Wearable devices and methods for evaluating respiratory infections

Abstract

The present application provides a wearable device and a method for assessing a respiratory infection. The wearable device may include a first sensor, a second sensor, and a processor. The first sensor may acquire an audio signal of a user. The second sensor may acquire a physiological parameter signal of the user. The processor may acquire a respiratory infection assessment report of the user based on the physiological parameter signal and the audio signal. Based on the respiratory infection assessment report, a respiratory infection can be detected at an early stage to facilitate confirmation, intervention, and treatment.

Description

Technical Field

[0001] This application claims the priority of Chinese Patent Application No. 202111489780.7, titled "Wearable Device and Method for Evaluating Respiratory Infection", filed with the China National Intellectual Property Administration on December 8, 2021. The entire content of the Chinese patent application is incorporated herein by reference.

[0002] This application relates to the technical field of wearable devices, and specifically, to wearable devices and methods for evaluating respiratory infections.

Background Art

[0003] Currently, wearable devices in the market have the function of detecting physiological parameters such as heart rate, blood oxygen concentration, and body temperature. However, wearable devices in the market cannot evaluate the risk that a user suffers from a disease such as a respiratory infection accompanied by multiple physiological parameters based on the detected physiological parameters.

Summary of the Invention

[0004] In view of this, this application provides a wearable device and a method for evaluating respiratory infection to solve the problem that existing wearable devices cannot evaluate the risk that a user suffers from a disease.

[0005] According to a first aspect, the present invention provides a wearable device. The wearable device may include a first sensor, a second sensor, and a processor. The first sensor is configured to acquire a user's voice signal. The second sensor is configured to acquire a user's physiological parameter signal. The processor is configured to acquire the user's respiratory rate based on the physiological parameter signal and to acquire a respiratory infection assessment report of the user based on the voice signal and respiratory rate. It should be understood that the wearable device has features such as portability and ease of use, and therefore helps the user to perform respiratory infection assessments as needed. The risk of the user contracting a respiratory infection can be assessed based on the respiratory infection assessment report. Furthermore, by using the wearable device, respiratory infections can be detected at an early stage, and as a result, confirmation, intervention, and treatment can be carried out as quickly as possible to prevent the worsening of the respiratory infection.

[0006] In some implementations, the second sensor may include a PPG sensor. The PPG sensor is configured to acquire the user's PPG signal. The processor is configured to acquire the user's first respiratory rate based on the PPG signal. In some cases, the wearable device may acquire a respiratory infection assessment report of the user based on the first respiratory rate and the voice signal.

[0007] In some implementations, the second sensor may further include an ACC sensor. The ACC sensor is configured to acquire the user's ACC signal. The processor is configured to acquire the user's second respiratory rate based on the ACC signal. In some cases, the wearable device may acquire a respiratory infection assessment report of the user based on the second respiratory rate and voice signal.

[0008] In some implementations, the processor is further configured to perform filtering and fusion on the first and second respiratory counts to obtain the user's third respiratory count. It should be understood that since the third respiratory count is obtained based on the first and second respiratory counts, the accuracy of the measured respiratory count may be improved. Based on this, the accuracy of the respiratory infection assessment report may be improved.

[0009] In some implementations, the processor is further configured to obtain user posture classification results based on the ACC signal, which include a first posture and a second posture, the first posture including a state where stress is applied to the user's wearing position for support, and the second posture including a state where the user's wearing position is in a natural position. It should be understood that the accuracy of the assessment report may be improved by using the corresponding respiratory rate, resulting from the detection of the user's posture, to obtain the respiratory infection assessment report.

[0010] In some implementations, when the user is in a first posture, the processor is configured to acquire a respiratory infection assessment report based on a first respiratory rate and voice signal. It should be understood that when the user is in a first posture, the PPG sensor may acquire a PPG signal with a relatively high signal-to-noise ratio. Based on this, the first respiratory rate may be acquired based on the PPG signal, and a respiratory infection assessment report may be further acquired.

[0011] In some implementations, when the user is in a second posture, the processor is configured to acquire a respiratory infection assessment report based on a third respiratory rate and voice signal. It should be understood that when the user is in a second posture, the PPG sensor may acquire a PPG signal with a relatively high signal-to-noise ratio, and similarly, the ACC sensor may acquire an ACC signal with a relatively high signal-to-noise ratio. Based on this, a third respiratory rate may be acquired based on the first and second respiratory rates, and a respiratory infection assessment report may be further acquired.

[0012] In some implementations, the wearable device may further include a low-pass filter. The low-pass filter is configured to perform low-pass filtering on the ACC signal. The cutoff frequency of the low-pass filter is 1 Hz. The processor is configured to calculate the mean and standard deviation of the ACC signal, calculate the power spectrum based on the low-pass filtered ACC signal, and obtain the position and amplitude of the peak points of the power spectrum. The processor is further configured to input the mean and standard deviation of the ACC signal, and the position and amplitude of the peak points of the power spectrum, into a classification model to obtain attitude classification results.

[0013] In some implementations, the processor may be configured to prompt the user to switch to a second posture if the user is in a first posture and the first respiratory rate exceeds a preset range; and, in response to the user performing the operation to switch to the second posture, to obtain a third respiratory rate based on the PPG signal and ACC signal; and to obtain a respiratory infection assessment report based on the third respiratory rate and voice signal.

[0014] In some implementations, the wearable device may be an electronic device such as a wearable watch, wearable blanket, or wearable monitor. The wearable monitor may be a multi-parameter monitor.

[0015] In some implementations, the wearable device may further include a first bandpass filter and a second bandpass filter. The first bandpass filter is configured to perform bandpass filtering on the PPG signal to obtain the locations of the peaks and troughs of the PPG signal. The second bandpass filter is configured to perform bandpass filtering on the PPG signal to obtain the amplitudes of the peaks and troughs of the PPG signal. The processor is further configured to obtain the user's first respiratory rate based on the locations and amplitudes of the peaks and troughs of the PPG signal. It should be understood that the waveform of the PPG signal may be obtained using the second bandpass filter, and the amplitudes of the peaks and troughs of the PPG signal may be obtained based on the locations of the peaks and troughs of the PPG signal.

[0016] In some implementations, the frequency band of the signal allowed to pass through the first bandpass filter is from 0.5 Hz to 10 Hz, and the frequency band of the signal allowed to pass through the second bandpass filter is from 0.1 Hz to 10 Hz.

[0017] In some implementations, the wearable device may further include a third bandpass filter. The third bandpass filter is configured to perform bandpass filtering on the baseline-removed ACC signal. The processor is further configured to obtain the user's second respiratory rate based on the bandpass-filtered ACC signal.

[0018] In some implementations, the frequency bandwidth of the signal allowed to pass through the third bandpass filter is from 0.1 Hz to 0.5 Hz.

[0019] In a second aspect, the present invention provides a method for evaluating respiratory infections based on a wearable device. The method may include the steps of: acquiring a user's voice signal; acquiring a user's physiological parameter signal; acquiring the user's respiratory rate based on the physiological parameter signal; and acquiring a user's respiratory infection assessment report based on the voice signal and respiratory rate. It should be understood that the risk of a user contracting a respiratory infection can be assessed based on the respiratory infection assessment report. Furthermore, according to the method, respiratory infections can be detected at an early stage. This helps healthcare professionals to perform confirmation, intervention, and treatment to prevent the worsening of respiratory infections.

[0020] In some implementations, the step of acquiring the user's physiological parameter signals specifically includes the step of acquiring the user's PPG signal, the PPG signal including the physiological parameter signals. The step of acquiring the user's respiratory rate based on the physiological parameter signals specifically includes the step of acquiring the user's first respiratory rate based on the PPG signal. In some cases, a respiratory infection assessment report of the user may be acquired based on the first respiratory rate and voice signals.

[0021] In some implementations, the step of acquiring the user's physiological parameter signals specifically includes acquiring the user's PPG signal and ACC signal, each of which includes a physiological parameter signal. The step of acquiring the user's respiratory rate based on the physiological parameter signals specifically includes acquiring the user's first respiratory rate based on the PPG signal; acquiring the user's second respiratory rate based on the ACC signal; and acquiring the user's third respiratory rate by performing filtering and fusion on the first and second respiratory rates. It should be understood that since the third respiratory rate is acquired based on the first and second respiratory rates, the accuracy of the measured respiratory rate may be improved. Based on this, the accuracy of the respiratory infection assessment report may be improved.

[0022] In some implementations, prior to the step of acquiring the user's physiological parameter signals, the method further includes a step of acquiring the user's ACC signal; and a step of acquiring the user's posture classification result based on the ACC signal, where the posture classification result includes a first posture and a second posture, the first posture including a state in which stress is applied to the user's mounting position for support, and the second posture including a state in which the user's mounting position is in a natural position. It should be understood that the accuracy of the assessment report can be improved by using the corresponding respiratory rate, resulting from the detection of the user's posture, to acquire the respiratory infection assessment report.

[0023] In some implementations, the steps to obtain user posture classification results based on the ACC signal specifically include: calculating the mean and standard deviation of the ACC signal; performing low-pass filtering on the ACC signal and calculating the power spectrum of the ACC signal to obtain the position and amplitude of the peak points of the power spectrum, where the cutoff frequency of the low-pass filtering is 1 Hz; and inputting the mean and standard deviation of the ACC signal, and the position and amplitude of the peak points of the power spectrum, into a classification model to obtain posture classification results.

[0024] In some implementations, the step of obtaining a user respiratory infection assessment report based on voice signals and respiratory rate specifically includes: a step of obtaining a respiratory infection assessment report based on a first respiratory rate and voice signal when the user is in a first posture; or a step of obtaining a respiratory infection assessment report based on a third respiratory rate and voice signal when the user is in a second posture. When the user is in the first posture, it should be understood that the PPG sensor may acquire a PPG signal with a relatively high signal-to-noise ratio. Based on this, a first respiratory rate may be acquired based on the PPG signal, and a respiratory infection assessment report may be further acquired. When the user is in the second posture, in addition to the PPG signal, the ACC sensor may similarly acquire an ACC signal with a relatively high signal-to-noise ratio. Based on this, a third respiratory rate may be acquired based on the first and second respiratory rates, and a respiratory infection assessment report may be further acquired.

[0025] In some implementations, the method may further include a step of prompting the user to switch to a second posture if the user is in a first posture and the first respiratory rate exceeds a preset range; and a step of obtaining a third respiratory rate based on the PPG signal and ACC signal in response to the user performing the operation to switch to the second posture.

[0026] According to a third aspect, the present invention further provides a wearable device. The wearable device may include a processor, an ACC sensor, and a PPG sensor. The ACC sensor is configured to acquire the user's ACC signal. The PPG sensor is configured to acquire the user's PPG signal. The processor is configured to acquire a user posture classification result based on the ACC signal, the posture classification result including a first posture and a second posture, the first posture including a state in which the user's wearing position is stressed for support, and the second posture including a state in which the user's wearing position is naturally positioned. If the user is in the first posture, the processor is further configured to acquire the user's first respiratory rate based on the PPG signal. If the user is in the second state, the processor is further configured to acquire the user's first respiratory rate based on the PPG signal, acquire the user's second respiratory rate based on the ACC signal, and perform filtering and fusion on the first and second respiratory rates to acquire the user's third respiratory rate. It should be understood that the wearable device may acquire a posture classification result based on the detection of the user's posture. In addition, wearable devices can improve the accuracy of measured respiratory rates by acquiring the user's respiratory rate in a manner that corresponds to the posture classification results.

[0027] According to a fourth aspect, the present application further provides a method for measuring a respiratory rate based on a wearable device. The method includes: obtaining the user's ACC signal and PPG signal; obtaining a posture classification result of the user based on the ACC signal, where the posture classification result includes a first posture and a second posture, the first posture includes a state where stress is applied to the user's wearing position for support, and the second posture includes a state where the user's wearing position is naturally arranged; when the user is in the first posture, obtaining the user's first respiratory rate based on the PPG signal; or when the user is in the second state, obtaining the user's first respiratory rate based on the PPG signal and obtaining the user's second respiratory rate based on the ACC signal, and performing filtering and fusion on the first respiratory rate and the second respiratory rate to obtain the user's third respiratory rate. It should be understood that the posture classification result can be obtained based on the detection of the user's posture. In addition, by obtaining the user's respiratory rate in a manner corresponding to the posture classification result, the accuracy of the measured respiratory rate can be improved.

[0028] According to the present application, a user's respiratory infection assessment report can be obtained by obtaining the user's voice signal and physiological parameter signal, and thereby, respiratory infection assessment can be implemented. Based on this, respiratory infection can be detected at an early stage, facilitating confirmation, intervention, and treatment.

Brief Description of Drawings

[0029] [Figure 1] It is a diagram of the framework of a wearable device according to an embodiment of the present application.

[0030] [Figure 2] It is a diagram of the framework of a wearable device according to an embodiment of the present application.

[0031] [Figure 3] It is a diagram of the framework for obtaining a first respiratory rate based on a PPG signal according to an embodiment of the present application.

[0032] [Figure 4] This is a diagram of a framework for acquiring a second respiratory rate based on an ACC signal, according to one embodiment of the present invention.

[0033] [Figure 5] This is a schematic diagram showing the user in their first position.

[0034] [Figure 6] This is a schematic diagram showing the user in the second position.

[0035] [Figure 7] This is a diagram of a framework for acquiring posture classification results based on ACC signals, according to one embodiment of the present invention.

[0036] [Figure 8] This is a schematic diagram of an interaction interface for a wearable device according to one embodiment of the present invention.

[0037] [Figure 9] This is a flowchart of a method for evaluating respiratory infections according to one embodiment of the present invention.

[0038] [Figure 10] This is a flowchart of a method for obtaining a first respiratory rate based on a PPG signal according to one embodiment of the present invention.

[0039] [Figure 11] This is a flowchart of a method for obtaining a first respiratory rate based on an ACC signal, according to one embodiment of the present invention.

[0040] [Figure 12] This is a flowchart of a method for extracting posture-related features according to one embodiment of the present invention. [Modes for carrying out the invention]

[0041] The terms used in the embodiments of this Application are intended solely to describe specific embodiments and are not intended to limit this Application. Unless otherwise explicitly stated in the context, the singular terms “1,” “one,” “that,” “the foregoing,” “this,” and “one” as used in this Specification and the appended Claims of this Application are also intended to include the plural form.

[0042] Any reference in this specification to “one embodiment” or “several embodiments” indicates that at least one embodiment of the Application includes certain features, structures, or characteristics described with reference to that embodiment. Accordingly, any phrases such as “in one embodiment,” “several embodiments,” “several other embodiments,” “in other embodiments,” and “in several implementations,” appearing elsewhere in this specification, do not necessarily refer to the same embodiment unless specifically emphasized otherwise, but mean “one or more embodiments, not all of them.” Unless specifically emphasized otherwise, the terms “include,” “equip,” and “have,” and their variations, all mean “include, but not limited to,” “include.”

[0043] A common wearable device may be worn by a user at a designated location to acquire the user's physiological parameters, such as heart rate, electrocardiogram signals, blood oxygen saturation, and body temperature. The designated location may be, for example, the user's wrist, upper arm, or forearm. Based on this, if the user needs to view these physiological parameters, they may do so using the wearable device; or they may view these parameters using a smart terminal wirelessly connected to the wearable device, which may be a mobile phone; or a healthcare professional may view these physiological parameters using a smart terminal wirelessly connected to the wearable device, which may be a bedside monitor or a central station.

[0044] While common wearable devices typically only offer the ability to acquire and display physiological parameters, it's important to understand that they cannot assess a user's risk of developing a disease based on these parameters.

[0045] Diseases such as respiratory infections typically affect a user's physical condition. For example, users tend to experience symptoms such as cough, shortness of breath, or fever. However, in the early stages of a respiratory infection, a user's physical condition changes only slightly, and physiological parameters fluctuate only slightly. Because of these changes, it is generally difficult for users to perceive the early symptoms of a respiratory infection, and common wearable devices cannot implement respiratory infection assessments based on acquired physiological parameters.

[0046] Due to the aforementioned problems, the following embodiments of the present application provide a wearable device and a method for evaluating respiratory infections based on the wearable device. According to the wearable device and method, a user's respiratory infection assessment report may be obtained by acquiring the user's voice signals and physiological parameter signals, and thus the risk of the user suffering from a respiratory infection can be recognized. In some cases, respiratory infections can be detected at an early stage based on the wearable device and method provided in the following embodiments, thereby advancing disease prevention and treatment techniques and facilitating intervention and treatment.

[0047] In some embodiments, the wearable device may be a wearable watch. In some other embodiments, the wearable device may alternatively be an electronic device such as a wearable blanket or a wearable monitor. This is not limited to these embodiments.

[0048] In addition to implementing respiratory infection assessment, it should be understood that the wearable devices provided in these embodiments may further implement functions such as fall detection, wireless communication, or message-based alerts. This is not limited to these functions.

[0049] Please refer to Figure 1. A wearable device 10 provided in one embodiment of the present application may include a processor 12 and a first sensor 14. The processor 12 may be electrically or wirelessly connected to the first sensor 14. This is not limited. The first sensor 14 may be configured to acquire an audio signal from a user 20. The audio signal may be used to measure sounds produced by the user 20 and emitted from the user 20's respiratory system. It should be understood that the first sensor 14 may further be configured to acquire sounds produced by the user 20 within a preset period. The preset period may be, for example, 10 seconds, 20 seconds, 30 seconds, or 1 minute. This is not limited.

[0050] In some embodiments, the wearable device 10 may prompt the user 20 to cough. A first sensor 14 may measure the cough and acquire an audio signal. Based on the audio signal, the processor 12 may perform an analysis by referencing another signal to assess the risk of the user 20 contracting a respiratory infection and present the risk in the form of a respiratory infection assessment report.

[0051] As described above, the wearable device 10 can prompt the user 20 to produce sound in various ways. For example, the wearable device 10 may prompt the user 20 to cough using voice, or it may display information prompting the user 20 to cough. Based on this, after the user 20 coughs, the wearable device 10 may acquire an audio signal in response to the cough made by the user 20.

[0052] In some other embodiments, the smart terminal may alternatively prompt the user 20 to cough in various ways, so that the first sensor 14 can collect the cough. For example, the smart terminal may prompt the user 20 by using voice or by displaying an image. After the user 20 coughs, the wearable device 10 may acquire an audio signal in response to the cough made by the user 20.

[0053] It should be understood that there are significant differences between the coughs emitted from the respiratory systems of users 20 who do not have any respiratory infections and users 20 who do have respiratory infections. Accordingly, these differences may be reflected in their respective voice signals. Based on this, the processor 12 may perform operations such as feature extraction on the voice signals and classify the voice signals through machine learning or the like, which will help to later assess the risk of user 20 developing a respiratory infection.

[0054] In some embodiments, the processor 12 may be, but is not limited to, an MCU (Microcontroller Unit) or a CPU (Central Processing Unit) within the wearable device 10. Alternatively, the processor 12 may be a circuit board component within the wearable device 10. The circuit board component may be integrated with electronic components such as an MCU, CPU, radio frequency chip, or filter.

[0055] Furthermore, please refer to Figure 1. In some embodiments, the wearable device 10 may further include a second sensor 16. Similar to the first sensor 14, the second sensor 16 may be electrically or wirelessly connected to the processor 12. The second sensor 16 may be configured to acquire physiological parameter signals of the user 20. The physiological parameter signals may include at least one signal used to measure the user 20's respiratory rate. For example, the physiological parameter signals may include, but are not limited to, the PPG signal and ACC signal described below. Depending on the number and type of sensors provided for the wearable device 10, the physiological parameter signals may further include signals used to measure physiological parameters such as the user 20's heart rate, electrocardiogram signal, or blood oxygen saturation.

[0056] It should be understood that for a user 20 suffering from a respiratory infection, the user 20's physiological parameters generally produce abnormal fluctuations. These abnormal fluctuations may indicate that at least some of the user 20's physiological parameters exceed normal limits. Based on this, in the wearable device 10 provided in the embodiments of the present application, the processor 12 may analyze the user 20's voice signal and physiological parameter signal to determine whether the user 20's physical condition has changed. Thus, the risk of the user 20 suffering from a respiratory infection can be comprehensively assessed.

[0057] In some embodiments, after a respiratory infection assessment is performed on user 20, the respiratory infection assessment report corresponding to user 20 may record information such as "no abnormalities found," "low risk of respiratory infection," "moderate risk of respiratory infection," or "high risk of respiratory infection." It should be understood that the wearable device 10 may display the corresponding respiratory infection assessment report on its interaction interface, so that user 20 or a healthcare professional can view this report. Alternatively, a smart terminal may similarly display the respiratory infection assessment report on its interaction interface. This is not limited to these embodiments.

[0058] For example, in the case of user 20 with an early respiratory infection, it is relatively difficult for user 20 to notice the physical changes that occur when user 20's body cope with the respiratory infection. However, the second sensor 16 of the wearable device 10 can acquire physiological parameter signals corresponding to user 20's physical changes. The processor 12 can analyze the physiological parameter signals to obtain user 20's respiratory rate. Based on this, it can be determined through analysis that user 20's respiratory rate is abnormal. Next, a respiratory infection assessment report for user 20 can be obtained by referring to user 20's voice signals. The assessment report may record information about the high risk of respiratory infection.

[0059] User 20 should understand that by using the wearable device 10 provided in the embodiments of the present application, respiratory infection assessment can be performed simply and quickly. In some cases, the wearable device 10 can be used to detect respiratory infections at an early stage, and as a result, healthcare workers can confirm, intervene in, and treat respiratory infections to prevent them from worsening.

[0060] Refer to Figure 2. In some embodiments, the second sensor 16 may include a PPG (Photoplethysmography) sensor 16a. The PPG sensor 16a can acquire the user 20's PPG signal. It should be understood that, due to the commonality of human anatomy, the user 20's breathing motion affects blood flow in the blood vessels. Accordingly, this effect may be reflected in the acquired PPG signal. Based on this, the processor 12 may extract characteristic signals related to the user 20's breathing motion as physiological parameter signals from the PPG signal, and thus calculate the user 20's first respiratory rate based on the physiological parameter signals.

[0061] In some embodiments, the second sensor 16 may further include an ACC (Accelerometer) sensor 16b. The ACC sensor 16b may acquire the ACC signal of the user 20. It should be understood that the user 20's breathing motion may cause fluctuations in the user 20's thoracic and abdominal cavities, and the user 20's upper limbs may oscillate. In some cases, the oscillating motion of the upper limbs may be imaged by the ACC sensor 16b and modulated in the ACC signal. Based on this, the processor 12 may extract characteristic signals related to the user 20's breathing motion as physiological parameter signals from the ACC signal, thereby calculating the user 20's second respiratory rate based on the physiological parameter signals.

[0062] In some embodiments, the PPG signal or ACC signal may also be understood as a physiological parameter signal. The wearable device 10 may process the PPG and ACC signals to obtain the user's respiratory rate, but it is not necessarily required to extract feature signals related to respiratory rate as physiological parameter signals from the PPG or ACC signal.

[0063] In some embodiments, multiple first sensors may be present to improve the accuracy of the acquired signal. In addition, multiple second sensors may also be present to improve the accuracy of the acquired signal.

[0064] Refer to Figure 3. In some embodiments, corresponding to the PPG signal, the wearable device 10 may further include a first bandpass filter 18a and a second bandpass filter 18b. The first bandpass filter 18a may be configured to perform bandpass filtering on the PPG signal to obtain the locations of the peaks and troughs of the PPG signal. Similarly, the second bandpass filter 18b may be configured to perform bandpass filtering on the PPG signal to obtain the waveform of the PPG signal.

[0065] The amplitudes of the peaks and troughs of the PPG signal can be obtained by matching the waveform of the PPG signal to the positions of the peaks and troughs. Baseline wander (BW), amplitude modulation (AM), and frequency modulation (FM) features related to respiratory rate can be obtained based on the positions and amplitudes of the peaks and troughs of the PPG signal. Based on this, a first respiratory rate based on the PPG signal can be obtained by performing calculations on the baseline wander, amplitude modulation, and frequency modulation features.

[0066] In some embodiments, the frequency band of the signal allowed to pass through the first bandpass filter 18a may be from 0.5 Hz to 10 Hz; and the frequency band of the signal allowed to pass through the second bandpass filter 18b may be from 0.1 Hz to 10 Hz.

[0067] Refer to Figure 4. In some embodiments, baseline removal may be performed on the ACC signal by software to prevent baseline fluctuation interference of the ACC signal. The wearable device 10 may further include a third bandpass filter 18c. The third bandpass filter 18c may be configured to perform bandpass filtering on the baseline-removed ACC signal to ensure a frequency bandwidth corresponding to the respiratory rate. The processor 12 may further be configured to perform wave peak extraction on the bandpass-filtered ACC signal to obtain a second respiratory rate based on the ACC signal.

[0068] In some embodiments, the frequency bandwidth of the signal allowed to pass through the third bandpass filter 18c may be from 0.1 Hz to 0.5 Hz.

[0069] In some embodiments, the processor 12 may be further configured to perform filtering and fusion on the first and second respiratory rates to obtain a third respiratory rate for the user 20.

[0070] It should be understood that the second respiratory rate obtained based on the ACC signal is more accurate than the first respiratory rate obtained based on the PPG signal. However, the ACC signal is also more susceptible to influences such as the way the wearable device 10 is worn and the user 20's movement and posture, resulting in a more limited scope of application. In some cases, a more accurate third respiratory rate can be obtained by performing filtering and fusion on the first and second respiratory rates. Thus, the accuracy of the respiratory infection assessment report can be improved.

[0071] In some embodiments, the first respiratory rate, second respiratory rate, and third respiratory rate may be understood as the respiratory rate of user 20, acquired in different ways. In the wearable device 10 provided in the embodiments, the respiratory infection assessment report of user 20 is acquired mainly based on the first respiratory rate or the third respiratory rate, but the present application is not limited to these.

[0072] In some other embodiments, the respiratory infection assessment report of user 20 may be obtained based on a second respiratory rate.

[0073] In some embodiments, the processor 12 may be further configured to acquire a user 20 posture classification result based on the ACC signal acquired by the ACC sensor 16b.

[0074] It should be understood that the ACC signal may include a 3-axis acceleration signal associated with user 20. The posture classification result of user 20 can be obtained by analyzing the ACC signal. The wearable device 10 may improve the accuracy of the respiratory infection assessment report by obtaining the user 20's respiratory rate in different ways based on the posture classification result.

[0075] The posture classification results may include a first posture and a second posture. The first posture may indicate a state in which stress is applied to the user 20's mounting position for support. The second posture may indicate a state in which the user 20's mounting position is in a natural position.

[0076] When user 20 is in a first posture, user 20's first respiratory rate can be obtained based on the PPG signal. When user 20 is in a second posture, user 20's third respiratory rate can be obtained based on the PPG signal and the ACC signal. It should be understood that the wearable device 10 can adaptively select the corresponding measurement method based on the posture classification result. Therefore, the accuracy of the measured respiratory rate can be improved.

[0077] In some embodiments, the wearable device 10 may prompt the user 20 on an interaction interface to perform respiratory rate measurement based on a first or second posture.

[0078] In some other embodiments, the wearable device 10 may further automatically acquire the user 20's posture classification result based on the ACC signal. A corresponding sensor (14, 16a, or 16b) may be selected based on the posture classification result to cooperate with the processor 12 in acquiring the user 20's respiratory rate.

[0079] It should be understood that when user 20 is in a specific first or second posture, interference caused by the external environment and user 20's inattentional shaking or trembling can be eliminated. Based on this, the wearable device 10 may acquire a PPG signal or ACC signal with a relatively high signal-to-noise ratio, which may improve the accuracy of the acquired first or third respiratory rate and the accuracy of the respiratory evaluation report.

[0080] For illustrative purposes, an example is used in which the wearable device 10 is a watch and is worn on the user 20's wrist.

[0081] Figure 5 shows a schematic diagram of the user in a first posture. Please refer to Figures 1 through 5 synchronously. Both forearms of the seated user 20 are stably positioned on the table; an interaction force exists between the user 20's forearms and the table. In this case, the user 20's PPG signal can be obtained based on the PPG sensor 16a. The user 20's first respiratory rate can be obtained by processing the PPG signal.

[0082] Figure 6 shows a schematic diagram of the user in the second posture. Please refer to Figures 1 through 6 synchronously. The palms of the seated user 20 are naturally placed on the user 20's thighs, and the user 20's wrists are naturally relaxed. In this case, when the user 20 breathes, the PPG sensor 16a may acquire the user 20's PPG signal, and the ACC sensor 16b may acquire the user 20's ACC signal. Accordingly, the first and second respiratory counts can be obtained by processing the PPG and ACC signals. The processor 12 may further be configured to perform filtering and fusion on the first and second respiratory counts to obtain the user 20's third respiratory count.

[0083] The first posture in Figure 5 and the second posture in Figure 6 are used only as illustrative examples, and it should be understood that the present invention is not limited to these. In some other embodiments, specifically, the first posture may further include a state in which both forearms of user 20 are placed on the armrests of a chair. Specifically, the second posture may further include a state in which the palms of both hands of a seated user 20 are placed on the user 20's abdomen, or a state in which both arms of a standing user 20 are naturally hanging down.

[0084] In some embodiments, the processor 12 may extract posture-related features from the ACC signal and input the extracted features into a classification model. The classification model may classify the user 20's posture based on these features to distinguish between a first posture and a second posture.

[0085] In some embodiments, the classification model may include SVM (Support Vector Machine), decision trees, XGBoost, neural networks, or deep learning.

[0086] Refer to Figure 7. In some embodiments, the processor 12 may calculate the mean and standard deviation of the ACC signal. In addition, the wearable device 10 may further include a low-pass filter 18d. The low-pass filter 18d is configured to perform low-pass filtering on the ACC signal. The cutoff frequency of the low-pass filter 18d is 1 Hz.

[0087] The processor 12 may further calculate the power spectrum of the low-pass filtered ACC signal to obtain the position and amplitude of the peak points in the power spectrum. A classification model may classify the ACC signal based on the mean and standard deviation of the ACC signal, and the position and amplitude of the peak points in the power spectrum, to determine the current attitude of user 20. For example, after the ACC signal has been processed, it may be determined that the current attitude of user 20 is the first attitude; or it may be determined that the current attitude of user 20 is the second attitude.

[0088] In some embodiments, the wearable device 10 may display guide pictures of a first or second posture on its interaction interface to guide the user 20 to adjust its posture accordingly. In some other embodiments, a smart terminal may alternatively display guide pictures of a first or second posture on its interaction interface. This is not limited to these embodiments.

[0089] In some embodiments, if the user 20's respiratory rate (e.g., a first respiratory rate or a third respiratory rate) exceeds a preset range, the wearable device 10 may further display prompt information on its interaction interface. The prompt information may encourage the user 20 to change posture and perform the measurement again. If the user 20 chooses to change posture and perform the measurement again, a signal may be acquired based on the posture after the change using the corresponding sensor. If the user 20 does not choose to change posture, or does not actively choose to change posture, the risk of the user 20 contracting a respiratory infection may be assessed based on the initially acquired respiratory rate.

[0090] In some embodiments, the prompt information of the wearable device 10 may alternatively prompt the user 20 to adjust the position of the wearable device 10 at the wearing location or to reattach the wearable device 10.

[0091] In some embodiments, the preset range of the respiratory rate may be from 9 BPM to 24 BPM, where BPM is an abbreviation for Breaths Per Minute.

[0092] For example, if user 20 is in the first posture, the respiratory rate of user 20 acquired by the wearable device 10 is 8 BPM, meaning the respiratory rate is above a preset range. Based on this, user 20 may be prompted on the interaction interface of the wearable device 10 to switch to the second posture and perform detection again. If user 20 chooses to switch, the wearable device 10 may, in response to user 20's action, measure user 20's respiratory rate again. If user 20 does not choose to switch, the wearable device 10 may, by referring to the audio signal, acquire a respiratory infection assessment report for user 20 based on the previously acquired respiratory rate (i.e., 8 BPM).

[0093] In some embodiments, during the process of implementing respiratory infection assessment, the wearable device 10 may further provide prompt information in the form of pictures, text, or audio to prompt the user 20 to perform the relevant operation.

[0094] Refer to Figure 8. In some embodiments, the wearable device 10 may display a respiratory infection assessment report and may further display information related to respiratory rate. For example, the wearable device may exemplify changes in the user 20's respiratory rate over a period of time. This period may be the last few minutes, tens of minutes, or hours. As shown in Figure 8, for example, the respiratory infection assessment report may indicate no abnormalities, and the wearable device 10 may further display changes in the user 20's respiratory rate from 1 hour to 7 hours.

[0095] In some embodiments, the wearable device 10 may be further configured to measure the user 20's respiratory rate during daily work and life. For example, a first or third respiratory rate may be obtained accordingly based on the user 20's posture. The first or third respiratory rate may be displayed in real time on the wearable device's main interface or default interface, so that the user 20 can see the first or third respiratory rate. The main interface may be a manufacturer-defined interface, or an interface that is displayed after the wearable device is powered on and is visible to the user without requiring any operation. The default interface may be an interface within the wearable device provided for user-defined settings, or the default interface may be the main interface.

[0096] For some wearable devices, after a different theme plugin is loaded, the main interface of these wearable devices may or may not display the respiratory rate, but it should be understood that the main interface should still be considered the main interface or default interface in the embodiments described above.

[0097] Please refer to Figure 9. One embodiment of the present invention further provides a method for evaluating respiratory infections based on a wearable device. Similar to the wearable device provided in the embodiments described above, this method can also implement respiratory infection evaluation. This method may include, but is not limited to, the following steps:

[0098] 101: Acquire the user's voice signal.

[0099] In some embodiments, the audio signal may be used to measure sounds produced by the user and emitted from the user's respiratory system. For example, the audio signal may be obtained by measuring a cough made by the user. Based on the audio signal, and by performing an analysis according to this method with reference to another signal, the risk of the user contracting a respiratory infection can be assessed, and the risk may be presented in the form of a respiratory infection assessment report.

[0100] In some embodiments, the user may be prompted on the interaction interface of a wearable device to produce a sound, for example, by coughing. The audio signal is then acquired in response to the cough made by the user. Alternatively, the user may be prompted on the interaction interface of a smart device to cough; and the wearable device may acquire the audio signal in response to the cough.

[0101] It should be understood that there are significant differences between the coughs of healthy users (without any respiratory infections) and users with respiratory infections. Accordingly, these differences can be reflected in their respective voice signals. Based on this, operations such as feature extraction may be performed on the voice signals; and the voice signals may be classified through machine learning or the like. This will be useful in later assessing the risk of a user developing a respiratory infection.

[0102] 102: Obtain the user's physiological parameter signals.

[0103] Physiological parameter signals may, but are not limited to, be used to measure a user's respiratory rate. Depending on the number and type of sensors provided for the wearable device, physiological parameter signals may further be used to measure physiological parameters such as a user's heart rate, electrocardiogram signal, or blood oxygen saturation.

[0104] Assuming there are no contradictions, please understand that there is no required order between these stages. For example, regarding stages 101 and 102, stage 101 may be performed before stage 102; or stage 102 may be performed before stage 101.

[0105] 103: Obtain the user's respiratory rate based on physiological parameter signals.

[0106] The physiological parameter signal may be part of the PPG signal or ACC signal. The physiological parameter signal can be obtained by analyzing the PPG signal or ACC signal. Next, the user's respiratory rate can be obtained.

[0107] It should be understood that the PPG signal or ACC signal can also be directly understood as a physiological parameter signal. According to this method, the PPG and ACC signals can be processed to obtain the user's respiratory rate; however, feature signals related to respiratory rate do not necessarily need to be extracted as physiological parameter signals from the PPG or ACC signal.

[0108] 104: Obtain a user respiratory infection assessment report based on voice signals and respiratory rate.

[0109] In some embodiments, the user's respiratory rate is directly correlated with the user's respiratory health, so whether the user's physical condition is changing can be determined by analyzing the user's voice signals and respiratory rate. Next, the user's risk of developing a respiratory infection can be comprehensively assessed.

[0110] In some embodiments, after a respiratory infection assessment is performed on a user, a respiratory infection assessment report corresponding to the user may record information such as "no abnormalities found," "low risk of respiratory infection," "moderate risk of respiratory infection," or "high risk of respiratory infection." The respiratory infection assessment report may be displayed on the interaction interface of a wearable device or smart terminal, and it should be understood that the user can view this report as a result.

[0111] For example, in the case of a user with an early respiratory infection, it is relatively difficult for the user to notice the changes in their physical condition that occur when their body cope with the respiratory infection. According to the method provided in the embodiments of the present invention, the user's respiratory rate can be obtained by acquiring and analyzing physiological parameter signals corresponding to changes in the user's physical condition. Based on this, it can be determined through the analysis that the user's respiratory rate is abnormal. Next, a respiratory infection assessment report of the user can be obtained by referring to the user's voice signals. This assessment report records information indicating that the user is at high risk of developing a respiratory infection.

[0112] It should be understood that, according to the method provided in the embodiments of this application, users can easily and quickly implement respiratory infection assessment using a wearable device. In some cases, respiratory infections can be detected at an early stage according to this method, and as a result, healthcare workers can confirm, intervene in, and treat respiratory infections to prevent their worsening.

[0113] In some embodiments, the respiratory infection assessment report further records information related to respiratory rate. For example, the respiratory infection assessment report may record the user's respiratory rate distribution over a period of time, which may be the last few minutes, tens of minutes, or hours.

[0114] Prior to step 101, the method provided in embodiments of the present application may further include a step of detecting whether the user is wearing a wearable device.

[0115] In some embodiments, if the user is wearing a wearable device, step 101 may be performed to implement a respiratory infection assessment for the user.

[0116] In some other embodiments, if the user is not wearing a wearable device, step 101 is not performed. In some cases, in order to perform step 101, the method may further prompt the user to wear a wearable device on the interaction interface of the wearable device or smart terminal.

[0117] In some embodiments, in a manner in which physiological parameter signals are acquired, the method may specifically include the step of acquiring the user's PPG signal.

[0118] It should be understood that respiratory-related characteristic signals can be extracted from the PPG signal as physiological parameter signals, and that the user's first respiratory rate is calculated based on these physiological parameter signals.

[0119] Please refer to Figure 10. In some embodiments corresponding to the PPG signal, this method may further include the following steps:

[0120] 111: Perform a first bandpass filtering on the PPG signal to obtain the locations of the peak and trough points of the PPG signal.

[0121] In some embodiments, this filtering can be performed on the PPG signal by using a first bandpass filter. The frequency bandwidth of the signal allowed to pass through the first bandpass filter may be from 0.5 Hz to 10 Hz.

[0122] 112: By performing a second bandpass filtering on the PPG signal, the amplitudes of the peaks and troughs of the PPG signal are obtained by referencing the positions of the peaks and troughs of the PPG signal.

[0123] In some embodiments, this filtering may be performed on the PPG signal by using a second bandpass filter to obtain the waveform of the PPG signal. The amplitudes of the peaks and troughs of the PPG signal may be obtained by matching the waveform of the PPG signal to the positions of the peaks and troughs.

[0124] In some embodiments, the frequency bandwidth of the signal allowed to pass through the second bandpass filter may be from 0.1 Hz to 10 Hz.

[0125] 113: Obtain the first respiratory rate based on the PPG signal, taking into account the position and amplitude of the peak and trough points of the PPG signal.

[0126] In some embodiments, baseline variation features, amplitude modulation features, and frequency modulation features related to respiratory rate can be obtained based on the location and amplitude of the peaks and troughs of the PPG signal. A first respiratory rate based on the PPG signal can be obtained by performing calculations on the baseline variation features, amplitude modulation features, and frequency modulation features.

[0127] In some embodiments, in a configuration for acquiring physiological parameter signals, the method may further include, specifically, the step of acquiring the user's ACC signal.

[0128] It should be understood that the user's breathing motion can cause fluctuations in the user's thoracic and abdominal cavities, and may cause the user's upper limbs to oscillate. In some cases, the oscillating motion of the upper limbs may be captured by an ACC sensor and modulated in the ACC signal. Based on this, respiratory-related feature signals may be extracted from the ACC signal as physiological parameter signals; and the user's second respiratory rate may be calculated based on the physiological parameter signals.

[0129] Please refer to Figure 11. In some embodiments, with respect to the ACC signal, this method may specifically include the following steps.

[0130] 121: Perform baseline removal on the ACC signal.

[0131] Based on step 121, the baseline of the ACC signal may be removed via software to prevent baseline fluctuation interference of the ACC signal, resulting in improved accuracy of the measured respiratory rate.

[0132] 122: Perform bandpass filtering on the ACC signal from which the baseline has been removed.

[0133] In some embodiments, this filtering may be performed on the ACC signal by using a third bandpass filter to ensure a frequency band corresponding to the respiratory rate of the ACC signal. The frequency band of the signal allowed to pass through the third bandpass filter may be from 0.1 Hz to 0.5 Hz.

[0134] 123: Wave peak extraction is performed on the filtered ACC signal to obtain a second respiratory rate based on the ACC signal.

[0135] In some embodiments, in a configuration for obtaining the respiratory rate, the method may further include the step of performing filtering and fusion on a first respiratory rate and a second respiratory rate to obtain a third respiratory rate for the user.

[0136] It should be understood that the second respiratory rate obtained based on the ACC signal is more accurate than the first respiratory rate obtained based on the PPG signal. However, the ACC signal is also more susceptible to influences such as the way the wearable device is worn and the user's movement and posture, resulting in a more limited scope of application. In some cases, a more accurate third respiratory rate can be obtained by performing filtering and fusion on the first and second respiratory rates. Thus, the accuracy of respiratory infection assessment reports can be improved.

[0137] In some embodiments, the first, second, and third respiratory rates may be understood as the user's respiratory rate, obtained in different ways. In the methods provided in embodiments, the user's respiratory infection assessment report is obtained primarily based on the first or third respiratory rate, but the present application is not limited to these. In some other embodiments, the user's respiratory infection assessment report may alternatively be obtained based on a second respiratory rate.

[0138] In some embodiments, in order to improve the accuracy of respiratory rate measurement, the method may further include a step of obtaining a user posture classification result based on the ACC signal before the step of obtaining physiological parameter signals.

[0139] It should be understood that the ACC signal may include a 3-axis acceleration signal associated with the user. User posture classification results can be obtained by analyzing the ACC signal. Based on the posture classification results, the user's respiratory rate can be obtained in different ways to improve the accuracy of the respiratory infection assessment report.

[0140] In some embodiments, the posture classification result may include a first posture and a second posture. The first posture may indicate a state in which stress is applied to the user's wearing position for support. The second posture may indicate a state in which the user's wearing position is in a natural position.

[0141] When the user is in the first posture, the user's first respiratory rate can be obtained based on the PPG signal. When the user is in the second posture, the user's third respiratory rate can be obtained based on the PPG signal and the ACC signal. It should be understood that the corresponding measurement method can be adaptively selected based on the posture classification result. Therefore, the accuracy of the measured respiratory rate and the accuracy of the respiratory infection assessment report can be improved.

[0142] In some embodiments, the user may be prompted on the wearable device's interaction interface to perform respiratory rate measurement based on a first or second posture, as well as to produce a sound.

[0143] In some other embodiments, the user's posture classification result may be automatically obtained based on the ACC signal. To obtain the user's respiratory rate, a corresponding method may be selected based on the posture classification result.

[0144] In some embodiments, in which a user's posture classification result is obtained based on an ACC signal, the method may specifically include the steps of extracting posture-related features from the ACC signal and inputting the extracted features into a classification model. The classification model can then classify the user's posture based on these features.

[0145] Please refer to Figure 12. In some embodiments, the method for extracting posture-related features may specifically include the following steps.

[0146] 131: Calculate the mean and standard deviation of the ACC signal.

[0147] 132: Perform low-pass filtering on the ACC signal.

[0148] In some embodiments, this filtering can be performed on the ACC signal by using a low-pass filter. The cutoff frequency of the low-pass filter may be 1 Hz.

[0149] 133: Calculate the power spectrum of the low-pass filtered ACC signal to obtain the position and amplitude of the peak points in the power spectrum.

[0150] In some embodiments, the classification model may determine the user's current posture by classifying the ACC signal based on the mean and standard deviation of the ACC signal, and the position and amplitude of the peak points in the power spectrum.

[0151] In some embodiments, a guide picture of a first or second posture may be displayed on the interaction interface of a wearable device to guide the user to adjust their posture accordingly. In some other embodiments, the guide picture of the first or second posture may instead be displayed on the interaction interface of a smart terminal. This is not limited to these embodiments.

[0152] In some embodiments, if the user's respiratory rate (e.g., a first respiratory rate or a third respiratory rate) exceeds a preset range, prompt information may be further displayed on the wearable device's interaction interface. The prompt information may encourage the user to change posture and perform the measurement again. If the user chooses to change posture and perform the measurement again, a signal may be acquired based on the posture after the change using the corresponding sensor. If the user does not choose to change posture, or does not actively choose to change posture, the risk of the user contracting a respiratory infection may be assessed based on the initially acquired respiratory rate.

[0153] In some embodiments, the prompt information may instead prompt the user to adjust the position of the wearable device at the wearing location or to reattach the wearable device.

[0154] In some embodiments, the preset range for respiratory rate may be from 9 BPM to 24 BPM. For example, if the user is in a first posture, the user's respiratory rate obtained based on the PPG signal is 25 BPM, meaning the respiratory rate exceeds the preset range. Based on this, the user may be prompted on the wearable device's interaction interface to switch to a second posture and perform detection again. If the user chooses to switch, the user's respiratory rate may be measured again in response to the user's action. If the user does not choose to switch, a respiratory infection assessment report for the user may be obtained based on the previously obtained respiratory rate (i.e., 26 BPM) by referring to the audio signal.

[0155] In some embodiments, the process of implementing a method for evaluating respiratory infections may involve further prompting information, such as in the form of pictures, text, or audio, to prompt the user to perform the relevant actions.

[0156] In some embodiments, the method may further include the step of providing guide information in the form of pictures, text, or audio. The guide information may be used to acquire and process audio signals and physiological parameter signals by guiding the user to perform the relevant operations. [Other possible items] [Item 1] A first sensor configured to acquire the user's voice signal; A second sensor configured to acquire the physiological parameter signals of the user; and A processor configured to acquire the user's respiratory rate based on the physiological parameter signals and to acquire a respiratory infection assessment report for the user based on the voice signals and the respiratory rate. A wearable device equipped with [features / equipment]. [Item 2] The wearable device according to item 1, wherein the second sensor has a PPG sensor configured to acquire the user's PPG signal, and the processor is configured to acquire the user's first respiratory rate based on the PPG signal. [Item 3] The wearable device according to item 2, wherein the second sensor further comprises an ACC sensor, the ACC sensor is configured to acquire the user's ACC signal, and the processor is further configured to acquire the user's second respiratory rate based on the ACC signal. [Item 4] The wearable device according to item 3, wherein the processor is further configured to perform filtering and fusion on the first and second respiratory rates to obtain the user's third respiratory rate. [Item 5] The processor is further configured to acquire a user posture classification result based on the ACC signal, the posture classification result including a first posture and a second posture, the first posture including a state in which stress is applied to the user's mounting position for support, and the second posture including a state in which the user's mounting position is in a natural position; When the user is in the first posture, the processor is configured to obtain the respiratory infection assessment report based on the first respiratory rate and the voice signal; and When the user is in the second posture, the processor is configured to obtain the respiratory infection assessment report based on the third respiratory rate and the voice signal. Wearable devices as described in item 3 or 4. [Item 6] The wearable device further comprises a low-pass filter, the low-pass filter configured to perform low-pass filtering on the ACC signal, and the cutoff frequency of the low-pass filter is 1 Hz; The processor is configured to calculate the mean and standard deviation of the ACC signal, calculate the power spectrum based on the low-pass filtered ACC signal, and obtain the position and amplitude of the peak points of the power spectrum; and The processor is further configured to input the mean value and standard deviation of the ACC signal, and the position and amplitude of the peak point of the power spectrum into a classification model to obtain the attitude classification result. Wearable devices as described in item 5. [Item 7] The processor is further configured to prompt the user to switch to the second posture if the user is in the first posture and the first respiratory rate exceeds a preset range; and The processor is further configured to acquire the third respiratory rate based on the PPG signal and the ACC signal in response to the user performing an operation to switch to the second posture, and to acquire the respiratory infection assessment report based on the third respiratory rate and the voice signal. Wearable devices as described in item 5. [Item 8] The wearable device is a wearable watch, a wearable blanket, or a wearable monitor, as described in any one of items 1 to 6. [Item 9] A method for evaluating respiratory infections based on wearable devices, The stage of acquiring the user's voice signal; A step of acquiring the physiological parameter signals of the user; A step of obtaining the user's respiratory rate based on the physiological parameter signals; and Steps to obtain a respiratory infection assessment report for the user based on the aforementioned audio signal and respiratory rate. A method that includes [a certain feature]. [Item 10] The step of acquiring the user's physiological parameter signals specifically involves, The step of acquiring the user's PPG signal, wherein the PPG signal includes the physiological parameter signal. Having; and The step of obtaining the user's respiratory rate based on the physiological parameter signals is, specifically, Steps to obtain the user's first respiratory rate based on the PPG signal. has The method described in item 9. [Item 11] The step of acquiring the user's physiological parameter signals specifically involves, The step of acquiring the user's PPG signal and ACC signal, wherein each of the PPG signal and the ACC signal includes the physiological parameter signal. Having; and The step of obtaining the user's respiratory rate based on the physiological parameter signals is, specifically, A step of obtaining the user's first respiratory rate based on the PPG signal; A step of obtaining the user's second respiratory rate based on the ACC signal; and Steps to obtain the user's third respiratory rate by performing filtering and fusion on the first respiratory rate and the second respiratory rate. has The method described in item 9. [Item 12] Prior to the step of acquiring the physiological parameter signals of the user, The step of obtaining the ACC signal of the user; and Step of acquiring the user's posture classification result based on the ACC signal. Furthermore, here, The posture classification results include a first posture and a second posture, the first posture including a state in which stress is applied to the user's mounting position for support, and the second posture including a state in which the user's mounting position is in a natural position; and The step of obtaining a respiratory infection assessment report for the user based on the aforementioned audio signal and respiratory rate specifically includes: If the user is in the first posture, the step of obtaining the respiratory infection assessment report based on the first respiratory rate and the audio signal; or If the user is in the second posture, the step of obtaining the respiratory infection assessment report based on the third respiratory rate and the audio signal. Having, The method described in any one of items 9 through 11. [Item 13] The step of obtaining the user's posture classification result based on the ACC signal is, specifically, A step of calculating the mean value and standard deviation of the ACC signal; The steps include performing low-pass filtering on the ACC signal, calculating the power spectrum of the ACC signal, and obtaining the position and amplitude of the peak point of the power spectrum, where the cutoff frequency of the low-pass filtering is 1 Hz; and Step 1: Inputting the mean value and standard deviation of the ACC signal, and the position and amplitude of the peak point of the power spectrum into a classification model to obtain the attitude classification result. Having, The method described in item 11. [Item 14] If the user is in the first posture and the first respiratory rate exceeds a preset range, A stage in which the user is prompted to switch to the second posture; A step of obtaining the third respiratory rate based on the PPG signal and the ACC signal in response to the user performing the operation for switching to the second posture; and Steps to obtain the respiratory infection assessment report based on the third respiratory rate and the voice signal. The method described in item 13, further comprising:

Claims

1. A first sensor configured to acquire the user's voice signal; A second sensor configured to acquire the user's physiological parameter signals, wherein the second sensor has a PPG sensor, and the PPG sensor is configured to acquire the user's PPG signal; and A processor configured to acquire the user's respiratory rate based on the PPG signal and to acquire a respiratory infection assessment report for the user based on the voice signal and the respiratory rate. Equipped with, A wearable device consisting of a single device.

2. The wearable device according to claim 1, wherein the second sensor further comprises an ACC sensor configured to acquire the user's ACC signal, and the processor is further configured to acquire the user's second respiratory rate based on the ACC signal.

3. The wearable device according to claim 2, wherein the processor is further configured to perform filtering and fusion on the user's first respiratory rate and the second respiratory rate to obtain the user's third respiratory rate.

4. The processor is further configured to acquire a user posture classification result based on the ACC signal, the posture classification result including a first posture, the first posture including a state in which stress is applied to the user's mounting position for support; When the user is in the first posture, the processor is configured to obtain the respiratory infection assessment report based on the user's first respiratory rate and the voice signal. The wearable device according to claim 2.

5. The processor is further configured to acquire a user posture classification result based on the ACC signal, the posture classification result including a second posture, the second posture including a state in which the user's wearing position is naturally positioned; When the user is in the second posture, the processor is configured to obtain the respiratory infection assessment report based on the user's third respiratory rate and the voice signal. The wearable device according to claim 2.

6. The wearable device further comprises a low-pass filter, the low-pass filter configured to perform low-pass filtering on the ACC signal, and the cutoff frequency of the low-pass filter is 1 Hz; The processor is configured to calculate the mean and standard deviation of the ACC signal, calculate the power spectrum based on the low-pass filtered ACC signal, and obtain the position and amplitude of the peak points of the power spectrum; and The processor is further configured to input the mean value and standard deviation of the ACC signal, and the position and amplitude of the peak point of the power spectrum into a classification model to obtain the attitude classification result. The wearable device according to claim 5.

7. The processor is further configured to prompt the user to switch to the second posture if the user is in the first posture and the user's first respiratory rate exceeds a preset range; and The processor is further configured to acquire the third respiratory rate based on the PPG signal and the ACC signal in response to the user performing an operation to switch to the second posture, and to acquire the respiratory infection assessment report based on the third respiratory rate and the voice signal. The wearable device according to claim 5.

8. The wearable device according to claim 1, wherein the wearable device is a wearable watch, a wearable blanket, or a wearable monitor.

9. The wearable device further comprises a first bandpass filter and a second bandpass filter; The first bandpass filter is configured to perform bandpass filtering on the PPG signal to obtain the positions of the peak and trough points of the PPG signal; The second bandpass filter is configured to perform bandpass filtering on the PPG signal to obtain the amplitudes of the peak and trough points of the PPG signal; The processor is further configured to obtain the user's first respiratory rate based on the position and amplitude of the peak and trough points of the PPG signal. The wearable device according to claim 1.

10. The wearable device according to claim 9, wherein the frequency band of the signal permitted to pass through the first bandpass filter includes 0.5 Hz to 10 Hz, and the frequency band of the signal permitted to pass through the second bandpass filter includes 0.1 Hz to 10 Hz.

11. The wearable device further comprises a third bandpass filter; The third bandpass filter is configured to perform bandpass filtering on the ACC signal from which the baseline has been removed; The processor is further configured to acquire the user's second respiratory rate based on a bandpass filtered ACC signal. The wearable device according to claim 9.

12. The wearable device according to claim 11, wherein the frequency band of the signal permitted to pass through the third bandpass filter includes a range of 0.1 Hz to 0.5 Hz.

13. A method for operating a wearable device comprising a single device, The stage of acquiring the user's voice signal; A step of acquiring the physiological parameter signals of the user; A step of obtaining the user's respiratory rate based on the physiological parameter signals; and Steps to obtain a respiratory infection assessment report for the user based on the aforementioned audio signal and respiratory rate. Equipped with; Here, the step of acquiring the user's physiological parameter signals is: The step of acquiring the user's PPG signal, wherein the PPG signal includes the physiological parameter signal. It has; and The step of obtaining the user's respiratory rate based on the physiological parameter signals is: Steps to obtain the user's first respiratory rate based on the PPG signal. has How to operate.

14. The step of acquiring the physiological parameter signals of the user is, The step of acquiring the user's PPG signal and ACC signal, wherein each of the PPG signal and the ACC signal includes the physiological parameter signal. It has; and The step of obtaining the user's respiratory rate based on the physiological parameter signals is: A step of obtaining the user's first respiratory rate based on the PPG signal; A step of obtaining the user's second respiratory rate based on the ACC signal; and Steps to obtain the user's third respiratory rate by performing filtering and fusion on the first respiratory rate and the second respiratory rate. has The operating method according to claim 13.

15. Prior to the step of acquiring the physiological parameter signals of the user, the operating method The step of acquiring the ACC signal of the user; and Step of acquiring the user's posture classification result based on the ACC signal. Furthermore, here, The posture classification results include a first posture and a second posture, the first posture including a state in which stress is applied to the user's mounting position for support, and the second posture including a state in which the user's mounting position is in a natural position; and The step of obtaining a respiratory infection assessment report for the user based on the aforementioned audio signal and respiratory rate is: If the user is in the first posture, the step of obtaining the respiratory infection assessment report based on the first respiratory rate and the audio signal; or When the user is in the second posture, the step of obtaining the respiratory infection assessment report based on the user's third respiratory rate and the voice signal. Having, The operating method according to claim 13.

16. The step of obtaining the user's posture classification result based on the ACC signal is: A step of calculating the mean value and standard deviation of the ACC signal; The steps include performing low-pass filtering on the ACC signal, calculating the power spectrum of the ACC signal, and obtaining the position and amplitude of the peak point of the power spectrum, where the cutoff frequency of the low-pass filtering is 1 Hz; and Step 1: Inputting the mean value and standard deviation of the ACC signal, and the position and amplitude of the peak point of the power spectrum into a classification model to obtain the attitude classification result. Having, The operating method according to claim 14.

17. If the user is in the first posture and the first respiratory rate exceeds a preset range, the operating method shall The stage of prompting the user to switch to the second posture; A step of obtaining the third respiratory rate based on the PPG signal and the ACC signal in response to the user performing the operation for switching to the second posture; and Steps to obtain the respiratory infection assessment report based on the third respiratory rate and the audio signal. The operating method according to claim 16, further comprising:

18. At least one ACC sensor configured to acquire the user's ACC signal; At least one PPG sensor configured to acquire the user's PPG signal; At least one processor configured to acquire a user posture classification result based on the ACC signal, wherein the posture classification result includes a first posture and a second posture, the first posture including a state in which stress is applied to the user's mounting position for support, and the second posture including a state in which the user's mounting position is in a natural position; Equipped with, If the user is in the first posture, the processor is further configured to obtain the user's first respiratory rate based on the PPG signal; If the user is in a second state, the processor is further configured to obtain a first respiratory rate of the user based on the PPG signal, a second respiratory rate of the user based on the ACC signal, and to perform filtering and fusion on the first and second respiratory rates to obtain a third respiratory rate of the user. Wearable devices.

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