A method and system for cough symptom assessment based on a wearable device

Abstract

The application relates to a cough symptom evaluation method and system based on a wearable device, wherein the method comprises the following steps: collecting an audio signal of a user, processing the audio signal to obtain a first cough evaluation coefficient of the user; obtaining an ACC signal and an ECG signal of the user through an electrocardiogram patch on the wearable device; processing the ACC signal to obtain a second cough evaluation coefficient of the user, processing the ECG signal to obtain a third cough evaluation coefficient of the user; and evaluating the cough symptom of the user based on the first cough evaluation coefficient, the second cough evaluation coefficient and the third cough evaluation coefficient. Through the application, the problem of how to accurately evaluate the cough symptom of the user is solved, the cough symptom evaluation of the fusion of three signals is realized, the misjudgment rate of the evaluation using the audio signal alone is reduced, and the accuracy of the cough symptom evaluation is improved in the case of environmental noise interference or small user coughing sound.

Description

Technical Field

[0001] This application relates to the field of medical data processing, and in particular to a method and system for assessing cough symptoms based on wearable devices. Background Technology

[0002] Coughing is one of the main clinical symptoms of respiratory diseases. Almost all respiratory diseases may present with coughing symptoms, and it is common for patients in the respiratory department to have a cough that lasts for more than a week.

[0003] However, in real life, people often overlook cough symptoms, making it difficult to treat the underlying disease causing the cough in a timely manner. Furthermore, the causes of cough symptoms are numerous and diverse, and currently, doctors primarily rely on patients' subjective feelings to assess the severity of cough symptoms. For patients with chronic cough who show no obvious abnormalities on chest imaging and have a mild cough, this can easily be overlooked by clinicians, delaying treatment. Currently, cough symptom assessment methods are mainly based on audio signals, as illustrated in patents with application numbers CN201911188230.4 and CN201811261389.X. However, assessments based on a single audio signal are often susceptible to noise and have low accuracy. Therefore, how to accurately assess and diagnose cough is a pressing issue that needs to be addressed.

[0004] Currently, no effective solution has been proposed for the problem of how to accurately assess a user's cough symptoms in related technologies. Summary of the Invention

[0005] This application provides a method and system for assessing cough symptoms based on wearable devices, to at least address the problem of how to accurately assess a user's cough symptoms in related technologies.

[0006] In a first aspect, embodiments of this application provide a cough symptom assessment method based on a wearable device, the method comprising:

[0007] The user's audio signal is collected, and the audio signal is processed to obtain the user's first cough assessment coefficient;

[0008] The user's ACC and ECG signals are obtained through an ECG patch on a wearable device;

[0009] The ACC signal is processed to obtain the user's second cough assessment coefficient, and the ECG signal is processed to obtain the user's third cough assessment coefficient.

[0010] The user's cough symptoms are assessed based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient.

[0011] In some embodiments, assessing the user's cough symptoms based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient includes:

[0012] 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, then the user is determined to have cough symptoms.

[0013] If, based on the first cough assessment coefficient, the user is assessed to have cough symptoms, and based on the second cough assessment coefficient, the user is assessed not to have cough symptoms; if the user is currently in motion, then the user is determined to have cough symptoms; if the user is currently in a stationary state, then the user is determined not to have cough symptoms.

[0014] If, based on the first cough assessment coefficient, the user is assessed as not having cough symptoms, and based on the second cough assessment coefficient, the user is assessed as having cough symptoms; if, based on the third cough assessment coefficient, the user is assessed as having cough symptoms, then the user is determined to have cough symptoms; if, based on the third cough assessment coefficient, the user is assessed as not having cough symptoms, then the user is determined to have no cough symptoms.

[0015] If, based on the first cough assessment coefficient, the user is assessed as not having cough symptoms, and based on the second cough assessment coefficient, the user is assessed as not having cough symptoms, then it is determined that the user does not have cough symptoms.

[0016] In some embodiments, processing the audio signal to obtain the user's first cough assessment coefficient includes:

[0017] The non-coughing frequency band components in the audio signal are filtered out by a bandpass filter to obtain the coughing frequency band components.

[0018] The cough frequency band components are processed by sliding window segment processing, and the cough frequency band components with amplitude energy greater than a preset threshold are retained. The audio signal features of the retained cough frequency band components are then extracted.

[0019] Based on the characteristics of the audio signal, the user's first cough assessment coefficient is output through a deep neural network model.

[0020] In some embodiments, extracting the audio signal features of the preserved cough frequency band component includes:

[0021] Extract the audio signal features of the retained cough frequency band component, wherein the audio signal features include one or more of the following: Mel-frequency cepstral coefficients, zero-crossing rate, variance, extreme value difference, amplitude average, centroid frequency, mean square frequency, variance frequency, frequency variance, average power, kurtosis, and skewness.

[0022] In some embodiments, processing the ACC signal to obtain the user's second cough assessment coefficient includes:

[0023] The ACC signal is processed by sliding window segmentation, and the ACC signal with amplitude energy greater than a preset threshold is retained. The ACC signal features of the retained ACC signal are then extracted.

[0024] Based on the characteristics of the ACC signal, a second cough assessment coefficient for the user is output through a deep neural network model.

[0025] In some embodiments, sliding window segmentation of the ACC signal includes:

[0026] Based on the historical ACC signal, it is determined whether the user is currently stationary. If the user is stationary, the ACC signal is processed by sliding window segmentation.

[0027] In some embodiments, extracting the ACC signal features of the retained ACC signal includes:

[0028] Extract the ACC signal features of the retained cough frequency band component, wherein the ACC signal features include one or more of the following: variance, standard deviation, extreme value difference, skewness, kurtosis, amplitude average, centroid frequency, mean square frequency, variance frequency, frequency variance, and average power.

[0029] In some embodiments, processing the ECG signal to obtain the user's third cough assessment coefficient includes:

[0030] The baseline signal in the ECG signal is extracted using a low-pass filter;

[0031] The baseline signal is processed by sliding window segmentation to retain baseline signals with amplitude energy greater than a preset threshold;

[0032] Short-term fluctuation detection is performed on the baseline signal to obtain the user's third cough assessment coefficient.

[0033] In some embodiments, assessing the user's cough symptoms based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient further includes:

[0034] The first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient are weighted and averaged. The user's cough symptoms are assessed based on the weighted average coefficient. The assessment includes assessing whether the user has cough symptoms and assessing the severity of the cough symptoms.

[0035] Secondly, embodiments of this application provide a cough symptom assessment system based on wearable devices, the system including a recording device, a wearable device, and a terminal device;

[0036] The recording device is used to collect the user's audio signal;

[0037] The wearable device is used to acquire the user's ACC signal and ECG signal through an ECG patch;

[0038] The terminal device is configured to process the audio signal to obtain a first cough assessment coefficient for the user; process the ACC signal to obtain a second cough assessment coefficient for the user; process the ECG signal to obtain a third cough assessment coefficient for the user; and assess the user's cough symptoms based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient.

[0039] This application provides a method and system for assessing cough symptoms based on a wearable device. The method involves acquiring a user's audio signal, processing the audio signal to obtain a first cough assessment coefficient, acquiring the user's ACC and ECG signals through an ECG patch on the wearable device, processing the ACC signal to obtain a second cough assessment coefficient, and processing the ECG signal to obtain a third cough assessment coefficient. Based on the first, second, and third cough assessment coefficients, the user's cough symptoms are assessed. This solves the problem of how to accurately assess a user's cough symptoms, achieving cough symptom assessment by fusing three signals, reducing the misjudgment rate of assessment using only audio signals, and improving the accuracy of cough symptom assessment even under conditions of environmental noise interference or low-volume coughing. Attached Figure Description

[0040] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0041] Figure 1 This is a flowchart of the steps of a cough symptom assessment method based on a wearable device according to an embodiment of this application;

[0042] Figure 2This is a structural block diagram of a cough symptom assessment system based on a wearable device according to an embodiment of this application;

[0043] Figure 3 This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application.

[0044] Attached figures: 21, Recording device; 22, Wearable device; 23, Terminal device. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0046] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0047] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0048] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0049] This application provides a method for assessing cough symptoms based on wearable devices. Figure 1 This is a flowchart illustrating the steps of a cough symptom assessment method based on a wearable device according to an embodiment of this application, as follows: Figure 1 As shown, the method includes the following steps:

[0050] Step S102: Collect the user's audio signal, process the audio signal, and obtain the user's first cough assessment coefficient;

[0051] Step S102 specifically includes the following steps:

[0052] Step S21: Collect the user's audio signal through a recording device. The recording device can be a standalone recording device (such as a mobile phone, microphone, etc.) or a recording device integrated into the wearable device in step S104.

[0053] Step S22: The non-coughing frequency band components in the audio signal are filtered out by a bandpass filter to obtain the coughing frequency band components;

[0054] Step S23: Perform sliding window segment processing on the cough frequency band components, retaining cough frequency band components with amplitude energy greater than a preset threshold, and extracting audio signal features of the retained cough frequency band components. These audio signal features include one or more of the following: Mel-spectral coefficients, zero-crossing rate, variance, extreme value difference, amplitude average, centroid frequency, mean square frequency, variance frequency, frequency variance, average power, kurtosis, and skewness.

[0055] Step S24: Based on the above-mentioned audio signal features, the user's first cough assessment coefficient is output through a deep neural network model. Alternatively, the user can be directly assessed for cough symptoms based on the output first cough assessment coefficient through the deep neural network model.

[0056] Step S104: Obtain the user's ACC signal and ECG signal through the ECG patch on the wearable device;

[0057] It's important to note that ECG stands for electrocardiogram, a graph showing the electrical activity of the heart over time using electrodes placed on the skin. These electrodes detect minute electrical changes caused by myocardial depolarization and subsequent repolarization during each cardiac cycle (heartbeat). Alterations in the normal ECG pattern occur in many cardiac abnormalities, including arrhythmias (such as atrial fibrillation and ventricular tachycardia), insufficient coronary blood flow (such as myocardial ischemia and myocardial infarction), and electrolyte disturbances (such as hypokalemia and hyperkalemia).

[0058] An accelerometer (ACC) is a tool for measuring normal acceleration. Normal acceleration is the acceleration (rate of change of velocity) of an object in its own instantaneous stationary coordinate system; this differs from coordinate acceleration, which is acceleration in a fixed coordinate system. For example, an accelerometer stationary on the Earth's surface would measure the acceleration caused by gravity, linearly upwards (by definition), g ≈ 9.81 m / s². 2 In contrast, free fall (at approximately 9.81 m / s²) 2 An accelerometer reading of zero (when the speed is decreasing towards the Earth's center) will read zero.

[0059] Step S106: Process the ACC signal to obtain the user's second cough assessment coefficient, and process the ECG signal to obtain the user's third cough assessment coefficient.

[0060] Step S106 specifically includes the following steps:

[0061] Step S61: Based on the historical ACC signal, determine whether the user is currently stationary. If the user is stationary, perform sliding window segment processing on the ACC signal, retain the ACC signal with amplitude energy greater than a preset threshold, and extract the ACC signal features of the retained ACC signal. The ACC signal features include one or more of the following: variance, standard deviation, extreme value difference, skewness, kurtosis, amplitude average, centroid frequency, mean square frequency, variance frequency, frequency variance, and average power.

[0062] Step S62: Based on the above-mentioned ACC signal characteristics, the user's second cough assessment coefficient is output through a deep neural network model. Alternatively, the user can be directly assessed for cough symptoms based on the output second cough assessment coefficient through the deep neural network model.

[0063] Step S63: Extract the baseline signal from the ECG signal using a low-pass filter;

[0064] Step S64: Perform sliding window segment processing on the baseline signal and retain the baseline signal with amplitude energy greater than a preset threshold;

[0065] Step S65: Perform short-term fluctuation detection on the baseline signal to obtain the user's third cough assessment coefficient. Alternatively, the user can be directly assessed for cough symptoms based on the third cough assessment coefficient. For example, if the third cough assessment coefficient is less than or equal to 0.5s, it indicates that short-term fluctuations have been detected, and the user is assessed for cough symptoms.

[0066] Step S108: Assess the user's cough symptoms based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient.

[0067] Specifically, Table 1 is an example table of how to assess a user's cough symptoms based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient. As shown in Table 1, the determination of a user's cough symptoms includes the following situations:

[0068] ① If, based on the first cough assessment coefficient, the user is assessed to have cough symptoms, and based on the second cough assessment coefficient, the user is assessed to have cough symptoms, then the user is determined to have cough symptoms.

[0069] ② If, based on the first cough assessment coefficient, the user is assessed to have cough symptoms, and based on the second cough assessment coefficient, the user is assessed not to have cough symptoms; if the user is currently in motion, the user is determined to have cough symptoms; if the user is currently in stillness, the user is determined not to have cough symptoms.

[0070] ③ If, based on the first cough assessment coefficient, the user is assessed as not having cough symptoms, and based on the second cough assessment coefficient, the user is assessed as having cough symptoms; if based on the third cough assessment coefficient, the user is assessed as having cough symptoms, then the user is determined to have cough symptoms; if based on the third cough assessment coefficient, the user is assessed as not having cough symptoms, then the user is determined to not have cough symptoms.

[0071] ④ If, based on the first cough assessment coefficient, the user is assessed as not having cough symptoms, and based on the second cough assessment coefficient, the user is assessed as not having cough symptoms, then it is determined that the user does not have cough symptoms.

[0072] Table 1

[0073] It should be noted that cough frequency is an important clinical indicator for many respiratory diseases. In recent years, with the advancement of audio recognition algorithms, some cough detection devices based on audio recognition have emerged. However, these methods have the following problems: (1) Audio acquisition is affected by noise from others or the environment, which may cause the user to record the coughs of everyone else near the device as well as environmental noises that sound like coughs, leading to an overestimation of the cough frequency. (2) In some cases, people cough very softly or even silently, such as with a muffled cough, as well as in patients with severe emphysema, vocal cord paralysis, or extreme exhaustion. In these situations, cough detection devices based on audio recognition cannot identify the cough, leading to an underestimation of the cough frequency.

[0074] Therefore, the above three signals are combined to determine the user's cough symptoms. Furthermore, when the audio and ACC signals show inconsistent cough detection results, ECG baseline signals and motion status are introduced to assist in determining the presence of a cough, resulting in the final algorithm output. By fusing the cough detection results from the three signals and considering their characteristics, the false positive rate of using audio alone to determine whether a patient is coughing is reduced, especially when there is environmental noise interference or the patient is speaking very softly, where the false positive rate of audio-based determination increases significantly.

[0075] In addition, step S108 can preprocess the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient, unify the coefficient standard, and perform a weighted average. The user's cough symptoms are assessed based on the weighted average coefficient. The assessment includes assessing whether the user has cough symptoms and assessing the severity of the cough symptoms.

[0076] Steps S102 to S108 in the embodiments of this application solve the problem of how to accurately assess a user's cough symptoms, realize the assessment of cough symptoms by fusing three signals, reduce the misjudgment rate of assessment using audio signals alone, and improve the accuracy of cough symptom assessment when affected by environmental noise or when the user's cough is weak.

[0077] It should be noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0078] This application provides a cough symptom assessment system based on wearable devices. Figure 2 This is a structural block diagram of a cough symptom assessment system based on a wearable device according to an embodiment of this application, such as... Figure 2 As shown, the system includes a recording device 21, a wearable device 22, and a terminal device 23;

[0079] Recording device 21 is used to collect the user's audio signal;

[0080] Wearable device 22 is used to acquire the user's ACC and ECG signals through an ECG patch;

[0081] Terminal device 23 is used to process audio signals to obtain a first cough assessment coefficient for the user; process ACC signals to obtain a second cough assessment coefficient for the user; process ECG signals to obtain a third cough assessment coefficient for the user; and assess the user's cough symptoms based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient.

[0082] The recording device 21, wearable device 22, and terminal device 23 in this embodiment solve the problem of how to accurately assess a user's cough symptoms, realize the assessment of cough symptoms by fusing three signals, reduce the misjudgment rate of assessment using audio signals alone, and improve the accuracy of cough symptom assessment when affected by environmental noise or when the user's cough is weak.

[0083] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0084] This embodiment also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0085] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0086] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0087] Furthermore, in conjunction with the cough symptom assessment method based on wearable devices in the above embodiments, this application embodiment can provide a storage medium for implementation. This storage medium stores a computer program; when executed by a processor, the computer program implements any of the cough symptom assessment methods based on wearable devices in the above embodiments.

[0088] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements a cough symptom assessment method based on a wearable device. The display screen of the computer device may be a liquid crystal display (LCD) or an e-ink display. The input device of the computer device may be a touch layer covering the display screen, or buttons, a trackball, or a touchpad located on the casing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0089] In one embodiment, Figure 3 This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application, such as... Figure 3 As shown, an electronic device is provided, which can be a server, and its internal structure diagram can be as follows. Figure 3 As shown, the electronic device includes a processor, a network interface, internal memory, and non-volatile memory connected via an internal bus. The non-volatile memory stores an operating system, computer programs, and a database. The processor provides computing and control capabilities, the network interface communicates with external terminals via a network connection, the internal memory provides an environment for the operation of the operating system and computer programs, the computer programs are executed by the processor to implement a cough symptom assessment method based on a wearable device, and the database stores data.

[0090] Those skilled in the art will understand that Figure 3The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0091] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile 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 various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0092] Those skilled in the art should understand that the technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0093] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for assessing cough symptoms based on wearable devices, characterized in that, The method includes: The user's audio signal is collected, and the audio signal is processed to obtain the user's first cough assessment coefficient; The user's ACC and ECG signals are acquired through a wearable device; The ACC signal is processed to obtain the user's second cough assessment coefficient, and the ECG signal is processed to obtain the user's third cough assessment coefficient. Assessing the user's cough symptoms based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient includes: 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, then the user is determined to have cough symptoms. If, based on the first cough assessment coefficient, the user is assessed to have cough symptoms, and based on the second cough assessment coefficient, the user is assessed not to have cough symptoms; if the user is currently in motion, then the user is determined to have cough symptoms; if the user is currently in a stationary state, then the user is determined not to have cough symptoms. If, based on the first cough assessment coefficient, the user is assessed as not having cough symptoms, and based on the second cough assessment coefficient, the user is assessed as having cough symptoms; if, based on the third cough assessment coefficient, the user is assessed as having cough symptoms, then the user is determined to have cough symptoms; if, based on the third cough assessment coefficient, the user is assessed as not having cough symptoms, then the user is determined to have no cough symptoms. If, based on the first cough assessment coefficient, the user is assessed as not having cough symptoms, and based on the second cough assessment coefficient, the user is assessed as not having cough symptoms, then it is determined that the user does not have cough symptoms.

2. The method according to claim 1, characterized in that, Processing the audio signal to obtain the user's first cough assessment coefficient includes: The non-coughing frequency band components in the audio signal are filtered out to obtain the coughing frequency band components; The cough frequency band components are processed, and cough frequency band components with amplitude energy greater than a preset threshold are retained. The audio signal features of the retained cough frequency band components are then extracted. Based on the characteristics of the audio signal, the user's first cough assessment coefficient is output through a deep neural network model.

3. The method according to claim 2, characterized in that, Extract the audio signal features of the retained cough frequency band component, wherein the audio signal features include one or more of the following: Mel-frequency cepstral coefficients, zero-crossing rate, variance, extreme value difference, amplitude average, centroid frequency, mean square frequency, variance frequency, frequency variance, average power, kurtosis, and skewness.

4. The method according to claim 1, characterized in that, The ACC signal is processed to obtain the user's second cough assessment coefficient, which includes: The ACC signal is processed to retain ACC signals with amplitude energy greater than a preset threshold, and the ACC signal features of the retained ACC signals are extracted. Based on the characteristics of the ACC signal, a second cough assessment coefficient for the user is output through a deep neural network model.

5. The method according to claim 4, characterized in that, Processing the ACC signal includes: Based on the historical ACC signal, it is determined whether the user is currently stationary. If the user is stationary, the ACC signal is processed by sliding window segmentation.

6. The method according to claim 4, characterized in that, Extract the ACC signal features of the retained cough frequency band component, wherein the ACC signal features include one or more of the following: variance, standard deviation, extreme value difference, skewness, kurtosis, amplitude average, centroid frequency, mean square frequency, variance frequency, frequency variance, and average power.

7. The method according to claim 1, characterized in that, The ECG signal is processed to obtain the user's third cough assessment coefficient, which includes: Extract the baseline signal from the ECG signal; The baseline signal is processed to retain baseline signals with amplitude energy greater than a preset threshold; Short-term fluctuation detection is performed on the baseline signal to obtain the user's third cough assessment coefficient.

8. The method according to claim 1, characterized in that, Assessing the user's cough symptoms based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient further includes: The first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient are weighted and averaged. The user's cough symptoms are assessed based on the weighted average coefficient. The assessment includes assessing whether the user has cough symptoms and assessing the severity of the cough symptoms.

9. A cough symptom assessment system based on wearable devices, characterized in that, The system includes a recording device, wearable devices, and terminal devices; The recording device is used to collect the user's audio signal; The wearable device is used to acquire the user's ACC signal and ECG signal; The terminal device is used to process the audio signal to obtain the user's first cough assessment coefficient; The ACC signal is processed to obtain the user's second cough assessment coefficient, and the ECG signal is processed to obtain the user's third cough assessment coefficient. Assessing the user's cough symptoms based on the first cough assessment coefficient, the second cough assessment coefficient, and the third cough assessment coefficient includes: 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, then the user is determined to have cough symptoms. If, based on the first cough assessment coefficient, the user is assessed to have cough symptoms, and based on the second cough assessment coefficient, the user is assessed not to have cough symptoms; if the user is currently in motion, then the user is determined to have cough symptoms; if the user is currently in a stationary state, then the user is determined not to have cough symptoms. If, based on the first cough assessment coefficient, the user is assessed as not having cough symptoms, and based on the second cough assessment coefficient, the user is assessed as having cough symptoms; if, based on the third cough assessment coefficient, the user is assessed as having cough symptoms, then the user is determined to have cough symptoms; if, based on the third cough assessment coefficient, the user is assessed as not having cough symptoms, then the user is determined to have no cough symptoms. If, based on the first cough assessment coefficient, the user is assessed as not having cough symptoms, and based on the second cough assessment coefficient, the user is assessed as not having cough symptoms, then it is determined that the user does not have cough symptoms.

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