System for detecting disease on the basis of physiological parameters, and terminal device

Abstract

A system for detecting a disease on the basis of physiological parameters, and a terminal device, said system comprising: a terminal device and a wearable device. On the basis of physiological data collected by the wearable device that a user daily wears, the terminal device processes the data after acquiring same, then compares the difference between physiological data of the current day and personal baseline data of the user so as to monitor the current-day disease risk of the user everyday, obtains a daily monitoring result and displays same to the user, said daily monitoring result, for example, comprising: the disease probability of the current day, the risk level of the current day, and whether physiological parameters of the current day are abnormal, etc. In addition, in view of historical monitoring results and historical physiological data and on the basis of the difference between physiological data of a user before suffering from a disease and after rehabilitation, the present invention can inform the user of whether a long-term sequelae is present after disease rehabilitation, thereby achieving long-term trend analysis on the user's health. Therefore, the present invention achieves monitoring of the current disease risk of users and long-term trend analysis on the user's health, thereby providing better health guidance services for users.

Description

Systems and terminal devices for disease detection based on physiological parameters

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on July 25, 2023, with application number 202310921608.7 and application name “System and terminal device for disease detection based on physiological parameters”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of disease monitoring, and more specifically, to a system and terminal device for disease detection based on physiological parameters. Background Art

[0003] Wearable devices (such as smart bracelets, smart watches, etc.) can be integrated with various sensors to detect various types of physiological data of users, such as blood oxygen, respiratory rate, heart rate variability, body temperature and other physiological parameters of users, making it possible to detect user health using these physiological parameters.

[0004] Currently, wearable devices can detect whether a user is ill that day based on their physiological data, thus promptly detecting the risk of illness. Alternatively, after accumulating sufficient physiological data, a historical baseline of the user's various physiological data can be obtained. The physiological data from a recent period (e.g., the past week, two weeks, or the past month) can then be compared with the historical baseline to identify any differences. This can provide a visual indication of changing trends in the user's physiological data.

[0005] However, current technical solutions cannot both detect disease risks in a timely manner and conduct long-term trend analysis of user health, that is, detect long-term sequelae of users, resulting in a poor user experience.

[0006] Summary of the Invention

[0007] This application provides a system and terminal device for disease detection based on physiological parameters. The system includes a wearable device and a terminal device used by the user. It can monitor the risk of illness (including respiratory diseases or other diseases, etc.) on a daily basis and obtain daily monitoring results. It can also inform the user whether there are long-term sequelae after recovery from the disease, realizing long-term trend analysis of the user's health. This achieves both monitoring the user's current disease risk and conducting long-term trend analysis of the user's health, thereby better providing health guidance services to users and improving user experience.

[0008] In a first aspect, a terminal device is provided, which includes: a processor; a memory; a display screen and a computer program, wherein the computer program is stored in the memory, and when the computer program is executed by the processor, the terminal device performs the following steps: obtaining physiological data of a user, wherein the physiological data of the user includes historical physiological data and physiological data of the current day of the user; determining personal baseline data of the user based on the historical physiological data, wherein the personal baseline data includes statistics of different types of physiological data of the user, wherein the statistics include at least one of mean, variance, minimum, maximum, quantile, and probability distribution; determining the physiological data of the user based on the physiological data of the current day and the physiological data of the user ... Personal baseline data is used to obtain the disease monitoring results of the day, which include: the probability of getting sick on the day, the risk level on the day, and whether the various physiological parameters of the day are abnormal, or more of them; based on historical physiological data, the physiological data of the day, and historical measurement results, long-term sequelae detection results are obtained, which include: whether the user has long-term sequelae, and historical measurement results include: the probability of getting sick every day before the day, the risk level every day before the day, and whether the various physiological parameters of the day before the day are abnormal, or more of them; the disease monitoring results of the day and / or long-term sequelae detection results are displayed to the user on the display screen.

[0009] The terminal device provided in the first aspect is based on the physiological data collected by the wearable device worn by the user on a daily basis. After obtaining this data, the terminal device processes the data, compares the difference between the physiological data of the day and the user's personal baseline data, monitors the risk of the user getting sick on a daily basis, obtains daily monitoring results and displays them to the user, such as: the probability of getting sick on the day, the risk level of the day, whether there are abnormalities in various physiological parameters on the day, etc., to help users understand their own physical condition and improve user experience. Subsequently, the historical monitoring results and historical physiological data will be combined, and based on the difference in the user's physiological data before and after illness, the user will be informed of whether there are long-term sequelae after recovery from the disease, thereby realizing a long-term trend analysis of the user's health. This achieves both monitoring the user's current disease risk and conducting a long-term trend analysis of the user's health, thereby better providing health guidance services to users.

[0010] Exemplarily, the terminal device can be an IoT device such as a smart phone, tablet computer, laptop computer, handheld computer, notebook computer, cellular phone, PDA, AR\VR device, or a television, large-screen device, etc.

[0011] Exemplarily, historical physiological data does not include physiological data for the current day. Historical measurement results include measurement results corresponding to each day before the current day, but do not include measurement results corresponding to the current day (i.e., current day measurement results). For example, historical measurement results include: the probability of illness for each day before the current day, the risk level for each day before the current day, whether various physiological parameters for each day before the current day are abnormal, etc. Optionally, historical measurement results may also include: whether the user has long-term sequelae, etc. "Concurrent day" can be understood as "today" or any day.

[0012] In a possible implementation of the first aspect, the user's personal baseline data is determined based on historical physiological data; including: if the historical physiological data does not meet the preset conditions, determining the population baseline data corresponding to the user based on the user's personal information, and determining the population baseline data as the personal baseline data; or, if the historical physiological data does not meet the preset conditions, determining initial personal baseline data based on the historical physiological data; determining the population baseline data corresponding to the user based on the user's personal information; and determining the personal baseline data based on the population baseline data and the initial personal baseline data. In this implementation, when the user's historical physiological data is insufficient, the population baseline data corresponding to the user is used as the user's personal baseline data, or updated personal baseline data is obtained by combining the user's population baseline data and the initial personal baseline data. The user does not need to wear the wearable device for a long time to obtain the personal baseline data, thereby improving the stability and accuracy of the personal baseline data. Monitoring results can be obtained on the first day the user wears the wearable device, thereby improving the user experience.

[0013] For example, if the length of time for collecting historical physiological data is less than a certain threshold (for example, the length of time for collecting historical physiological data is less than six months); or, the amount of data included in the historical physiological data is less than a preset threshold, for example, the number of data such as heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, or skin temperature is less than a preset threshold, etc., then the historical physiological data does not meet the preset conditions.

[0014] In one possible implementation of the first aspect, obtaining a disease monitoring result for the day based on the physiological data for the day and the personal baseline data includes: calibrating the physiological data for the day using the personal baseline data to obtain calibrated physiological data for the day; and obtaining the disease monitoring result for the day based on the calibrated physiological data for the day, the physiological data for the day before calibration, and the personal baseline data. In this implementation, calibrating the physiological data for the day using the personal baseline data can improve the accuracy of the respiratory disease monitoring result for the day, so that the respiratory disease monitoring result for the day better reflects the user's health status for the day.

[0015] Exemplarily, the calibration method may include: calculating the difference or ratio between a certain type of physiological data on the day and the personal baseline data of the same type; normalizing the user's physiological data on the day using the personal baseline data; estimating probability distribution parameters based on the personal baseline data; and training at least one of the anomaly detection models or algorithms using the user's historical physiological data (i.e., personal baseline data).

[0016] In a possible implementation of the first aspect, based on historical physiological data, current day physiological data, and historical measurement results; or based on historical physiological data, current day physiological data, historical measurement results, and current day respiratory disease monitoring results; or based on historical physiological data and historical measurement results, a long-term sequelae detection result is obtained, including: determining the start and end time of the user's illness based on the historical measurement results; determining the baseline data corresponding to each physiological data of the user before the illness and after recovery; and obtaining the long-term sequelae detection result based on the baseline data corresponding to each physiological data of the user before the illness and after recovery. In this implementation, determining the sequelae detection result based on the baseline data corresponding to each physiological data of the user before the illness and after recovery can improve the accuracy of the long-term sequelae detection result and enhance the user experience.

[0017] For example, the baseline of a certain physiological parameter after recovery can be compared with the baseline of the same physiological parameter before the illness to determine whether there is a significant difference. If there is a significant difference, the user is considered to be at risk of long-term sequelae. For example, if the difference or ratio of the baseline of a certain physiological parameter before and after recovery is greater than a certain threshold, it is considered that there is a significant difference; otherwise, it is considered that there is no significant difference, that is, there is no risk of long-term sequelae.

[0018] In one possible implementation of the first aspect, if the probability of illness on the day is less than or equal to a preset first threshold, the risk level for that day is low risk; if the probability of illness on the day is greater than the preset first threshold and less than or equal to a preset second threshold, the risk level for that day is medium risk; and if the probability of illness on the day is greater than a preset second threshold, the risk level for that day is high risk, where the second threshold is greater than the first threshold. This implementation allows for quick and convenient determination of the risk level for the day, improving the user experience.

[0019] In one possible implementation of the first aspect, the terminal device further performs the following steps: determining a risk level for the current day based on the current day's probability of illness and the current day's physiological data. In this implementation, the current day's probability of illness is calibrated based on the user's current day's physiological data, ensuring that the current day's risk level is consistent with the user's actual symptoms on that day, thereby improving the accuracy of the predicted current day's risk level and enhancing the user experience.

[0020] For example, if the probability of illness on that day is less than or equal to a preset first threshold (e.g., 0.5), the risk level for that day is determined to be low risk; if the probability of illness on that day is greater than the preset first threshold, the risk level for that day is determined to be medium risk or high risk. Whether it is medium risk or high risk depends on the user's physiological data for that day. For example, if the heart rate is greater than a certain threshold and the respiratory rate is greater than another threshold, the risk level for that day is determined to be high risk; if the heart rate does not meet the requirements or the respiratory rate does not meet the requirements, the risk level for that day is determined to be medium risk.

[0021] In one possible implementation of the first aspect, the terminal device further performs the following steps: if the user is at risk of illness, the terminal device determines the type of illness the user suffers from in combination with non-physiological information and displays the information to the user. The non-physiological information includes at least one of: whether infectious diseases are prevalent in the user's area, whether epidemic diseases are prevalent in the current season, and whether the user suffers from an underlying disease. In this implementation, if the user is at risk of illness on that day, the terminal device determines the type of illness the user may suffer from in combination with non-physiological information and displays the information to the user, thereby notifying the user of the possible illness type. This further improves the efficiency and accuracy of notifying the user of possible illnesses, helps the user select their own treatment options, and enhances the user experience.

[0022] In one possible implementation of the first aspect, the terminal device further performs the following steps: displaying health advice to the user on the display screen based on the current day's risk level and the type of illness the user suffers from; or displaying health advice to the user on the display screen based on the current day's risk level. In this implementation, health advice appropriate to the user's current health status is displayed to the user, and corresponding response plans can be presented to the user, further improving the user experience.

[0023] For example, health advice may include dietary recommendations, exercise recommendations, and treatment recommendations. For example, treatment recommendations may prompt the user to take medication or seek medical attention promptly. Dietary recommendations may include avoiding spicy foods. Exercise recommendations may include reducing outdoor exercise and getting more rest.

[0024] For example, treatment recommendations could prompt users to take common medications that may be effective for treating a potential illness, or to seek medical attention promptly. Dietary recommendations could include avoiding foods that may aggravate a potential illness, or consuming foods that may alleviate or alleviate the symptoms of a potential illness. Exercise recommendations could include exercises that may alleviate or restore a potential illness, or avoiding exercises that may aggravate a potential illness.

[0025] In a possible implementation of the first aspect, the user's physiological data includes at least one of heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, skin temperature, sleep information, and exercise information.

[0026] In one possible implementation of the first aspect, the terminal device further includes a physiological data collector, and the terminal device further performs the following steps: using the physiological data collector to obtain physiological data of the user. For example, the terminal device may include sensors for measuring physiological parameters such as heart rate, heart rate variability, blood oxygen, respiratory rate, body temperature, sleep, and exercise, and may collect various physiological parameters of the user.

[0027] In a possible implementation of the first aspect, the terminal device communicates wirelessly with the wearable device of the user, and the terminal device further performs the following steps: receiving physiological data of the user sent by the wearable device, and obtaining the physiological data of the user using a physiological data collector on the wearable device.

[0028] In a second aspect, a system for disease detection based on physiological parameters is provided, comprising: a terminal device provided in the first aspect or any possible implementation of the first aspect, and a wearable device. The terminal device wirelessly communicates with a user's wearable device, the wearable device including a physiological data collector, and the wearable device uses the physiological data collector to obtain the user's physiological data and transmit the user's physiological data to the terminal device.

[0029] The second aspect provides a system for disease detection based on physiological parameters. Based on the physiological data collected by the wearable devices worn by users on a daily basis (for example, one or more combinations of heart rate, heart rate variability, blood oxygen, respiratory rate, body temperature, sleep, exercise, etc.), the terminal device processes the data after obtaining the data, monitors the risk of illness on that day (including respiratory diseases or other diseases, etc.) every day, and obtains daily monitoring results (also called test results), such as: the probability of illness on that day, the risk level on that day, whether there are abnormalities in various physiological parameters on that day, etc. Subsequently, the historical monitoring results and historical physiological data will be combined, and based on the differences in the user's physiological data before and after illness, the user will be informed of whether there are long-term sequelae after recovery from the disease, thereby realizing a long-term trend analysis of the user's health. Thus, it is possible to monitor the user's current disease risk and conduct a long-term trend analysis of the user's health, thereby better providing health guidance services to the user and improving the user experience.

[0030] In a third aspect, a communication device is provided, which includes a unit for executing each step executed by the terminal device in the above first aspect or any possible implementation of the first aspect.

[0031] In a fourth aspect, a terminal device is provided, which includes the communication device provided in the third aspect.

[0032] In a fifth aspect, a computer program product is provided, which includes a computer program. When the computer program is executed by a processor, the terminal device executes the steps performed in the first aspect or any possible implementation of the first aspect.

[0033] In a sixth aspect, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed, the terminal device executes the steps executed in the first aspect or any possible implementation method of the first aspect.

[0034] In the seventh aspect, a chip is provided, which includes: a processor for calling and running a computer program from a memory, so that a device equipped with the chip executes the steps performed by the terminal device in the first aspect or any possible implementation of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] FIG1 is a schematic architecture diagram of a system for disease detection based on physiological parameter detection provided in this application.

[0036] FIG2 is a schematic diagram of a disease detection process based on physiological parameter detection provided in an embodiment of the present application.

[0037] FIG3 is a schematic diagram of another example of a disease detection process based on physiological parameter detection provided in an embodiment of the present application.

[0038] FIG4 is a schematic block diagram of a communication device structure provided in an embodiment of the present application.

[0039] FIG5 is a schematic block diagram of a communication device structure provided in an embodiment of the present application.

[0040] FIG6 is a schematic block diagram of a chip system structure provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application.

[0042] The terms used in the following embodiments are only for the purpose of describing specific embodiments and are not intended to be limiting of the present application. As used in the specification and appended claims of this application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include expressions such as "one or more", unless there is a clear contrary indication in the context. It should also be understood that in the embodiments of the present application, "one or more" refers to one or more (including two); "and / or" describes the association relationship of associated objects, indicating that three relationships may exist; for example, A and / or B can represent: the existence of A alone, the existence of A and B at the same time, and the existence of B alone, where A and B can be singular or plural. The character " / " generally indicates that the related objects before and after are in an "or" relationship.

[0043] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in yet other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0044] The "multiple" involved in the embodiments of the present application means greater than or equal to two. It should be noted that in the description of the embodiments of the present application, the words "first" and "second" are only used for the purpose of distinguishing the description and cannot be understood as indicating or implying relative importance or order.

[0045] In addition, various aspects or features of the present application can be implemented as methods, devices or products using standard programming and / or engineering techniques. The term "product" used in the embodiments of the present application covers computer programs that can be accessed from any computer-readable device, carrier or medium. For example, computer-readable media can include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks or tapes, etc.), optical disks (e.g., compact discs (CDs), digital versatile discs (DVDs), etc.), smart cards and flash memory devices (e.g., erasable programmable read-only memories (EPROMs), cards, sticks or key drives, etc.). In addition, the various storage media described herein can represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" can include, but is not limited to, wireless channels and various other media capable of storing, containing and / or carrying instructions and / or data.

[0046] Various aspects or features of the present application can be implemented as methods, devices or products using standard programming and / or engineering techniques. The term "product" used in the embodiments of the present application covers computer programs that can be accessed from any computer-readable device, carrier or medium. For example, computer-readable media can include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks or magnetic tapes, etc.), optical disks (e.g., compact discs (CDs), digital versatile discs (DVDs), etc.), smart cards and flash memory devices (e.g., erasable programmable read-only memories (EPROMs), cards, sticks or key drives, etc.). In addition, the various storage media described herein can represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" can include, but is not limited to, wireless channels and various other media capable of storing, containing and / or carrying instructions and / or data.

[0047] Respiratory diseases can be roughly divided into two categories: acute and chronic. Acute respiratory diseases include influenza, lung infections, etc., while chronic respiratory diseases include chronic obstructive pulmonary disease (also known as COPD), etc.

[0048] Different types of respiratory diseases require different prevention and treatment methods. For example, lung infections are characterized by high morbidity and mortality, affecting 450 million people annually, and the mortality rate from lung infections ranks fourth among the top ten causes of death worldwide. Lung infections require early detection and treatment to significantly reduce mortality and medical costs. As a chronic disease, COPD requires a combination of medication, exercise, and long-term monitoring of lung function to prevent worsening of the disease.

[0049] Monitoring for sequelae of respiratory diseases is equally important. For example, the World Health Organization estimates that approximately 10%-20% of patients with acute COVID-19 infection will experience long-term COVID-19 symptoms, such as fatigue, difficulty breathing, chest pain, and cough, for weeks to months after recovery. Long-term monitoring of patients' recovery can help doctors effectively assess their recovery and develop treatment plans.

[0050] With technological advancements, wearable devices (such as smart bracelets and smart watches) are now capable of detecting physiological parameters such as blood oxygen, respiratory rate, heart rate variability, and body temperature. When a person develops a respiratory disease, their physiological data changes. For example, pneumonia may cause an increase in heart rate, respiratory rate, and body temperature, while blood oxygen levels may decrease. Chronic obstructive pulmonary disease may also cause an increase in respiratory rate and a decrease in blood oxygen levels, making it possible to monitor respiratory health.

[0051] In order to adapt to the different needs of different users for respiratory health monitoring, a technology is needed that can not only detect the risk of acute respiratory diseases in a timely manner, but also detect the sequelae of respiratory diseases.

[0052] Currently, related technologies exist for monitoring respiratory diseases. These technologies collect various types of physiological data (e.g., heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, skin temperature, etc.) from a user over a recent period of time (e.g., within a day) through a wearable device worn by the user. By establishing a mapping relationship between physiological data and respiratory diseases, the user is determined to have a respiratory disease, such as influenza, pneumonia, or chronic obstructive pulmonary disease, on the current day (e.g., that day). Some technologies also require collecting physiological data from the user before the respiratory disease as baseline data. By detecting whether the physiological data at the current time (e.g., that day) differs from the baseline data, the user is determined to have a respiratory disease. Baseline data can be understood as various physiological data statistics calculated by collecting different types of physiological data while the user wears the wearable device for an extended period of time (e.g., several months, a year, or longer). For example, physiological data can include different types of physiological data, such as heart rate, heart rate variability, respiratory rate, body temperature, sleep, and exercise. Statistics can include, for example, mean, variance, minimum, maximum, and quantiles.

[0053] Using the above solution, there are several problems:

[0054] First, technologies that don't require baseline data and determine whether a user has a respiratory disease based solely on the mapping between current physiological data and respiratory diseases can lead to inaccurate results. For example, if a user has other underlying conditions that cause abnormal physiological data, the above solution may frequently generate false positives for such a user. Alternatively, if a user is generally healthy and experiences mild symptoms, and the difference between their physiological data during illness and their normal health is minimal, and the abnormality is not obvious, the technology may easily miss a positive result.

[0055] Second, technologies that require baseline data require users to wear wearable devices for a long period of time (e.g., several months, a year, or even longer) to obtain baseline data from the various physiological data collected by the wearable device during this period. Only after obtaining baseline data can it be determined whether the user currently has a respiratory disease, which makes it inconvenient for users. For example, baseline data cannot be obtained on the first day a user wears a wearable device.

[0056] Third, the above solution can only determine whether the user currently (for example, on the same day) has respiratory disease, that is, it can only detect the risk of acute respiratory disease in a timely manner, but cannot monitor the long-term changes in the user's respiratory health trends (that is, it cannot perform long-term trend analysis of respiratory health), nor can it detect the sequelae of respiratory diseases.

[0057] Fourth, respiratory diseases such as influenza, COVID-19, and pneumonia can cause increases in heart rate, respiratory rate, and body temperature, and decreases in blood oxygen. It is difficult to determine the specific disease based on physiological data.

[0058] In addition to the above solutions, there are some other solutions. For example, the user wears a wearable device for a long time (such as several months, a year or a longer period of time) and records the user's daily physiological data during this period. After accumulating enough data, the user's various physiological data are obtained to obtain historical baseline data. Then compare the various physiological data of the recent period (such as the last week or two weeks, the last month, etc.) with the historical baseline data to see if there are any differences, so as to prompt the user's physiological data change trend. For example, it prompts the user that the heart rate in the past week has increased significantly compared to the past year. In other words, this technology uses the timeline as the dimension to compare the changes or differences between the user's various physiological data in the recent period and the historical baselines of various physiological data.

[0059] Using the above solution, it is necessary to accumulate the user's physiological data for a long period of time before the long-term trend of the user's physiological data can be counted. If the user suffers from an acute respiratory disease, this technology cannot promptly remind the user of abnormal physiological data. For example, it cannot determine whether the user currently (for example, on the same day) has a respiratory disease. In addition, this technology can only count simple trend changes in various physiological data, such as increase, decrease, and no change. It cannot monitor the long-term trend changes in the user's respiratory health (that is, it cannot perform long-term trend analysis of respiratory health), nor can it detect sequelae of respiratory diseases.

[0060] In summary, the existing technology mainly has the following technical problems:

[0061] First, existing technologies cannot monitor the risk of acute respiratory diseases and conduct long-term trend analysis of respiratory health, that is, detect long-term sequelae of users.

[0062] Second, when monitoring respiratory diseases, introducing baseline data will result in users being unable to measure in the absence of historical baseline data, but not using baseline data will lead to decreased accuracy.

[0063] Third, many respiratory diseases manifest similarly in physiological data, and current technology makes it difficult to distinguish specific disease types.

[0064] In view of this, the present application provides a system and terminal device for disease detection based on physiological parameters. The system includes a wearable device and a terminal device used by the user. Based on the physiological data collected by the wearable device worn by the user daily (for example, one or more combinations of heart rate, heart rate variability, blood oxygen, respiratory rate, body temperature, sleep, exercise, etc.), the terminal device processes the data after obtaining the data, monitors the risk of illness on that day (including respiratory diseases or other diseases, etc.) every day, and obtains daily monitoring results (also referred to as test results), for example, including: the probability of illness on that day, the risk level on that day, whether there are abnormalities in various physiological parameters on that day, etc. Subsequently, the historical monitoring results and historical physiological data will be combined, and based on the differences in the user's physiological data before and after illness, the user will be informed of whether there are long-term sequelae after recovery from the disease, thereby realizing a long-term trend analysis of the user's health. In this way, it is possible to monitor the user's current disease risk and conduct a long-term trend analysis of the user's health, thereby better providing health guidance services to the user and improving the user experience.

[0065] The following describes the disease detection system based on physiological parameters provided by the present application with reference to specific examples.

[0066] Referring to FIG1 , FIG1 is a schematic architecture diagram of a system for disease detection based on physiological parameter detection provided in an embodiment of the present application. As shown in FIG1 , the system includes: a wearable device 112 used by a user and a terminal device 111. In the example shown in FIG1 , the wearable device 112 used by the user is a smartwatch, and the terminal device 111 is a smartphone. In the present application, various sensors are provided or configured on the wearable device 112. For example, sensors for measuring physiological parameters such as heart rate, heart rate variability, blood oxygen, respiratory rate, body temperature, sleep, and exercise can be included. The wearable device 112 can collect various physiological parameters of the user. The smartphone 111 can obtain the various physiological parameters of the user collected by the wearable device 112, calculate the daily monitoring results and long-term sequelae data based on the various physiological parameters of the user, and display them on the smartphone 111 interface for the user to view.

[0067] For example, in the example shown in FIG1 , the wearable device 112 and the terminal device 111 can communicate with each other via wireless communication technologies such as Bluetooth (BT), wireless-fidelity (WiFi), and near-field communication (NFC). In other words, the terminal device and the wearable device can form a network (i.e., networking) according to certain communication protocols and networking strategies, so that the terminal device and the wearable device can communicate with each other.

[0068] It should be understood that in other implementations of the present application, if the wearable device supports computing functions, after the wearable device 112 collects various physiological parameters of the user, the wearable device 112 itself also calculates daily monitoring results and long-term sequelae data based on these physiological parameters, and then sends these calculation results to the smartphone 111, which displays the results on the smartphone interface for the user to view. In other words, the system composed of wearable devices and terminal devices can implement the technical solution provided by the present application.

[0069] Of course, if the wearable device supports both computing and display functions, for example, if the wearable device has a large display interface, then after the wearable device 112 collects various physiological parameters of the user, the wearable device 112 also calculates daily monitoring results and long-term sequelae data based on the various physiological parameters, and then displays these calculation results on the interface for the user to view. In other words, the technical solution provided by this application can also be implemented by a wearable device alone.

[0070] It should be understood that FIG1 is merely exemplary and should not impose any limitations on the system provided by the embodiments of the present application. For example, the scenario shown in FIG1 may include more terminal devices and wearable devices, etc. The present application does not impose any limitations on this.

[0071] In the embodiments of the present application, the terminal device may include: smart TV, tablet computer, netbook, personal digital assistant (PDA), computer handheld communication device, handheld computing device and other portable electronic devices. Wearable devices may also include: smart bracelet, wearable wrist device, smart glasses, augmented reality (AR) / virtual reality (VR) devices and the like. Exemplary embodiments of terminal devices or wearable devices include but are not limited to devices equipped with Terminal devices running Harmony or other operating systems.

[0072] It should be understood that in the embodiments of the present application, wearable devices can also be a general term for wearable devices that are intelligently designed and developed by applying wearable technology to daily wear, such as glasses, gloves, watches, clothing and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are full-featured, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0073] That is to say, the terminal device and the wearable device can form a network (i.e., networking) according to certain communication protocols and networking strategies, so that the terminal device and the wearable device can communicate with each other.

[0074] In the following example, a system consisting of a wearable device and a terminal device will be used as an example to illustrate the technical solution provided in the embodiment of the present application. However, it should be understood that if the wearable device supports computing and display functions, the wearable device alone can also implement the technical solution provided in the present application.

[0075] It should also be understood that the following description will use respiratory diseases as an example, but the technical solution provided in this application can also be applied to other diseases, such as cardiovascular diseases, as long as the symptoms corresponding to the disease can be detected through the patient's physiological data. The embodiments of this application are not limited here.

[0076] Figure 2 is a schematic diagram of an overall process (ie, a detection method) for disease detection using the system provided by the present application. The process shown in Figure 2 will be described in detail below. The process shown in Figure 2 includes S201 to S208.

[0077] In the example shown in FIG2 , the wearable device and the terminal device can communicate with each other through wireless communication technologies such as Bluetooth technology, WiFi technology, and NFC technology.

[0078] As shown in FIG2 , in S201 , the wearable device periodically collects physiological data of the user.

[0079] Optionally, in some possible implementations, the wearable device can continuously or periodically collect physiological data of the user, for example, collecting physiological data of 1 minute or once every 10 minutes. Exemplarily, physiological data may include: heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, skin temperature, sleep (data such as time to fall asleep, time to wake up, sleep stage staging), exercise (exercise type, step length, pace, heart rate during exercise, heart rate after exercise, etc.), etc. One or more. The embodiments of the present application do not limit the specific types of physiological data.

[0080] It should be understood that the wearable device can collect user physiological data for a long period of time, for example, for several months or even longer. Optionally, the collected user physiological data can be stored with "daily" as a granularity.

[0081] S202: The wearable device sends the collected physiological data to the terminal device.

[0082] The wearable device sends the collected physiological data to the terminal device through the wireless network between the wearable device and the terminal device.

[0083] For example, the terminal device may be a mobile phone used by a user, etc. The embodiment of the present application does not limit the specific type of the terminal device.

[0084] It should be understood that the physiological data collected by the wearable device includes the user's physiological data of the day (also referred to as the physiological data of the day) and the physiological data before the day. For example, in the embodiment of the present application, the physiological data before the day can also be referred to as historical physiological data.

[0085] S203: The terminal device determines the user's personal baseline data based on the user's historical physiological data.

[0086] Optionally, as another possible implementation, in S203, the terminal device may also determine the user's personal baseline data based on the user's historical physiological data and the physiological data of the day. The following description will take the example of the terminal device determining the user's personal baseline data based on the user's historical physiological data as an example.

[0087] In the embodiments of the present application, personal baseline data can be understood as statistics of various (or each) physiological data calculated by using the physiological data collected by the user wearing the wearable device for a long period of time (e.g., several months, a year, or longer). For example, physiological data may include: heart rate, heart rate variability, respiratory rate, body temperature, sleep, exercise, and other different types or types of physiological data. Statistics may include, for example, mean, variance, minimum, maximum, or quantile.

[0088] In some possible implementations, if the historical physiological data is insufficient or does not meet the requirements, for example, the length of time for collecting historical physiological data is less than a certain threshold (for example, the length of time for collecting historical physiological data is less than six months); or, the amount of data included in the historical physiological data is less than a preset threshold, for example, the number of data such as heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, or skin temperature is less than a preset threshold, etc., then the terminal device can select the corresponding population baseline data to replace the user's personal baseline data based on the user's gender, age, height, weight, region and other personal information, that is, the population baseline data with the same personal information under statistical big data is used as the user's personal baseline data.

[0089] In other possible implementations, if the historical physiological data is insufficient or does not meet the requirements, the terminal device can first use these historical physiological data that do not meet the requirements to determine the user's personal baseline data (for the purpose of distinction, called initial personal baseline data). After obtaining the initial personal baseline data, it can also be combined with the user's corresponding population baseline data and the initial personal baseline data, and the personal baseline data can be determined by using the weighted average of the population baseline data and the initial personal baseline data.

[0090] Through the above method, when the user's historical physiological data is insufficient, the population baseline data is used to replace the personal baseline data, or the personal baseline data is determined by combining the user's corresponding population baseline data and the initial personal baseline. The user does not need to wear the wearable device for a long time to obtain the personal baseline data. The user can obtain the personal baseline data and monitoring results on the first day of wearing the wearable device, thereby improving the user experience.

[0091] S204, the terminal device performs respiratory disease monitoring for the day based on the user's personal baseline data and the user's physiological data for the day.

[0092] In some possible implementations, the terminal device can use personal baseline data to calibrate the user's physiological data for the day, and use the personal baseline data, the calibrated physiological data for the day, and the uncalibrated physiological data for the day as parameters to input into a machine learning model on the terminal device. The machine learning model is used to monitor the respiratory disease risk for the day and obtain the respiratory disease monitoring results for the day. For example, the respiratory disease monitoring results (measurement results) for the day output by the machine learning model may include: one or more of the following: the probability of illness for the day, the risk level for the day, and whether the various physiological parameters for the day are abnormal.

[0093] For example, S204 may be calculated and output based on the dimension of “every day”.

[0094] Through the above technical solution, it is possible to monitor the user's current (daily) disease risk, that is, to achieve real-time detection of respiratory diseases, improve the efficiency and accuracy of prompting users of diseases, and improve user experience.

[0095] S205 , the terminal device collects baseline data corresponding to the physiological data before, during and after illness based on all the physiological data of the user and historical measurement results.

[0096] In S205 , all physiological data may include historical physiological data and physiological data of the current day.

[0097] For example, in S205, the historical measurement results may include one or more of the following: the probability of illness for each day in history (excluding the current day), the risk level for each day in history (excluding the current day), and whether each physiological parameter is abnormal for each day in history (excluding the current day).

[0098] Alternatively, as another possible implementation, S205 may be replaced by: the terminal device collects baseline data corresponding to the physiological data before, during, and after illness based on the user's entire physiological data, historical measurement results, and current day measurement results. The current day measurement results are the respiratory disease monitoring results for that day.

[0099] The terminal device can mark the start and end time of the last respiratory illness based on historical measurement results, and then statistically calculate the physiological baseline data corresponding to before, during, and after the illness. For example, the physiological baseline data corresponding to heart rate, respiratory rate, blood oxygen, and sleep time before, during, and after the illness can be the average of heart rate, respiratory rate, blood oxygen, and sleep time.

[0100] S206 , the terminal device determines whether the user has long-term sequelae based on the baseline data corresponding to the various physiological data before and after the illness.

[0101] For example, the terminal device can compare the baseline data of a certain physiological parameter (or parameters) after recovery with the baseline data of the same (or these) physiological parameters before the illness to see if there is a significant difference. If there is a significant difference, the user is considered to be at risk of long-term sequelae. For example, if the difference or ratio of the baseline of a certain physiological parameter before and after recovery is greater than a certain threshold, it is considered that there is a significant difference; otherwise, it is considered that there is no significant difference, that is, there is no risk of long-term sequelae.

[0102] Through S206, the long-term trend analysis result of the user's health can be achieved, that is, whether the user suffers from long-term sequelae can be detected. The user can be informed of the long-term trend changes caused by respiratory diseases, thereby improving the user experience.

[0103] S207, the terminal device displays the respiratory disease monitoring results of the day and the results of whether there are long-term sequelae to the user on the interface.

[0104] For example, the respiratory disease monitoring results of the day can be presented to the user through the probability of illness on the day, the risk level of the day, whether the various physiological parameters of the day are abnormal, etc. Among them, the probability of illness on the day can be presented to the user through numbers such as probability or percentage. The risk level of the day can be presented to the user in the form of text such as "low risk", "medium risk" or "high risk", and the risk level of the day is used to remind the user of the risk level of the disease on the day. The results of whether there are long-term sequelae can be displayed through the long-term trend analysis results of the user's health (such as the baseline data comparison results corresponding to the various physiological data before and after illness), or, it can also be presented to the user in the form of text such as "there is a risk of sequelae" or "there is no risk of sequelae". The embodiment of the present application does not limit the form displayed to the user by the terminal device on the interface.

[0105] Optionally, the process may further include S208.

[0106] S208: If the user is at risk of getting sick on that day, the terminal device determines the type of disease the user may have based on non-physiological information and displays it to the user.

[0107] When the terminal device determines that the user is at risk of illness on that day based on the user's physiological parameters, since respiratory diseases such as the new crown, influenza A, pneumonia, etc. will cause an increase in the values ​​of physiological parameters such as heart rate, respiratory rate, and body temperature, it may not be determined at the physiological data level which disease the user may have. In order to clarify the type of disease as much as possible, the type of disease can be marked by non-physiological information. Therefore, in an embodiment of the present application, when the user is at risk of illness on that day, for example, the probability of illness on that day is greater than a preset threshold, or the risk level on that day is medium risk or high risk, or when most of the physiological parameters on that day are abnormal, it can be determined that the user is at risk of illness on that day. In this case, the terminal device can combine non-physiological information, such as a questionnaire filled out by the user, the season of the day, the weather of the day, local epidemiological information or other disease detection technologies, to inform the user of the possible type of disease.

[0108] For example, as a possible implementation method, when it is determined that the user is at risk of illness on that day, the terminal device can determine whether there is an epidemic of infectious diseases in the user's location based on the area filled in by the user and combined with Internet information. If there is an epidemic of infectious diseases in the local area, the user will be prompted on the interface that he or she may be infected with the infectious disease.

[0109] For another example, as another possible implementation, when it is determined that the user has a risk of illness on that day, if the season on that day is a high-incidence season for influenza, a prompt is displayed on the interface that the user may have influenza.

[0110] For example, information collected through questionnaires or other testing technologies can be used to determine whether a user has underlying conditions. If the user does have an underlying condition, the terminal device can display a warning on the user's screen indicating possible complications or exacerbations of the underlying condition. For example, if a questionnaire or COPD testing technology indicates that a user has chronic obstructive pulmonary disease, the user can be warned of the possibility of an acute exacerbation of COPD.

[0111] Of course, in other implementations of the present application, other non-physiological factors may also be combined, such as the user's diet, the user's living habits, etc., to inform the user of the possible disease type when the user is at risk of disease that day. The embodiments of the present application do not limit the specific type or form of non-physiological factors.

[0112] Through the above-mentioned S208, the user can be notified of the possible disease type of the user, further improving the efficiency and accuracy of prompting the user of possible diseases, and can help the user to self-select treatment plans, thereby improving user experience.

[0113] Optionally, as a possible implementation method, the process shown in Figure 2 may also include: when it is determined that the risk level of the day is "medium risk" or "high risk", the terminal device or wearable device may also prompt the user (for example, through text or other means) some health advice on the display interface. For example, the health advice may include: dietary advice, exercise advice, treatment advice, etc. Exemplarily, treatment advice may prompt the user that he needs to take some medicine, or needs to seek medical treatment in time. Dietary advice may include: avoiding spicy and irritating foods, etc. Exercise advice may include: reducing outdoor exercise, getting more rest, etc. The embodiments of the present application do not limit the specific content or form of the health advice. In this way, health advice suitable for the user's current health status is displayed to the user, and corresponding response plans can be displayed to the user, which can further improve the user experience.

[0114] Optionally, as another possible implementation, the process shown in FIG2 may further include: if the risk level for the day is determined to be "medium risk" or "high risk," the terminal device or wearable device may further provide the user with some health advice on the display interface (e.g., via text or other means) based on the type of disease the user may have. For example, the health advice may include: dietary advice, exercise advice, treatment advice, etc. For example, treatment advice may prompt the user to take some common medications that are effective for treating the possible disease, or to seek medical attention promptly. Dietary advice may include: avoiding foods that may aggravate the possible disease, or consuming foods that may alleviate or reduce the possible disease. Exercise advice may include: exercising that has a mitigating or restorative effect on the possible disease, or avoiding exercise that may aggravate the possible disease. The specific content or form of the health advice is not limited in the embodiments of the present application. In this way, health advice appropriate to the user's current health status is displayed to the user, and corresponding response plans can be displayed to the user, further improving the user experience.

[0115] It should be understood that S201 to S208 shown in Figure 2 can also be executed by the wearable device. Alternatively, S203 to S206 shown in Figure 2 can also be executed by the wearable device.

[0116] FIG3 is a schematic diagram of another example of the overall process (i.e., detection method) for disease detection using the system provided by this application. The process shown in FIG3 will be described in detail below. In the example shown in FIG3, the terminal device used by the user will be described using a smartphone (hereinafter referred to as a mobile phone) as an example.

[0117] S301: The user logs in to the account on the mobile phone and the wearable device, and binds the account on the application of the mobile phone and the wearable device.

[0118] For example, after the mobile phone and the wearable device are bound, the user can log in to the account on the mobile phone and the wearable device respectively. After successful login, the user can bind the account on the mobile phone and the wearable device application respectively. After the account is successfully bound, the mobile phone and the wearable device can automatically execute the steps performed by the mobile phone and the wearable device respectively in each embodiment of the present application.

[0119] As a possible implementation, the application can be a newly developed application. When the wearable device runs the application, it can implement the steps performed by the wearable device in each embodiment of the present application. When the mobile phone runs the application, it can implement the steps performed by the mobile phone in each embodiment of the present application. Of course, in other implementations of the present application, the application can also be an existing application, but the application integrates new functions, so that when the wearable device runs the application, it can implement the steps performed by the wearable device in each embodiment of the present application. When the mobile phone runs the application, it can implement the steps performed by the mobile phone in each embodiment of the present application.

[0120] S302: The wearable device periodically collects the user's physiological data.

[0121] Wearable devices are equipped with various sensors, such as sensors for measuring heart rate, heart rate variability, blood oxygen, respiratory rate, body temperature, sleep, exercise and other physiological parameters. Wearable devices can continuously or periodically collect physiological data of users, such as collecting physiological data every 10 minutes. For example, physiological data may include: heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, skin temperature, sleep (information such as time to fall asleep, time to wake up, sleep stages), exercise (type of exercise, step length, pace, heart rate during exercise, heart rate after exercise, etc.) and one or more thereof.

[0122] It should be understood that wearable devices can collect user physiological data for a long period of time, for example, for several months or even longer.

[0123] It should also be understood that the physiological data collected by the wearable device in S301 may include historical physiological data and the user's physiological data of the day (i.e., the physiological data of the day). The historical physiological data includes the physiological data measured every day before the day, but does not include the user's physiological data of the day.

[0124] Optionally, the physiological data collected by the wearable device in S301 may be stored in the wearable device or in the cloud.

[0125] S303, the mobile phone obtains historical physiological data, physiological data of the day, and historical measurement results collected by the wearable device.

[0126] For example, the wearable device sends the collected physiological data to the terminal device via a wireless network between the wearable device and the terminal device.

[0127] For another example, wearable devices can store collected physiological data in the cloud, and mobile phones can obtain the collected physiological data from the cloud.

[0128] In some possible implementations, after a user opens an app on their phone, they can access all physiological data (i.e., historical and current physiological data) and historical measurement results collected by the wearable device from the wearable device or the cloud through certain operations (e.g., clicking on a control in the app, pulling down an interface, etc.). Optionally, after the user authorizes the app, all physiological data and historical measurement results collected by the wearable device can also be automatically synchronized to the phone.

[0129] In some possible implementations, the historical measurement results in S303 may include one or more of the following: the probability of illness for each day in history (excluding the current day), the risk level for each day in history (excluding the current day), whether various physiological parameters were abnormal for each day in history (excluding the current day), etc. In other words, for these indicators, each day may correspond to a measurement result, or the measurement results are based on the time granularity of "day".

[0130] In other possible implementations, the historical measurement results in S303 may further include whether the user has long-term sequelae, etc. For this indicator, the time unit may be the period from "the time after the user recovers from the illness (e.g., the day after the user recovers from the illness)" to (or ending on) "the day".

[0131] Optionally, the step corresponding to S303 may also be referred to as “data synchronization”.

[0132] It should be understood that the historical physiological data in S303 does not include the physiological data of the current day. The historical measurement results include the measurement results corresponding to each day before the current day, and do not include the measurement results corresponding to the current day (i.e., the measurement results of the current day). For example, the historical measurement results include: the probability of illness for each day before the current day, the risk level for each day before the current day, whether the various physiological parameters for each day before the current day are abnormal, etc. Optionally, the historical measurement results may also include: whether the user has long-term sequelae, etc. It should be understood that in the embodiment of the present application, "current day" can be understood as "today" or any day.

[0133] S304, the mobile phone determines whether the historical physiological data is sufficient.

[0134] Alternatively, as another possible implementation, S304 can be replaced by the mobile phone determining whether the historical physiological data and the current day's physiological data are sufficient or meet requirements. In other words, the object used by the mobile phone to determine whether the physiological data meets the requirements can be the sum of the historical physiological data and the current day's physiological data, or the historical physiological data alone. The following uses historical physiological data as an example for illustration; the determination method for historical physiological data and the sum of the current day's physiological data is similar, and reference can be made to the description of historical physiological data. For the sake of brevity, this is not repeated here.

[0135] In S304 , illustratively, after the mobile phone data synchronization is completed, the mobile phone will determine whether the historical physiological data (eg, data from 24 hours ago from the current moment, or historical physiological data before the current day) meets the requirements or is sufficient.

[0136] Exemplarily, if the time length of historical physiological data collection is less than a certain threshold, the cutoff time of the time length is the day before the current day, for example, the time length of historical physiological data collection is counted from the day before the current day to the start time of historical physiological data collection (that is, physiological data 24 hours ago from the current moment), and the length of this period is less than six months; or, the amount of data included in the historical physiological data (that is, the historical physiological data before the current day) is less than a preset threshold, for example, the number of data such as heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, or skin temperature is less than a preset threshold, etc., then the mobile phone determines that the historical physiological data does not meet the requirements or is insufficient; otherwise, the mobile phone determines that the historical physiological data meets the requirements or is sufficient.

[0137] Of course, in other implementations of the present application, the mobile phone may also use other methods to determine whether the historical physiological data is sufficient. The present application does not limit the specific method by which the mobile phone determines whether the historical physiological data is sufficient.

[0138] If the mobile phone determines that the historical physiological data does not meet the requirements or is insufficient, one possible implementation method is that the mobile phone can prompt the user on the interface: the historical physiological data is insufficient to establish personal baseline data, and continue to collect data. In this case, S309 can be directly executed after S304, that is, S305 to S308 are skipped.

[0139] If the mobile phone determines that the historical physiological data does not meet the requirements or is insufficient, another possible implementation is: the mobile phone continues to execute S305.

[0140] Of course, if the mobile phone determines that the historical physiological data meets the requirements or is sufficient, the mobile phone continues to execute S305.

[0141] S305 , the mobile phone determines personal baseline data based on historical physiological data.

[0142] Alternatively, as another possible implementation, S305 may be replaced by the mobile phone determining the personal baseline data based on the historical physiological data and the physiological data of the current day. In other words, the data used by the mobile phone to determine the personal baseline data may be the sum of the historical physiological data and the physiological data of the current day, or may be the historical physiological data alone. The following description will use historical physiological data as an example.

[0143] If the mobile phone determines that the historical physiological data does not meet the requirements or is insufficient, for example, if there is no historical physiological data for the user, one possible implementation method is that the mobile phone can use the user's personal information such as gender, age, height, weight, and region to collect baseline data from a group of people with the same personal information in the big data as the user's personal baseline data. In this case, S305 to S309 can be executed after S304.

[0144] If the mobile phone determines that the historical physiological data does not meet the requirements or is insufficient, another possible implementation method is: the mobile phone can first use these historical physiological data that do not meet the requirements to determine the user's personal baseline data (for distinction, called initial personal baseline data). After obtaining the initial personal baseline data, it can also combine the user's corresponding population baseline data and the initial personal baseline data, and use the weighted average of the population baseline data and the initial personal baseline data to determine the personal baseline data to improve the stability and accuracy of the personal baseline data. In this case, S305 to S309 can be executed after S304.

[0145] For example, the average of the initial personal heart rate baseline data obtained based on the user's historical physiological data is 60, while the heart rate baseline data of the group with the same personal information is 65. The personal heart rate baseline data obtained after fusion (updated personal heart rate baseline data) is (60+65) / 2=62.5.

[0146] For example, if the historical physiological data meets the requirements, the mobile phone can calculate statistical information such as the mean, variance, minimum, maximum, quantile, or probability distribution of various types of historical physiological data (such as heart rate, heart rate variability, respiratory rate, blood oxygen, sleep, etc.) as personal baseline data.

[0147] Through this approach, when a user's historical physiological data is insufficient, the user's corresponding population baseline data can be used as the user's personal baseline data, or the user's corresponding population baseline data and initial personal baseline data can be combined to obtain updated personal baseline data. This eliminates the need for the user to wear the wearable device for an extended period of time to obtain personal baseline data, thereby improving the stability and accuracy of personal baseline data. Monitoring results can be obtained as early as the first day the user wears the wearable device, improving the user experience.

[0148] S306: The mobile phone determines whether the physiological data of the day meets the requirements.

[0149] After data synchronization is complete, the phone will determine whether there is physiological data for the current day (for example, the past 24 hours from now). The physiological data for the current day includes various types of physiological data, such as heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, skin temperature, sleep (sleep onset time, wake-up time, sleep stages, etc.), exercise (exercise type, step length, pace, heart rate during exercise, heart rate after exercise, etc.), and other different types of physiological data.

[0150] If there is physiological data on that day (i.e., physiological data measured on that day exists) and the physiological data measured on that day meets the requirements or is sufficient. For example, if the amount of data included in a certain type of physiological data on that day is greater than a preset threshold (such as the number of heart rate data > N, and the number of respiratory rate data is greater than M, and the number of blood oxygen data is greater than L), then it is determined that the physiological data on that day meets the requirements; otherwise, it is determined that the physiological data on that day does not meet the requirements.

[0151] Of course, in other implementations of the present application, the mobile phone can also use other methods to determine whether the physiological data of the day meets the requirements. The present application does not limit the specific method for the mobile phone to determine whether the physiological data of the day meets the requirements.

[0152] If the physiological data for the day meets the requirements, S307 to S309 are executed. If the physiological data for the day does not meet the requirements, S307 is skipped and S308 and S309 are executed directly; or S307 and S308 are skipped and S309 is executed directly. Optionally, if the physiological data for the day does not meet the requirements, the mobile phone may also prompt the user on the interface that insufficient data is available for the day and disease risk monitoring cannot be performed for the day.

[0153] S307, the mobile phone obtains the respiratory disease monitoring result of the day based on the user's physiological data of the day, personal baseline data, and historical physiological data.

[0154] In this embodiment of the present application, the respiratory disease monitoring results for the day (or also referred to as the measurement results for the day) may include one or more of the following: the probability of illness for the day, the risk level for the day, and whether various physiological parameters for the day are abnormal. The long-term sequelae detection results may include whether the user has long-term sequelae.

[0155] In some possible implementations, the probability of illness on the day can be divided into different levels. For example, if the probability of illness on the day is less than or equal to a preset first threshold, the risk level for the day is determined to be low risk; if the probability of illness on the day is greater than the preset first threshold and less than or equal to a preset second threshold, the risk level for the day is determined to be medium risk; and if the probability of illness on the day is greater than a preset second threshold, the risk level for the day is determined to be high risk, where the second threshold is greater than the first threshold.

[0156] S308 , the mobile phone obtains a long-term sequelae detection result based on the user's historical physiological data, the physiological data of the day, and the historical measurement results; or based on the historical physiological data and the historical measurement results.

[0157] In some possible implementations, the historical measurement results may include one or more of: the probability of illness for each day before the current day, the risk level for each day before the current day, and whether each physiological parameter for each day before the current day is abnormal. The mobile phone can mark the start and end time of the last respiratory disease based on the historical measurement results, and then obtain the physiological baseline data corresponding to before the illness, during the illness, and after recovery from the historical physiological data, or from the historical physiological data and the current day's physiological data. For example, the physiological baseline data corresponding to the heart rate, respiratory rate, blood oxygen, and sleep time before the illness, during the illness, and after recovery can be the average of the heart rate, respiratory rate, blood oxygen, and sleep time. By comparing the baseline data corresponding to the physiological data before the illness and after recovery, it is determined whether the user has long-term sequelae.

[0158] Of course, the mobile phone can also be combined with the respiratory disease monitoring results of the day to determine the long-term sequelae detection results.

[0159] S309, the mobile phone prompts the user on the display interface with the respiratory disease monitoring results and / or long-term sequelae detection results of the day.

[0160] In some possible implementations, if the process includes S301 to S308 , the respiratory disease monitoring results and long-term sequelae detection results of the day can be displayed to the user on the display screen of the mobile phone.

[0161] Optionally, if the measurement result for that day indicates illness, for example, if the probability of illness for that day is greater than a certain threshold, or if the risk level for that day is medium or high, the phone will further determine the number of days the user has been ill and whether their condition is improving or worsening. For example, if the user's probability of illness has been greater than a certain threshold for the past N days, it is considered that the user has been ill for multiple days, and the user will be prompted that they have been ill for N days. Furthermore, if the user has been ill for multiple days, the current probability of illness is compared with the average of the previous days' probabilities. If the current probability of illness is higher than the average of the previous days' probabilities, the user is prompted that their condition may be worsening. Conversely, if the current probability of illness is lower than the average of the previous days' probabilities, the user is prompted that their condition is improving.

[0162] In some possible implementations, in S304, if the historical physiological data does not meet the requirements or is insufficient, and the population baseline data corresponding to the user is not used to determine the user's personal baseline, in this case, regardless of whether the physiological data of the day meets the requirements, the process does not include S305 to S308, that is, the process includes: S301 to S304 and S309, then in S309, the mobile phone displays the historical physiological data and historical measurement results to the user on the display interface, and does not display the respiratory disease monitoring results of the day to the user.

[0163] In some possible implementations, in S304, if the historical physiological data meets the requirements, or although the historical physiological data does not meet the requirements, the user's personal baseline is determined using the population baseline data corresponding to the user; and if it is determined in S306 that the physiological data of the day does not meet the requirements, the process does not include S307, that is, the process includes: S301 to S306, S308 and S309; ​​or, the process does not include S307 and S308, that is, the process includes: S301 to S306, and S309. Then in S309, the mobile phone displays the historical physiological data and historical measurement results to the user on the display interface, and does not display the respiratory disease monitoring results of the day (that is, the measurement results of the day) to the user. Optionally, in S309, the long-term sequelae detection results can be displayed to the user, of course, the long-term sequelae detection results can also not be displayed to the user.

[0164] The system for disease detection based on physiological parameters provided by this application is based on the physiological data collected by the wearable device worn by the user on a daily basis. After the terminal device obtains this data, it processes the data, compares the difference between the physiological data of the day and the baseline data, monitors the risk of the user getting sick on a daily basis, obtains daily monitoring results and displays them to the user, for example, including: the probability of getting sick on the day, the risk level of the day, whether there are abnormalities in various physiological parameters on the day, etc., to help users understand their own physical condition and improve user experience. Subsequently, the system will combine historical monitoring results and historical physiological data, and based on the difference in the user's physiological data before and after illness, inform the user whether there are long-term sequelae after recovery from the disease, thereby realizing long-term trend analysis of the user's health. In this way, it is possible to monitor the user's current disease risk and conduct long-term trend analysis of the user's health, so as to better provide health guidance services to the user.

[0165] It should also be understood that S301 to S309 shown in FIG3 may also be executed by a wearable device. Alternatively, S304 to S308 may also be executed by a wearable device.

[0166] Optionally, if the mobile phone determines that the user is at risk of illness that day, the mobile phone can also combine non-physiological information to determine the type of illness the user may have and display it to the user. For a detailed description of this, please refer to the detailed description of S208 above, which will not be repeated here for the sake of brevity.

[0167] Optionally, if the mobile phone or wearable device determines that the user is at risk of illness on that day (for example, "medium risk" or "high risk"), the mobile phone or wearable device can also display health advice to the user on the display screen based on the risk level of the day and the type of disease the user may have. Alternatively, if the mobile phone or wearable device determines that the user is at risk of illness on that day (for example, "medium risk" or "high risk"), the mobile phone or wearable device can display health advice to the user on the display screen. For specific instructions, please refer to the above-mentioned specific instructions for the relevant parts of the process shown in Figure 2. For the sake of brevity, we will not repeat them here.

[0168] The specific processes of S307 and S308 will be described below with reference to specific examples.

[0169] Optionally, as a possible implementation method, the mobile phone may include a respiratory disease monitoring module and a sequelae detection module. The respiratory disease monitoring module is used to determine the user's respiratory disease risk on the day, and the sequelae detection is used to determine whether the user has long-term sequelae after recovering from the last respiratory disease. Since the long-term effects of respiratory diseases on patients are not the same, the sequelae symptoms and definitions corresponding to different respiratory diseases are also different. Therefore, as a possible implementation method, in the embodiment of the present application, long-term sequelae can be understood as: after the patient recovers, the baseline data of a certain physiological data of the individual or the baseline data of certain physiological data have not returned to the level before the illness. For example, before the patient was infected with the new crown, the average baseline data of the individual respiratory rate was 15. Within a few months after recovering from the new crown, the average baseline data of the individual respiratory rate was 17. At this time, it is considered that the patient's baseline data has not returned to the level before the illness.

[0170] As a possible implementation, the mobile phone can first call the respiratory disease monitoring module to monitor the risk of illness on that day and obtain the respiratory disease monitoring results for that day. It then determines whether the historical measurement results have detected respiratory disease and whether the time since the last respiratory disease recovery is greater than N days. The value of N is a preset threshold, for example, N is five days, or N can be greater than 1, etc. This embodiment of the application does not limit the value of N.

[0171] Optionally, if the mobile phone detects a respiratory disease and the time since the last respiratory disease recovery is greater than N days, the sequelae detection module is called to determine whether the user has long-term sequelae; if a respiratory disease is detected and the time since the last respiratory disease recovery is less than or equal to N days, no determination is made as to whether the user has long-term sequelae.

[0172] For the respiratory disease monitoring module, i.e. S307, the data input into the module include: historical physiological data and physiological data of the day; or, the data input into the module include: personal baseline data and physiological data of the day, and the output is the respiratory disease monitoring result of the day (i.e. disease monitoring result of the day), including: one or more of the probability of illness on the day, the risk level on the day, and whether the various physiological parameters of the day are abnormal.

[0173] The following describes the specific processing of the respiratory disease monitoring module:

[0174] The respiratory disease monitoring module may first determine personal baseline data using historical physiological data. The specific process may refer to the above description of S305 and will not be described here for the sake of brevity.

[0175] Then, the respiratory disease monitoring module uses the personal baseline data to calibrate the physiological data of the day. The respiratory disease monitoring module can use the baseline data of different types of physiological data to calibrate the physiological data of each corresponding type on the day.

[0176] Exemplarily, the calibration method may include the following:

[0177] The first calibration method is to calculate the difference or ratio between a certain type of physiological data for the day and the individual's baseline data of the same type. For example, calculate the difference or ratio between the average of all heart rates for the day and the average of all historical heart rates (i.e., the individual's baseline heart rate data);

[0178] The second calibration method: normalize the user's physiological data for the day using personal baseline data;

[0179] For example, minimum or maximum normalization processing: Calculate the minimum and maximum values ​​of all historical heart rate data of the user (i.e., personal heart rate baseline data). The user's average heart rate for the day can be obtained by calculating the minimum or maximum normalization result based on the minimum and maximum values ​​of all historical heart rate data of the user;

[0180] Another example is Z-Score normalization: Calculate the mean and variance of all the user's historical heart rate data (i.e., personal heart rate baseline data). The user's average heart rate for the day can be obtained by calculating the Z-Score normalization result based on the mean and variance of all the user's historical heart rate data.

[0181] The third calibration method is to estimate the probability distribution parameters based on the user's baseline data. For example, the Gaussian probability distribution is used to calculate the probability corresponding to a certain physiological data of the user on that day (the larger the probability, the more consistent the physiological data is with the baseline distribution);

[0182] The fourth calibration method: Use the user's historical physiological data (i.e., personal baseline data) to train an anomaly detection model or algorithm. For example, the model or algorithm may include: a single-category support vector machine (SVM) model or algorithm, an isolation forest model or algorithm, etc. The trained model or algorithm is used to calculate the abnormal probability of the user's physiological parameters on that day (the greater the abnormal probability, the more abnormal the physiological data).

[0183] It should be understood that the above-mentioned calibration methods are merely exemplary and should not impose any limitations on the use of baseline data to calibrate various types of physiological data of the same day in the embodiments of the present application. In other implementations of the present application, other calibration methods may also be used to calibrate various types of physiological data of the same day using baseline data. The embodiments of the present application are not limiting in this regard.

[0184] After calibrating the day's physiological data using the individual's baseline data, the respiratory disease monitoring module uses a machine learning model to predict the probability of respiratory disease for the day, using a combination of the individual's baseline data, the uncalibrated day's physiological data, and the calibrated day's physiological data. This calibrated day's physiological data using the individual's baseline data improves the accuracy of the respiratory disease monitoring results, ensuring they better reflect the user's health status for the day.

[0185] Optionally, as a possible implementation, one or more thresholds can be set to divide the probability of illness on the day into different risk levels. For example, if the probability of illness on the day is less than or equal to a preset first threshold, the risk level for the day is determined to be low risk; if the probability of illness on the day is greater than the preset first threshold and less than or equal to a preset second threshold, the risk level for the day is determined to be medium risk; if the probability of illness on the day is greater than the preset second threshold, the risk level for the day is determined to be high risk. The second threshold is greater than the first threshold. In this way, the risk level for the day can be determined quickly and conveniently, improving the user experience.

[0186] Optionally, as another possible implementation method, the risk level for the day can be determined in combination with the probability of illness on the day and the physiological data of the user on the day. For example, if the probability of illness on the day is less than or equal to the preset first threshold (for example, 0.5), the risk level for the day is determined to be low risk; if the probability of illness on the day is greater than the preset first threshold, the risk level for the day is determined to be medium risk or high risk, or the risk level for the day is determined to be at least medium risk. As for whether it is medium risk or high risk, it needs to be determined in combination with the physiological data of the user on the day. For example, it can be set that if the heart rate is greater than a certain threshold, and the respiratory rate is greater than another threshold, the risk level for the day is determined to be high risk; if the heart rate does not meet the requirements, or the respiratory rate does not meet the requirements, the risk level for the day is determined to be medium risk. The probability of illness on the day is calibrated according to the physiological data of the user on the day, so that the risk level for the day is consistent with the actual symptoms of the user on the day, thereby improving the accuracy of the predicted risk level for the day and improving the user experience.

[0187] It should be understood that the above example of combining the probability of illness on that day and the user's physiological data on that day to comprehensively determine the risk level for that day is merely exemplary and should not impose any limitations on the embodiments of this application. In other implementations of this application, other calculation methods or preset rules may be used to comprehensively determine the risk level for that day by combining the probability of illness on that day and the user's physiological data on that day. This embodiment of this application is not limiting in this regard.

[0188] In an embodiment of the present application, in order to mark which physiological data are abnormal when presenting the results to the user, the respiratory disease monitoring module can determine which physiological data are abnormal based on the calibrated user's physiological data for the day, and mark which physiological parameters are abnormal and which are not abnormal when displaying them to the user on the mobile phone, to help the user understand his or her own physical condition and improve the user experience.

[0189] Exemplarily, when the respiratory disease monitoring module determines which physiological data are abnormal based on the calibrated physiological data of the user on the day, the judgment method varies with the calibration method. For example, for the calibration method of calculating the difference or ratio (the first calibration method), if the difference or ratio corresponding to a certain physiological data of the user on the day after calibration is greater than a certain threshold, then the physiological data is abnormal; for the probability distribution calibration method (the third calibration method), if the probability corresponding to a certain physiological data of the user on the day after calibration is less than a certain threshold, then the physiological data is abnormal; for the anomaly detection calibration method (the fourth calibration method), if the probability of abnormality corresponding to a certain physiological data of the user on the day after calibration is greater than a certain threshold, then the physiological data is abnormal.

[0190] It should be understood that the above-mentioned determination of which physiological data of the user on the day are abnormal is merely exemplary and should not impose any limitation on the embodiments of the present application. In other implementations of the present application, other methods can also be used to determine which physiological data of the user on the day are abnormal, and the embodiments of the present application are not limited thereto.

[0191] Through the above-mentioned method, the accuracy of the respiratory disease monitoring results of the day can be guaranteed, so that the respiratory disease monitoring results of the day can better reflect the health condition of the user on the day.

[0192] For the sequelae detection module, that is, for S308, the input of the sequelae detection module may include: historical physiological data, physiological data of the current day, historical measurement results, and respiratory disease monitoring results of the current day; or, the input of the sequelae detection module may include: historical physiological data, physiological data of the current day, historical measurement results; or, the input of the sequelae detection module may include: historical physiological data and historical measurement results. The module outputs a determination result of whether there is a sequelae.

[0193] For example, the sequelae detection module can mark the start and end times of the last respiratory illness based on historical measurement results, and then statistically obtain the physiological baseline data corresponding to before, during, and after the illness. For example, the physiological baseline data corresponding to heart rate, respiratory rate, blood oxygen, and sleep time before, during, and after the illness can be the average of heart rate, respiratory rate, blood oxygen, and sleep time. Based on the baseline data corresponding to the physiological data before and after the illness, it is determined whether the user has long-term sequelae.

[0194] For example, the baseline of a physiological parameter after recovery can be compared with the baseline of the same physiological parameter before the illness to see if there is a significant difference. If there is a significant difference, the user is considered to be at risk of long-term sequelae. For example, if the difference or ratio of the baseline of a physiological parameter before and after recovery is greater than a certain threshold, it is considered to be a significant difference; otherwise, it is considered that there is no significant difference, that is, there is no risk of long-term sequelae.

[0195] Through the above-mentioned method, the accuracy of the long-term sequelae detection results can be guaranteed, so that the long-term sequelae detection results can better reflect the long-term health status of the user.

[0196] The physiological parameter-based disease detection system provided in this application is based on physiological data collected by wearable devices worn daily by users. After acquiring this data, the terminal device processes the data, compares the user's physiological data for the day with the user's personal baseline data, monitors the user's risk of illness daily, obtains daily monitoring results, and displays them to the user. Subsequently, the system combines historical monitoring results with historical physiological data and, based on the differences in the user's physiological data before and after illness, informs the user of whether there are long-term sequelae after recovery, thus enabling long-term trend analysis of the user's health. This allows for both monitoring the user's current disease risk and analyzing long-term health trends, thereby providing better health guidance services to the user. Furthermore, if a user's historical physiological data is insufficient, the user's corresponding population baseline data can be used as the user's personal baseline data, or the user's corresponding population baseline data and initial personal baseline data can be combined to obtain personal baseline data. This eliminates the need for the user to wear the wearable device for an extended period of time to obtain personal baseline data, allowing monitoring results to be obtained as early as the first day the wearable device is worn, thus improving the user experience. When determining the user's risk level for the day, the probability of illness for the day is calibrated based on the user's physiological data for the day, so that the risk level for the day is consistent with the user's actual symptoms for the day, thereby improving the accuracy of the predicted risk level for the day and enhancing the user experience.

[0197] It should be understood that the above is only intended to help those skilled in the art better understand the embodiments of the present application, and is not intended to limit the scope of the embodiments of the present application. Based on the above examples given, those skilled in the art can obviously make various equivalent modifications or changes. For example, certain steps in each embodiment of the above method may be unnecessary, or certain new steps may be added. Or a combination of any two or any multiple embodiments described above. Such modifications, changes, or combined solutions also fall within the scope of the embodiments of the present application.

[0198] It should also be understood that the above description of the embodiments of the present application focuses on emphasizing the differences between the various embodiments. The same or similar points that are not mentioned can be referenced with each other. For the sake of brevity, they will not be repeated here.

[0199] It should also be understood that the division of the modes, situations, categories and embodiments in the embodiments of the present application is only for the convenience of description and should not constitute a special limitation. The features of various modes, categories, situations and embodiments can be combined without contradiction.

[0200] It should also be understood that in the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0201] The above description, in combination with Figures 1 to 3, describes an embodiment of the system for detecting lower limb arterial disease provided by an embodiment of the present application. The following describes the terminal device and wearable device provided by an embodiment of the present application.

[0202] In this embodiment, the terminal device and the wearable device can be divided into functional modules according to the steps respectively performed by the terminal device and the wearable device. For example, each function can be divided into functional modules, or two or more functions can be integrated into one processing module. The above-mentioned integrated modules can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic and is only a logical function division. In actual implementation, there may be other division methods.

[0203] It should be noted that the relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module and will not be repeated here.

[0204] The terminal device and wearable device provided in the embodiments of the present application are used to execute the above-mentioned method for disease detection based on physiological parameters as shown in Figures 2 and 3, and thus can achieve the same effect as the above-mentioned implementation method. In the case of adopting an integrated unit, the terminal device and the wearable device may include a processing module, a storage module and a communication module. Among them, the processing module can be used to control and manage the actions of the terminal device and the wearable device. For example, it can be used to support the terminal device and the wearable device to execute the steps performed by the processing unit. The storage module can be used to support the storage of program code and data, etc. The communication module can be used to support communication between the terminal device and the wearable device and other devices.

[0205] The processing module may be a processor or a controller. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, and so on. The storage module may be a memory. The communication module may specifically be a device that interacts with other devices, such as a radio frequency circuit, a Bluetooth chip, or a Wi-Fi chip.

[0206] For example, FIG4 shows a hardware structure diagram of a communication device 400 provided in this application, which can be the terminal device or wearable device mentioned above. As shown in FIG4, the communication device 400 may include a processor 410, an external memory interface 420, an internal memory 430, a universal serial bus (USB) interface 440, a charging management module 450, a power management module 451, a battery 452, an antenna 1, an antenna 2, a wireless communication module 460, a sensor module 470, and a display screen 480.

[0207] It is understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the communication device 400. In other embodiments of the present application, the communication device 400 may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The illustrated components can be implemented in hardware, software, or a combination of software and hardware. For example, when the communication device 400 is a wearable device, the communication device 400 may not include the display screen 480. For another example, when the communication device 400 is a terminal device, the communication device 400 may not include the sensor module 470.

[0208] Processor 410 may include one or more processing units. For example, processor 410 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU). The different processing units may be independent components or integrated into one or more processors. In some embodiments, communication device 400 may also include one or more processors 410. The controller may generate operation control signals based on instruction opcodes and timing signals to control instruction fetching and execution. For example, processor 410 may determine a user's personal baseline data based on the user's historical physiological data; perform daily respiratory disease monitoring based on the user's personal baseline data and the user's daily physiological data; compile baseline data corresponding to the user's physiological data before, during, and after illness based on all of the user's physiological data and historical measurement results; and determine whether the user has long-term sequelae based on the baseline data corresponding to each physiological data before and after illness.

[0209] In some embodiments, the processor 410 may include one or more interfaces. The interfaces may include an inter-integrated circuit (I2C) interface, an integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a SIM card interface, and / or a USB interface. Among them, the USB interface 440 is an interface that complies with the USB standard specification, and specifically can be a Mini USB interface, a Micro USB interface, a USB Type C interface, etc. The USB interface 440 can be used to connect a charger to charge the communication device 400, and can also be used to transmit data between the communication device 400 and peripheral devices.

[0210] It is understood that the interface connection relationship between the modules illustrated in the embodiments of the present application is merely an illustrative illustration and does not constitute a structural limitation on the communication device 400. In other embodiments of the present application, the communication device 400 may also adopt a different interface connection method from the above embodiments, or a combination of multiple interface connection methods.

[0211] The wireless communication function of the communication device 400 can be implemented through the antenna 1, the antenna 2 and the wireless communication module 460.

[0212] The wireless communication module 460 can provide wireless communication solutions including Wi-Fi (including Wi-Fi sensing and Wi-Fi AP), Bluetooth (BT), wireless data transmission modules (for example, 433MHz, 868MHz, 915MHz) applied to the communication device 400. The wireless communication module 460 can be one or more devices integrating at least one communication processing module. The wireless communication module 460 receives electromagnetic waves via antenna 1 or antenna 2 (or, antenna 1 and antenna 2), filters and frequency modulates the electromagnetic wave signals, and sends the processed signals to the processor 410. The wireless communication module 460 can also receive the signal to be transmitted from the processor 410, frequency modulate it, amplify it, and convert it into electromagnetic waves for radiation through antenna 1 or antenna 2.

[0213] Communication device 400 implements display functionality through a GPU, display screen 480, and an application processor. The GPU is a microprocessor for image processing that connects display screen 480 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. Processor 410 may include one or more GPUs that execute program instructions to generate or modify display information.

[0214] External memory interface 420 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of communication device 400. The external memory card communicates with processor 410 via external memory interface 420 to implement data storage functions. For example, files such as music and videos can be stored on the external memory card.

[0215] The internal memory 430 can be used to store one or more computer programs, which include instructions. The processor 410 can execute the above instructions stored in the internal memory 430, so that the communication device 400 performs the methods provided in some embodiments of the present application, as well as various applications and data processing. The internal memory 430 may include a code storage area and a data storage area. Among them, the code storage area can store an operating system. The data storage area can store data created during the use of the communication device 400, etc. In addition, the internal memory 430 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more disk storage components, flash memory components, universal flash storage (UFS), etc. In some embodiments, the processor 410 can execute the instructions stored in the internal memory 430, and / or the instructions stored in the memory provided in the processor 410, so that the communication device 400 executes the processing flow for disease detection based on physiological parameters provided in the embodiments of the present application, as well as other applications and data processing.

[0216] Display screen 480 is used to display one or more of the following to the user: the probability of illness for the day, the risk level for the day, and whether various physiological parameters for the day are abnormal. Optionally, display screen 480 may also display a determination result indicating whether the user has any sequelae. Optionally, display screen 480 may also display corresponding health advice to the user.

[0217] The sensor module 470 may include sensors for measuring the user's heart rate, heart rate variability, blood oxygen, respiratory rate, body temperature, sleep, exercise and other physiological parameters. The sensor module 470 can collect various physiological parameters of the user.

[0218] The communication device 400 may include, for example, smart TVs, large-screen devices, smart air conditioners, mobile phones, tablet computers, notebooks, large-screen TVs, smart home products, PDAs, laptops, printers, smart watches, smart bracelets, wearable wrist devices, smart glasses, augmented reality (AR) / virtual reality (VR) devices, and other devices, but the embodiments of the present application are not limited to this.

[0219] It should be understood that the communication device 400 shown in FIG. 4 may be a terminal device or a wearable device, or the terminal device or the wearable device may include the communication device 400 shown in FIG. 4 .

[0220] The charging management module 450 can charge the communication device 400. The power management module 451 can manage the battery 452.

[0221] It should be understood that the specific process of the communication device 400 executing the above corresponding steps can be referred to the relevant description of the terminal device or wearable device executing steps in the embodiments described in conjunction with Figures 2 and 3 in the previous text. For the sake of brevity, it is not repeated here.

[0222] FIG5 shows a schematic block diagram of another example of a communication device 500 provided in an embodiment of the present application. The communication device 500 may correspond to the terminal device or wearable device described in the various embodiments shown in FIG2 or FIG3 above. It may also be a chip or component applied to the terminal device or wearable device, and the modules or units included in the communication device 500 are respectively used to execute the various actions or processing processes performed by the terminal device or wearable device described in the various embodiments shown in FIG2 or FIG3 above. As shown in FIG5 , the communication device 500 may include: a processing unit 510 and a communication unit 520. Optionally, the communication device 500 may also include a storage unit 530.

[0223] It should be understood that the specific process of each unit in the communication device 500 executing the above corresponding steps can be referred to the relevant description of the electronic device or wearable device executing the steps described in conjunction with Figure 4 in the previous text. For the sake of brevity, it is not repeated here.

[0224] Optionally, the communication unit 520 may include a receiving unit (module) and a sending unit (module) for performing the steps of receiving information and sending information by the electronic device in the aforementioned various method embodiments. The storage unit 530 is used to store instructions executed by the processing unit 510 and the communication unit 520. The processing unit 510, the communication unit 520, and the storage unit 530 are communicatively connected, the storage unit 530 stores instructions, the processing unit 510 is used to execute the instructions stored in the storage unit, and the communication unit 520 is used to perform specific signal transmission and reception under the drive of the processing unit 510.

[0225] It should be understood that the communication unit 520 may be a transceiver, an input / output interface, or an interface circuit, and may be implemented, for example, by the wireless communication module 460 in the embodiment shown in FIG4 . The storage unit may be a memory, and may be implemented, for example, by the external memory interface 420 and the internal memory 430 in the embodiment shown in FIG4 . The processing unit 510 may be implemented by the processor 410 in the embodiment shown in FIG4 , or may be implemented by the processor 410, the external memory interface 420, and the internal memory 430.

[0226] It should also be understood that the communication device 500 shown in FIG. 5 may be a terminal device or a wearable device, or the terminal device or the wearable device may include the communication device 500 shown in FIG. 5 .

[0227] It should also be understood that the division of units in the above device is merely a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. Moreover, the units in the device can all be implemented in the form of software called through processing elements; or all be implemented in the form of hardware; or some units can be implemented in the form of software called through processing elements, and some units can be implemented in the form of hardware. For example, each unit can be a separately established processing element, or it can be integrated into a certain chip of the device. In addition, it can also be stored in a memory in the form of a program, and called by a certain processing element of the device to execute the function of the unit. Here, the processing element can also be called a processor, which can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each unit above can be implemented by the integrated logic circuit of the hardware in the processor element or in the form of software called through the processing element. In one example, the unit in any of the above devices may be one or more integrated circuits configured to implement the above method, such as one or more application specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms. For another example, when the unit in the device can be implemented in the form of a processing element scheduler, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call a program. For another example, these units can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0228] An embodiment of the present application also provides a system for disease detection based on physiological parameters, which includes any terminal device and any wearable device provided in the embodiment of the present application.

[0229] An embodiment of the present application also provides a chip system, as shown in Figure 6, which includes at least one processor 601 and at least one interface circuit 602. The processor 601 and the interface circuit 602 can be interconnected via lines. For example, the interface circuit 602 can be used to receive signals from other devices (such as the wearable device mentioned above). For another example, the interface circuit 602 can be used to send signals to other devices (such as the processor 601). Exemplarily, the interface circuit 602 can read instructions stored in the memory and send the instructions to the processor 601. When the instructions are executed by the processor 601, the terminal device can execute the various steps performed by the terminal device in any of the implementation methods in the above embodiments. Of course, the chip system can also include other discrete components, which is not specifically limited in the embodiment of the present application.

[0230] The present invention also provides a computer-readable storage medium for storing computer program code, wherein the computer program includes instructions for executing any of the methods for disease detection based on physiological parameters provided in the embodiments of the present invention. The computer-readable storage medium may be a read-only memory (ROM) or a random access memory (RAM), which is not limited in the present invention.

[0231] The present application also provides a computer program product, which includes instructions. When the instructions are executed, the electronic device performs corresponding operations in the above method flow.

[0232] Embodiments of the present application also provide a chip within a communication device, comprising a processing unit and a communication unit. The processing unit may be, for example, a processor, and the communication unit may be, for example, an input / output interface, a pin, or an interface circuit. The processing unit may execute computer instructions to cause the communication device to perform any of the aforementioned methods for disease detection based on physiological parameters provided in the embodiments of the present application.

[0233] Optionally, the computer instructions are stored in a storage unit.

[0234] Optionally, the storage unit is a storage unit within the chip, such as a register, cache, etc. The storage unit can also be a storage unit located outside the chip within the terminal, such as a ROM or other type of static storage device that can store static information and instructions, random access RAM, etc. The processor mentioned in any of the above can be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the program of the above-mentioned feedback information transmission method. The processing unit and the storage unit can be decoupled and respectively set on different physical devices, and connected by wired or wireless means to implement the respective functions of the processing unit and the storage unit to support the system chip to implement the various functions in the above-mentioned embodiments. Alternatively, the processing unit and the memory can also be coupled on the same device.

[0235] Among them, the communication device, computer-readable storage medium, computer program product or chip provided in this embodiment are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0236] It will be appreciated that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a ROM, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a RAM, which is used as an external cache. There are many different types of RAM, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0237] Various objects such as various messages / information / equipment / network elements / systems / devices / actions / operations / processes / concepts that may appear in this application are named. It can be understood that these specific names do not constitute a limitation on the relevant objects. The names assigned may change with factors such as scenarios, contexts or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from the functions and technical effects embodied / executed in the technical solutions.

[0238] In the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0239] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0240] The methods in the embodiments of the present application can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instruction is loaded and executed on a computer, the process or function described in the embodiments of the present application is executed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program or instruction can be stored in a computer-readable storage medium or transmitted via the computer-readable storage medium. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server that integrates one or more available media.

[0241] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0242] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0243] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0244] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0245] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a readable storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned readable storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0246] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A terminal device for disease detection based on physiological parameters, characterized in that: The terminal device comprises: processor; Memory; Display screen; and a computer program, the computer program being stored in the memory and causing the terminal device to perform the following steps when the computer program is executed by the processor: Acquiring physiological data of a user, wherein the physiological data of the user includes historical physiological data and physiological data of the current day of the user; Determine the user's personal baseline data according to the historical physiological data, wherein the personal baseline data includes statistics of different types of physiological data of the user, wherein the statistics include at least one of a mean, a variance, a minimum value, a maximum value, a quantile, and a probability distribution; Obtaining disease monitoring results for the day according to the physiological data for the day and the personal baseline data, wherein the disease monitoring results for the day include: one or more of the probability of illness for the day, the risk level for the day, and whether various physiological parameters for the day are abnormal; Obtaining a long-term sequelae detection result according to the historical physiological data, the physiological data of the day, and the historical measurement results, wherein the long-term sequelae detection result includes: whether the user has a long-term sequelae or not, and the historical measurement results include: one or more of: the probability of illness every day before the day, the risk level every day before the day, and whether each physiological parameter every day before the day is abnormal; The disease monitoring results for the day and / or the long-term sequelae detection results are displayed to the user on the display screen.

2. The terminal device according to claim 1, characterized in that: Determining the user's personal baseline data based on the historical physiological data includes: If the historical physiological data does not meet the preset conditions, determine the population baseline data corresponding to the user according to the personal information of the user, and determine the population baseline data as the personal baseline data; or If the historical physiological data does not meet the preset conditions, determining initial personal baseline data based on the historical physiological data; Determine the population baseline data corresponding to the user according to the personal information of the user; The personal baseline data is determined based on the population baseline data and the initial personal baseline data.

3. The terminal device according to claim 1 or 2, characterized in that: The disease monitoring result of the day is obtained according to the physiological data of the day and the personal baseline data, including: Using the personal baseline data to calibrate the physiological data of the day to obtain calibrated physiological data of the day; The disease monitoring result of the day is obtained according to the physiological data of the day after calibration, the physiological data of the day before calibration and the personal baseline data.

4. The terminal device according to any one of claims 1 to 3, characterized in that: The long-term sequelae detection results are obtained according to the historical physiological data, the physiological data of the day and the historical measurement results, including: Determine the start and end time of the user's illness according to the historical measurement results; Determine baseline data corresponding to various physiological data of the user before and after illness; The long-term sequelae detection result is obtained according to the baseline data corresponding to the various physiological data of the user before the illness and after recovery.

5. The terminal device according to any one of claims 1 to 4, characterized in that: When the probability of illness on the day is less than or equal to a preset first threshold, the risk level on the day is low risk; When the probability of illness on the day is greater than a preset first threshold and less than or equal to a preset second threshold, the risk level on the day is medium risk; When the probability of illness on that day is greater than a preset second threshold, the risk level on that day is high risk, and the second threshold is greater than the first threshold.

6. The terminal device according to any one of claims 1 to 4, characterized in that: The terminal device further performs the following steps: The risk level of the day is determined according to the probability of illness on the day and the physiological data on the day.

7. The terminal device according to any one of claims 1 to 6, characterized in that: The terminal device further performs the following steps: In the case that the user is at risk of disease, the type of disease the user suffers from is determined and displayed to the user in combination with non-physiological information, wherein the non-physiological information includes at least one of: whether infectious diseases are prevalent in the area where the user is located, whether epidemic diseases are prevalent in the season of the day, and whether the user suffers from an underlying disease.

8. The terminal device according to any one of claims 1 to 7, characterized in that: The terminal device further performs the following steps: Displaying health advice to the user on the display screen according to the risk level of the day and the type of disease the user suffers from; or, Health advice is displayed to the user on the display screen according to the risk level of the day.

9. The terminal device according to any one of claims 1 to 8, characterized in that: The user's physiological data includes at least one of heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, skin temperature, sleep information, and exercise information.

10. The terminal device according to any one of claims 1 to 9, characterized in that: The terminal device also includes a physiological data collector, and the terminal device further performs the following steps: The physiological data collector is used to obtain the physiological data of the user.

11. The terminal device according to any one of claims 1 to 10, characterized in that: The terminal device communicates wirelessly with the wearable device of the user, and the terminal device further performs the following steps: Receive physiological data of the user sent by the wearable device.

12. A system for disease detection based on physiological parameters, characterized in that: The system includes a terminal device and a wearable device according to any one of claims 1 to 11, wherein the terminal device communicates wirelessly with the wearable device of the user, the wearable device includes a physiological data collector, and the wearable device uses the physiological data collector to obtain the physiological data of the user and sends the physiological data of the user to the terminal device.

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