Disease detection system based on physiological parameters and terminal equipment

Abstract

The invention discloses a system and terminal equipment for disease detection based on physiological parameters. The system comprises the terminal equipment and wearable equipment. Based on physiological data collected by wearable equipment worn by a user in daily life, terminal equipment processes the data after obtaining the data, compares the difference between the physiological data of the current day and personal baseline data of the user, monitors the disease risk of the user in the current day every day, obtains a monitoring result of every day and displays the monitoring result to the user. For example, the current-day illness probability, the current-day risk level, whether various physiological parameters of the current day are abnormal or not and the like are included. Moreover, in combination with the historical monitoring result and the historical physiological data, the user is informed of whether long-term sequelae exist after disease rehabilitation according to the difference between the physiological data of the user before and after disease rehabilitation, and long-term trend analysis of user health is realized. Therefore, the current disease risk of the user is monitored, and the long-term trend analysis of the health of the user is carried out, so that the health guidance service is better provided for the user.

Description

Technical Field

[0001] 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

[0002] 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.

[0003] At present, based on the user's physiological data detected by wearable devices, it is possible to detect whether the user is sick on the day, that is, to detect the risk of disease in a timely manner. Alternatively, after accumulating enough physiological data of the user, various physiological data of the user are obtained to obtain historical baseline data, and then compare 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 is any difference, so as to prompt the changing trend of the user's physiological data.

[0004] 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. Summary of the invention

[0005] 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. The system 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, and realize the 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 and improve the user experience.

[0006] In a first aspect, a terminal device is provided, the terminal device comprising: a processor; a memory; a display screen and a computer program, the computer program being 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, the physiological data of the user comprising historical physiological data and physiological data of the day of the user; determining personal baseline data of the user based on the historical physiological data, the personal baseline data comprising statistics of different types of physiological data of the user, the statistics comprising at least one of mean, variance, minimum, maximum, quantile, and probability distribution; determining a personal baseline data based on the physiological data of the day of the user; determining a personal baseline data based on the historical physiological data, the personal baseline data comprising statistics of different types of physiological data of the user; determining a personal baseline data based on the historical physiological data, the personal baseline data comprising statistics of different types of physiological data of the user, the personal baseline data comprising at least one of mean, variance, minimum, maximum, quantile, and probability distribution; determining a personal baseline data based on the physiological data of the day of the user ... The personal baseline data is used to obtain the disease monitoring results of the day, which include: the probability of illness on the day, the risk level on the day, and whether the various physiological parameters of the day are abnormal, one or more of them; based on the historical physiological data, the physiological data of the day and the historical measurement results, the long-term sequelae detection results are obtained, and the long-term sequelae detection results include: whether the user has long-term sequelae, and the historical measurement results include: the probability of illness 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, one or more of them; the disease monitoring results of the day and / or the long-term sequelae detection results are displayed to the user on the display screen.

[0007] 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 the terminal device obtains these data, it 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's illness on the day, obtains the daily monitoring results and displays them to the user, for example, including: the probability of illness 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 whether there are long-term sequelae after recovery from the disease, and the long-term trend analysis of the user's health will be realized. Thereby, it is possible to monitor the user's current disease risk and conduct a long-term trend analysis of the user's health, so as to better provide health guidance services to users.

[0008] Exemplarily, the terminal device can be an IOT device such as a smart phone, a tablet computer, a laptop, a handheld computer, a notebook computer, a cellular phone, a PDA, an AR\VR device, or a television, a large-screen device, etc.

[0009] Exemplarily, historical physiological data does not include physiological data of 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., measurement results of the current day). 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. "The current day" may be understood as "today" or any day.

[0010] In a possible implementation of the first aspect, the personal baseline data of the user is determined based on historical physiological data; including: if the historical physiological data does not meet the preset conditions, the population baseline data corresponding to the user is determined based on the user's personal information, and the population baseline data is determined as the personal baseline data; or, if the historical physiological data does not meet the preset conditions, the initial personal baseline data is determined based on the historical physiological data; the population baseline data corresponding to the user is determined based on the user's personal information; the personal baseline data is determined 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 the updated personal baseline data is obtained by combining the population baseline data corresponding to the user and the initial personal baseline data, and the user does not need to wear the wearable device for a long time to obtain the personal baseline data, so as to improve the stability and accuracy of the personal baseline data. It can be achieved that the monitoring results can be obtained on the first day the user wears the wearable device, thereby improving the user experience.

[0011] Exemplarily, 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.

[0012] In a possible implementation of the first aspect, obtaining the disease monitoring result of the day based on the physiological data of the day and the personal baseline data includes: calibrating the physiological data of the day using the personal baseline data to obtain the calibrated physiological data of the day; obtaining the disease monitoring result of the day based on the calibrated physiological data of the day, the physiological data of the day before calibration, and the personal baseline data. In this implementation, calibrating the physiological data of the day using the personal baseline data can improve the accuracy of the respiratory disease monitoring result of the day, so that the respiratory disease monitoring result of the day better reflects the health condition of the user on the day.

[0013] Exemplarily, the calibration method may include: calculating the difference or ratio between a certain type of physiological data of the day and the personal baseline data of the same type; normalizing the user's physiological data of 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).

[0014] In a possible implementation of the first aspect, based on historical physiological data, physiological data of the day, and historical measurement results; or based on historical physiological data, physiological data of the day, historical measurement results, and respiratory disease monitoring results of the day; or based on historical physiological data and historical measurement results, long-term sequelae detection results are obtained, including: determining the start and end time of the user's illness based on historical measurement results; determining the baseline data corresponding to each physiological data of the user before the illness and after recovery; obtaining long-term sequelae detection results 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 results 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 results and improve user experience.

[0015] For example, the baseline of a certain physiological parameter after recovery can be compared with the baseline of the physiological parameter before the illness to see if there is a significant difference. If there is a significant difference, the user is considered to have a risk of long-term sequelae. For example, if the difference or ratio of the baseline of a certain physiological parameter before the illness 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.

[0016] In a 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 on the 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 on the day is medium risk; if the probability of illness on the day is greater than the preset second threshold, the risk level on the day is high risk, and the second threshold is greater than the first threshold. In this implementation, the risk level of the day can be determined quickly and conveniently, thereby improving user experience.

[0017] In a possible implementation of the first aspect, the terminal device further performs the following steps: determining the risk level of the day according to the probability of illness on the day and the physiological data of the day. In this implementation, 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 of the day is consistent with the actual symptoms of the user on the day, thereby improving the accuracy of the predicted risk level of the day and improving the user experience.

[0018] Exemplarily, 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 of 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 of the day is determined to be medium risk or high risk. As for whether it is medium risk or high risk, it needs to be determined in combination with the user's physiological data 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 of 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 of the day is determined to be medium risk.

[0019] In a possible implementation of the first aspect, the terminal device further performs the following steps: when the user is at risk of illness, the type of disease the user suffers from is determined in combination with non-physiological information and displayed to the user, and the non-physiological information includes at least one of: whether there is an epidemic of infectious diseases in the area where the user is located, whether there is an epidemic disease in the season of the day, and whether the user suffers from an underlying disease. In this implementation, when the user is at risk of illness on the day, the type of disease the user may suffer from is determined in combination with non-physiological information and displayed to the user, so as to notify the user of the possible type of disease, further improve the efficiency and accuracy of prompting the user of possible diseases, and can help the user to self-select treatment plans and improve user experience.

[0020] In a possible implementation of the first aspect, 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 displaying health advice to the user on the display screen according to the risk level of the day. In this implementation, health advice suitable for the user's current health status is displayed to the user, and a corresponding response plan can be displayed to the user, which can further improve the user experience.

[0021] For example, health advice may include: diet advice, exercise advice, treatment advice, etc. For example, treatment advice may prompt the user to take some medicine, or to seek medical treatment in time, etc. Diet advice may include: avoid spicy food, etc. Exercise advice may include: reduce outdoor exercise, take more rest, etc.

[0022] For another example, treatment suggestions may prompt the user to take some common medicines that have therapeutic effects on the possible disease, or to seek medical treatment in a timely manner. Dietary suggestions may include: avoiding foods that may aggravate the possible disease, or eating some foods that may relieve or alleviate the possible disease. Exercise suggestions may include: exercises that have a relieving or restorative effect on the possible disease, and avoiding exercises that may aggravate the possible disease.

[0023] 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.

[0024] In a 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 the physiological data of the user. For example, it may include sensors for measuring physiological parameters such as heart rate, heart rate variability, blood oxygen, respiratory rate, body temperature, sleep, exercise, etc., and various physiological parameters of the user may be collected.

[0025] 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 the 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.

[0026] In a second aspect, a system for disease detection based on physiological parameters is provided, the system comprising: a terminal device and a wearable device provided in the first aspect or any possible implementation of the first aspect. The terminal device communicates wirelessly with the wearable device of the user, the wearable device comprises a physiological data collector, 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.

[0027] The second aspect provides a system for disease detection based on physiological parameters. 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 obtains these data and processes the data, monitors the risk of illness on the day (including respiratory diseases or other diseases, etc.) every day, and obtains daily monitoring results (also called test results), for example: the probability of illness on the day, the risk level on the day, whether there are abnormalities in various physiological parameters on the day, etc. Subsequently, the historical monitoring results and historical physiological data will be combined, and the user will be informed of whether there are long-term sequelae after recovery from the disease based on the difference in the user's physiological data before and after illness, so as to realize the long-term trend analysis of the user's health. Thereby, it is realized that the user's current disease risk is monitored and the long-term trend analysis of the user's health is carried out, so as to better provide health guidance services to users and improve user experience.

[0028] In a third aspect, a communication device is provided, which includes a unit for executing each step executed by a terminal device in the above first aspect or any possible implementation of the first aspect.

[0029] In a fourth aspect, a terminal device is provided, which includes the communication apparatus provided in the third aspect.

[0030] 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 executed in the first aspect or any possible implementation of the first aspect.

[0031] 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.

[0032] In the seventh aspect, a chip is provided, comprising: 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 a terminal device in the first aspect or any possible implementation method of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a schematic architecture diagram of a system for disease detection based on physiological parameter detection provided in this application.

[0034] Figure 2 This is a schematic diagram of an example of a disease detection process based on physiological parameter detection provided in an embodiment of the present application.

[0035] Figure 3 This is another schematic diagram of a disease detection process based on physiological parameter detection provided in an embodiment of the present application.

[0036] Figure 4 It is a schematic block diagram of an example of a communication device structure provided in an embodiment of the present application.

[0037] Figure 5 It is a schematic block diagram of an example of a communication device structure provided in an embodiment of the present application.

[0038] Figure 6 It is a schematic block diagram of a chip system structure provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0040] The terms used in the following embodiments are only for the purpose of describing specific embodiments, and are not intended to be used as limitations on the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind", "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: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0041] References to "one embodiment" or "some embodiments" etc. described in this specification mean that a particular feature, structure or characteristic described in conjunction with the embodiment is included in one or more embodiments of the present application. Thus, the phrases "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear at different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0042] 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", "second", etc. are only used for the purpose of distinguishing the description, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying an order.

[0043] 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 present application embodiment covers a computer program that can be accessed from any computer-readable device, carrier or medium. For example, a computer-readable medium may include, but is not limited to: a magnetic storage device (e.g., a hard disk, a floppy disk or a tape, etc.), an optical disk (e.g., a compact disc (CD), a digital versatile disc (DVD), etc.), a smart card and a flash memory device (e.g., an erasable programmable read-only memory (EPROM), a card, a stick or a key drive, etc.). In addition, various storage media described herein may represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing and / or carrying instructions and / or data.

[0044] 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 a computer program that can be accessed from any computer-readable device, carrier or medium. For example, a computer-readable medium may include, but is not limited to: a magnetic storage device (e.g., a hard disk, a floppy disk or a magnetic tape, etc.), an optical disk (e.g., a compact disc (CD), a digital versatile disc (DVD), etc.), a smart card and a flash memory device (e.g., an erasable programmable read-only memory (EPROM), a card, a stick or a key drive, etc.). In addition, the various storage media described herein may represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing and / or carrying instructions and / or data.

[0045] 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.

[0046] For different types of respiratory diseases, the means of prevention and treatment are not the same. For example, lung infection is characterized by high morbidity and mortality, with 450 million people suffering from the disease each year, and the mortality rate of lung infection ranks fourth among the top ten causes of death in the world. Lung infection needs to be detected and treated early to significantly reduce mortality and medical expenses. As a chronic disease, COPD treatment requires a combination of drug intervention, exercise and other measures, and long-term monitoring of lung function changes to prevent worsening of the disease.

[0047] It is equally important to monitor the sequelae of respiratory diseases. For example, according to the World Health Organization, about 10-20% of patients with acute COVID-19 infection will have long-term COVID-19 symptoms for weeks to months after recovery, such as fatigue, dyspnea, chest pain, cough, etc. Long-term monitoring of the patient's recovery can help doctors effectively judge the patient's recovery level and formulate treatment plans.

[0048] With the development of technology, wearable devices (such as smart bracelets, smart watches, etc.) have the ability to detect physiological parameters such as blood oxygen, respiratory rate, heart rate variability, body temperature, etc. When the human body has respiratory diseases, physiological data will change. For example, when suffering from pneumonia, the heart rate, respiratory rate, body temperature may increase, and blood oxygen may decrease. When suffering from chronic obstructive pulmonary disease, the respiratory rate may increase and the blood oxygen may decrease, which makes it possible to monitor the health of the respiratory system.

[0049] In order to adapt to the different needs of different users for respiratory health monitoring, a technology is needed that can both detect the risk of acute respiratory diseases in a timely manner and detect the sequelae of respiratory diseases.

[0050] At present, there is a technology for monitoring respiratory diseases in the related art. This technology collects various types of physiological data (such as heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, skin temperature, etc.) of the user in the recent period of time (such as within a day) through the wearable device worn by the user, and determines whether the user currently (such as that day) suffers from a respiratory disease, such as influenza, pneumonia, chronic obstructive pulmonary disease, etc. by establishing a mapping relationship between physiological data and respiratory diseases. Some technologies also need to collect the physiological data of the user when he does not suffer from a respiratory disease as baseline data, and determine whether he suffers from a respiratory disease at that time by detecting whether the physiological data at that time (such as that day) is different from the baseline data. Baseline data can be understood as: statistics of various physiological data calculated by collecting different types of physiological data by wearing a wearable device for a long time (such as several months, a year or a longer period of time). Exemplarily, physiological data can include different types or types of physiological data such as heart rate, heart rate variability, respiratory rate, body temperature, sleep, exercise, etc. Statistics can include, for example, mean, variance, minimum and maximum values, quantiles, etc.

[0051] Using the above solution, there are several problems:

[0052] First, the technology that does not require baseline data and only determines whether the user has a respiratory disease based on the mapping relationship between the current physiological data and respiratory diseases may lead to inaccurate results. For example, when the user has other underlying diseases that cause abnormal physiological data, the above solution may frequently misreport such users; or the user is usually very healthy, and the symptoms after illness are very mild, and the difference between the various physiological data during the illness and the various physiological data when healthy is very small, and the physiological data abnormality is not obvious, the technology is prone to miss reports.

[0053] Second, the technology requires baseline data. Users need to wear the wearable device for a long time (such as several months, a year or longer) and obtain baseline data through various physiological data collected by the wearable device during this period. Only after obtaining the baseline data can it be determined whether the user currently suffers from respiratory diseases, which makes it inconvenient for users to use. For example, the user cannot get the monitoring results on the first day of wearing the wearable device.

[0054] Third, the above scheme 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 is impossible to perform long-term trend analysis of respiratory health), nor can it detect the sequelae of respiratory disease.

[0055] Fourth, respiratory diseases such as influenza, COVID-19, and pneumonia will cause increased heart rate, respiratory rate, and body temperature, and decreased blood oxygen. It is difficult to determine what specific disease it is based on physiological data.

[0056] 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, the user's 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.

[0057] Using the above solution, it is necessary to accumulate the user's physiological data for a long time before the changing trend of the user's long-term 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, today) has a respiratory disease. In addition, this technology can only count simple changing trends of various physiological data, such as rising, falling, and unchanged, and cannot monitor the user's long-term respiratory health trend changes (that is, it cannot perform long-term trend analysis of respiratory health), nor can it detect sequelae of respiratory diseases.

[0058] In summary, there are mainly the following technical problems in the prior art:

[0059] 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.

[0060] Second, when monitoring respiratory diseases, the introduction of baseline data will result in users being unable to measure in the absence of historical baseline data, but not using baseline data will result in decreased accuracy.

[0061] Third, many respiratory diseases manifest similarly in physiological data, and current technology makes it difficult to distinguish specific disease types.

[0062] 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, including one or more combinations of heart rate, heart rate variability, blood oxygen, respiratory rate, body temperature, sleep, exercise, etc.), the terminal device obtains these data and processes the data, monitors the risk of illness on the 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 the day, the risk level on the day, whether there are abnormalities in various physiological parameters on the day, etc. Subsequently, the historical monitoring results and historical physiological data will be combined, and the user will be informed whether there are long-term sequelae after recovery from the disease based on the difference in the user's physiological data before and after illness, so as to realize the long-term trend analysis of the user's health. Thereby, it is realized that the user's current disease risk is monitored and the long-term trend analysis of the user's health is performed, so as to better provide health guidance services to the user and improve the user experience.

[0063] The following is a description of the disease detection system based on physiological parameters provided by the present application with reference to specific examples.

[0064] refer to Figure 1 , Figure 1 The schematic diagram of the system for disease detection based on physiological parameter detection provided by an embodiment of the present application is shown in FIG. Figure 1As shown, the system includes: a user uses a wearable device 112 and a terminal device 111, Figure 1 In the example shown, the wearable device 112 used by the user takes a smart watch as an example, and the terminal device 111 takes a smart phone as an example. In the present application, various sensors are set 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, exercise, etc., and the wearable device 112 can collect various physiological parameters of the user. The smart phone 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 interface of the smart phone 111 for the user to view.

[0065] For example, in Figure 1 In the example shown, the wearable device 112 and the terminal device 111 can communicate with each other through wireless communication technologies such as Bluetooth (BT) technology, wireless-fidelity (WiFi) technology, and near-field communication (NFC) technology. 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.

[0066] It should be understood that in other implementations of the present application, if the wearable device supports the calculation function, after the wearable device 112 collects various physiological parameters of the user, the wearable device 112 itself also calculates the daily monitoring results and the data of long-term sequelae based on the various physiological parameters, and sends these calculation results to the smartphone 111, which is displayed on the smartphone interface for the user to view. In other words, the system composed of the wearable device and the terminal device can implement the technical solution provided by the present application.

[0067] Of course, if the wearable device supports computing and display functions, for example, the wearable device has a larger 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 displays the results on the interface for the user to view. In other words, the wearable device alone can also implement the technical solution provided by this application.

[0068] It should be understood that Figure 1 This is merely an example and should not impose any limitation on the system provided in the embodiments of the present application. Figure 1The illustrated scenario may also include more terminal devices and wearable devices, etc. This application does not limit this.

[0069] 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, etc. 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.

[0070] 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 realize powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, 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.

[0071] 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.

[0072] 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.

[0073] It should also be understood that the following description will take 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.

[0074] Figure 2 The figure shows a schematic diagram of the overall process (i.e., detection method) of disease detection using the system provided by the present application. Figure 2 The process shown, Figure 2 The process shown includes S201 to S208.

[0075] exist Figure 2 In the example shown, 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.

[0076] like Figure 2 As shown, in S201, the wearable device periodically collects physiological data of the user.

[0077] Optionally, in some possible implementations, the wearable device can continuously or periodically collect the user's physiological data, for example, collecting physiological data of 1 minute or once every 10 minutes. Exemplarily, the physiological data may include: heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, skin temperature, sleep (data such as falling asleep, waking up, and 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.

[0078] It should be understood that the wearable device can collect the user's physiological data for a long time, for example, it can continuously collect the user's physiological data for several months or even longer. Optionally, the collected user's physiological data can be stored with "daily" as a granularity.

[0079] S202, the wearable device sends the collected physiological data to the terminal device.

[0080] The wearable device sends the collected physiological data to the terminal device through the wireless network between the wearable device and the terminal device.

[0081] Exemplarily, 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.

[0082] 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. Exemplarily, in the embodiment of the present application, the physiological data before the day can also be referred to as historical physiological data.

[0083] S203, the terminal device determines the user's personal baseline data based on the user's historical physiological data.

[0084] 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 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.

[0085] In the embodiment of the present application, personal baseline data can be understood as: statistics of various (or items) of physiological data calculated by using the physiological data collected by the user wearing the wearable device for a long time (for example, several months, a year or a longer period of time). Exemplarily, physiological data may include: heart rate, heart rate variability, respiratory rate, body temperature, sleep, exercise and other different types of physiological data. Statistics may include, for example, mean, variance, minimum, maximum, or quantile.

[0086] 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.

[0087] In other possible implementations, if the historical physiological data is insufficient or does not meet the requirements, the terminal device may first use the historical physiological data that does not meet the requirements to determine the user's personal baseline data (for the sake of distinction, referred to as initial personal baseline data). After obtaining the initial personal baseline data, the user's corresponding population baseline data and the initial personal baseline data may be combined to determine the personal baseline data using methods such as the weighted average of the population baseline data and the initial personal baseline data.

[0088] 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 in combination with 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.

[0089] 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.

[0090] 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 the machine learning model on the terminal device, and use the machine learning model to monitor the respiratory disease risk for the day to 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 probability of illness for the day, the risk level for the day, and whether the various physiological parameters for the day are abnormal.

[0091] Exemplarily, S204 may be calculated and output based on the dimension of “every day”.

[0092] 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.

[0093] 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.

[0094] In S205 , all physiological data may include historical physiological data and physiological data of the day.

[0095] Exemplarily, in S205, the historical measurement results may include: one or more of 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 for each day in history (excluding the current day) is abnormal.

[0096] Optionally, as another possible implementation, S205 may also be replaced by: the terminal device collects baseline data corresponding to the physiological data before, during and after illness based on all the user's physiological data, historical measurement results and the measurement results of the day. The measurement results of the day are the respiratory disease monitoring results of the day.

[0097] The terminal device can mark the start and end time of the last respiratory disease according to the historical measurement results, and then obtain the physiological baseline data corresponding to before, during and after the disease. For example, the physiological baseline data corresponding to the heart rate, respiratory rate, blood oxygen and sleep time before, during and after the disease can be the average of the heart rate, respiratory rate, blood oxygen and sleep time.

[0098] 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.

[0099] For example, the terminal device can compare the baseline data of a certain (or some) physiological parameter after recovery with the baseline data of the same (or these) physiological parameter before the illness to see if there is a significant difference. If there is a significant difference, the user is considered to have a risk of long-term sequelae. For example, if the difference or ratio of the baseline of a certain physiological parameter before the illness 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.

[0100] 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.

[0101] S207, the terminal device displays the respiratory disease monitoring results of the day and whether there are long-term sequelae to the user on the interface.

[0102] 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 that day. 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 the illness and after recovery), or it can also be displayed 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 in which the terminal device displays to the user on the interface.

[0103] Optionally, the process may also include S208.

[0104] S208, if the user is at risk of illness 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.

[0105] When the terminal device determines that the user is at risk of illness on that day based on the user's physiological parameters, 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.

[0106] 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 an infectious disease is prevalent in the user's location based on the area filled in by the user and Internet information. If an infectious disease is prevalent in the local area, the user will be prompted on the interface that he or she may be infected with the infectious disease.

[0107] 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 of influenza, the user is prompted on the interface that he or she may have influenza.

[0108] For another example, information can be collected through questionnaires or other detection technologies to determine whether the user has some underlying diseases. If the user has an underlying disease, the terminal device can prompt the user on the display interface: the user may have complications of the underlying disease or the underlying disease may be aggravated. For example, if the user is found to have chronic obstructive pulmonary disease through questionnaires or COPD detection technology, the user may be prompted that COPD may be acutely aggravated.

[0109] 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.

[0110] Through the above-mentioned S208, it is possible to notify the user of the possible disease type, further improve the efficiency and accuracy of prompting the user of possible diseases, and help the user to self-select treatment plans, thereby improving the user experience.

[0111] Optionally, as a possible implementation, Figure 2The process shown 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, the 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 specific content or form of the health advice included in the embodiments of the present application is not limited. 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.

[0112] Optionally, as another possible implementation, Figure 2 The process shown 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 be combined with the type of disease that the user may suffer from, and some health advice may be prompted to the user on the display interface (for example, by text or other means, etc.). For example, the health advice may include: dietary advice, exercise advice, treatment advice, etc. Exemplarily, the treatment advice may prompt the user to take some common medicines that have therapeutic effects on the possible disease, or to seek medical treatment in time. Dietary advice may include: avoiding foods that aggravate the possible disease, or eating some foods that relieve or alleviate the possible disease. Exercise advice may include: exercises that have a mitigating or restorative effect on the possible disease, avoiding exercises that aggravate the possible disease, etc. The content or form of the health advice specifically included in the embodiment of the present application is not limited. In this way, it is realized that health advice suitable for the user's current health status is displayed to the user, and the corresponding response plan can be displayed to the user, which can further improve the user experience.

[0113] It should be understood that Figure 2 S201 to S208 shown in can also be performed by the wearable device. Alternatively, Figure 2 S203 to S206 shown in FIG. 1 may also be executed by a wearable device.

[0114] Figure 3 The diagram shown is another example of the overall process (i.e., detection method) of disease detection using the system provided by the present application. Figure 3 The process shown in Figure 3 In the example shown, the terminal device used by the user will be described by taking a smart phone (referred to as a mobile phone) as an example.

[0115] S301, the user logs in to the account on the mobile phone and the wearable device, and binds the account on the application programs of the mobile phone and the wearable device.

[0116] 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 the login is successful, 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 respectively executed by the mobile phone and the wearable device in each embodiment of the present application.

[0117] As a possible implementation, the application may be a newly developed application, and when the wearable device runs the application, the steps performed by the wearable device in each embodiment of the present application can be implemented, and when the mobile phone runs the application, the steps performed by the mobile phone in each embodiment of the present application can be implemented. Of course, in other implementations of the present application, the application may also be an existing application, but the application integrates new functions, so that when the wearable device runs the application, the steps performed by the wearable device in each embodiment of the present application can be implemented, and when the mobile phone runs the application, the steps performed by the mobile phone in each embodiment of the present application can be implemented.

[0118] S302, the wearable device periodically collects the user's physiological data.

[0119] Wearable devices are provided or configured with various sensors, for example, sensors for measuring physiological parameters such as heart rate, heart rate variability, blood oxygen, respiratory rate, body temperature, sleep, and exercise. 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, etc.), exercise (exercise type, step length, pace, heart rate during exercise, heart rate after exercise, etc.), etc.

[0120] It should be understood that the wearable device can collect the user's physiological data for a long time or for a long time, for example, it can continue to collect data for several months or even longer.

[0121] 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.

[0122] Optionally, the physiological data collected by the wearable device in S301 may be stored in the wearable device or in the cloud.

[0123] S303, the mobile phone obtains historical physiological data, physiological data of the day, and historical measurement results collected by the wearable device.

[0124] For example, the wearable device sends the collected physiological data to the terminal device through the wireless network between the wearable device and the terminal device.

[0125] For another example, wearable devices can store the collected physiological data in the cloud, and mobile phones can obtain the collected physiological data from the cloud.

[0126] In some possible implementations, after the user opens the application on the mobile phone, the user can obtain all physiological data (i.e., historical physiological data and physiological data of the day) and historical measurement results collected by the wearable device from the wearable device side or the cloud through certain operations (such as the user clicking on the control on the application, pulling down the interface, etc.). Optionally, after the user authorizes on the application, all physiological data and historical measurement results collected by the wearable device in the past can also be automatically synchronized to the mobile phone.

[0127] In some possible implementations, the historical measurement results in S303 may include one or more of: 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 each physiological parameter is 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 these measurement results are based on the time granularity of "day".

[0128] In some other possible implementations, the historical measurement results in S303 may also 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 (for example, the day after the user recovers from the illness)" to (or ending at) "the day".

[0129] Optionally, the step corresponding to S303 may also be referred to as “data synchronization”.

[0130] It should be understood that the historical physiological data in S303 does not include the physiological data of the day. The historical measurement results include the measurement results corresponding to each day before the day, and do not include the measurement results corresponding to the day (i.e., the measurement results of the day). For example, the historical measurement results include: the probability of illness for each day before the day, the risk level for each day before the day, whether the various physiological parameters for each day before the 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, "that day" can be understood as "today" or any day.

[0131] S304, the mobile phone determines whether the historical physiological data is sufficient.

[0132] Optionally, as another possible implementation method, S304 may also be replaced by the mobile phone judging whether the historical physiological data and the physiological data of the day are sufficient or whether they meet the requirements. In other words, the object for the mobile phone to judge whether the physiological data meets the requirements may be: the sum of the historical physiological data and the physiological data of the day, or the historical physiological data. The historical physiological data is used as an example for explanation below. For the historical physiological data and the sum of the physiological data of the day, the judgment method is similar. You can refer to the description of the historical physiological data. For the sake of brevity, it will not be repeated here.

[0133] In S304, illustratively, after the mobile phone data synchronization is completed, the mobile phone will determine whether the historical physiological data (eg, data 24 hours ago from the current time, or historical physiological data before the current day) meets the requirements or is sufficient.

[0134] 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, and ends at the start time of historical physiological data collection (i.e., physiological data 24 hours ago from the current time), and this period of time is less than six months; or, the amount of data included in the historical physiological data (i.e., 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.

[0135] Of course, in other implementations of the present application, the mobile phone can also use other methods to determine whether the historical physiological data is sufficient. The present application does not limit the specific method for the mobile phone to determine whether the historical physiological data is sufficient.

[0136] If the mobile phone determines that the historical physiological data does not meet the requirements or is insufficient, a possible implementation method is: 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.

[0137] 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.

[0138] 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.

[0139] S305, the mobile phone determines personal baseline data based on historical physiological data.

[0140] Optionally, as another possible implementation, S305 may also be replaced by the mobile phone determining the personal baseline data based on the historical physiological data and the physiological data of the 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 day, or the historical physiological data. The historical physiological data is used as an example for explanation below.

[0141] If the mobile phone determines that the historical physiological data does not meet the requirements or is insufficient, for example, when there is no historical physiological data of the user, a possible implementation method is: the mobile phone can collect the baseline data of the group with the same personal information under the big data as the personal baseline data of the user according to the personal information of the user such as gender, age, height, weight, region, etc. In this case, S305 to S309 can be executed after S304.

[0142] 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 to determine the personal baseline data using the weighted average of the population baseline data and the initial personal baseline data, so as to improve the stability and accuracy of the personal baseline data. In this case, S305 to S309 can be executed after S304.

[0143] 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.

[0144] For example, if the historical physiological data meets the requirements, the mobile phone can calculate the mean, variance, minimum, maximum, quantile, or probability distribution and other statistical information of various types of historical physiological data (such as heart rate, heart rate variability, respiratory rate, blood oxygen, sleep, etc.) as personal baseline data.

[0145] Through the above method, when the user's historical physiological data is insufficient, the user's corresponding population baseline data is used as the user's personal baseline data, or the user's corresponding population baseline data and the initial personal baseline data are combined to obtain updated personal baseline data, so that the user does not need to wear the wearable device for a long time to obtain personal baseline data, thereby improving the stability and accuracy of personal baseline data. It is possible to obtain monitoring results on the first day the user wears the wearable device, thereby improving the user experience.

[0146] S306, the mobile phone determines whether the physiological data of the day meets the requirements.

[0147] After data synchronization is completed, the phone will determine whether there is physiological data for the day (for example, from now on, in the past 24 hours). The physiological data of the day includes various types of physiological data of the day, such as: heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, skin temperature, sleep (sleep time, wake time, sleep stage, etc.), exercise (exercise type, step length, pace, heart rate during exercise, heart rate after exercise, etc.) and other different types of physiological data.

[0148] 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 (or some) 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), 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.

[0149] 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.

[0150] If the physiological data of the day meets the requirements, S307 to S309 are executed. If the physiological data of the day does not meet the requirements, S307 is skipped and S308 and S309 are directly executed; or S307 and S308 are skipped and S309 is directly executed. Optionally, if the physiological data of the day does not meet the requirements, the mobile phone can also prompt the user on the interface that the data of the day is insufficient and the disease risk monitoring of the day cannot be performed.

[0151] 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.

[0152] In the embodiment of the present application, the respiratory disease monitoring result of the day (or also referred to as the measurement result of the day) may include: one or more of the probability of illness on the day, the risk level on the day, and whether the physiological parameters on the day are abnormal. The long-term sequelae detection result may include: whether the user has long-term sequelae.

[0153] 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 of 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 of 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 of the day is determined to be high risk. The second threshold is greater than the first threshold.

[0154] 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.

[0155] 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 according to 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 mean of the heart rate, respiratory rate, blood oxygen, and sleep time. Compare the baseline data corresponding to the physiological data before the illness and after recovery to determine whether the user has long-term sequelae.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Optionally, if the measurement result of the day is determined to be sick, for example, the probability of illness on the day is greater than a certain threshold, or the risk level on the day is medium risk or high risk, the mobile phone will further determine the number of days the user is sick and whether the condition is getting better or worse. For example, if the probability of illness of the user has been greater than a certain threshold in the past N days, it is considered that the user has been sick for many days, and the user will be prompted that he has been sick for N days. At the same time, if the user has been sick for many days, the probability of illness on the day and the average of the probability of illness in the previous few days will be compared. If the probability of illness on the day is higher than the average of the probability of illness in the previous few days, the user is prompted that the condition may worsen. On the contrary, if the probability of illness on the day is lower than the average of the probability of illness in the previous few days, the user is prompted that the condition is improving.

[0160] 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.

[0161] 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 (i.e., 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.

[0162] The system for disease detection based on physiological parameters provided by the present 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 these 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 the day, obtains the 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 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 whether there are long-term sequelae after recovery from the disease, and the long-term trend analysis of the user's health is realized. Thereby, it is realized that the user's current disease risk is monitored, and the long-term trend analysis of the user's health is performed, so as to better provide health guidance services to users.

[0163] It should also be understood that Figure 3 The steps S301 to S309 shown may also be all executed by the wearable device. Alternatively, steps S304 to S308 may also be executed by the wearable device.

[0164] Optionally, if the mobile phone determines that the user has a risk of illness on that day, the mobile phone can also combine non-physiological information to determine the type of disease the user may have and display it to the user. For a specific description of this, please refer to the specific description of S208 above, which will not be repeated here for brevity.

[0165] Optionally, if the mobile phone or wearable device determines that the user is at risk of disease 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 disease 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 detailed instructions, please refer to the above Figure 2 For the sake of brevity, the specific description of the relevant parts of the process shown will not be repeated here.

[0166] The specific process of S307 and S308 will be described below with reference to a specific example.

[0167] Optionally, as a possible implementation, 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 for the day, and the sequelae detection is used to determine whether the user has long-term sequelae after the last respiratory disease recovery. 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, 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's respiratory rate was 15. Within a few months after recovering from the new crown, the average baseline data of the individual's 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.

[0168] As a possible implementation method, the mobile phone can first call the respiratory disease monitoring module to monitor the risk of illness on the day and obtain the respiratory disease monitoring results of the day. Then, it is determined whether the historical measurement results have detected respiratory diseases and whether the time from the last respiratory disease recovery on the day is greater than N days. The value of N is a preset threshold, for example, the value of N is five days, or the value of N can be greater than 1, etc. The embodiment of the present application does not limit the value of N.

[0169] Optionally, if the mobile phone detects 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 respiratory disease is detected and the time since the last respiratory disease recovery is less than or equal to N days, it is not determined whether the user has long-term sequelae.

[0170] 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. the disease monitoring result of the day), including: one or more of the probability of illness on the day, the risk level of the day, and whether the various physiological parameters of the day are abnormal.

[0171] The following describes the specific processing of the respiratory disease monitoring module:

[0172] The respiratory disease monitoring module may first determine the personal baseline data using the historical physiological data. The specific process may refer to the above description of S305 , which will not be described here for brevity.

[0173] 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 various corresponding types of physiological data of the day.

[0174] Exemplarily, the calibration method may include the following:

[0175] The first calibration method: Calculate the difference or ratio between a certain type of physiological data of the day and the same type of personal baseline data. For example, calculate the difference or ratio between the mean of all heart rates of the day and the mean of all heart rates in history (i.e. personal heart rate baseline data);

[0176] The second calibration method: using personal baseline data to normalize the user's physiological data for the day;

[0177] For example, minimum or maximum normalization processing: calculate the minimum and maximum values ​​of all the user's historical heart rate data (i.e., personal heart rate baseline data), and 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 the user's historical heart rate data;

[0178] For another example, Z-Score normalization processing: calculate the mean and variance of all the user's historical heart rate data (i.e., personal heart rate baseline data), and the user's heart rate average 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;

[0179] The third calibration method: estimate the probability distribution parameters based on personal baseline data. For example, Gaussian probability distribution is used to calculate the probability corresponding to a certain physiological data of the user on that day (the greater the probability, the more consistent the physiological data is with the baseline distribution);

[0180] 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 (Support Vector Machine) model or algorithm, an isolation forest model or algorithm, etc. Use the trained model or algorithm 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).

[0181] It should be understood that the above-mentioned several calibration methods are merely exemplary and should not impose any limitation on the use of baseline data in the embodiments of the present application to calibrate various types of physiological data of the day. In other implementations of the present application, other calibration methods can also be used to calibrate various types of physiological data of the day using baseline data. The embodiments of the present application are not limited here.

[0182] After the physiological data of the day is calibrated using the personal baseline data, the respiratory disease monitoring module uses a combination of the personal baseline data, the uncalibrated physiological data of the day, and the calibrated physiological data of the day to use a machine learning model to predict the probability of having a respiratory disease on the day, that is, to obtain the probability of illness on the day. By calibrating the physiological data of the day using the personal baseline data, the accuracy of the respiratory disease monitoring results of the day can be improved, so that the respiratory disease monitoring results of the day can better reflect the health status of the user on the day.

[0183] Optionally, as a possible implementation method, one or more thresholds may 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 the preset first threshold, the risk level on 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 the preset second threshold, the risk level on 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 on the day is determined to be high risk. Among them, the second threshold is greater than the first threshold. In this way, the risk level of the day can be determined quickly and conveniently, improving the user experience.

[0184] Optionally, as another possible implementation method, the risk level of 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 of 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 of the day is determined to be medium risk or high risk, or the risk level of the day is determined to be medium risk at the lowest. 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 of 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 of 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 of the day is consistent with the actual symptoms of the user on the day, thereby improving the accuracy of the predicted risk level of the day and improving the user experience.

[0185] It should be understood that the above-mentioned example of comprehensively determining the risk level of the day by combining the probability of illness on the day and the physiological data of the user on the day is merely exemplary and should not cause any limitation to the embodiments of the present application. In other implementations of the present application, other calculation methods or preset rules can also be used to comprehensively determine the risk level of the day by combining the probability of illness on the day and the physiological data of the user on the day. The embodiments of the present application are not limited here.

[0186] 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 the mobile phone displays them to the user, thereby helping the user understand his or her own physical condition and improving the user experience.

[0187] Exemplarily, in the process of judging which physiological data are abnormal based on the calibrated physiological data of the user on the day, the respiratory disease monitoring module changes the way of judging according to 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 abnormal detection calibration method (the fourth calibration method), if the abnormal probability 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.

[0188] It should be understood that the above determination of which physiological data of the user on that day is abnormal is merely exemplary and should not cause any limitation to the embodiments of the present application. In other implementations of the present application, other methods may be used to determine which physiological data of the user on that day is abnormal, which is not limited in the embodiments of the present application.

[0189] 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.

[0190] 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 day, historical measurement results, and respiratory disease monitoring results of the day; or, the input of the sequelae detection module may include: historical physiological data, physiological data of the day, historical measurement results; or, the input of the sequelae detection module includes: historical physiological data and historical measurement results. Output the judgment result of whether there is a sequelae.

[0191] For example, the sequelae detection module can mark the start and end time of the last respiratory disease according to the historical measurement results, and then obtain the corresponding physiological baseline data before, during and after the disease. For example, the physiological baseline data corresponding to the heart rate, respiratory rate, blood oxygen, and sleep time before, during and after the disease can be the mean of the heart rate, respiratory rate, blood oxygen, and sleep time. Then, based on the baseline data corresponding to the physiological data before and after the disease, it is determined whether the user has long-term sequelae.

[0192] For example, the baseline of a physiological parameter after recovery can be compared with the baseline of the physiological parameter before the illness to see if there is a significant difference. If there is a significant difference, the user is considered to have a risk of long-term sequelae. For example, if the difference or ratio of the baseline of a physiological parameter before the illness 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.

[0193] 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 condition of the user.

[0194] The system for disease detection based on physiological parameters provided by the present application is based on the physiological data collected by the wearable device worn by the user daily. After the terminal device obtains these data, it processes the data, compares the difference between the user's physiological data of the day and the user's personal baseline data, monitors the risk of the user's illness on the day every day, obtains the daily monitoring results and displays them to the user. Subsequently, the historical monitoring results and historical physiological data will be combined, and the user will be informed whether there are long-term sequelae after recovery from the disease based on the difference between the user's physiological data before and after illness, so as to achieve the long-term trend analysis of the user's health. Thus, it is realized that both the current disease risk of the user is monitored and the long-term trend analysis of the user's health is performed, so as to better provide health guidance services for the user. In addition, when the user's historical physiological data is insufficient, the user's corresponding population baseline data is used as the user's personal baseline data, or the user's corresponding population baseline data and the initial personal baseline data are combined to obtain the personal baseline data. The user does not need to wear the wearable device for a long time to obtain the personal baseline data, and the monitoring results can be obtained on the first day the user wears the wearable device, thereby 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 improving the user experience.

[0195] It should be understood that the above is only to help those skilled in the art better understand the embodiments of the present application, rather than to limit the scope of the embodiments of the present application. According to the above examples given, those skilled in the art can obviously make various equivalent modifications or changes. For example, some steps in each embodiment of the above method may be unnecessary, or some new steps may be added. Or a combination of any two or any multiple embodiments of the above. Such modifications, changes or combined solutions also fall within the scope of the embodiments of the present application.

[0196] 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 to each other. For the sake of brevity, they will not be repeated here.

[0197] It should also be understood that the division of the methods, 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 methods, categories, situations and embodiments can be combined without contradiction.

[0198] 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.

[0199] The above combination Figures 1 to 3 An embodiment of a system for detecting lower limb arterial disease provided by an embodiment of the present application is described. The following describes a terminal device and a wearable device provided by an embodiment of the present application.

[0200] This embodiment can divide the terminal device and the wearable device into functional modules according to the steps respectively performed by the above-mentioned terminal device and the wearable device. For example, each function can be divided into various functional modules, or two or more functions can be integrated into one processing module. The above-mentioned integrated module 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. There may be other division methods in actual implementation.

[0201] It should be noted that the relevant contents of each step involved in the above method embodiment can all be referred to the functional description of the corresponding functional module, which will not be repeated here.

[0202] The terminal device and the wearable device provided in the embodiments of the present application are used to perform the above-mentioned Figure 2 and Figure 3 The method for disease detection based on physiological parameters shown can therefore achieve the same effect as the above-mentioned implementation method. In the case of 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 codes and data, etc. The communication module can be used to support the communication between the terminal device and the wearable device and other devices.

[0203] Among them, the processing module can be a processor or a controller. It can implement or execute various exemplary logic boxes, modules and circuits described in conjunction with the disclosure of this application. The processor can 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, etc. The storage module can be a memory. The communication module can specifically be a device that interacts with other devices, such as a radio frequency circuit, a Bluetooth chip, a Wi-Fi chip, etc.

[0204] For example, Figure 4 FIG. 4 is a schematic diagram showing a hardware structure of a communication device 400 provided in the present application. The communication device 400 may be the above-mentioned terminal device or wearable device. Figure 4 As shown, 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.

[0205] It is to be 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, or combine certain components, or split certain components, or arrange the components differently. The illustrated components may 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 a display screen 480. For another example, when the communication device 400 is a terminal device, the communication device 400 may not include a sensor module 470.

[0206] The processor 410 may include one or more processing units. For example, the processor 410 may include an application processor (AP), a modem processor, a graphics processor (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). Different processing units may be independent components or integrated into one or more processors. In some embodiments, the communication device 400 may also include one or more processors 410. The controller may generate an operation control signal according to the instruction opcode and the timing signal to complete the control of fetching and executing instructions. For example, the processor 410 may determine the user's personal baseline data based on the user's historical physiological data; perform respiratory disease monitoring on the day based on the user's personal baseline data and the user's physiological data on the day; and perform statistical analysis of the baseline data corresponding to the physiological data before, during and after the illness according to all the user's physiological data and historical measurement results; and determine whether the user has long-term sequelae according to the baseline data corresponding to each physiological data before and after the illness.

[0207] In some embodiments, the processor 410 may include one or more interfaces. The interface 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, etc. 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.

[0208] It is understandable that the interface connection relationship between the modules illustrated in the embodiment of the present application is only a schematic 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 different interface connection methods in the above embodiments, or a combination of multiple interface connection methods.

[0209] 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.

[0210] 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 signal, and sends the processed signal to the processor 410. The wireless communication module 460 can also receive the signal to be sent from the processor 410, frequency modulate it, amplify it, and convert it into electromagnetic waves for radiation through antenna 1 or antenna 2.

[0211] The communication device 400 implements the display function through a GPU, a display screen 480, and an application processor. The GPU is a microprocessor for image processing, which connects the display screen 480 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor 410 may include one or more GPUs, which execute program instructions to generate or change display information.

[0212] The 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 the communication device 400. The external memory card communicates with the processor 410 through the external memory interface 420 to implement a data storage function, such as storing music, video and other files in the external memory card.

[0213] The internal memory 430 may be used to store one or more computer programs, which include instructions. The processor 410 may enable the communication device 400 to execute the methods provided in some embodiments of the present application, as well as various applications and data processing, etc. by running the above instructions stored in the internal memory 430. The internal memory 430 may include a code storage area and a data storage area. Among them, the code storage area may store an operating system. The data storage area may 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, a flash memory component, a universal flash storage (UFS), etc. In some embodiments, the processor 410 may enable the communication device 400 to execute 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 by running instructions stored in the internal memory 430, and / or instructions stored in a memory provided in the processor 410.

[0214] Display screen 480 is used to display one or more of the probability of illness on the day, the risk level on the day, and whether the physiological parameters on the day are abnormal. Optionally, display screen 480 can also display the judgment result of whether the user has sequelae. Optionally, display screen 480 can also display corresponding health advice to the user.

[0215] The sensor module 470 may include sensors for measuring physiological parameters such as the user's heart rate, heart rate variability, blood oxygen, respiratory rate, body temperature, sleep, exercise, etc. The sensor module 470 can collect various physiological parameters of the user.

[0216] 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.

[0217] It should be understood that Figure 4 The communication device 400 shown may be a terminal device or a wearable device, or the terminal device or the wearable device may include Figure 4 The communication device 400 is shown.

[0218] The charging management module 450 can charge the communication device 400. The power management module 451 can manage the battery 452.

[0219] It should be understood that the specific process of the communication device 400 performing the above corresponding steps can be referred to in conjunction with the above Figure 2 and Figure 3 The relevant description of the steps executed by the terminal device or wearable device in the embodiment is described, and for the sake of brevity, it is not repeated here.

[0220] Figure 5 A schematic block diagram of another example of a communication device 500 provided in an embodiment of the present application is shown. The communication device 500 may correspond to the above Figure 2 or Figure 3 The terminal device or wearable device described in each embodiment shown in the figure may also be a chip or component applied to the terminal device or wearable device, and each module or unit included in the communication device 500 is used to execute the above Figure 2 or Figure 3 The actions or processing processes performed by the terminal device or wearable device described in each embodiment shown in FIG. Figure 5 As shown, 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.

[0221] It should be understood that the specific process of each unit in the communication device 500 performing the above corresponding steps can be referred to in conjunction with the above Figure 4 For the sake of brevity, the relevant description of the steps executed by the electronic device or wearable device described in the description is not repeated here.

[0222] Optionally, the communication unit 520 may include a receiving unit (module) and a sending unit (module), which are used to perform 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 connected in communication, 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.

[0223] It should be understood that the communication unit 520 may be a transceiver, an input / output interface or an interface circuit, etc., for example, Figure 4 The wireless communication module 460 in the illustrated embodiment is implemented. The storage unit may be a memory, for example, Figure 4 The external memory interface 420 and the internal memory 430 in the illustrated embodiment are implemented. The processing unit 510 may be composed of Figure 4 The processor 410 in the illustrated embodiment may be implemented by the processor 410 , the external memory interface 420 , and the internal memory 430 .

[0224] It should also be understood that Figure 5 The communication device 500 shown may be a terminal device or a wearable device, or the terminal device or the wearable device may include Figure 5 The communication device 500 is shown.

[0225] It should also be understood that the division of units in the above device is only 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. And the units in the device can all be implemented in the form of software calling through processing elements; they can also be all implemented in the form of hardware; some units can also be implemented in the form of software calling 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 in a certain chip of the device. In addition, it can also be stored in the memory in the form of a program, and called and executed by a certain processing element of the device. The processing element here 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 an integrated logic circuit of hardware in the processor element or in the form of software calling through a 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 (ASIC), or one or more digital signal processors (DSP), or one or more field programmable gate arrays (FPGA), 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 can 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).

[0226] 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.

[0227] The present application also provides a chip system, such as Figure 6As shown, the chip system includes at least one processor 601 and at least one interface circuit 602. The processor 601 and the interface circuit 602 can be interconnected through 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 implementation method in the above embodiments. Of course, the chip system can also include other discrete devices, which are not specifically limited in the embodiments of the present application.

[0228] The embodiment of the present application also provides a computer-readable storage medium for storing computer program code, the computer program including instructions for executing any one of the methods for disease detection based on physiological parameters provided in the above-mentioned embodiment of the present application. The readable medium may be a read-only memory (ROM) or a random access memory (RAM), which is not limited in the embodiment of the present application.

[0229] 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.

[0230] The embodiment of the present application also provides a chip located in a communication device, the chip comprising: a processing unit and a communication unit, the processing unit, for example, may be a processor, the communication unit, for example, may be an input / output interface, a pin or an interface circuit, etc. The processing unit may execute computer instructions to enable the communication device to execute any of the above-mentioned methods for disease detection based on physiological parameters provided in the embodiment of the present application.

[0231] Optionally, the computer instructions are stored in a storage unit.

[0232] Optionally, the storage unit is a storage unit within the chip, such as a register, a cache, etc. The storage unit can also be a storage unit located outside the chip within the terminal, such as a ROM or other types of static storage devices that can store static information and instructions, random RAM, etc. Among them, 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.

[0233] 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.

[0234] It is understood that the memory in the embodiment of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a ROM, a programmable read-only memory (programmable ROM, PROM), an erasable programmable read-only memory (erasable PROM, EPROM), an electrically erasable programmable read-only memory (electrically EPROM, EEPROM) or a flash memory. The volatile memory can be a RAM, which is used as an external cache. There are many different types of RAM, such as static random access memory (static RAM, SRAM), dynamic random access memory (dynamic RAM, DRAM), synchronous dynamic random access memory (synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (doubledata rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (enhanced SDRAM, ESDRAM), synchronous connection dynamic random access memory (synch link DRAM, SLDRAM) and direct memory bus random access memory (direct rambus RAM, DRRAM).

[0235] 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 scheme.

[0236] In the various embodiments of the present application, unless otherwise specified or provided for in any logical conflict, the terms and / or descriptions between the different embodiments are consistent and may be referenced to each other, and the technical features in the different embodiments may be combined to form new embodiments according to their inherent logical relationships.

[0237] Those of ordinary skill 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 to be beyond the scope of this application.

[0238] The method in the embodiment of the present application can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it 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 embodiment 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 a computer can access or a data storage device such as a server that integrates one or more available media.

[0239] Those skilled in the art can 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.

[0240] In the several embodiments provided in the present 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, 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.

[0241] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0242] 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.

[0243] 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, which is stored in a readable storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned readable storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0244] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope 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.

Images