CN116763288A - Cough symptom assessment method and system based on wearable equipment - Google Patents

Abstract

The application relates to a method and a system for evaluating cough symptoms based on wearable equipment, wherein the method comprises the following steps: collecting an audio signal of a user, and processing the audio signal to obtain a first cough assessment coefficient of the user; acquiring ACC signals and ECG signals of a user through an electrocardiograph patch on the wearable device; processing the ACC signal to obtain a second cough assessment coefficient of the user, and processing the ECG signal to obtain a third cough assessment coefficient of the user; based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient, a cough symptom of the user is assessed. The application solves the problem of accurately evaluating the cough symptom of the user, realizes the cough symptom evaluation combining three signals, reduces the misjudgment rate of the evaluation by singly using the audio signal, and improves the accuracy rate of the cough symptom evaluation under the condition of being interfered by environmental noise or having small cough sounding of the user.

Description

Cough symptom assessment method and system based on wearable equipment

Technical Field

The application relates to the field of medical data processing, in particular to a cough symptom assessment method and system based on wearable equipment.

Background

Cough is one of the major clinical symptoms of respiratory disease, and almost all respiratory diseases may be symptomatic of cough, and it is common for patients with continuous cough for more than one week in respiratory patients.

However, in real life, people often neglect cough symptoms easily, so that it is difficult to treat the disease causing cough symptoms in time. Meanwhile, the cause of the cough symptoms is various and involved clinically, the judgment of the severity of the cough symptoms by a doctor at present mainly depends on subjective feeling of patients, and for the chronic cough patients with no obvious abnormality and weak cough feeling in chest imaging examination, the treatment process is easily delayed by negligence of the clinician. Currently, the scheme for evaluating cough symptoms is mainly based on audio signals, such as the patent with application numbers CN201911188230.4 and CN201811261389.X, but the evaluation based on audio signals is often susceptible to noise and has low accuracy, i.e. how to accurately evaluate and diagnose cough is a problem to be solved.

At present, no effective solution is proposed for the problem of how to accurately evaluate cough symptoms of users in the related art.

Disclosure of Invention

The embodiment of the application provides a method and a system for evaluating cough symptoms based on wearing equipment, which at least solve the problem of how to accurately evaluate the cough symptoms of a user in the related technology.

In a first aspect, an embodiment of the present application provides a method for evaluating cough symptoms based on a wearable device, the method including:

collecting an audio signal of a user, and processing the audio signal to obtain a first cough assessment coefficient of the user;

acquiring ACC signals and ECG signals of the user through an electrocardiograph patch on the wearable device;

processing the ACC signal to obtain a second cough assessment coefficient of the user, and processing the ECG signal to obtain a third cough assessment coefficient of the user;

based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient, the cough symptoms of the user are assessed.

In some of these embodiments, estimating the cough symptom of the user based on the first, second, and third cough assessment coefficients comprises:

if the user is assessed to have cough symptoms based on the first cough assessment coefficient and the user is assessed to have cough symptoms based on the second cough assessment coefficient, determining that the user has cough symptoms;

in a case where the user is estimated to have cough symptoms based on the first cough estimation coefficient, and the user is estimated to have no cough symptoms based on the second cough estimation coefficient; if the user is in a motion state currently, judging that the user has cough symptoms, and if the user is in a static state currently, judging that the user does not have cough symptoms;

in a case where the user is estimated to have no cough symptoms based on the first cough assessment coefficient, and the user is estimated to have cough symptoms based on the second cough assessment coefficient; if the user is estimated to have cough symptoms based on the third cough estimation coefficient, the user is determined to have cough symptoms, and if the user is estimated to have no cough symptoms based on the third cough estimation coefficient, the user is determined to have no cough symptoms;

if the user is assessed to have no cough symptoms based on the first cough assessment coefficient and the user is assessed to have no cough symptoms based on the second cough assessment coefficient, then the user is determined to have no cough symptoms.

In some of these embodiments, processing the audio signal to obtain a first cough assessment coefficient for the user includes:

filtering the non-cough frequency band component in the audio signal through a band-pass filter to obtain a cough frequency band component;

performing sliding window segment processing on the cough frequency band component, reserving the cough frequency band component with amplitude energy larger than a preset threshold, and extracting the audio signal characteristics of the reserved cough frequency band component;

and outputting a first cough assessment coefficient of the user through a deep neural network model based on the audio signal characteristics.

In some of these embodiments, extracting the audio signal characteristics of the reserved cough band component comprises:

extracting audio signal features of the reserved cough frequency band component, wherein the audio signal features comprise one or more of mel-frequency cepstrum coefficient, zero-crossing rate, variance, extremum difference, amplitude average value, center of gravity frequency, mean square frequency, variance frequency, frequency variance, average power, kurtosis and skewness.

In some of these embodiments, processing the ACC signal to obtain a second cough assessment coefficient for the user includes:

performing sliding window segment processing on the ACC signal, reserving an ACC signal with amplitude energy larger than a preset threshold value, and extracting ACC signal characteristics of the reserved ACC signal;

and outputting a second cough assessment coefficient of the user through a deep neural network model based on the ACC signal characteristics.

In some of these embodiments, sliding window segment processing of the ACC signal includes:

and judging whether the user is in a static state or not based on the historical ACC signal, and when the user is in the static state, carrying out sliding window segment processing on the ACC signal.

In some of these embodiments, extracting ACC signal features of the retained ACC signal comprises:

and extracting ACC signal characteristics of the reserved cough frequency band component, wherein the ACC signal characteristics comprise one or more of variance, standard deviation, extremum difference, skewness, kurtosis, amplitude average value, center of gravity frequency, mean square frequency, variance frequency, frequency variance and average power.

In some of these embodiments, processing the ECG signal to obtain a third cough assessment coefficient for the user includes:

extracting a baseline signal from the ECG signal by a low pass filter;

carrying out sliding window segment processing on the baseline signals, and reserving the baseline signals with amplitude energy larger than a preset threshold value;

and detecting short-time fluctuation of the baseline signal to obtain a third cough assessment coefficient of the user.

In some of these embodiments, based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient, assessing the cough symptoms of the user further comprises:

and performing weighted average on the first cough assessment coefficient, the second cough assessment coefficient and the third cough assessment coefficient, and assessing the cough symptom of the user based on the weighted average coefficient, wherein the assessment comprises assessing whether the user has the cough symptom and assessing the severity of the cough symptom.

In a second aspect, an embodiment of the present application provides a system for evaluating cough symptoms based on a wearable device, where the system includes a recording apparatus, a wearable device, and a terminal device;

the recording device is used for collecting audio signals of a user;

the wearable device is used for acquiring ACC signals and ECG signals of the user through an electrocardiograph patch;

the terminal equipment is used for processing the audio signal to obtain a first cough assessment coefficient of the user; processing the ACC signal to obtain a second cough assessment coefficient of the user, and processing the ECG signal to obtain a third cough assessment coefficient of the user; based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient, the cough symptoms of the user are assessed.

The embodiment of the application provides a cough symptom evaluation method and a cough symptom evaluation system based on wearable equipment, wherein the method is used for acquiring an audio signal of a user, and processing the audio signal to obtain a first cough evaluation coefficient of the user; acquiring ACC signals and ECG signals of a user through an electrocardiograph patch on the wearable device; processing the ACC signal to obtain a second cough assessment coefficient of the user, and processing the ECG signal to obtain a third cough assessment coefficient of the user; based on the first cough assessment coefficient, the second cough assessment coefficient and the third cough assessment coefficient, the cough symptom of the user is assessed, the problem of how to accurately assess the cough symptom of the user is solved, the cough symptom assessment integrating three signals is realized, the misjudgment rate of the independent use of the audio signal assessment is reduced, and the accuracy rate of the cough symptom assessment is improved under the condition that the cough symptom of the user is interfered by environmental noise or the cough of the user is little.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:

FIG. 1 is a flowchart of steps of a method for assessing cough symptoms based on a wearable device according to an embodiment of the application;

FIG. 2 is a block diagram of a wearable device-based cough symptom assessment system, according to an embodiment of the present application;

fig. 3 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application.

The attached drawings are identified: 21. a recording device; 22. a wearable device; 23. and a terminal device.

Detailed Description

The present application will be described and illustrated with reference to the accompanying drawings and examples in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application. All other embodiments, which can be made by a person of ordinary skill in the art based on the embodiments provided by the present application without making any inventive effort, are intended to fall within the scope of the present application.

It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is possible for those of ordinary skill in the art to apply the present application to other similar situations according to these drawings without inventive effort. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.

Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the described embodiments of the application can be combined with other embodiments without conflict.

Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. The terms "a," "an," "the," and similar referents in the context of the application are not to be construed as limiting the quantity, but rather as singular or plural. The terms "comprising," "including," "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in connection with the present application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The terms "first," "second," "third," and the like, as used herein, are merely distinguishing between similar objects and not representing a particular ordering of objects.

An embodiment of the present application provides a method for evaluating cough symptoms based on a wearable device, and fig. 1 is a flowchart of steps of the method for evaluating cough symptoms based on a wearable device according to an embodiment of the present application, as shown in fig. 1, the method includes the following steps:

step S102, collecting an audio signal of a user, and processing the audio signal to obtain a first cough assessment coefficient of the user;

step S102 specifically includes the steps of:

step S21, collecting audio signals of a user through a recording device, wherein the recording device can be an independent recording device (such as a mobile phone, a microphone and the like) or an integrated recording device on the wearable device in step S104;

step S22, filtering the non-cough frequency band component in the audio signal through a band-pass filter to obtain a cough frequency band component;

and S23, carrying out sliding window segment processing on the cough frequency band component, reserving the cough frequency band component with the amplitude energy larger than a preset threshold value, and extracting the audio signal characteristics of the reserved cough frequency band component, wherein the audio signal characteristics comprise one or more of a Mel cepstrum coefficient, a zero-crossing rate, a variance, an extremum difference, an amplitude average value, a gravity center frequency, a mean square frequency, a variance frequency, a frequency variance, an average power, kurtosis and skewness. The method comprises the steps of carrying out a first treatment on the surface of the

Step S24, based on the audio signal characteristics, the first cough assessment coefficient of the user is output through the deep neural network model, and in addition, whether the user has a cough symptom may be assessed directly through the deep neural network model based on the output first cough assessment coefficient.

Step S104, acquiring ACC signals and ECG signals of a user through an electrocardiograph patch on the wearable device;

the ECG is an electrocardiogram of the heart, which is a graph of voltage versus time of the electrical activity of the heart using electrodes placed on the skin. These electrodes detect small electrical changes caused by myocardial depolarization followed by repolarization during each cardiac cycle (heartbeat). Changes in normal electrocardiogram patterns occur in many cardiac abnormalities, including cardiac arrhythmias (e.g., atrial fibrillation and ventricular tachycardia), coronary insufficiency (e.g., myocardial ischemia and myocardial infarction), and electrolyte disorders (e.g., hypokalemia and hyperkalemia).

ACC (accelerometer) is a tool for measuring the proper acceleration. Positive acceleration is the acceleration (rate of change of velocity) of an object in its own instantaneous stationary coordinate system; this is different from the coordinate acceleration, which is acceleration in a fixed coordinate system. For example, an accelerometer resting on the earth's surface will measure acceleration due to the earth's gravity, with a straight line going upwards (by definition) g.apprxeq.9.81 m/s ² . In contrast, free fall (at about 9.81m/s ² Is declining toward the center of the earth) will measure zero.

Step S106, processing the ACC signal to obtain a second cough assessment coefficient of the user, and processing the ECG signal to obtain a third cough assessment coefficient of the user;

step S106 specifically includes the steps of:

step S61, judging whether a user is in a static state currently based on a historical ACC signal, when the user is in the static state, carrying out sliding window segment processing on the ACC signal, reserving an ACC signal with amplitude energy larger than a preset threshold value, and extracting ACC signal characteristics of the reserved ACC signal, wherein the ACC signal characteristics comprise one or more of variance, standard deviation, extremum difference, skewness, kurtosis, amplitude average value, gravity center frequency, mean square frequency, variance frequency, frequency variance and average power;

step S62, based on the ACC signal characteristics, the second cough assessment coefficient of the user is output through the deep neural network model, and in addition, whether the user has a cough symptom may be assessed directly through the deep neural network model based on the output second cough assessment coefficient.

Step S63, extracting a baseline signal in the ECG signal through a low-pass filter;

step S64, sliding window segment processing is carried out on the baseline signals, and the baseline signals with amplitude energy larger than a preset threshold value are reserved;

in step S65, short-time fluctuation detection is performed on the baseline signal to obtain a third cough evaluation coefficient of the user, and in addition, whether the user has a cough symptom may be evaluated directly based on the third cough evaluation coefficient, for example: and if the third cough assessment coefficient is less than or equal to 0.5s, the short-time fluctuation is detected, and the user is assessed to have cough symptoms.

Step S108, based on the first cough assessment coefficient, the second cough assessment coefficient and the third cough assessment coefficient, the cough symptom of the user is assessed.

Specifically, table 1 is an example table of how the cough symptoms of the user are evaluated based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient, and as shown in table 1, the determination of the cough symptoms of the user has the following cases:

(1) if the user is estimated to have cough symptoms based on the first cough assessment coefficient and the user is estimated to have cough symptoms based on the second cough assessment coefficient, determining that the user has cough symptoms;

(2) in a case where the user is estimated to have cough symptoms based on the first cough estimation coefficient, and the user is estimated to have no cough symptoms based on the second cough estimation coefficient; if the user is in a motion state currently, the user is judged to have cough symptoms, and if the user is in a static state currently, the user is judged to have no cough symptoms;

(3) in a case where the user is estimated to have no cough symptom based on the first cough assessment coefficient, and the user is estimated to have a cough symptom based on the second cough assessment coefficient; if the user is estimated to have cough symptoms based on the third cough assessment coefficient, the user is judged to have cough symptoms, and if the user is estimated to have no cough symptoms based on the third cough assessment coefficient, the user is judged to have no cough symptoms;

(4) if the user is assessed to have no cough symptoms based on the first cough assessment coefficient and the user is assessed to have no cough symptoms based on the second cough assessment coefficient, then the user is determined to have no cough symptoms.

TABLE 1

It should be noted that, the cough frequency is an important clinical index of various respiratory diseases, and in recent years, with the progress of audio recognition algorithms, some cough detection devices based on audio recognition are presented, but these methods have the following problems: (1) The audio acquisition is affected by ambient others or noise in the environment, and cough from all other people in the vicinity of the device and ambient noise like the cough is recorded as the user's cough, resulting in overestimated cough frequency. (2) Humans produce little or no sound during some coughs, such as stuffy cough, and severe emphysema, vocal cord paralysis, and extremely exhausted patients. In these cases the cough detection device based on audio recognition cannot recognize the cough, resulting in an underestimation of the cough frequency.

Therefore, the three signals are combined to judge the cough symptom of the user, and under the condition that the detected result of the cough is inconsistent by the audio signal and the ACC signal, an ECG baseline signal and a motion state are introduced to assist in judging whether the cough exists or not, so that the final algorithm output is obtained. The cough detection results of the three signals are fused, and the characteristics of the three signals are combined, so that the misjudgment rate of whether the patient cough is judged by singly using the audio is reduced, and particularly under the condition that the patient is interfered by environmental noise or the patient has very small sounding, the misjudgment rate of the audio judgment is greatly increased.

In addition, step S108 may further perform preprocessing on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient, perform weighted average after unifying coefficient standards, and assess the cough symptom of the user based on the coefficient after weighted average, where assessing includes assessing whether the user has the cough symptom and assessing the severity of the cough symptom.

Through the steps S102 to S108 in the embodiment of the application, the problem of how to accurately evaluate the cough symptom of the user is solved, the cough symptom evaluation integrating three signals is realized, the misjudgment rate of the evaluation by independently using the audio signal is reduced, and the accuracy of the cough symptom evaluation is improved under the condition of being interfered by environmental noise or the cough of the user sounding is small.

It should be noted that the steps illustrated in the above-described flow or flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.

An embodiment of the present application provides a cough symptom evaluation system based on a wearable device, fig. 2 is a block diagram of a structure of the cough symptom evaluation system based on the wearable device according to an embodiment of the present application, as shown in fig. 2, the system includes a sound recording apparatus 21, a wearable device 22, and a terminal device 23;

recording means 21 for collecting audio signals of a user;

a wearable device 22 for acquiring ACC signals and ECG signals of a user through an electrocardiographic patch;

a terminal device 23 for processing the audio signal to obtain a first cough assessment coefficient of the user; processing the ACC signal to obtain a second cough assessment coefficient of the user, and processing the ECG signal to obtain a third cough assessment coefficient of the user; based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient, a cough symptom of the user is assessed.

Through the recording device 21, the wearing equipment 22 and the terminal equipment 23 in the embodiment of the application, the problem of how to accurately evaluate the cough symptom of the user is solved, the cough symptom evaluation integrating three signals is realized, the misjudgment rate of the evaluation by independently using the audio signal is reduced, and the accuracy of the cough symptom evaluation is improved under the condition of being interfered by environmental noise or the cough of the user sounding is small.

The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.

The present embodiment also provides an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.

Optionally, the electronic apparatus may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input/output device is connected to the processor.

It should be noted that, specific examples in this embodiment may refer to examples described in the foregoing embodiments and alternative implementations, and this embodiment is not repeated herein.

In addition, in combination with the method for evaluating cough symptoms based on the wearable device in the above embodiment, the embodiment of the application may be implemented by providing a storage medium. The storage medium has a computer program stored thereon; the computer program, when executed by a processor, implements any of the wearable device-based cough symptom assessment methods of the above embodiments.

In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method of assessing cough symptoms based on a wearable device. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.

In one embodiment, fig. 3 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application, and as shown in fig. 3, an electronic device, which may be a server, is provided, and an internal structure diagram thereof may be as shown in fig. 3. The electronic device includes a processor, a network interface, an internal memory, and a non-volatile memory connected by an internal bus, where the non-volatile memory stores an operating system, computer programs, and a database. The processor is used for providing computing and control capabilities, the network interface is used for communicating with an external terminal through a network connection, the internal memory is used for providing an environment for the operation of an operating system and a computer program, the computer program is executed by the processor to realize a cough symptom assessment method based on the wearable device, and the database is used for storing data.

It will be appreciated by those skilled in the art that the structure shown in fig. 3 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the electronic device to which the present inventive arrangements are applied, and that a particular electronic device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

It should be understood by those skilled in the art that the technical features of the above-described embodiments may be combined in any manner, and for brevity, all of the possible combinations of the technical features of the above-described embodiments are not described, however, they should be considered as being within the scope of the description provided herein, as long as there is no contradiction between the combinations of the technical features.

The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.

Claims (10)

1. A method of assessing cough symptoms based on a wearable device, the method comprising:

collecting an audio signal of a user, and processing the audio signal to obtain a first cough assessment coefficient of the user;

acquiring ACC signals and ECG signals of the user through a wearable device;

processing the ACC signal to obtain a second cough assessment coefficient of the user, and processing the ECG signal to obtain a third cough assessment coefficient of the user;

based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient, the cough symptoms of the user are assessed.

2. The method of claim 1, wherein evaluating the cough symptoms of the user based on the first, second, and third cough assessment coefficients comprises:

if the user is assessed to have cough symptoms based on the first cough assessment coefficient and the user is assessed to have cough symptoms based on the second cough assessment coefficient, determining that the user has cough symptoms;

in a case where the user is estimated to have cough symptoms based on the first cough estimation coefficient, and the user is estimated to have no cough symptoms based on the second cough estimation coefficient; if the user is in a motion state currently, judging that the user has cough symptoms, and if the user is in a static state currently, judging that the user does not have cough symptoms;

in a case where the user is estimated to have no cough symptoms based on the first cough assessment coefficient, and the user is estimated to have cough symptoms based on the second cough assessment coefficient; if the user is estimated to have cough symptoms based on the third cough estimation coefficient, the user is determined to have cough symptoms, and if the user is estimated to have no cough symptoms based on the third cough estimation coefficient, the user is determined to have no cough symptoms;

if the user is assessed to have no cough symptoms based on the first cough assessment coefficient and the user is assessed to have no cough symptoms based on the second cough assessment coefficient, then the user is determined to have no cough symptoms.

3. The method of claim 1, wherein processing the audio signal to obtain a first cough estimate coefficient for the user comprises:

filtering the non-cough frequency band component in the audio signal to obtain a cough frequency band component;

processing the cough frequency band component, reserving the cough frequency band component with amplitude energy larger than a preset threshold, and extracting the audio signal characteristics of the reserved cough frequency band component;

and outputting a first cough assessment coefficient of the user through a deep neural network model based on the audio signal characteristics.

4. The method of claim 3, wherein extracting the audio signal characteristics of the reserved cough frequency band component, wherein the audio signal characteristics comprise one or more of mel-frequency cepstral coefficients, zero-crossing rate, variance, extremum difference, magnitude average, center of gravity frequency, mean square frequency, variance frequency, frequency variance, average power, kurtosis, and skewness.

5. The method of claim 1, wherein processing the ACC signal to obtain a second cough estimate coefficient for the user comprises:

processing the ACC signal, reserving an ACC signal with amplitude energy larger than a preset threshold value, and extracting ACC signal characteristics of the reserved ACC signal;

and outputting a second cough assessment coefficient of the user through a deep neural network model based on the ACC signal characteristics.

6. The method of claim 5, wherein processing the ACC signal comprises:

and judging whether the user is in a static state or not based on the historical ACC signal, and when the user is in the static state, carrying out sliding window segment processing on the ACC signal.

7. The method of claim 5, wherein the ACC signal features of the reserved cough frequency band component are extracted, wherein the ACC signal features include one or more of variance, standard deviation, extremum difference, skewness, kurtosis, amplitude average, center of gravity frequency, mean square frequency, variance frequency, frequency variance, and average power.

8. The method of claim 1, wherein processing the ECG signal to obtain a third cough assessment coefficient for the user comprises:

extracting a baseline signal in the ECG signal;

processing the baseline signal, and reserving the baseline signal with amplitude energy larger than a preset threshold value;

and detecting short-time fluctuation of the baseline signal to obtain a third cough assessment coefficient of the user.

9. The method of claim 1, wherein evaluating the cough symptoms of the user based on the first, second, and third cough assessment coefficients further comprises:

and performing weighted average on the first cough assessment coefficient, the second cough assessment coefficient and the third cough assessment coefficient, and assessing the cough symptom of the user based on the weighted average coefficient, wherein the assessment comprises assessing whether the user has the cough symptom and assessing the severity of the cough symptom.

10. A cough symptom evaluation system based on a wearable device, which is characterized by comprising a sound recording device, the wearable device and a terminal device;

the recording device is used for collecting audio signals of a user;

the wearable device is used for acquiring ACC signals and ECG signals of the user;

the terminal equipment is used for processing the audio signal to obtain a first cough assessment coefficient of the user; processing the ACC signal to obtain a second cough assessment coefficient of the user, and processing the ECG signal to obtain a third cough assessment coefficient of the user; based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient, the cough symptoms of the user are assessed.