System and terminal device for disease detection based on physiological parameters

Abstract

The system and terminal equipment for disease detection based on physiological parameters, the system comprises: a terminal equipment and a wearable device. Based on the physiological data collected by the wearable device worn by the user daily, the terminal equipment processes the data after obtaining the data, compares the differences between the physiological data of the day and the personal baseline data of the user, monitors the risk of the user's illness of the day every day, obtains the monitoring result of each day and shows it to the user, for example, including: the probability of illness of the day, the risk level of the day, whether there is an abnormality of each physiological parameter of the day, etc. Moreover, combined with the historical monitoring results and historical physiological data, according to the differences of the physiological data of the user before and after the disease, whether there is a long-term sequelae after the recovery of the disease is informed to the user, the long-term trend analysis of the health of the user is realized. Thus, both the current disease risk of the user and the long-term trend analysis of the health of the user are monitored, so as to better provide the health guidance service for the user.

Description

Technical Field

[0001] This application relates to the field of disease surveillance, and more specifically, to a system and terminal device for disease detection based on physiological parameters. Background Technology

[0002] Wearable devices (such as smart bracelets and smartwatches) can integrate 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, which provide the possibility of monitoring users' health.

[0003] Currently, based on user physiological data detected by wearable devices, it is possible to detect whether a user is ill on a given day, thus enabling timely detection of disease risk. Alternatively, after accumulating sufficient user physiological data, historical baseline data can be obtained from various user physiological data. Then, the various physiological data from recent periods (such as the last week or two weeks, or the last month) can be compared with the historical baseline data to identify any differences and thus indicate the changing trends 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, i.e., detect long-term sequelae of users, resulting in a poor user experience. Summary of the Invention

[0005] This application provides a system and terminal device for disease detection based on physiological parameters. The system includes a wearable device and a user-operated terminal device, which can monitor the risk of illness (including respiratory diseases or other illnesses) daily and obtain daily monitoring results. It can also inform users about the possibility of long-term sequelae after disease recovery, enabling long-term health trend analysis. Therefore, it achieves both monitoring the user's current disease risk and analyzing the user's long-term health trends, thus providing better health guidance services and improving the user experience.

[0006] In a first aspect, a terminal device is provided, comprising: a processor; a memory; a display screen; and a computer program stored in the memory. When the computer program is executed by the processor, the terminal device performs the following steps: acquiring a user's physiological data, the user's physiological data including the user's historical physiological data and current day's physiological data; determining the user's personal baseline data based on the historical physiological data, the personal baseline data including statistics of different types of physiological data of the user, the statistics including at least one of: mean, variance, minimum, maximum, quantile, and probability distribution; and determining the user's personal baseline data based on the current day's physiological data and the... Personal baseline data is used to obtain the disease monitoring results for the day, which include: the probability of illness on the day, the risk level on the day, and whether one or more of the physiological parameters on the day are abnormal. Based on historical physiological data, the physiological data on the day, and historical measurement results, long-term sequelae detection results are obtained, which include: whether the user has or does not have long-term sequelae. Historical measurement results include: the probability of illness on each day before the day, the risk level on each day before the day, and whether one or more of the physiological parameters on each day before the day are abnormal. The disease monitoring results and / or long-term sequelae detection results for the day are displayed to the user on the screen.

[0007] The first aspect involves providing terminal devices that collect physiological data from wearable devices worn by users daily. After acquiring this data, the terminal devices process it, comparing the daily physiological data with the user's baseline data to monitor the user's risk of illness daily. Daily monitoring results are then displayed to the user, including the probability of illness that day, the risk level, and whether any abnormalities exist in various physiological parameters, helping users understand their physical condition and improving the user experience. Subsequently, historical monitoring results and physiological data are combined to inform users about potential long-term sequelae after illness, based on the differences in physiological data before and after illness, thus enabling long-term health trend analysis. This achieves both monitoring the user's current disease risk and analyzing long-term health trends, providing better health guidance services.

[0008] For example, the terminal device can be an IoT device such as a smartphone, tablet, laptop, handheld computer, cellular phone, PDA, AR / VR device, or a TV or large-screen device.

[0009] For example, historical physiological data does not include physiological data for the current day. Historical measurement results include the measurement results for each day prior to the current day, but exclude the measurement results for the current day (i.e., the measurement results for the current day). For example, historical measurement results may include: the probability of disease for each day prior to the current day, the risk level for each day prior to the current day, and whether various physiological parameters were abnormal for each day prior to the current day. Optionally, historical measurement results may also include: whether the user has long-term sequelae, etc. "The current day" can be understood as "today" or any day.

[0010] In one possible implementation of the first aspect, the user's personal baseline data is determined based on historical physiological data. This includes: if the historical physiological data does not meet preset conditions, determining the population baseline data corresponding to the user based on the user's personal information, and using the population baseline data as the personal baseline data; or, if the historical physiological data does not meet preset conditions, determining initial personal baseline data based on historical physiological data; determining the population baseline data corresponding to the user based on the user's personal information; and determining the personal baseline data based on the population baseline data and the initial personal baseline data. In this implementation, when the user's historical physiological data is insufficient, the population baseline data corresponding to the user is used as the user's personal baseline data, or an updated personal baseline data is obtained by combining the user's population baseline data and the initial personal baseline data. This eliminates the need for the user to wear the wearable device for an extended period to obtain personal baseline data, thereby improving the stability and accuracy of the personal baseline data. It allows monitoring results to be obtained on the first day the user wears the wearable device, improving the user experience.

[0011] For example, if the duration of historical physiological data collection is less than a certain threshold (e.g., less than six months); or if the amount of data included in the historical physiological data is less than a preset threshold, such as the number of data points including heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, or skin temperature being less than a preset threshold, then the historical physiological data does not meet the preset conditions.

[0012] In one possible implementation of the first aspect, the disease monitoring result for the day is obtained based on the day's physiological data and the individual's baseline data. This includes: calibrating the day's physiological data using the individual's baseline data to obtain calibrated day's physiological data; and obtaining the day's disease monitoring result based on the calibrated day's physiological data, the day's physiological data before calibration, and the individual's baseline data. In this implementation, calibrating the day's physiological data using the individual's baseline data can improve the accuracy of the day's respiratory disease monitoring results, allowing the day's respiratory disease monitoring results to better reflect the user's health status on that day.

[0013] For example, the calibration method may include: calculating the difference or ratio between a certain type of physiological data on the day and the individual's baseline data of the same type; normalizing the user's physiological data on the day using the individual's baseline data; estimating probability distribution parameters based on the individual's baseline data; and training at least one of the following anomaly detection models or algorithms using the user's historical physiological data (i.e., individual baseline data).

[0014] In one possible implementation of the first aspect, long-term sequelae detection results are obtained based on historical physiological data, current day's physiological data, and historical measurement results; or, based on historical physiological data, current day's physiological data, historical measurement results, and current day's respiratory disease monitoring results; or, based on historical physiological data and historical measurement results. This includes: determining the start and end times of the user's illness based on historical measurement results; determining the baseline data corresponding to various physiological data of the user before and after the illness; and obtaining long-term sequelae detection results based on the baseline data corresponding to various physiological data of the user before and after the illness. In this implementation, determining the sequelae detection results based on the baseline data corresponding to various physiological data of the user before and after the illness can improve the accuracy of long-term sequelae detection results and enhance the user experience.

[0015] For example, a comparison can be made between the baseline of a certain physiological parameter after recovery and the baseline of the same physiological parameter before the illness to determine 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 instance, if the difference or ratio between the baseline of a certain physiological parameter before and after the illness is greater than a certain threshold, it is considered to have a significant difference; otherwise, it is considered not to have a significant difference, i.e., there is no risk of long-term sequelae.

[0016] In one possible implementation of the first aspect, if the probability of contracting the disease on a given day is less than or equal to a preset first threshold, then the risk level for that day is low risk; if the probability of contracting the disease on a given day is greater than the preset first threshold and less than or equal to a preset second threshold, then the risk level for that day is medium risk; if the probability of contracting the disease on a given day is greater than the preset second threshold, then the risk level for that day is high risk, where the second threshold is greater than the first threshold. This implementation allows for convenient and rapid determination of the risk level for a given day, improving the user experience.

[0017] In one possible implementation of the first aspect, the terminal device further performs the following steps: determining the risk level for the day based on the probability of illness on that day and the user's physiological data on that day. In this implementation, the probability of illness on that day is calibrated based on the user's physiological data for that day, ensuring that the risk level for that day is consistent with the user's actual symptoms on that day, thereby improving the accuracy of the predicted risk level for that day and enhancing the user experience.

[0018] For example, if the probability of contracting the disease on a given day is less than or equal to a preset first threshold (e.g., 0.5), the risk level for that day is determined to be low risk; if the probability of contracting the disease on a given day is greater than the preset first threshold, the risk level for that day is determined to be medium risk or high risk. The specific risk level (medium or high risk) needs to be determined based on the user's physiological data for that day. For instance, it can be set that if the heart rate is greater than a certain threshold and the respiratory rate is greater than another threshold, the risk level for that day is determined to be high risk; if the heart rate or respiratory rate does not meet the requirements, the risk level for that day is determined to be medium risk.

[0019] In one possible implementation of the first aspect, the terminal device further performs the following steps: when the user is at risk of illness, it determines the type of illness the user may have by combining non-physiological information and displays it to the user. This non-physiological information includes at least one of the following: whether there is an infectious disease outbreak in the user's area, whether there is an infectious disease outbreak in the current season, and whether the user has an underlying disease. In this implementation, when the user is at risk of illness on a given day, the non-physiological information is combined to determine the possible type of illness the user may have and displays it to the user. This notifies the user of the possible type of illness, further improving the efficiency and accuracy of alerting the user to potential illnesses, and can help the user choose a treatment plan, thus improving the user experience.

[0020] In one possible implementation of the first aspect, the terminal device further performs the following steps: displaying health advice to the user on the display screen based on the 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 based on the risk level of the day. This implementation displays health advice suitable for the user's current health status and can show the user corresponding solutions, further improving the user experience.

[0021] For example, health advice can include dietary recommendations, exercise recommendations, and treatment recommendations. For instance, treatment recommendations might suggest that the user needs to take certain medications or seek medical attention promptly. Dietary recommendations might include avoiding spicy and irritating foods. Exercise recommendations might include reducing outdoor activities and getting more rest.

[0022] For example, treatment recommendations could suggest that users take commonly used medications effective for their potential illness, or seek medical attention promptly. Dietary recommendations could include avoiding foods that might worsen their potential illness, or consuming foods that might alleviate or reduce their symptoms. Exercise recommendations could include exercises that might help alleviate or recover from a potential illness, or exercises that might worsen it.

[0023] In one 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 one possible implementation of the first aspect, the terminal device further includes a physiological data acquisition device, which also performs the following steps: acquiring the user's physiological data using the physiological data acquisition device. For example, it may include sensors for measuring physiological parameters such as heart rate, heart rate variability, blood oxygen, respiratory rate, body temperature, sleep, and exercise, and can collect various physiological parameters of the user.

[0025] In one possible implementation of the first aspect, the terminal device and the user's wearable device communicate wirelessly, and the terminal device further performs the following steps: receiving the user's physiological data transmitted by the wearable device; and acquiring the user's physiological data using a physiological data acquisition device on the wearable device.

[0026] Secondly, a system for disease detection based on physiological parameters is provided. This system includes: a terminal device and a wearable device as provided in the first aspect or any possible implementation of the first aspect. The terminal device and the user's wearable device communicate wirelessly. The wearable device includes a physiological data acquisition device, which acquires the user's physiological data and transmits the user's physiological data to the terminal device.

[0027] The second aspect provides a system for disease detection based on physiological parameters. Based on physiological data collected by wearable devices worn by users daily (e.g., heart rate, heart rate variability, blood oxygen saturation, respiratory rate, body temperature, sleep, exercise, etc., or a combination thereof), the terminal device processes this data to monitor the risk of illness (including respiratory diseases or other illnesses) daily, obtaining daily monitoring results (also called detection results). These results include, for example, the probability of illness on that day, the risk level for that day, and whether there are any abnormalities in various physiological parameters for that day. Subsequently, by combining historical monitoring results and historical physiological data, and based on the differences in the user's physiological data before and after illness, the system informs the user about the possibility of long-term sequelae after disease recovery, achieving long-term trend analysis of the user's health. This system not only monitors the user's current disease risk but also analyzes the user's long-term health trends, thereby providing better health guidance services and improving the user experience.

[0028] Thirdly, a communication device is provided, which includes units for performing the steps executed by a terminal device in any possible implementation of the first aspect or any aspect thereof.

[0029] Fourthly, a terminal device is provided, which includes the communication device provided in the third aspect above.

[0030] Fifthly, a computer program product is provided, comprising a computer program that, when executed by a processor, causes a terminal device to perform the steps performed in the first aspect or any possible implementation thereof.

[0031] In a sixth aspect, a computer-readable storage medium is provided, which stores a computer program that, when executed, causes a terminal device to perform the steps performed in the first aspect or any possible implementation thereof.

[0032] In a seventh aspect, a chip is provided, the chip comprising: a processor for calling and running a computer program from a memory, causing a device having the chip mounted to perform the steps performed by a terminal device in the first aspect or any possible implementation thereof. Attached Figure Description

[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 a disease detection process based on physiological parameter detection provided in an embodiment of this application.

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

[0036] Figure 4 This is a schematic block diagram of an example communication device structure provided in the embodiments of this application.

[0037] Figure 5 This is a schematic block diagram of an example communication device structure provided in the embodiments of this application.

[0038] Figure 6 This is a schematic block diagram of an example chip system structure provided in an embodiment of this application. Detailed Implementation

[0039] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0040] The terminology used in the following embodiments is for the purpose of describing specific embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to also include expressions such as “one or more,” unless the context clearly indicates otherwise. It should also be understood that in the embodiments of this application, “one or more” means one or more (including two); “and / or” describes the relationship between related objects, indicating that three relationships may exist; for example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character “ / ” generally indicates that the preceding and following related objects are in an “or” relationship.

[0041] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0042] The "multiple" mentioned in the embodiments of this application refers to two or more. It should be noted that in the description of the embodiments of this application, terms such as "first" and "second" are used only for the purpose of distinguishing descriptions and should not be construed as indicating or implying relative importance, nor should they be construed as indicating or implying order.

[0043] Furthermore, various aspects or features of this application can be implemented as methods, apparatus, or articles of manufacture using standard programming and / or engineering techniques. The term "article of manufacture" as used in embodiments of this application encompasses a computer program accessible from any computer-readable device, carrier, or medium. For example, computer-readable media may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tapes), optical discs (e.g., compact discs (CDs), digital versatile discs (DVDs), etc.), smart cards, and flash memory devices (e.g., erasable programmable read-only memory (EPROMs), cards, sticks, or key drives, etc.). Additionally, 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.

[0044] Various aspects or features of this application can be implemented as methods, apparatus, or articles of manufacture using standard programming and / or engineering techniques. As used in embodiments of this application, the term "article of manufacture" encompasses a computer program accessible from any computer-readable device, carrier, or medium. For example, computer-readable media may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tapes), optical discs (e.g., compact discs (CDs), digital versatile discs (DVDs), etc.), smart cards, and flash memory devices (e.g., erasable programmable read-only memory (EPROMs), cards, sticks, or key drives, etc.). Additionally, 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 broadly classified into two categories: acute and chronic. Acute respiratory diseases include influenza, lung infections, etc., while chronic respiratory diseases include chronic obstructive pulmonary disease (COPD).

[0046] Prevention and treatment methods differ for different types of respiratory diseases. For example, lung infections are characterized by high morbidity and mortality, affecting 450 million people annually and ranking fourth among the top ten causes of death worldwide. Early detection and treatment of lung infections are crucial to significantly reduce mortality and medical costs. COPD, as a chronic disease, requires a combination of medication, exercise, and long-term monitoring of lung function to prevent disease progression.

[0047] Monitoring for the sequelae of respiratory illnesses is equally important. For example, according to estimates by the World Health Organization, approximately 10-20% of patients with acute COVID-19 infection will experience prolonged COVID-19 symptoms, such as fatigue, shortness of breath, chest pain, and cough, for weeks to months after recovery. Long-term monitoring of patients' recovery can help doctors effectively assess the degree of recovery and develop treatment plans.

[0048] With technological advancements, wearable devices (such as smart bracelets and smartwatches) have acquired the ability to detect physiological parameters such as blood oxygen saturation, respiratory rate, heart rate variability, and body temperature. When a person experiences respiratory illnesses, these physiological data change. For example, pneumonia may cause an increase in heart rate, respiratory rate, and body temperature, while blood oxygen saturation may decrease. Similarly, COPD may cause an increase in respiratory rate and a decrease in blood oxygen saturation, thus providing a possibility for monitoring respiratory health.

[0049] To meet the diverse 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] Currently, technologies for monitoring respiratory diseases exist. These technologies collect various types of physiological data (such as heart rate, heart rate variability, respiratory rate, blood oxygen saturation, body temperature, and skin temperature) from a user over a recent period (e.g., within a day) using wearable devices. By establishing a mapping relationship between physiological data and respiratory diseases, it determines whether the user currently (e.g., on that day) has a respiratory disease, such as influenza, pneumonia, or COPD. Some technologies also require collecting physiological data from a time when the user did not have a respiratory disease as baseline data. By detecting differences between the current (e.g., on that day) physiological data and the baseline data, it determines whether the user had a respiratory disease at that time. Baseline data can be understood as statistical measures of various physiological data collected by the user over a long period (e.g., several months, a year, or longer) while wearing wearable devices. For example, physiological data may include different types of physiological data such as heart rate, heart rate variability, respiratory rate, body temperature, sleep, and exercise. Statistical measures may include, for example, the mean, variance, minimum and maximum values, and quantiles.

[0051] Using the above solution will have the following problems:

[0052] First, techniques that do not require baseline data and rely solely on the mapping between current physiological data and respiratory diseases to determine whether a user has a respiratory illness may lead to inaccurate results. For example, if a user has other underlying conditions that cause abnormal physiological data, the above approach may frequently result in false alarms for such users; or, if a user is normally very healthy but experiences mild symptoms after becoming ill, with minimal differences in physiological data between the illness and healthy states, and the abnormalities are not obvious, the technology may easily miss cases.

[0053] Secondly, the technology requires baseline data. Users need to wear the wearable device for a relatively long period (e.g., several months, a year, or longer) to collect various physiological data during this time to obtain baseline data. Only after obtaining baseline data can it be determined whether the user currently has a respiratory disease, which makes it inconvenient for users. For example, monitoring results may not be available on the first day a user wears the wearable device.

[0054] Third, the above-mentioned solutions can only determine whether a user has a respiratory disease at present (e.g., on the same day), that is, they can only detect the risk of acute respiratory diseases in a timely manner, but cannot monitor the long-term respiratory health trend changes of users (i.e., they cannot conduct long-term respiratory health trend analysis), nor can they detect the sequelae of respiratory diseases.

[0055] Fourth, respiratory diseases such as influenza, COVID-19, and pneumonia can all cause increases in heart rate, respiratory rate, and body temperature, as well as decreases in blood oxygen levels. It is difficult to determine the specific disease based on physiological data alone.

[0056] Besides the solutions mentioned above, other approaches exist. For example, users can wear wearable devices for extended periods (e.g., several months, a year, or longer) to record their daily physiological data. After accumulating sufficient data, historical baseline data is obtained from various physiological data points. This data is then compared with the historical baseline data over a more recent period (e.g., the last week, two weeks, or the last month) to identify trends in the user's physiological data. For instance, it might indicate that the user's heart rate has significantly increased in the last week compared to the past year. In other words, this technology uses a timeline to compare the changes or differences between the user's recent physiological data and historical baseline data.

[0057] Using the above approach, it is necessary to accumulate users' physiological data over a relatively long period before it is possible to statistically analyze the long-term trends of these data. If a user suffers from an acute respiratory illness, this technology cannot promptly alert the user to abnormal physiological data; for example, it cannot determine whether the user currently (e.g., on that day) has a respiratory illness. Furthermore, this technology can only statistically analyze simple trends in various physiological data, such as increases, decreases, or no change; it cannot monitor long-term respiratory health trends (i.e., it cannot perform long-term respiratory health trend analysis) or detect the sequelae of respiratory diseases.

[0058] In summary, the existing technology mainly has the following technical problems:

[0059] First, current technology cannot both 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, introducing baseline data can prevent users from taking measurements if historical baseline data is unavailable, but not using baseline data can lead to decreased accuracy.

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

[0062] In view of this, this application provides a system and terminal device for disease detection based on physiological parameters. The system includes a wearable device and a user-used terminal device. Based on physiological data collected by the wearable device worn daily by the user (e.g., one or more combinations of heart rate, heart rate variability, blood oxygen saturation, respiratory rate, body temperature, sleep, and exercise), the terminal device processes this data to monitor the risk of illness (including respiratory diseases or other diseases) daily, obtaining daily monitoring results (also called detection results), such as: the probability of illness on that day, the risk level on that day, and whether there are any abnormalities in various physiological parameters on that day. Subsequently, by combining historical monitoring results and historical physiological data, and based on the differences in the user's physiological data before and after illness, the system informs the user about the possibility of long-term sequelae after disease recovery, achieving long-term trend analysis of the user's health. This achieves both monitoring the user's current disease risk and conducting long-term trend analysis of the user's health, thereby providing better health guidance services and improving the user experience.

[0063] The following examples illustrate the system for disease detection based on physiological parameters provided in this application.

[0064] refer to Figure 1 , Figure 1 The diagram shown is a schematic architecture of a system for disease detection based on physiological parameter detection, as provided in an embodiment of this application. Figure 1As shown, the system includes: a user using a wearable device 112 and a terminal device 111, in Figure 1 In the example shown, the wearable device 112 used by the user is a smartwatch, and the terminal device 111 is a smartphone. In this application, the wearable device 112 is equipped with various sensors, such as sensors for measuring physiological parameters like heart rate, heart rate variability, blood oxygen saturation, respiratory rate, body temperature, sleep, and exercise. The wearable device 112 can collect various physiological parameters from the user. The smartphone 111 can obtain these physiological parameters collected by the wearable device 112, calculate daily monitoring results and long-term sequelae data based on these parameters, and display them on the smartphone 111 for the user to view.

[0065] For example, in Figure 1 In the example shown, wearable device 112 and terminal device 111 can communicate via wireless communication technologies such as Bluetooth (BT), Wireless-Fidelity (WiFi), and Near Field Communication (NFC). In other words, the terminal device and wearable device can form a network (i.e., a network) according to certain communication protocols and networking strategies, enabling them to communicate with each other.

[0066] It should be understood that in other implementations of this application, if the wearable device supports computing functions, after the wearable device 112 collects various physiological parameters of the user, the wearable device 112 itself also calculates the daily monitoring results and long-term sequelae data based on these physiological parameters. These calculation results are then sent to the smartphone 111 for display 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 in this application.

[0067] Of course, if the wearable device supports both computing and display functions, such as having a large display interface, then after the wearable device 112 collects various physiological parameters from the user, it can also calculate daily monitoring results and long-term sequelae data based on these parameters. These calculation results are then displayed on the interface for the user to view. In other words, the wearable device can also implement the technical solution provided in this application independently.

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

[0069] In this application embodiment, the terminal device may include: a smart TV, tablet computer, netbook, personal digital assistant (PDA), handheld computer communication device, handheld computing device, and other portable electronic devices. Wearable devices may further include: smart bracelets, wearable wrist devices, smart glasses, augmented reality (AR) / virtual reality (VR) devices, etc. Exemplary embodiments of the terminal device or wearable device include, but are not limited to, carrying... Terminal devices using Harmony or other operating systems.

[0070] It should be understood that, in the embodiments of this application, wearable devices can also be a general term for devices that utilize wearable technology to intelligently design and develop everyday wearables, such as glasses, gloves, watches, clothing, and shoes. Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not merely hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly defined, wearable smart devices include those with comprehensive functions, large size, and the ability to achieve complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses, as well as those focused on a specific type of application function that require the use of other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0071] In other words, terminal devices and wearable devices can form a network (i.e., networking) according to certain communication protocols and networking strategies, enabling terminal devices and wearable devices to communicate with each other.

[0072] In the examples below, a system consisting of a wearable device and a terminal device will be used to illustrate the technical solutions provided in the embodiments of this application. However, it should be understood that if the wearable device supports computing and display functions, the wearable device can also implement the technical solutions provided in this application on its own.

[0073] It should also be understood that the following description will use respiratory diseases as an example, but the technical solutions provided in this application can also be applied to other diseases, such as cardiovascular diseases, as long as the symptoms of the disease can be detected by the patient's physiological data. The embodiments of this application are not limited here.

[0074] Figure 2 The diagram shown illustrates the overall workflow (i.e., detection method) for disease detection using the system provided in this application. The details will be explained below. Figure 2 The process shown is as follows: Figure 2 The process shown includes S201 to S208.

[0075] exist Figure 2 In the example shown, wearable devices and terminal devices can communicate using wireless communication technologies such as Bluetooth, WiFi, and NFC.

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

[0077] Optionally, in some possible implementations, the wearable device can continuously or periodically collect the user's physiological data, such as collecting physiological data for 1 minute every 10 minutes or collecting physiological data once. Exemplary examples of physiological data may include one or more of the following: heart rate, heart rate variability, respiratory rate, blood oxygen saturation, body temperature, skin temperature, sleep (sleep onset time, wake-up time, sleep stage segmentation, etc.), and exercise (exercise type, step length, step speed, heart rate during exercise, heart rate after exercise, etc.). This application does not limit the specific type of physiological data.

[0078] It should be understood that wearable devices can collect users' physiological data over long periods, such as for several months or even longer. Optionally, the collected user physiological data can be stored at a granular level of "daily".

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

[0080] Wearable devices transmit collected physiological data to terminal devices via a wireless network.

[0081] For example, the terminal device can be a mobile phone or similar device used by the user. This application does not limit the specific type of terminal device.

[0082] It should be understood that the physiological data collected by wearable devices includes the user's physiological data for the current day (also referred to as current day physiological data) and physiological data from previous days. For example, in the embodiments of this application, physiological data from previous days 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 can also determine the user's personal baseline data based on the user's historical physiological data and the physiological data of the current day. The following will illustrate this by taking the example of the terminal device determining the user's personal baseline data based on the user's historical physiological data.

[0085] In this embodiment of the application, personal baseline data can be understood as: statistical measures of various (or specific) physiological data calculated using physiological data collected by the user over a long period (e.g., several months, a year, or longer) while wearing a wearable device. For example, physiological data may include different types or categories of physiological data such as heart rate, heart rate variability, respiratory rate, body temperature, sleep, and exercise. Statistical measures may include, for example, the mean, variance, minimum value, maximum value, or quantiles.

[0086] In some possible implementations, if historical physiological data is insufficient or does not meet the requirements—for example, the duration of historical physiological data collection is less than a certain threshold (e.g., less than six months); or, the amount of data included in historical physiological data is less than a preset threshold—for example, the number of data points such as heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, or skin temperature is less than a preset threshold—then the terminal device can select baseline data of the corresponding population to replace the user's personal baseline data based on the user's personal information such as gender, age, height, weight, and region. That is, the baseline data of the population with the same personal information under statistical big data is used as the user's personal baseline data.

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

[0088] Using the methods described above, when a user's historical physiological data is insufficient, population baseline data can be used to replace personal baseline data, or personal baseline data can be determined by combining the user's corresponding population baseline data and the initial personal baseline. This eliminates the need for users to wear wearable devices for extended periods to obtain personal baseline data, allowing users to obtain personal baseline data and monitoring results on the first day they wear the wearable device, thus improving the user experience.

[0089] S204, The terminal device monitors respiratory diseases on the same day based on the user's personal baseline data and the user's physiological data for that day.

[0090] In some possible implementations, the terminal device can use personal baseline data to calibrate the user's physiological data for the day. The personal baseline data, the calibrated physiological data for the day, and the uncalibrated physiological data for the day are then input into a machine learning model on the terminal device. The machine learning model monitors the respiratory disease risk for the day, obtaining the respiratory disease monitoring results for that day. For example, the respiratory disease monitoring results (measurement results) output by the machine learning model for the day may include: the probability of illness on that day, the risk level for that day, and whether one or more of the various physiological parameters are abnormal on that day.

[0091] For example, S204 can be calculated and output in terms of "daily".

[0092] The above technical solution enables the monitoring of a user's current (day's) disease risk, achieving real-time detection of respiratory diseases, improving the efficiency and accuracy of alerting users to diseases, and enhancing the user experience.

[0093] S205, the terminal device calculates baseline data corresponding to the physiological data before, during and after the onset of illness, based on all of the user's physiological data and historical measurement results.

[0094] In S205, all physiological data may include historical physiological data and current day's physiological data.

[0095] For example, in S205, historical measurement results may include one or more of the following: the probability of disease on each historical day (excluding the current day), the risk level on each historical day (excluding the current day), and whether various physiological parameters were abnormal on each historical day (excluding the current day).

[0096] Optionally, as another possible implementation, S205 can also be replaced by: the terminal device calculating baseline data corresponding to the physiological data before, during, and after the onset of illness, based on all of 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 for that day.

[0097] The terminal device can mark the start and end times of the last respiratory illness based on historical measurement results, and then statistically obtain the corresponding physiological baseline data before, during, and after the illness. For example, the physiological baseline data for heart rate, respiratory rate, blood oxygen, and sleep time before, during, and after the illness can be the mean values ​​of heart rate, respiratory rate, blood oxygen, and sleep time, respectively.

[0098] S206, the terminal device determines whether the user has long-term sequelae based on the baseline data corresponding to various physiological data before and after the onset of the disease.

[0099] For example, the terminal device can compare the baseline data of a certain physiological parameter (or parameters) after recovery with the baseline data of the same physiological parameter (or parameters) 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 between the baseline of a certain physiological parameter before the illness and after recovery is greater than a certain threshold, it is considered to have a significant difference; otherwise, it is considered not to have a significant difference, that is, there is no risk of long-term sequelae.

[0100] S206 enables long-term trend analysis of user health, specifically detecting whether a user suffers from long-term sequelae. It allows users to understand long-term trend changes caused by respiratory diseases, improving the user experience.

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

[0102] For example, the respiratory disease monitoring results for the day can be presented to the user through the probability of illness on that day, the risk level on that day, and whether various physiological parameters are abnormal on that day. The probability of illness on that day can be displayed to the user as a probability or percentage. The risk level on that day can be displayed to the user in the form of text such as "low risk," "medium risk," or "high risk," indicating the user's risk level for the disease that day. The results regarding the possibility of long-term sequelae can be displayed through long-term health trend analysis results (e.g., comparison results of baseline data corresponding to various physiological data before and after illness), or it can be displayed to the user in the form of text such as "risk of sequelae exists" or "no risk of sequelae exists." This application embodiment does not limit the form in which the terminal device displays information to the user on the interface.

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

[0104] S208: If a user is at risk of contracting an illness on a given day, the terminal device combines non-physiological information to determine the type of illness the user may have and displays it to the user.

[0105] When a terminal device determines that a user is at risk of illness on a given day based on their physiological parameters, it's important to note that respiratory illnesses such as COVID-19, H1N1, and pneumonia can cause increases in physiological parameters like heart rate, respiratory rate, and body temperature. At the physiological data level, it may not be possible to definitively identify the specific disease. To clarify the disease type as much as possible, non-physiological information can be used to label the disease type. Therefore, in this embodiment, when a user is at risk of illness on a given day—for example, if the probability of illness exceeds a preset threshold, or if the risk level is medium or high, or if most physiological parameters are abnormal—it can be determined that the user is at risk of illness that day. In this case, the terminal device can combine non-physiological information, such as questionnaires completed by the user, the season, the weather, local epidemiological information, or other disease detection technologies, to inform the user of the possible disease type.

[0106] For example, as a possible implementation, when it is determined that a user is at risk of contracting an infectious disease on a given day, the terminal device can determine whether there is an outbreak of infectious diseases in the user's location based on the location entered by the user and information from the Internet. If there is an outbreak of infectious diseases in the area, the device can prompt the user on the interface that they may be infected with the infectious disease.

[0107] For example, as another possible implementation, if it is determined that a user is at risk of getting sick on a given day, and if the season in question is the peak season for influenza, then the user may be prompted on the interface that they may have the flu.

[0108] For example, information can be collected through questionnaires or other detection technologies to determine if a user has any underlying medical conditions. If the user has an underlying condition, the terminal device can display a message indicating that the user may have complications or an exacerbation of that underlying condition. For instance, if a questionnaire or COPD detection technology reveals that a user has chronic obstructive pulmonary disease (COPD), the device may indicate that the user may experience an acute exacerbation of COPD.

[0109] Of course, in other implementations of this application, other non-physiological factors, such as the user's diet and lifestyle, can be combined to inform the user of possible disease types when the user is at risk of illness on a given day. This application does not limit the specific type or form of non-physiological factors.

[0110] Through the above-mentioned S208, the user can be notified of possible disease types, further improving the efficiency and accuracy of prompting the user about possible diseases, and helping the user to choose a treatment plan, thus improving the user experience.

[0111] Optional, as one possible implementation, Figure 2The illustrated process may further include: if the risk level for the day is determined to be "medium risk" or "high risk," the terminal device or wearable device may also display health advice to the user on the screen (e.g., through text or other means). For example, this health advice may include dietary suggestions, exercise suggestions, and treatment suggestions. For instance, treatment suggestions may suggest that the user needs to take some medication or seek medical attention promptly. Dietary suggestions may include avoiding spicy and irritating foods. Exercise suggestions may include reducing outdoor activities and getting more rest. This embodiment does not limit the specific content or form of the health advice. In this way, health advice suitable for the user's current health status can be displayed, and corresponding solutions can be shown, further improving the user experience.

[0112] Optionally, as another possible implementation, Figure 2 The illustrated process may further include: if the risk level for the day is determined to be "medium risk" or "high risk," the terminal device or wearable device may, based on the user's potential illness, provide health advice on the display interface (e.g., through text or other means). For example, this health advice may include dietary recommendations, exercise recommendations, and treatment recommendations. For instance, treatment recommendations may suggest taking commonly used medications effective for the potential illness, or seeking timely medical attention. Dietary recommendations may include avoiding foods that aggravate the potential illness, or consuming foods that alleviate or reduce the potential illness. Exercise recommendations may include exercises that alleviate or help recover from the potential illness, or exercises that aggravate the potential illness. This application embodiment does not limit the specific content or form of the health advice. In this way, health advice suitable for the user's current health status is displayed, and corresponding solutions are shown, further improving the user experience.

[0113] It should be understood that, Figure 2 S201 to S208 shown can also be performed by a wearable device. Alternatively, Figure 2 S203 to S206 shown can also be performed by wearable devices.

[0114] Figure 3 The diagram shown illustrates another example of the overall workflow (i.e., detection method) for disease detection using the system provided in this application. The details will be explained below. Figure 3 The process shown is in Figure 3 In the example shown, the user's terminal device will be illustrated using a smartphone (referred to as a mobile phone).

[0115] S301 allows users to log in to their accounts on mobile phones and wearable devices, and bind their accounts to the applications on their mobile phones and wearable devices.

[0116] For example, after the mobile phone and wearable device are paired, the user can log in to their account on both the mobile phone and the wearable device. After successful login, the user can then bind the account on the application on both the mobile phone and the wearable device. After successful account binding, the mobile phone and the wearable device can automatically execute the steps performed by the mobile phone and the wearable device respectively in the various embodiments of this application.

[0117] As one possible implementation, the application could be a newly developed application. When a wearable device runs the application, it can perform the steps executed by the wearable device in the various embodiments of this application. When a mobile phone runs the application, it can perform the steps executed by the mobile phone in the various embodiments of this application. Of course, in other implementations of this application, the application could also be an existing application, but this application integrates new functions, thereby enabling the wearable device to perform the steps executed by the wearable device in the various embodiments of this application, and the mobile phone to perform the steps executed by the mobile phone in the various embodiments of this application, when running the application.

[0118] S302, wearable devices periodically collect users' physiological data.

[0119] Wearable devices are equipped with various sensors, such as sensors for measuring physiological parameters like heart rate, heart rate variability, blood oxygen saturation, respiratory rate, body temperature, sleep, and exercise. Wearable devices can continuously or periodically collect users' physiological data, such as data collected every 10 minutes. Physiological data may include one or more of the following: heart rate, heart rate variability, respiratory rate, blood oxygen saturation, body temperature, skin temperature, sleep (sleep onset time, wake-up time, sleep stage information, etc.), and exercise (exercise type, step length, step speed, heart rate during exercise, heart rate after exercise, etc.).

[0120] It should be understood that wearable devices can collect users' physiological data for a long period of time, for example, 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 for the current day (i.e., the physiological data for that day). Historical physiological data includes physiological data measured each day prior to the current day, but does not include the user's physiological data for the current day.

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

[0123] S303: The mobile phone can acquire historical physiological data, current day's physiological data, and historical measurement results collected by wearable devices.

[0124] For example, wearable devices send collected physiological data to terminal devices via a wireless network.

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

[0126] In some possible implementations, after a user opens the application on their phone, they can obtain all physiological data (i.e., historical and current day's physiological data) and historical measurement results collected by the wearable device from the wearable device or the cloud through certain operations (such as clicking controls on the application, pulling down the interface, etc.). Optionally, after the user authorizes the application, all previously collected physiological data and historical measurement results from the wearable device can also be automatically synchronized to the phone.

[0127] In some possible implementations, the historical measurement results in S303 may include one or more of the following: the probability of disease on each historical day (excluding the current day), the risk level on each historical day (excluding the current day), and whether various physiological parameters were abnormal on each historical day (excluding the current day). In other words, for these indicators, each day can correspond to a measurement result, or these measurement results are in terms of "day" as the time granularity.

[0128] In 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 can be the period from "the time after the user recovered from the illness (e.g., the day the user recovered from the illness)" to (or up to) "today".

[0129] Optionally, the steps corresponding to S303 can also be called "data synchronization".

[0130] It should be understood that the historical physiological data in S303 does not include the physiological data for the current day. Historical measurement results include the measurement results for each day prior to the current day, but exclude the measurement results for the current day (i.e., the measurement results for the current day). For example, historical measurement results may include: the probability of disease for each day prior to the current day, the risk level for each day prior to the current day, and whether various physiological parameters were abnormal for each day prior to the current day. Optionally, historical measurement results may also include: whether the user has long-term sequelae, etc. It should be understood that in this embodiment, "the current day" can be understood as "today" or any day.

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

[0132] Optionally, as another possible implementation, S304 can also be replaced by the phone determining whether the historical physiological data and the current day's physiological data are sufficient or meet the requirements. In other words, the object the phone determines whether the physiological data meets the requirements can be the sum of the historical physiological data and the current day's physiological data, or it can be the historical physiological data itself. The following will use historical physiological data as an example for explanation. The method for determining the sum of historical physiological data and the current day's physiological data is similar, and you can refer to the explanation of historical physiological data. For the sake of brevity, it will not be repeated here.

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

[0134] For example, if the duration of historical physiological data collection is less than a certain threshold, where the cutoff time is the day before the current day (e.g., the duration of historical physiological data collection starts from the day before the current day and ends at the start time of historical physiological data collection (i.e., physiological data from 24 hours prior to the current time), and this duration is less than six months); or, if the amount of data included in the historical physiological data (i.e., historical physiological data from the current day) is less than a preset threshold (e.g., the number of data points such as heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, or skin temperature is less than a preset threshold), then the phone determines that the historical physiological data does not meet the requirements or is insufficient; otherwise, the phone determines that the historical physiological data meets the requirements or is sufficient.

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

[0136] If the phone determines that the historical physiological data is insufficient or does not meet the requirements, one possible approach is for the phone to display a message to the user: "Insufficient historical physiological data to establish personal baseline data; please continue collecting data." In this case, S309 can be executed directly after S304, skipping S305 to S308.

[0137] If the phone determines that the historical physiological data does not meet the requirements or is insufficient, another possible implementation is that the phone continues to execute S305.

[0138] Of course, if the phone determines that the historical physiological data meets the requirements or is sufficient, the phone will continue to execute S305.

[0139] The S305 phone determines an individual's baseline data based on historical physiological data.

[0140] Optionally, as another possible implementation, S305 can also be replaced by the mobile phone determining personal baseline data based on historical physiological data and current day's physiological data. In other words, the data used by the mobile phone to determine personal baseline data can be the sum of historical physiological data and current day's physiological data, or it can be historical physiological data alone. The following will use historical physiological data as an example for explanation.

[0141] If the phone determines that the historical physiological data is insufficient or unavailable (e.g., no historical physiological data is available for the user), one possible approach is to use the user's gender, age, height, weight, region, and other personal information to generate baseline data of a large group of people with similar personal information as the user's personal baseline data. In this case, steps S305 through 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 as follows: The mobile phone can first use the historical physiological data that does not meet the requirements to determine the user's personal baseline data (for distinction, it is called initial personal baseline data). After obtaining the initial personal baseline data, it can also combine the user's corresponding population baseline data and the initial personal baseline data, and use methods such as weighted average of the population baseline data and the initial personal baseline data to determine the 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 initial personal heart rate baseline data obtained from a user's historical physiological data is 60, while the heart rate baseline data of a group with the same personal information is 65. The resulting personal heart rate baseline data (updated personal heart rate baseline data) after merging 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 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] By employing the methods described above, when a user's historical physiological data is insufficient, the baseline data of the user's corresponding population can be used as the user's personal baseline data. Alternatively, an updated personal baseline data can be obtained by combining the user's corresponding population baseline data with the initial personal baseline data. This eliminates the need for users to wear the wearable device for extended periods to obtain personal baseline data, thereby improving the stability and accuracy of the personal baseline data. Monitoring results can be obtained as early as the first day the user wears the wearable device, enhancing the user experience.

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

[0147] After data synchronization is complete, the phone will determine whether there is any physiological data for the day (e.g., the past 24 hours from now). The physiological data for the day includes various types of physiological data, such as: heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, skin temperature, sleep (sleep onset time, wake-up time, sleep stage information, etc.), and exercise (exercise type, step length, step speed, heart rate during exercise, heart rate after exercise, etc.).

[0148] If physiological data is available for the day (i.e., physiological data measured on the day exists) and the physiological data measured on the day meets the requirements or is sufficient. For example, if the amount of data of a certain type (or several types) of physiological data on the day is greater than a preset threshold (e.g., the number of heart rate data points > N, the number of respiratory rate data points > M, and the number of blood oxygen data points > L), then the physiological data for the day is determined to meet the requirements; otherwise, the physiological data for the day is determined not to meet the requirements.

[0149] Of course, in other implementations of this application, the mobile phone can also use other methods to determine whether the physiological data of the day meets the requirements. This application does not limit the specific method by which the mobile phone determines whether the physiological data of the day meets the requirements.

[0150] If the physiological data for the day meets the requirements, then steps S307 to S309 are executed. If the physiological data for the day does not meet the requirements, then S307 is skipped, and steps S308 and S309 are executed directly; or S307 and S308 are skipped, and step S309 is executed directly. Optionally, if the physiological data for the day does not meet the requirements, the phone can also display a message on the interface indicating that there is insufficient data for the day and that disease risk monitoring cannot be performed for that day.

[0151] The S307 mobile phone obtains the day's respiratory disease monitoring results based on the user's daily physiological data, personal baseline data, and historical physiological data.

[0152] In this embodiment, the results of respiratory disease monitoring on the same day (or the measurement results on the same day) may include one or more of the following: the probability of illness on the same day, the risk level on the same day, and whether various physiological parameters are abnormal on the same day. The results of long-term sequelae detection may include: whether the user has long-term sequelae.

[0153] In some possible implementations, the probability of contracting the disease on a given day can be divided into different levels. For example, if the probability of contracting the disease on a given day is less than or equal to a preset first threshold, the risk level for that day is determined to be low risk; if the probability of contracting the disease on a given day is greater than the preset first threshold but less than or equal to a preset second threshold, the risk level for that day is determined to be medium risk; and if the probability of contracting the disease on a given day is greater than the preset second threshold, the risk level for that day is determined to be high risk. The second threshold is greater than the first threshold.

[0154] The S308 mobile phone obtains long-term sequelae detection results based on the user's historical physiological data, current day's physiological data, and historical measurement results; or based on historical physiological data and historical measurement results.

[0155] In some possible implementations, historical measurement results can include: the probability of illness on each day prior to the current day, the risk level on each day prior to the current day, and one or more abnormalities in various physiological parameters on each day prior to the current day. The mobile phone can mark the start and end times of the last respiratory illness based on historical measurement results, and then statistically analyze the corresponding baseline physiological data before, during, and after the illness from historical physiological data, or from historical physiological data and the current day's physiological data. For example, the baseline physiological data for heart rate, respiratory rate, blood oxygen, and sleep time before, during, and after the illness could be the mean values ​​of heart rate, respiratory rate, blood oxygen, and sleep time. By comparing the baseline data corresponding to the physiological data before and after the illness, it can be determined whether the user has long-term sequelae.

[0156] Of course, mobile phones can also be used in conjunction with the respiratory disease monitoring results of the day to determine the long-term sequelae detection results.

[0157] The S309 displays the user's daily respiratory disease monitoring results and / or long-term sequelae detection results on the screen.

[0158] In some possible implementations, if the process includes S301 to S308, the user can be shown the day's respiratory disease monitoring results and long-term sequelae detection results on the phone's display screen.

[0159] Optionally, if the measurement result for the day indicates illness—for example, if the probability of illness on that day exceeds a certain threshold, or if the risk level for that day is medium or high—the phone will further determine which day the user contracted the illness and whether the condition is improving or worsening. For instance, if the user's probability of illness has consistently exceeded a certain threshold for the past N days, it is assumed that the user has been continuously ill for several days, and the phone will then notify the user that they have been continuously ill for N days. Simultaneously, if the user has been continuously ill for several days, the phone will compare the current day's probability of illness with the average probability of illness over the previous few days. If the current day's probability of illness is higher than the average probability of illness over the previous few days, it will suggest that the user's condition may be worsening; conversely, if the current day's probability of illness is lower than the average probability of illness over the previous few days, it will suggest that the user's 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 user's personal baseline is not determined using the population baseline data corresponding to the user, then 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. In S309, the mobile phone displays the historical physiological data and historical measurement results to the user on the display interface, but 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 even if the historical physiological data does not meet the requirements, but the user's personal baseline is determined using the corresponding population baseline data; and if in S306 it is determined that the physiological data for the current day does not meet the requirements, then the process does not include S307, i.e., the process includes: S301 to S306, S308, and S309; ​​or, the process does not include S307 and S308, i.e., 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, but does not display the respiratory disease monitoring results for the current day (i.e., the measurement results for the current day). 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 be omitted.

[0162] The system for disease detection based on physiological parameters provided in this application collects physiological data from wearable devices worn by users daily. The terminal device processes this data, compares the differences between the daily physiological data and baseline data, monitors the user's risk of developing illness daily, and displays daily monitoring results to the user, including: the probability of developing illness that day, the risk level for that day, and whether there are any abnormalities in various physiological parameters, helping users understand their own physical condition and improving user experience. Subsequently, it combines historical monitoring results and historical physiological data, based on the differences in the user's physiological data before and after illness, to inform the user about the possibility of long-term sequelae after disease recovery, achieving long-term trend analysis of the user's health. Thus, it achieves both monitoring the user's current disease risk and conducting long-term trend analysis of the user's health, thereby providing users with better health guidance services.

[0163] It should also be understood that, Figure 3 S301 to S309 shown can also be executed by a wearable device. Alternatively, S304 to S308 can also be executed by a wearable device.

[0164] Optionally, if the phone determines that the user is at risk of becoming ill that day, it can also combine non-physiological information to determine the type of disease the user may have and display it to the user. For a detailed explanation of this, please refer to the above description of S208; for the sake of brevity, it will not be repeated here.

[0165] Optionally, if the phone or wearable device determines that the user has a risk of illness that day (e.g., "medium risk" or "high risk"), the phone or wearable device can also display health advice on the screen based on the risk level and the types of illnesses the user may have. Alternatively, if the phone or wearable device determines that the user has a risk of illness that day (e.g., "medium risk" or "high risk"), the phone or wearable device can display health advice on the screen. For specific details, please refer to the above explanation regarding... Figure 2 For the sake of brevity, the specific details of the relevant parts in the process shown will not be repeated here.

[0166] The specific processes of S307 and S308 above will be explained below with specific examples.

[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 on a given day, and the sequelae detection module is used to determine whether the user has long-term sequelae after recovering from a previous respiratory disease. Since the long-term effects of respiratory diseases on patients vary, and the symptoms and definitions of sequelae differ for different respiratory diseases, as a possible implementation, in this embodiment, long-term sequelae can be understood as: after a patient's recovery, the baseline data of a certain physiological data point or the baseline data of certain physiological data points have not returned to the pre-illness level. For example, if a patient's baseline respiratory rate was 15 before COVID-19 infection, and within a few months after recovery, the average baseline respiratory rate was 17, then the patient's baseline data is considered not to have returned to the pre-illness level.

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

[0169] Optionally, if the phone detects a respiratory illness and the time since the last recovery from the respiratory illness is greater than N days, the sequelae detection module is invoked to determine whether the user has long-term sequelae; if a respiratory illness is detected and the time since the last recovery from the respiratory illness is less than or equal to N days, the determination of whether the user has long-term sequelae is not performed.

[0170] For the respiratory disease monitoring module, namely S307, the data input to this module includes: historical physiological data and current day's physiological data; or, the data input to this module includes: personal baseline data and current day's physiological data, and the output is the respiratory disease monitoring results for the day (i.e., the disease monitoring results for the day), including: the probability of disease on the day, the risk level on the day, and whether one or more of the various physiological parameters on the day are abnormal.

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

[0172] The respiratory disease monitoring module can first determine an individual's baseline data using historical physiological data. For details, please refer to the above explanation of S305. For the sake of brevity, it will not be repeated here.

[0173] Then, the respiratory disease monitoring module uses 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 corresponding types of physiological data of the day.

[0174] For example, calibration methods may include the following:

[0175] The first calibration method involves calculating the difference or ratio between a specific type of physiological data for the day and the individual's baseline data for the same type. For example, calculating the difference or ratio between the mean of all heart rates for the day and the mean of all historical heart rates (i.e., the individual's baseline heart rate data).

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

[0177] For example, minimum or maximum normalization: calculate the minimum and maximum values ​​of all historical heart rate data of a user (i.e., personal heart rate baseline data). The average heart rate of a user on a given day can be obtained by calculating the minimum or maximum normalization results based on the minimum and maximum values ​​of all historical heart rate data of the user.

[0178] For example, Z-score normalization: calculate the mean and variance of all historical heart rate data of a user (i.e., personal heart rate baseline data). The average heart rate of a user on a given day can be obtained by calculating the Z-score normalization result based on the mean and variance of all historical heart rate data of the user.

[0179] The third calibration method involves estimating probability distribution parameters based on individual baseline data. For example, a Gaussian probability distribution can be used to calculate the probability corresponding to a certain physiological data point for a given day (a higher probability indicates a greater consistency between the physiological data and the baseline distribution).

[0180] The fourth calibration method is to train an anomaly detection model or algorithm using the user's historical physiological data (i.e., personal baseline data). For example, the model or algorithm may include: a single-class support vector machine (SVM) model or algorithm, an isolated forest model or algorithm, etc. The trained model or algorithm is used to calculate the probability of anomalies in the user's physiological parameters on that day (the higher the probability of anomalies, the more abnormal the physiological data).

[0181] It should be understood that the above-described calibration methods are merely exemplary and should not impose any limitations on the calibration of various corresponding types of physiological data for the day using baseline data in the embodiments of this application. In other implementations of this application, other calibration methods can also be used to calibrate various corresponding types of physiological data for the day using baseline data. This application embodiment does not impose any limitations on these methods.

[0182] After calibrating the day's physiological data using personal baseline data, the respiratory disease monitoring module uses a combination of these three data points—personal baseline data, uncalibrated daily physiological data, and calibrated daily physiological data—and a machine learning model to predict the probability of having a respiratory disease that day, thus obtaining the probability of illness for that day. By calibrating the daily physiological data using personal baseline data, the accuracy of the day's respiratory disease monitoring results can be improved, allowing the results to better reflect the user's health status for that day.

[0183] Optionally, as a possible implementation, one or more thresholds can be set to classify the probability of contracting the disease on a given day into different risk levels. For example, if the probability of contracting the disease on a given day is less than or equal to a preset first threshold, the risk level for that day is determined to be low risk; if the probability of contracting the disease on a given day is greater than the preset first threshold but less than or equal to a preset second threshold, the risk level for that day is determined to be medium risk; and if the probability of contracting the disease on a given day is greater than the preset second threshold, the risk level for that day is determined to be high risk. The second threshold is greater than the first threshold. This allows for convenient and quick determination of the risk level for the day, improving the user experience.

[0184] Optionally, as another possible implementation, the risk level for the day can be determined by combining the probability of illness on that day with the user's physiological data. For example, if the probability of illness on that day is less than or equal to a preset first threshold (e.g., 0.5), the risk level for that day is determined to be low risk; if the probability of illness on that day is greater than the preset first threshold, the risk level for that day is determined to be medium risk or high risk, or at least medium risk. The specific level of medium or high risk needs to be determined based on the user's physiological data for that day. For example, it can be set that if the heart rate is greater than a certain threshold and the respiratory rate is greater than another threshold, the risk level for that day is determined to be high risk; if the heart rate or respiratory rate does not meet the requirements, the risk level for that day is determined to be medium risk. Calibrating the probability of illness on that day based on the user's physiological data ensures that the risk level for that day is consistent with the user's actual symptoms, improving the accuracy of the predicted risk level and enhancing the user experience.

[0185] It should be understood that the above example of combining the probability of illness on a given day with the user's physiological data to determine the risk level for that day is merely exemplary and should not impose any limitations on the embodiments of this application. In other implementations of this application, other calculation methods or preset rules can also be used to combine the probability of illness on a given day with the user's physiological data to determine the risk level for that day. The embodiments of this application are not limited here.

[0186] In this embodiment of the application, in order to mark which physiological data are abnormal when presenting results to the user, the respiratory disease monitoring module can determine which physiological data are abnormal based on the calibrated physiological data of the user on that day, and mark which physiological parameters are abnormal and which are not when the results are displayed to the user on the mobile phone, so as to help the user understand his or her own physical condition and improve the user experience.

[0187] For example, in the process of determining which physiological data is abnormal based on the calibrated user's daily physiological data, the respiratory disease monitoring module uses different methods depending on the calibration method. For instance, for the calibration method that calculates differences or ratios (the first calibration method), if the difference or ratio corresponding to a certain physiological data point of the user's daily data after calibration is greater than a certain threshold, then that physiological data point is abnormal; for the probability distribution calibration method (the third calibration method), if the probability corresponding to a certain physiological data point of the user's daily data after calibration is less than a certain threshold, then that physiological data point is abnormal; for the anomaly detection calibration method (the fourth calibration method), if the abnormal probability corresponding to a certain physiological data point of the user's daily data after calibration is greater than a certain threshold, then that physiological data point is abnormal.

[0188] It should be understood that the above-described method for determining which physiological data of a user are abnormal on a given day is merely exemplary and should not impose any limitations on the embodiments of this application. Other implementations of this application may also utilize other methods to determine which physiological data of a user are abnormal on a given day, and these embodiments are not limited thereto.

[0189] By using the methods described above, the accuracy of the respiratory disease monitoring results on that day can be guaranteed, allowing the results to better reflect the user's health status on that day.

[0190] For the sequelae detection module, specifically S308, the inputs can include: historical physiological data, current day's physiological data, historical measurement results, and current day's respiratory disease monitoring results; or, the inputs can include: historical physiological data, current day's physiological data, and historical measurement results; or, the inputs can include: historical physiological data and historical measurement results. The output is a judgment result indicating whether sequelae exist.

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

[0192] For example, a comparison can be made between the baseline of a certain physiological parameter after recovery and the baseline of the same physiological parameter before the illness to determine 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 and after the illness is greater than a certain threshold, it is considered to have a significant difference; otherwise, it is considered not to have a significant difference, i.e., there is no risk of long-term sequelae.

[0193] By using the methods described above, the accuracy of long-term sequelae detection results can be guaranteed, allowing these results to better reflect the user's long-term health status.

[0194] The system for disease detection based on physiological parameters provided in this application collects physiological data from wearable devices worn by users daily. After acquiring this data, the terminal device processes it, compares the user's daily physiological data with their personal baseline data, monitors the user's risk of developing illness daily, and displays the daily monitoring results to the user. Subsequently, it combines historical monitoring results and historical physiological data, and based on the differences in the user's physiological data before and after illness, it informs the user about the possibility of long-term sequelae after disease recovery, achieving long-term trend analysis of the user's health. This achieves both monitoring the user's current disease risk and conducting long-term health trend analysis, thus providing better health guidance services to users. Furthermore, when historical physiological data is insufficient, the system uses the baseline data of the user's corresponding population as the user's personal baseline data, or combines the user's corresponding population baseline data with the initial personal baseline data to obtain personal baseline data. This eliminates the need for the user to wear the wearable device for an extended period to obtain personal baseline data, allowing monitoring results to be obtained from the first day the user wears the wearable device, improving the user experience. When determining a user's risk level for the day, the probability of illness for that day is calibrated based on the user's physiological data for that day, so that the risk level for that day is consistent with the user's actual symptoms for that day, thereby improving the accuracy of the predicted risk level for that day and improving the user experience.

[0195] It should be understood that the above description is merely to help those skilled in the art better understand the embodiments of this application, and is not intended to limit the scope of the embodiments of this application. Based on the examples given above, those skilled in the art can obviously make various equivalent modifications or changes. For example, some steps in the various embodiments of the above methods may be unnecessary, or new steps may be added, etc. Alternatively, any combination of two or more of the above embodiments may be used. Such modifications, changes, or combinations also fall within the scope of the embodiments of this application.

[0196] It should also be understood that the above description of the embodiments of this application focuses on highlighting the differences between the various embodiments. Any similarities or differences not mentioned can be referred to each other. For the sake of brevity, they will not be repeated here.

[0197] It should also be understood that the methods, situations, categories, and classifications of embodiments in this application are for the convenience of description only and should not constitute a special limitation. Various methods, categories, situations, and features in embodiments can be combined without contradiction.

[0198] It should also be understood that, in the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced by each other, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0199] The above combination Figures 1 to 3 This application describes an embodiment of a system for detecting lower extremity arterial diseases. The following describes the terminal device and wearable device provided in the embodiments of this application.

[0200] This embodiment divides the terminal device and wearable device into functional modules based on the steps executed by the terminal device and wearable device respectively. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing module. The integrated modules can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0201] It should be noted that the relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0202] The terminal device and wearable device provided in this application embodiment are used to perform the above-described... Figure 2 and Figure 3 The method for disease detection based on physiological parameters shown can therefore achieve the same effect as the method described above. When using an integrated unit, the terminal device and wearable device can include a processing module, a storage module, and a communication module. The processing module can be used to control and manage the actions of the terminal device and wearable device. For example, it can be used to support the terminal device and wearable device in executing the steps performed by the processing unit. The storage module can be used to support the storage of program code and data, etc. The communication module can be used to support communication between the terminal device and wearable device and other devices.

[0203] The processing module can be a processor or a controller. It can implement or execute various exemplary logic blocks, 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 radio frequency circuit, a Bluetooth chip, a Wi-Fi chip, or other devices that interact with other devices.

[0204] For example, Figure 4 A schematic diagram of the hardware structure of a communication device 400 provided in this application is shown. This communication device 400 can be the aforementioned terminal device or wearable device. For example... 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 understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the communication device 400. In other embodiments of this application, the communication device 400 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. 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 the display screen 480. As another example, when the communication device 400 is a terminal device, the communication device 400 may not include the sensor module 470.

[0206] Processor 410 may include one or more processing units. For example, processor 410 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). 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 can generate operation control signals based on instruction opcodes and timing signals to control instruction fetching and execution. For example, processor 410 can determine a user's personal baseline data based on historical physiological data; perform daily respiratory disease monitoring based on the user's personal baseline data and daily physiological data; statistically analyze baseline data corresponding to pre-illness, during-illness, and post-recovery physiological data based on all of the user's physiological data and historical measurement results; and determine whether the user has long-term sequelae based on the baseline data corresponding to various physiological data before and after illness.

[0207] In some embodiments, the processor 410 may include one or more interfaces. These interfaces may include an inter-integrated circuit (I2C) interface, an integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a SIM card interface, and / or a USB interface, etc. The USB interface 440 is a USB standard-compliant interface, specifically 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 for data transfer between the communication device 400 and peripheral devices.

[0208] It is understood that the interface connection relationships between the modules illustrated in the embodiments of this application are merely illustrative and do not constitute a structural limitation on the communication device 400. In other embodiments of this application, the communication device 400 may also employ different interface connection methods or combinations of multiple interface connection methods as described in the above embodiments.

[0209] The wireless communication function of the communication device 400 can be realized through antenna 1, antenna 2 and wireless communication module 460, etc.

[0210] The wireless communication module 460 can provide solutions for wireless communication applications on the communication device 400, including Wi-Fi (including Wi-Fi sensing and Wi-Fi AP), Bluetooth (BT), and wireless data transmission modules (e.g., 433MHz, 868MHz, 915MHz). The wireless communication module 460 can be one or more devices integrating at least one communication processing module. The wireless communication module 460 receives electromagnetic waves via antenna 1 or antenna 2 (or antenna 1 and antenna 2), filters and frequency-modulates the electromagnetic wave signals, and sends the processed signal to the processor 410. The wireless communication module 460 can also receive signals to be transmitted from the processor 410, frequency-modulate and amplify them, and then convert them into electromagnetic waves for radiation via antenna 1 or antenna 2.

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

[0212] The external storage interface 420 can be used to connect an external storage card, such as a Micro SD card, to expand the storage capacity of the communication device 400. The external storage card communicates with the processor 410 through the external storage interface 420 to perform data storage functions. For example, music, video, and other files can be saved on the external storage card.

[0213] Internal memory 430 can be used to store one or more computer programs, which include instructions. Processor 410 can execute the instructions stored in internal memory 430, thereby causing communication device 400 to perform the methods provided in some embodiments of this application, as well as various applications and data processing. Internal memory 430 may include a code storage area and a data storage area. The code storage area may store the operating system. The data storage area may store data created during the use of communication device 400. In addition, internal memory 430 may include high-speed random access memory, and may also include non-volatile memory, such as one or more disk storage components, flash memory components, universal flash storage (UFS), etc. In some embodiments, processor 410 can execute instructions stored in internal memory 430 and / or instructions stored in memory disposed in processor 410, thereby causing communication device 400 to perform the disease detection process based on physiological parameters provided in embodiments of this application, as well as other applications and data processing.

[0214] The display screen 480 is used to show the user one or more of the following: the probability of illness on that day, the risk level on that day, and whether various physiological parameters are abnormal on that day. Optionally, the display screen 480 can also show the user the assessment results regarding whether the user will experience any sequelae. Optionally, the display screen 480 can also show the user corresponding health advice, etc.

[0215] The sensor module 470 may include sensors for measuring physiological parameters such as heart rate, heart rate variability, blood oxygen, respiratory rate, body temperature, sleep, and exercise. 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, tablets, laptops, large-screen TVs, smart home devices, PDAs, laptops, printers, smartwatches, smart bracelets, wearable wrist devices, smart glasses, augmented reality (AR) / virtual reality (VR) devices, etc., but this application embodiment is not limited to these.

[0217] It should be understood that Figure 4 The communication device 400 shown can be a terminal device or a wearable device, or the terminal device or wearable device can include... Figure 4 The communication device 400 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 by which the communication device 400 performs the above-mentioned steps is described in the preceding text. Figure 2 and Figure 3 The descriptions of the steps performed by the terminal device or wearable device in the embodiments are omitted here for the sake of brevity.

[0220] Figure 5 A schematic block diagram of another communication device 500 provided in this application embodiment is shown, which can correspond to the above-described communication device 500. Figure 2 or Figure 3 The terminal devices or wearable devices described in the various embodiments shown may also be chips or components applied to terminal devices or wearable devices, and the various modules or units included in the communication device 500 are respectively used to perform the above-described functions. Figure 2 or Figure 3 The various actions or processing procedures performed by the terminal device or wearable device described in the various embodiments shown, such as... 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 by which each unit in the communication device 500 performs the above-mentioned corresponding steps is described in the preceding text. Figure 4 For the sake of brevity, the relevant descriptions of the steps performed by the electronic or wearable devices described herein will not be repeated here.

[0222] Optionally, the communication unit 520 may include a receiving unit (module) and a transmitting unit (module) for performing the steps of receiving and transmitting information by the electronic device in the foregoing method embodiments. The storage unit 530 stores instructions executed by the processing unit 510 and the communication unit 520. The processing unit 510, the communication unit 520, and the storage unit 530 are communicatively connected. The storage unit 530 stores instructions, the processing unit 510 executes the instructions stored in the storage unit, and the communication unit 520 performs specific signal transmission and reception under the drive of the processing unit 510.

[0223] It should be understood that the communication unit 520 can be a transceiver, an input / output interface, or an interface circuit, for example, it can be composed of... Figure 4 The wireless communication module 460 in the illustrated embodiment is implemented. The storage unit can be a memory, for example, it can be made of... Figure 4 The external memory interface 420 and internal memory 430 in the illustrated embodiment are implemented. The processing unit 510 can be implemented by... 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 can be a terminal device or a wearable device, or the terminal device or wearable device can include... Figure 5 The communication device 500 shown.

[0225] It should also be understood that the division of units in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, all units in the device can be implemented entirely through software calls from processing elements; all units can be implemented entirely in hardware; or some units can be implemented through software calls from processing elements, while others are implemented in hardware. For example, each unit can be a separate processing element, or it can be integrated into a chip within the device. Alternatively, it can be stored as a program in memory, and its function can be called and executed by a processing element within the device. Here, the processing element can also be called a processor, which can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above units can be implemented through integrated logic circuits in the processor element or through software calls from processing elements. In one example, a unit in any of the above devices can be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these forms of integrated circuits. As another example, when a 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 capable of calling programs. Furthermore, these units can be integrated together to implement a system-on-a-chip (SOC).

[0226] This application also provides a system for disease detection based on physiological parameters, which includes any of the terminal devices and wearable devices provided in this application.

[0227] This 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 are interconnected via lines. For example, the interface circuit 602 can be used to receive signals from other devices (such as the wearable device described above). As 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 memory and send those instructions to the processor 601. When the instructions are executed by the processor 601, the terminal device can perform the various steps performed by the terminal device in any of the implementations of the above embodiments. Of course, the chip system may also include other discrete components, which are not specifically limited in this application embodiment.

[0228] This application also provides a computer-readable storage medium for storing computer program code, the computer program including instructions for executing any of the methods for disease detection based on physiological parameters provided in the embodiments of this application. The readable medium may be a read-only memory (ROM) or a random access memory (RAM), and this application does not impose any limitations on this.

[0229] This application also provides a computer program product including instructions that, when executed, cause the electronic device to perform corresponding operations in the above-described method flow.

[0230] This application also provides a chip located in a communication device, the chip including a processing unit and a communication unit. The processing unit may be, for example, a processor, and the communication unit may be, for example, an input / output interface, pins, or interface circuits. The processing unit can execute computer instructions to cause the communication device to perform any of the disease detection methods based on physiological parameters provided in the above-described embodiments of this application.

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

[0232] Optionally, the storage unit can be an internal storage unit within the chip, such as a register or cache. Alternatively, it can be an external storage unit located within the terminal, such as a ROM or other types of static storage devices capable of storing static information and instructions, such as random access RAM. The processor mentioned above can be a CPU, microprocessor, ASIC, or one or more integrated circuits used to control the execution of a program for transmitting the aforementioned feedback information. The processing unit and the storage unit can be decoupled and located on different physical devices, connected via wired or wireless means to implement their respective functions, thereby supporting the system chip in implementing the various functions described in the above embodiments. Alternatively, the processing unit and the memory can also be coupled to the same device.

[0233] In this embodiment, the communication device, computer-readable storage medium, computer program product or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred 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 embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory can be ROM, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be RAM, which is used as an external cache. RAM has various different types, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus RAM (DRRAM).

[0235] In this application, various objects such as messages / information / devices / network elements / systems / apparatus / actions / operations / processes / concepts may be named. It is understood that these specific names do not constitute a limitation on the relevant objects. The names may be changed depending on the scenario, context, or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from their functions and technical effects embodied / performed in the technical solution.

[0236] In the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of different embodiments are consistent and can be referenced by each other. The technical features of different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0237] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0238] The methods in the embodiments of this application can be implemented, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, they can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are performed, 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 instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server integrating one or more available media.

[0239] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0240] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

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

[0242] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0243] If the aforementioned functions are implemented as 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 this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned readable storage medium includes various media capable of storing program code, such as USB flash drives, external hard drives, ROM, RAM, magnetic disks, or optical disks.

[0244] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A terminal device for disease detection based on physiological parameters, characterized in that, The terminal device includes: processor; Memory; Display screen; And a computer program, stored in the memory, which, when executed by the processor, causes the terminal device to perform the following steps: Acquire the user's physiological data, which includes the user's historical physiological data and current day's physiological data; Based on the historical physiological data, the user's personal baseline data is determined. The personal baseline data includes statistics on different types of physiological data of the user, and the statistics include at least one of the following: mean, variance, minimum, maximum, quantile, and probability distribution. Based on the daily physiological data and the individual baseline data, the daily disease monitoring results are obtained, which include: the probability of disease on the day, the risk level on the day, and whether one or more of the daily physiological parameters are abnormal. Based on the historical physiological data, the current day's physiological data, and historical measurement results, long-term sequelae detection results are obtained. These results include whether the user has or does not have long-term sequelae. The historical measurement results include one or more of the following: the probability of illness each day before the current day, the risk level each day before the current day, and whether various physiological parameters were abnormal each day before the current day. The presence of long-term sequelae is indicated by the difference or ratio between the user's baseline first physiological data after recovery and the user's baseline first physiological data before the illness being greater than or equal to a third threshold. The absence of long-term sequelae is indicated by the difference or ratio between the user's baseline first physiological data after recovery and the user's baseline first physiological data before the illness being less than the third threshold. The first physiological data includes any one of the user's physiological data, and the third threshold corresponds to the first physiological data. The display screen shows the user the results of the disease monitoring for the day and the results of the long-term sequelae detection.

2. The terminal device according to claim 1, characterized in that, The step of 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 based on the user's personal information, and use the population baseline data as the individual 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. Determine the baseline data of the population corresponding to the user based on the user's personal information; The individual baseline data is determined based on the population baseline data and the initial individual baseline data.

3. The terminal device according to claim 1 or 2, characterized in that, The process of obtaining the daily disease monitoring results based on the daily physiological data and the individual baseline data includes: The daily physiological data is calibrated using the individual baseline data to obtain the calibrated daily physiological data; The disease monitoring results for the day are obtained based on the calibrated physiological data of the day, the physiological data of the day before calibration, and the individual baseline data.

4. The terminal device according to claim 1 or 2, characterized in that, If the probability of contracting the disease on a given day is less than or equal to a preset first threshold, then the risk level for that day is low risk. If the probability of contracting the disease on a given day is greater than a preset first threshold and less than or equal to a preset second threshold, then the risk level for that day is medium risk. If the probability of contracting the disease on a given day is greater than a preset second threshold, then the risk level for that day is high risk, and the second threshold is greater than the first threshold.

5. The terminal device according to claim 1 or 2, characterized in that, The terminal device also performs the following steps: The risk level for that day is determined based on the probability of illness on that day and the physiological data for that day.

6. The terminal device according to claim 1 or 2, characterized in that, The terminal device also performs the following steps: In cases where a user is at risk of developing a disease, the type of disease the user has is determined and displayed to the user by combining non-physiological information, which includes at least one of the following: whether there is an infectious disease outbreak in the user's region, whether there is an infectious disease outbreak in the current season, and whether the user has an underlying disease.

7. The terminal device according to claim 1 or 2, characterized in that, The terminal device also performs the following steps: Based on the risk level of the day and the type of illness the user suffers from, health advice is displayed to the user on the screen; or, Based on the risk level of the day, health advice is displayed to the user on the screen.

8. The terminal device according to claim 1 or 2, characterized in that, The user's physiological data includes at least one of the following: heart rate, heart rate variability, respiratory rate, blood oxygen, body temperature, skin temperature, sleep information, and exercise information.

9. The terminal device according to claim 1 or 2, characterized in that, The terminal device also includes a physiological data acquisition device, and the terminal device further performs the following steps: The physiological data of the user is acquired using the physiological data acquisition device.

10. The terminal device according to claim 1 or 2, characterized in that, The terminal device and the user's wearable device communicate wirelessly, and the terminal device further performs the following steps: Receive the user's physiological data sent by the wearable device.

11. A system for disease detection based on physiological parameters, characterized in that, The system includes a terminal device as described in any one of claims 1 to 10 and a wearable device, wherein the terminal device and the user's wearable device communicate wirelessly, the wearable device includes a physiological data collector, and the wearable device uses the physiological data collector to acquire the user's physiological data and send the user's physiological data to the terminal device.

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