocessing operation is executed, and preprocessed multi-modal physiological signals are obtained; extracting features by adopting an improved space-time convolutional neural network based on the preprocessed multi-modal physiological signals to obtain optic nerve function features; according to the optic nerve function features and the visual stimulation parameters, establishing an optic nerve conduction pathway model; evaluating the optic nerve function state based on the optic nerve conduction pathway model to obtain an optic nerve function evaluation result; generating an evaluation report containing the optic nerve function evaluation result; predicting the optic nerve function change trend, and generating a prediction report.

The invention discloses an automatic calibration system and method for an audiometer, and particularly relates to the technical field of automatic calibration of audiometers. The system comprises a calibration preprocessing unit, a calibration processing unit and a database, and the calibration preprocessing unit comprises a calibration judgment module, a calibration environment evaluation module and a calibration starting module and is used for preparing before the self-service physical examination machine audiometer is calibrated. And the calibration processing unit comprises a calibration parameter selection module, a calibration parameter synchronization module, a verification error evaluation module and a verification error processing module, and is used for the calibration control platform to execute various operations in the calibration process of the target self-service physical examination machine audiometer. The parameters of the self-service physical examination machine audiometer are monitored, all the to-be-calibrated self-service physical examination machine audiometers are calibrated, a full-automatic calibration mode is adopted to replace traditional manual calibration, and the calibration efficiency of the self-service physical examination machine audiometers is improved.

The invention discloses a polarization-based multichannel fNIRS auditory stimulation detection system, which comprises a signal generator module, a light source and detector module, an amplifier module, a voltage-current module, a power supply module, a data acquisition module and an intelligent terminal, the light source and detector module generates two emergent light sources with different wavelengths to a to-be-detected area according to the current signal with the carrier wave, and receives second polarized light and third polarized light reflected by the to-be-detected area; the amplifier module amplifies and converts the second polarized light and the third polarized light into voltage signals; the intelligent terminal is used for calculating the blood oxygen concentration change through the electric signal. According to the invention, the interference of reflected light and noise of surface tissues on blood oxygen concentration signals of a brain area is reduced.

The invention discloses a speech training hearing aid system carrying an AI speech anomaly assessment algorithm, and relates to the AI speech anomaly assessment algorithm, which is characterized in that comprehensive hearing assessment is carried out by collecting a composite signal and formulating an age adaptation rule base, hearing threshold dispersion and signal-to-noise ratio attenuation are obtained, and then a hearing level assessment coefficient is obtained; daily speech evaluation is carried out by collecting speech feature data and formulating a standard speech comparison library, a daily speech anomaly evaluation model is constructed by using a convolutional neural network learning model, and then a daily speech evaluation coefficient is obtained; by collecting multi-dimensional data, professional speech evaluation is carried out, and then a professional speech evaluation coefficient is obtained; and according to the hearing level evaluation coefficient, the daily speech evaluation coefficient and the professional speech evaluation coefficient, analysis is performed to obtain an analysis result, and a comprehensive analysis scheme is formulated, so that the accuracy is greatly improved.

The present invention relates to an AI-based hearing loss diagnosis method using voice data. The AI-based hearing loss diagnosis method can: accurately and effectively diagnose the presence or absence of hearing loss regardless of the age or gender of a subject by utilizing, as voice data used to diagnose the presence or absence of hearing loss, voice data obtained by reading negative prose (long scenario-type text of approximately one minute in length) and voice data obtained by reading positive prose, especially, voice data obtained by reading negative prose with both the feelings of being wronged and overwhelmed and voice data obtained by reading positive prose without feelings; accurately diagnose the presence or absence of hearing loss by utilizing effectively acquired voice data while reducing overfitting due to amount of data by utilizing data obtained by augmenting the original data of the voice data that is difficult to obtain due to problems in the collection process, security, or the like; and easily and accurately diagnose the presence or absence of hearing loss more effectively in the early stage by identifying voice features and a diagnostic algorithm that is more effective for the early diagnosis of the presence or absence of hearing loss in the acquired voice data.

The present disclosure generally relates to techniques and user interfaces for interacting with research studies. In some embodiments, an electronic device displays a user interface that includes a task view with active tasks from multiple research studies. In some embodiments, an electronic device, while displaying a research study user interface, displays an indication of a problem that prevents enrollment in the research study when enrollment problem criteria are met. In some embodiments, an electronic device, while performing a hearing test, suspends the test and displays a restart affordance when the ambient noise level exceeds a threshold.

The invention relates to the technical field of wearable equipment and intelligent audio processing, in particular to an intelligent control method for TWS earphone heart rate detection and earphone function adaptive adjustment, which comprises the following steps: S1, synchronously acquiring an original heart rate signal, a contact pressure signal and a motion acceleration signal; s2, performing real-time baseline correction on the original heart rate signal; s3, the motion acceleration signals are fused to calculate motion interference weight factors, and a multi-modal physiological feature set is generated; s4, outputting a multi-dimensional physiological state vector; s5, matching a predefined adjustment strategy library according to the multi-dimensional physiological state vector; and S6, analyzing the combined control instruction set. According to the TWS earphone, physiological signals are accurately collected through the ear canal composite sensor array, high-precision correction of heart rate detection is achieved in combination with dynamic compensation, feature extraction and physiological state classification, noise reduction, audio equalization and ear canal pressure adjustment parameters are intelligently matched based on an adjustment strategy library, and the TWS earphone can adapt to the physiological state of a user.

Hearing management, using a portable device or integrated portable devices, can include generating during a hearing diagnostics phase an audiogram based on responses of a user to signals conveyed to the user. In response to detecting ambient noises during the hearing diagnostics phase, noise cancellation can be performed to cancel the ambient noises in conjunction with conveying the signals to the user. During a hearing enhancement phase, sounds can be captured with the portable device. The captured sounds can be enhanced in real-time during the hearing enhancement phase by amplifying select frequencies of the captured sounds using signal gain. The frequencies can be selected, and the signal gain determined based on the audiogram. The captured sounds, now enhanced, can be conveyed to the user as frequency-enhanced sounds.

Presented herein are a system and a method comprising obtaining one or more electrophysiological measures of acoustic hearing associated with a hearing device recipient, obtaining one or more hearing performance measures of the recipient, analyzing the one or more electrophysiological measures with respect to the one or more hearing performance measure, and generating an output based on the analyzing the one or more electrophysiological measures with respect to the one or more hearing performance measures.

Configurations are disclosed for a health system to be used in various healthcare applications, e.g., for patient diagnostics, monitoring, and / or therapy. The health system may comprise a light generation module to transmit light or an image to a user, one or more sensors to detect a physiological parameter of the user's body, including their eyes, and processing circuitry to analyze an input received in response to the presented images to determine one or more health conditions or defects.

Disclosed herein are systems and methods for classifying a tympanic membrane by using a classifier. The classifier is a machine learning algorithm. A method for classifying a tympanic membrane includes steps of: receiving, from an interrogation system, one or more datasets relating to the tympanic membrane; determining a set of parameters from the one or more datasets, wherein at least one parameter of the set of parameters is related to a dynamic property or a static position of the tympanic membrane; and outputting a classification of the tympanic membrane based on a classifier model derived from the set of parameters. The classification comprises one or more of a state, a condition, or a mobility metric of the tympanic membrane.

The invention relates to a method for testing a participant's ability to accurately detect speech under background noise, comprising: (a) audibly presenting a series of 2-bit numbers, 3-bit numbers, 4-bit numbers and / or 5-bit numbers in a random or fixed order to a participant's first ear at a fixed or varying signal-to-noise ratio (SNR) in a quiet or noisy environment, (b) audibly presenting a series of 2-bit numbers, 3-bit numbers, 4-bit numbers and / or 5-bit numbers in a random or fixed order to the second ear of the participant at a fixed or random signal-to-noise ratio (SNR) in a quiet or noisy environment, where the numbers are presented in the native language of the participant or the fluent language of the participant, (c) recording the numbers or sequence of numbers correctly recognized by the participant, and (c) storing the recorded numbers or sequence of numbers at the second ear of the participant. And (d) calculating a score as a function of the percentage of the number or sequence of numbers correctly replicated by the subject, or as a function of the sound intensity difference between the speech and noise required to achieve the correct recognition of 50% sequence of numbers. The invention also relates to a device and a method for measuring the digital recognition capability related to hearing loss, and to a computer-implemented method for evaluating the speech recognition capability of a participant.

The invention is suitable for the technical field of intelligent equipment, and provides a hearing loss risk assessment method and system, intelligent equipment and a storage medium, and the method comprises the steps: a near-ear wearable device collects an audio signal through a configured acoustic sensor, carries out the sound source classification marking of the audio signal based on a current working mode, and carries out the sound source classification marking of the audio signal; and according to a set time interval or a trigger condition, counting a signal intensity mean value of each sound source type audio signal, sending a sound source classification mark and the counted signal intensity mean value to a cloud server corresponding to the user, and indicating the cloud server to carry out hearing loss risk assessment on the user according to the sound source classification mark and the counted signal intensity mean value. The hearing loss risk assessment method and device can effectively improve the convenience, accuracy and universality of hearing loss risk assessment.

The invention provides an optimized otoacoustic reflected signal detection method, and aims to improve the accuracy of quality analysis and anomaly recognition of otoacoustic signals, the method mainly comprises the steps of TE noise threshold optimization, artifact elimination, similarity calculation, wavelet noise reduction, DP data processing and the like, the noise threshold is dynamically adjusted through self-adaptive threshold control, and the accuracy of the quality analysis and anomaly recognition of the otoacoustic signals is improved. The signal reliability is enhanced; a T-shaped window and a band-pass filtering technology are utilized, noise is effectively filtered, and the signal-to-noise ratio is improved; the signal matching degree and stability are further enhanced through the frequency domain coherence spectrum method and wavelet noise reduction; the continuous sound processing and small cavity identification strategy ensures the data integrity and accurately distinguishes the influence of different environments and earplug states.

The invention discloses a health assessment system and method based on wearable device multi-source data fusion analysis. Wearable equipment health assessment features are obtained through the hearing feature vectors obtained through processing and the heart rate and blood pressure features based on a multi-source data fusion analysis method, and a wearable equipment health assessment model is constructed through the wearable equipment health assessment features and an adopted wearable health assessment result. According to the method, the convolutional neural network model is adjusted based on the auditory features acquired and processed by the wearable device in combination with the physiological parameters, so that data with highly structured features are efficiently processed, and the health assessment features of the wearable device, which are more suitable for fusion analysis of the multi-source data of the wearable device, are obtained by imaging the multi-source data, so that the health assessment accuracy of the wearable device is improved. And the trained model has less parameter quantity and stronger generalization ability, and the health assessment of the wearable device on the wearer is more objective under the multi-source processing of the health assessment features generated by fusion on the model.

The embodiment of the invention provides an HRTF acquisition method and device and electronic equipment. The method comprises the following steps: acquiring a plurality of candidate HRTFs from a plurality of stored HRTFs; performing at least one audition feedback experiment on a target user according to the plurality of candidate HRTFs, and generating evaluation parameters of the plurality of candidate HRTFs; and according to the evaluation parameters of the plurality of candidate HRTFs, determining a personalized HRTF of the target user from the plurality of candidate HRTFs. In the embodiment of the invention, the electronic equipment can only carry out the audition feedback experiment aiming at the candidate HRTF, so that the complexity and the workload of obtaining the personalized HRTF operation are reduced; the electronic equipment determines the personalized HRTF from the plurality of candidate HRTFs through the audition feedback experiment, the real feeling of a user is considered in the process of obtaining the personalized HRTF, and the accuracy of obtaining the personalized HRTF is improved.

Cochlear implant systems can include an inner ear sensor configured to receive a stimulus signal from the cochlear tissue of a wearer and generate an input signal based on the received stimulus signal. The inner ear sensor can be configured to detect pressure, for example, within a wearer's cochlear tissue and generate an input signal based on the detected pressure. The inner ear sensor can be integrated with a cochlear electrode implanted in the cochlear tissue. Systems can include a signal processor programmed with a transfer function and configured to receive an input signal and output a stimulation signal based on the received input signal and transfer function. Systems can include an implantable battery and / or communication module in communication with the signal processor. The implantable battery and / or communication module can be configured to interface with and update the transfer function of the signal processor.

The invention belongs to the field of biological medicine research and development, discloses a method and a system for collecting and analyzing auditory electrophysiological data of experimental animals, and is particularly suitable for non-human primates. The method comprises the following steps: delivering a drug to a middle ear cavity through an external auditory canal, and synchronously recording auditory electrophysiological data before and after drug administration; aBR and DPOAE multi-mode detection is adopted, and the frequency range of 4 kHz to 32 kHz is covered; setting anesthesia depth and three-electrode positioning to reduce interference; optimizing the signal-to-noise ratio through noise suppression and dynamic signal superposition; an auditory threshold database is constructed, and a machine learning model is combined to judge an injured part. The system integrates a multi-modal stimulation module, a minimally invasive drug delivery device and a data analysis platform, and supports cross-species data compatibility. The accuracy of otology medicine research is remarkably improved, the surgical operation risk is avoided, and the method is suitable for gene therapy, ototoxicity screening and other scenes.