Health management method and device based on a hearing aid system, hearing aid and medium

Abstract

The application discloses a health management method and device based on a hearing aid system, a hearing aid and a medium, and relates to the technical field of hearing aids. The method is applied to a health management system integrated on a hearing aid. The health management system comprises a vital sign monitoring module. The method comprises the following steps: monitoring first real-time vital sign data of a target user through the vital sign monitoring module; in the case that the first real-time vital sign data exceeds a preset health vital sign range, constructing a target feature vector based on the first real-time vital sign data; inputting the target feature vector into a target model to obtain a predicted symptom root cause of the target user in the case of the first real-time vital sign data; determining a preset target relief scheme corresponding to the predicted symptom root cause, and outputting the target relief scheme through the hearing aid to assist the target user in recovering the vital sign data to the health vital sign range. The application can avoid the problem of low user health management efficiency caused by the single function of the hearing aid.

Description

Technical Field

[0001] This application relates to the field of hearing aid technology, and in particular to a health management method, device, hearing aid, and medium based on a hearing aid system. Background Technology

[0002] With the rapid development of technology and the increasing aging of the population, hearing impairment has become one of the key factors affecting the quality of life of the elderly. Hearing aids, as the main assistive devices for people with hearing impairments, greatly improve their daily communication abilities.

[0003] Currently, a significant proportion of hearing aid users are elderly. These users not only face hearing impairment but also inevitably suffer from chronic diseases and declining physiological functions. However, most hearing aids on the market primarily focus on hearing compensation, striving to provide users with more precise and comfortable hearing compensation solutions, while neglecting users' needs for health management. This forces users to rely on various other hearing aids for health management, resulting in inefficient health management practices.

[0004] Therefore, how to avoid the inefficiency of user health management due to the limited functionality of hearing aids is an urgent problem that needs to be solved. Summary of the Invention

[0005] The main purpose of this application is to provide a health management method, device, hearing aid, and medium based on a hearing aid system, aiming to avoid the low efficiency of user health management due to the limited functionality of hearing aids.

[0006] To achieve the above objectives, this application provides a health management method based on a hearing aid system. This method is applied to a health management system integrated into a hearing aid, the health management system including a vital signs monitoring module. The health management method based on the hearing aid system includes:

[0007] The vital signs monitoring module monitors the first real-time vital signs data of the target user, wherein the target user is a user wearing the hearing aid;

[0008] If the first real-time vital signs data exceeds the preset range of health vital signs, a target feature vector is constructed based on the first real-time vital signs data;

[0009] The target feature vector is input into the target model to obtain the predicted symptom root cause of the target user exhibiting the first real-time vital signs data. The target model is trained by using the feature vector constructed based on the user's vital signs data as the model input data and the label data constructed based on the user's real symptom root cause as the model training label.

[0010] The predicted symptom root cause is determined to correspond to a preset target relief plan, and the target relief plan is output through the hearing aid to assist the target user's vital sign data to return to the healthy vital sign range.

[0011] In one embodiment, the vital signs monitoring module includes a body temperature monitoring unit, a heart rate monitoring unit, and a blood oxygen monitoring unit. The real-time vital signs data includes real-time body temperature data, real-time heart rate data, and real-time blood oxygen data. The body temperature monitoring unit is used to generate the real-time body temperature data, the heart rate monitoring unit is used to generate the real-time heart rate data, and the blood oxygen monitoring unit is used to generate the real-time blood oxygen data.

[0012] In one embodiment, the step of constructing a target feature vector based on the first real-time vital sign data includes:

[0013] The real-time body temperature data, real-time heart rate data, and real-time blood oxygen data in the first real-time vital signs data are preprocessed to obtain each feature quantity;

[0014] The target feature vector is obtained by combining the aforementioned feature quantities.

[0015] In one embodiment, the range of health signs is determined based on the actual physical condition of the target user, wherein the actual physical condition represents the target user's age, gender, medical history, and lifestyle habits.

[0016] In one embodiment, the health management system further includes a positioning module, and the health management system is communicatively connected to a user terminal;

[0017] After the step of inputting the target feature vector into the target model to obtain the predicted symptom root cause of the target user exhibiting the first real-time vital sign data, the method further includes:

[0018] The positioning module detects the geographical location of the hearing aid and integrates the geographical location and the predicted symptom root cause into an alarm message.

[0019] The alarm information is sent to the user terminal to notify medical staff or the target user's family.

[0020] In one embodiment, the method further includes:

[0021] In response to a voice control command for the hearing aid, the vital sign query information in the voice control command is identified;

[0022] The second real-time vital sign data corresponding to the vital sign to be queried in the vital sign query information is obtained from the vital sign monitoring module, and the second real-time vital sign data is output through the hearing aid to inform the target user.

[0023] In one embodiment, the method further includes:

[0024] Obtain a sample dataset, wherein the sample dataset includes multiple sample data, each sample data includes a user's vital sign data and the true root cause of the symptoms, and the user's vital sign data includes the user's body temperature data, heart rate data and blood oxygen data at the same time;

[0025] Feature vectors are constructed based on the vital signs data in each of the aforementioned sample data, and label data are constructed based on the true symptom root causes in each of the aforementioned sample data.

[0026] The feature vectors corresponding to each of the sample data are used as model input data, and the label data corresponding to each of the sample data are used as model training labels to train the initial model to be trained, thereby obtaining the target model.

[0027] Furthermore, to achieve the above objectives, this application also provides a health management device based on a hearing aid system. This health management device is applied to a health management system integrated into a hearing aid. The health management system includes a vital signs monitoring module. The hearing aid system-based health management device includes:

[0028] A monitoring module is used to monitor the first real-time vital signs data of a target user through the vital signs monitoring module, wherein the target user is a user wearing the hearing aid;

[0029] The construction module is used to construct a target feature vector based on the first real-time vital signs data when the first real-time vital signs data exceeds the preset range of health vital signs.

[0030] The prediction module is used to input the target feature vector into the target model to obtain the predicted symptom root cause of the target user exhibiting the first real-time vital signs data. The target model is trained by using the feature vector constructed based on the user's vital signs data as the model input data and the label data constructed based on the user's real symptom root cause as the model training label.

[0031] The output module is used to determine the preset target relief plan corresponding to the predicted symptom root cause, and output the target relief plan through the hearing aid to assist the target user's vital sign data to return to the healthy vital sign range.

[0032] In addition, to achieve the above objectives, this application also provides a storage medium, which is a computer-readable storage medium, storing a program that implements a health management method based on a hearing aid system. The program that implements the health management method based on a hearing aid system is executed by a processor to implement the steps of the health management method based on a hearing aid system as described above.

[0033] In addition, to achieve the above objectives, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the health management method based on a hearing aid system as described above.

[0034] This application provides a health management method based on a hearing aid system. This method is applied to a health management system integrated into a hearing aid. The health management system includes a vital sign monitoring module. This application monitors the first real-time vital sign data of a target user (the user wearing the hearing aid). If the first real-time vital sign data exceeds a preset range of health signs, a target feature vector is constructed based on the real-time vital sign data. This target feature vector is then input into a target model to obtain the predicted root cause of the symptoms exhibited by the target user. The target model is trained using the feature vector constructed based on the user's vital sign data as input data and labeled data constructed based on the user's actual symptom root cause as training labels. Finally, a preset target relief plan corresponding to the predicted symptom root cause is found, and the relief plan is output through the hearing aid to help the target user's vital sign data return to a healthy range.

[0035] In summary, compared to traditional hearing aids that lack health monitoring capabilities, this application integrates a health management system that performs health monitoring into the hearing aid. This system monitors the first real-time vital signs data of the target user wearing the hearing aid. If the first real-time vital signs data exceeds a preset range, a pre-trained target model predicts the root cause of the user's symptoms based on the data, and then identifies the corresponding relief measures to help the user recover to normal vital signs. Thus, this application not only enables the monitoring of user vital signs data through the hearing aid, thus diversifying its functions, but also provides relief measures based on the monitored vital signs data to help users recover to normal vital signs, thereby improving the efficiency of health management based on the hearing aid. Attached Figure Description

[0036] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart illustrating the first embodiment of the health management method based on a hearing aid system according to this application;

[0039] Figure 2 This is a schematic diagram of a hearing aid-based health management process according to an embodiment of the hearing aid-based health management method of this application;

[0040] Figure 3 This is a schematic diagram of the modular structure of the health management device based on the hearing aid system in this application;

[0041] Figure 4 This is a schematic diagram of the hardware operating environment involved in the health management method based on a hearing aid system in the embodiments of this application.

[0042] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0043] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0044] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0045] The main solution of this application is as follows: First real-time vital sign data of a target user are monitored through the vital sign monitoring module, wherein the target user is a user wearing the hearing aid; when the first real-time vital sign data exceeds a preset range of healthy vital signs, a target feature vector is constructed based on the first real-time vital sign data; the target feature vector is input into a target model to obtain the predicted symptom root cause exhibited by the target user in relation to the first real-time vital sign data, wherein the target model is trained using the feature vector constructed based on the user's vital sign data as model input data and the labeled data constructed based on the user's actual symptom root cause as model training labels; a preset target relief plan is determined corresponding to the predicted symptom root cause, and the target relief plan is output through the hearing aid to assist the target user's vital sign data in returning to the healthy vital sign range.

[0046] Currently, a significant proportion of hearing aid users are elderly. These users not only face hearing impairment but also inevitably suffer from chronic diseases and declining physiological functions. However, most hearing aids on the market primarily focus on hearing compensation, striving to provide users with more precise and comfortable hearing compensation solutions, while neglecting users' needs for health management. This forces users to rely on various other hearing aids for health management, resulting in inefficient health management practices.

[0047] This application integrates a health management system that performs health monitoring into a hearing aid. It monitors the first real-time vital signs data of the target user wearing the hearing aid. If the first real-time vital signs data exceeds a preset range, a pre-trained target model predicts the root cause of the target user's symptoms based on the first real-time vital signs data. Then, it identifies the corresponding relief plan to assist the target user in restoring normal vital signs. Thus, this application not only enables the monitoring of user vital signs data based on the hearing aid, thus diversifying the hearing aid's functions, but also provides relief plans based on the monitored vital signs data to help users restore normal vital signs, thereby improving the efficiency of health management based on the hearing aid.

[0048] It should be noted that the execution subject of the health management method based on the hearing aid system in this application is the health management system integrated on the hearing aid, which includes a vital signs monitoring module. The following description uses the health management system as the execution subject to illustrate this embodiment and the subsequent embodiments.

[0049] Based on this, this application proposes a health management method based on a hearing aid system according to the first embodiment. Please refer to... Figure 1 The health management method based on the hearing aid system includes steps S10 to S40:

[0050] Step S10: Monitor the first real-time vital signs data of the target user through the vital signs monitoring module, wherein the target user is a user wearing the hearing aid;

[0051] It should be noted that users wearing hearing aids with integrated health management systems are referred to as target users.

[0052] The health management system monitors the target user's vital signs data at the current moment through its vital signs monitoring module (hereinafter referred to as the first real-time vital signs data for distinction). It should be understood that real-time vital signs data refers to the user's vital signs data monitored by the vital signs monitoring module in real time.

[0053] In this embodiment, the vital signs monitoring module includes a body temperature monitoring unit, a heart rate monitoring unit, and a blood oxygen monitoring unit. The real-time vital signs data includes real-time body temperature data, real-time heart rate data, and real-time blood oxygen data. The body temperature monitoring unit is used to generate the real-time body temperature data, the heart rate monitoring unit is used to generate the real-time heart rate data, and the blood oxygen monitoring unit is used to generate the real-time blood oxygen data.

[0054] It should be noted that the aforementioned vital signs monitoring module includes a body temperature monitoring unit, a heart rate monitoring unit, and a blood oxygen monitoring unit. The real-time vital signs data monitored by the vital signs monitoring module includes real-time body temperature data, real-time heart rate data, and real-time blood oxygen data. Specifically, the body temperature monitoring unit is used to monitor the target user's body temperature and generate real-time body temperature data; the heart rate monitoring unit is used to monitor the target user's heart rate and generate real-time heart rate data; and the blood oxygen monitoring unit is used to monitor the target user's blood oxygen and generate real-time blood oxygen data.

[0055] For example, since hearing aids are typically placed close to the ear, a miniaturized, high-precision temperature sensor (such as a thermistor or infrared temperature sensor) can be designed and integrated directly into the inside or edge of the hearing aid housing to ensure accurate measurement of skin temperature near the ear. The output of the temperature sensor needs to be connected to the hearing aid's main control chip. Using PPG (Photoplethysmography) technology, an LED light source and photoelectric sensor can be integrated on one side or the back of the hearing aid. The LED light source illuminates the skin of the ear, and the photoelectric sensor detects changes in the intensity of the reflected light to monitor the heartbeat. It should be noted that since the blood vessels in the ear are not as abundant as those in the fingers, the algorithm may need to be optimized to improve the accuracy of heart rate monitoring. Also based on PPG technology, the LED light source and photoelectric sensor are shared with the heart rate monitoring unit, but different wavelengths of LED (red light and infrared light) are used to distinguish the absorption differences between oxyhemoglobin and deoxyhemoglobin, thereby calculating blood oxygen saturation.

[0056] Step S20: If the first real-time vital signs data exceeds the preset range of health vital signs, construct a target feature vector based on the first real-time vital signs data;

[0057] It should be noted that the health indicators range for the target user are preset, namely the range of body temperature, heart rate, and blood oxygen.

[0058] After acquiring the first real-time vital signs data, it is determined whether the real-time body temperature data in the first real-time vital signs data is within the body temperature range specified for healthy vital signs, whether the real-time heart rate data in the first real-time vital signs data is within the heart rate range specified for healthy vital signs, and whether the real-time blood oxygen data in the first real-time vital signs data is within the blood oxygen range specified for healthy vital signs. If at least one of the above three vital signs data exceeds the healthy vital signs range, it is considered that the first real-time vital signs data exceeds the healthy vital signs range, and a feature vector (hereinafter referred to as the target feature vector for distinction) is constructed based on the first real-time vital signs data.

[0059] In this embodiment, the range of health signs is determined based on the actual physical condition of the target user, wherein the actual physical condition represents the target user's age, gender, medical history, and lifestyle habits.

[0060] It should be noted that the range of normal physical signs for the target user is determined based on their actual physical condition, i.e., the aforementioned range of healthy physical signs. Here, "actual physical condition" refers to the target user's age, gender, medical history, and lifestyle habits.

[0061] For example, a normal body temperature fluctuation range is set based on age and physiological characteristics. For instance, for adults, normal body temperature is generally maintained between 36.5℃ and 37.2℃. Reasonable ranges for resting heart rate and maximum heart rate are set considering factors such as age, gender, and exercise habits. For example, an adult's resting heart rate is typically between 60-100 beats per minute; a healthy adult's blood oxygen saturation is generally not lower than 95%, but for users with a history of respiratory diseases or heart disease, more rigorous monitoring and lower threshold settings may be required. It should be understood that the above-mentioned ranges of health indicators can be dynamically adjusted according to changes in the user's physical condition.

[0062] In this embodiment, step S20 may include:

[0063] Step S201: Perform data preprocessing on the real-time body temperature data, real-time heart rate data, and real-time blood oxygen data in the first real-time vital signs data to obtain each feature quantity;

[0064] Step S202: Combine the aforementioned feature quantities to obtain the target feature vector.

[0065] The real-time body temperature data in the real-time vital signs data is preprocessed with noise reduction and standardization to obtain the corresponding feature value. The real-time heart rate data in the real-time vital signs data is preprocessed with filtering and heart rate extraction to obtain the corresponding feature value. The real-time blood oxygen data in the real-time vital signs data is preprocessed with noise reduction and calibration to obtain the corresponding feature value. Then, the target feature vector is obtained by concatenating and combining the various feature values.

[0066] Step S30: Input the target feature vector into the target model to obtain the predicted symptom root cause of the target user exhibiting the first real-time vital signs data. The target model is trained by using the feature vector constructed based on the user's vital signs data as the model input data and the label data constructed based on the user's real symptom root cause as the model training label.

[0067] It should be noted that the user's vital signs data includes body temperature, heart rate, and blood oxygen saturation. The user's true symptom root cause refers to the reason why the user presents the above-mentioned vital signs data. The initial model is trained using feature vectors pre-constructed based on the user's vital signs data as input data, and labeled data constructed based on the user's true symptom root causes as training labels, resulting in a target model for predicting symptom root causes. This application embodiment does not limit the specific type of the initial model; it can be a deep neural network model or a convolutional neural network model, etc.

[0068] The target feature vector is input into the target model to obtain the symptom root cause of the target user's first real-time vital signs data (hereinafter referred to as the predicted symptom root cause for distinction).

[0069] In this embodiment, the health management method based on the hearing aid system of this application further includes:

[0070] Step A10: Obtain a sample dataset, wherein the sample dataset includes multiple sample data, each sample data includes one vital sign data and the true root cause of the symptoms of a user, and the vital sign data of the user includes the user's body temperature data, heart rate data and blood oxygen data at the same time.

[0071] Step A20: Construct feature vectors based on the vital sign data in each of the sample data, and construct label data based on the true symptom root causes in each of the sample data;

[0072] Step A30: Use the feature vectors corresponding to each of the sample data as model input data, and use the label data corresponding to each of the sample data as model training labels to train the initial model to obtain the target model.

[0073] The training process of the target model is as follows: A sample dataset is constructed based on multiple sample data. Each sample data includes one vital sign data of the user and the user's true symptom root cause. The vital sign data includes the user's body temperature data, heart rate data, and blood oxygen data at the same time (hereinafter referred to as the first time for distinction). The true symptom root cause is the user's symptom root cause at the first time. Feature vectors are constructed based on the vital sign data in each sample data, and label data is constructed based on the true symptom root cause in each sample data. The feature vectors corresponding to each sample data are used as the model input data, and the label data corresponding to each sample data are used as the model training labels. The initial model to be trained is trained to obtain the target model for symptom root cause prediction.

[0074] Step S40: Determine the preset target relief plan corresponding to the predicted symptom root cause, and output the target relief plan through the hearing aid to assist the target user's vital sign data to return to the healthy vital sign range.

[0075] It should be noted that a mapping relationship between symptom root causes and relief plans is pre-defined, and a mapping relationship table is generated. The relief plans can serve as reminders to users to reuse targeted medications.

[0076] After the target model outputs the predicted symptom root cause, the corresponding relief plan (hereinafter referred to as the target relief plan for distinction) is found from the preset mapping table. Then, the voice prompt corresponding to the target relief plan is output through the speaker of the hearing aid to assist the target user in restoring normal vital signs.

[0077] For example, such as Figure 2 The diagram illustrates a health management process based on a hearing aid system. It monitors the real-time vital signs of users wearing hearing aids; determines whether these vital signs fall within a preset range; if not, it constructs a feature vector based on the real-time vital signs data and inputs this feature vector into the target model to obtain the cause of the user's abnormal vital signs (i.e., predict the root cause of symptoms); it determines the preset relief plan corresponding to the predicted root cause of symptoms and plays the corresponding voice prompt through the hearing aid's speaker.

[0078] In summary, compared to traditional hearing aids that lack health monitoring functions, this application integrates a health management system that performs health monitoring into the hearing aid. It monitors the first real-time vital sign data of the target user wearing the hearing aid, and when the first real-time vital sign data exceeds a preset range, it uses a pre-trained target model to predict the root cause of the target user's symptoms based on the first real-time vital sign data. Then, it finds the corresponding relief plan to assist the target user in restoring normal vital signs. Thus, this application not only enables the monitoring of user vital sign data based on the hearing aid, achieving greater functionality, but also provides relief plans based on the monitored vital sign data to help users restore normal vital signs, thereby improving the efficiency of health management based on the hearing aid.

[0079] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter. In addition, the health management system further includes a positioning module, and the health management system is communicatively connected to a user terminal. The health management method based on the hearing aid system of this application further includes:

[0080] Step B10: The location of the hearing aid is detected by the positioning module, and the location and the predicted symptom root cause are integrated into alarm information;

[0081] Step B20: Send the alarm information to the user terminal to notify medical staff or the target user's family members.

[0082] It should be noted that the health management system of this application includes a positioning module, which is used to determine the geographical location of the hearing aid. The health management system communicates with one or more user terminals. It should be understood that the user terminals are terminal devices belonging to medical personnel or family members of the target user.

[0083] After detecting that the first real-time vital sign data is outside the preset range of health signs and generating a predicted symptom root cause through the target model, the location module monitors the geographical location of the hearing aid and integrates the geographical location and predicted symptom root cause to obtain an alarm message. That is, it should be understood that this alarm message represents the geographical location of the hearing aid and the predicted symptom root cause of the target user. Then, this alarm message is sent to the user's terminal, thereby informing medical staff or the target user's family members to pay attention to the target user's health status.

[0084] Thus, this embodiment of the application, by setting a positioning module on the hearing aid, enables the hearing aid to be located and establishes a communication connection between the health management system and the user terminal. When the user's vital signs data are abnormal, an alarm message is generated based on the real-time location of the hearing aid and the user's abnormal condition, and this alarm message is sent to the user terminal to achieve a distress call function. This further ensures the health and safety of hearing aid users.

[0085] Based on the first and / or second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description and will not be repeated hereafter. Furthermore, the health management method based on a hearing aid system in this application also includes:

[0086] Step C10: In response to a voice control command for the hearing aid, identify the vital sign query information in the voice control command;

[0087] Step C20: Obtain the second real-time vital sign data corresponding to the vital sign to be queried from the vital sign query information from the vital sign monitoring module, and output the second real-time vital sign data through the hearing aid to inform the target user.

[0088] It should be noted that the hearing aid in this application has a built-in voice assistant. Users can wake up the voice assistant by using a preset wake word to control the hearing aid to perform data feedback functions.

[0089] In response to a target user's voice control command for the hearing aid, the system determines the text information corresponding to the voice control command, compares the semantics of this text information with a preset query text, and if the semantics match, determines that the preset query text is the vital sign query information in the voice control command. The system then obtains the real-time vital sign data corresponding to the queried vital sign in the vital sign query information (hereinafter referred to as the second real-time vital sign data for distinction) from the vital sign monitoring module, and then plays the voice prompt corresponding to the second real-time vital sign data through the hearing aid's speaker to inform the target user of the current real-time vital sign data.

[0090] For example, the voice assistant is awakened when it hears "Hi, Xiao Er". It obtains the text information corresponding to the voice control command initiated by the target user, which is "What is my current body temperature?". It determines that the text information is semantically consistent with the preset query text "body temperature data query". Therefore, it determines that the vital sign query information in the voice control command is "body temperature data query", that is, the vital sign to be queried in the vital sign query information is body temperature. So, it obtains the user's current body temperature data as 37.1° from the vital sign monitoring module and plays the information through the hearing aid's speaker.

[0091] Thus, because the hearing aid integrates a health management system that can detect the user's vital signs data in real time, and the hearing aid has a voice control function, it provides users with a more convenient health monitoring solution. That is, users can query current vital signs data by voice, which improves the efficiency of users' health monitoring based on hearing aids.

[0092] This application also provides a health management device based on a hearing aid system. Please refer to... Figure 3 The hearing aid system-based health management device is applied to a health management system integrated into a hearing aid. The health management system includes a vital signs monitoring module. The hearing aid system-based health management device includes:

[0093] The monitoring module 10 is used to monitor the first real-time vital signs data of the target user through the vital signs monitoring module, wherein the target user is a user wearing the hearing aid;

[0094] Construction module 20 is used to construct a target feature vector based on the first real-time vital signs data when the first real-time vital signs data exceeds the preset range of health vital signs.

[0095] Prediction module 30 is used to input the target feature vector into the target model to obtain the predicted symptom root cause of the target user exhibiting the first real-time vital signs data. The target model is trained by using the feature vector constructed based on the user's vital signs data as the model input data and the label data constructed based on the user's real symptom root cause as the model training label.

[0096] The output module 40 is used to determine the preset target relief plan corresponding to the predicted symptom root cause, and output the target relief plan through the hearing aid to assist the target user's vital sign data to return to the healthy vital sign range.

[0097] Optionally, the vital signs monitoring module includes a body temperature monitoring unit, a heart rate monitoring unit, and a blood oxygen monitoring unit. The real-time vital signs data includes real-time body temperature data, real-time heart rate data, and real-time blood oxygen data. The body temperature monitoring unit is used to generate the real-time body temperature data, the heart rate monitoring unit is used to generate the real-time heart rate data, and the blood oxygen monitoring unit is used to generate the real-time blood oxygen data.

[0098] Optionally, the building module 20 is further configured to:

[0099] The real-time body temperature data, real-time heart rate data, and real-time blood oxygen data in the first real-time vital signs data are preprocessed to obtain each feature quantity;

[0100] The target feature vector is obtained by combining the aforementioned feature quantities.

[0101] Optionally, the range of health signs is determined based on the actual physical condition of the target user, wherein the actual physical condition represents the target user's age, gender, medical history, and lifestyle habits.

[0102] Optionally, the health management system further includes a positioning module, and the health management system is communicatively connected to the user terminal;

[0103] The health management device based on the hearing aid system further includes an alarm module, which is used for:

[0104] The positioning module detects the geographical location of the hearing aid and integrates the geographical location and the predicted symptom root cause into an alarm message.

[0105] The alarm information is sent to the user terminal to notify medical staff or the target user's family.

[0106] Optionally, the health management device based on the hearing aid system further includes a query module, which is used for:

[0107] In response to a voice control command for the hearing aid, the vital sign query information in the voice control command is identified;

[0108] The second real-time vital sign data corresponding to the vital sign to be queried in the vital sign query information is obtained from the vital sign monitoring module, and the second real-time vital sign data is output through the hearing aid to inform the target user.

[0109] Optionally, the health management device based on the hearing aid system further includes a training module, which is used for:

[0110] Obtain a sample dataset, wherein the sample dataset includes multiple sample data, each sample data includes a user's vital sign data and the true root cause of the symptoms, and the user's vital sign data includes the user's body temperature data, heart rate data and blood oxygen data at the same time;

[0111] Feature vectors are constructed based on the vital signs data in each of the aforementioned sample data, and label data are constructed based on the true symptom root causes in each of the aforementioned sample data.

[0112] The feature vectors corresponding to each of the sample data are used as model input data, and the label data corresponding to each of the sample data are used as model training labels to train the initial model to be trained, thereby obtaining the target model.

[0113] The health management device based on a hearing aid system provided in this application adopts the health management method based on a hearing aid system in the above embodiments, which can solve the technical problem of how to avoid low efficiency in user health management due to the single function of hearing aids. Compared with the prior art, the beneficial effects of the health management device based on a hearing aid system provided in this application are the same as the beneficial effects of the health management method based on a hearing aid system provided in the above embodiments, and other technical features in the health management device based on a hearing aid system are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0114] This application provides a hearing aid, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the health management method based on the hearing aid system in Embodiment 1 described above.

[0115] The following is for reference. Figure 4 It shows a structural schematic diagram suitable for implementing the embodiments of this application. Figure 4 The hearing aid shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0116] like Figure 4As shown, the hearing aid may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the hearing aid. The processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the hearing aid to communicate wirelessly or wiredly with other devices to exchange data. Although hearing aids with various systems are shown in the figures, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0117] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0118] The hearing aid provided in this application employs the hearing aid system-based health management method described in the above embodiments, which solves the technical problem of how to avoid low efficiency in user health management due to the limited functionality of hearing aids. Compared with the prior art, the beneficial effects of the hearing aid provided in this application are the same as those of the hearing aid system-based health management method provided in the above embodiments, and other technical features of this hearing aid are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0119] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0120] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0121] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the health management method based on the hearing aid system in the above embodiments.

[0122] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0123] The aforementioned computer-readable storage medium may be included in the hearing aid or may exist independently and not incorporated into the hearing aid.

[0124] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a hearing aid, cause the hearing aid to: monitor first real-time vital sign data of a target user through the vital sign monitoring module, wherein the target user is a user wearing the hearing aid; construct a target feature vector based on the first real-time vital sign data when the first real-time vital sign data exceeds a preset range of healthy vital signs; input the target feature vector into a target model to obtain a predicted symptom root cause exhibited by the target user in relation to the first real-time vital sign data, wherein the target model is trained using the feature vector constructed based on the user's vital sign data as model input data and the label data constructed based on the user's actual symptom root cause as model training labels; determine a preset target relief plan corresponding to the predicted symptom root cause, and output the target relief plan through the hearing aid to assist the target user's vital sign data in returning to the range of healthy vital signs.

[0125] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, SmallTalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0126] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0127] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0128] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned health management method based on a hearing aid system. This addresses the technical problem of how to avoid low efficiency in user health management due to the limited functionality of hearing aids. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the health management method based on a hearing aid system provided in the above embodiments, and will not be repeated here.

[0129] This application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the health management method based on a hearing aid system as described above.

[0130] The computer program product provided in this application can avoid the low efficiency of user health management caused by the limited functionality of hearing aids. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the health management method based on the hearing aid system provided in the above embodiments, and will not be repeated here.

[0131] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. A health management method based on a hearing aid system, characterized by, The hearing aid system-based health management method is applied to a health management system integrated on a hearing aid, and the health management system comprises a vital sign monitoring module. First real-time vital sign data of a target user is monitored by the vital sign monitoring module, wherein the target user is a user wearing the hearing aid. In a case where the first real-time vital sign data exceeds a preset health vital sign range, a target feature vector is constructed based on the first real-time vital sign data, wherein the health vital sign range is determined based on an actual physical condition of the target user, and the actual physical condition represents the age, gender, medical history and living habit of the target user. The target feature vector is input into a target model to obtain a predicted symptom root cause of the target user in terms of the first real-time vital sign data, wherein the target model is obtained by training a feature vector constructed based on user's vital sign data as model input data and label data constructed based on the actual symptom root cause of the user as model training label. A preset target relief scheme corresponding to the predicted symptom root cause is determined, and the target relief scheme is output by the hearing aid to assist the target user in recovering the vital sign data to the health vital sign range. The method further comprises: In response to a voice control instruction for the hearing aid, body sign query information in the voice control instruction is recognized. Second real-time vital sign data corresponding to a to-be-queried body sign in the body sign query information is obtained from the vital sign monitoring module, and the second real-time vital sign data is output by the hearing aid to inform the target user. The method further comprises: A sample data set is obtained, wherein the sample data set comprises a plurality of sample data, and each sample data comprises user's vital sign data and an actual symptom root cause, and the user's vital sign data comprises the user's body temperature data, heart rate data and blood oxygen data at the same time. Feature vectors are respectively constructed based on the vital sign data in each sample data, and label data are respectively constructed based on the actual symptom root cause in each sample data. An initial model to be trained is trained by taking the feature vectors corresponding to each sample data as model input data and taking the label data corresponding to each sample data as model training label, to obtain the target model.

2. The method of claim 1, wherein, The vital sign monitoring module comprises a body temperature monitoring unit, a heart rate monitoring unit and a blood oxygen monitoring unit, the real-time vital sign data comprises real-time body temperature data, real-time heart rate data and real-time blood oxygen data, the body temperature monitoring unit is configured to generate the real-time body temperature data, the heart rate monitoring unit is configured to generate the real-time heart rate data, and the blood oxygen monitoring unit is configured to generate the real-time blood oxygen data.

3. The method of claim 2, wherein, The step of constructing a target feature vector based on the first real-time vital sign data comprises: The real-time body temperature data, the real-time heart rate data and the real-time blood oxygen data in the first real-time vital sign data are respectively preprocessed to obtain feature quantities. The feature quantities are combined to obtain a target feature vector.

4. The method of claim 1, wherein, The health management system further comprises a positioning module, and the health management system is in communication connection with a user terminal; After the step of inputting the target feature vector into a target model to obtain a predicted symptom root cause of the target user showing the first real-time physical sign data, the method further comprises: Detecting a geographic location where the hearing aid is located through the positioning module, and integrating the geographic location and the predicted symptom root cause into alarm information; Sending the alarm information to the user terminal to inform medical staff or family members of the target user.

5. A health management device based on a hearing aid system, characterized by The health management device based on the hearing aid system is applied to a health management system integrated on a hearing aid, and the health management system comprises a physical sign monitoring module. A monitoring module is configured to monitor first real-time physical sign data of a target user through the physical sign monitoring module, wherein the target user is a user wearing the hearing aid. A construction module is configured to, in a case where the first real-time physical sign data exceeds a preset health sign range, construct a target feature vector based on the first real-time physical sign data, wherein the health sign range is determined based on an actual physical condition of the target user, and the actual physical condition represents the age, gender, medical history and living habit of the target user. A prediction module is configured to input the target feature vector into a target model to obtain a predicted symptom root cause of the target user showing the first real-time physical sign data, wherein the target model is obtained by training an initial model with feature vectors constructed based on physical sign data of a user as model input data and with label data constructed based on actual symptom root causes of the user as model training labels. An output module is configured to determine a preset target relief scheme corresponding to the predicted symptom root cause, and output the target relief scheme through the hearing aid to assist the target user in recovering the physical sign data to the health sign range. A query module is configured to, in response to a voice control instruction for the hearing aid, identify physical sign query information in the voice control instruction, acquire second real-time physical sign data corresponding to to-be-queried physical sign data in the physical sign query information from the physical sign monitoring module, and output the second real-time physical sign data through the hearing aid to inform the target user. A training module is configured to acquire a sample data set, wherein the sample data set comprises a plurality of sample data, one piece of sample data comprises physical sign data and an actual symptom root cause of a user, the physical sign data of the user comprises body temperature data, heart rate data and blood oxygen data of the user at the same time, feature vectors are constructed based on the physical sign data in each piece of sample data, and label data are constructed based on the actual symptom root causes in each piece of sample data; the feature vectors corresponding to each piece of sample data are used as model input data, and the label data corresponding to each piece of sample data are used as model training labels, an initial model to be trained is trained, and the target model is obtained.

6. A hearing aid, characterized in that The hearing aid comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the hearing aid system-based health management method according to any one of claims 1 to 4.

7. A storage medium, characterized by The storage medium is a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the hearing aid system-based health management method according to any one of claims 1 to 4.

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