Cardiac activity monitoring method and wearable device

Abstract

The application provides a heart activity monitoring method and a wearable device, and relates to the technical field of terminals. The application monitors heart activity of a user through a wearable device, determines whether the user is in a plurality of preset scenes which are prone to induce coronary heart disease, realizes more timely coronary heart disease warning, and avoids missed diagnosis. The method comprises the following steps: after the wearable device determines that the user is in a preset scene according to at least one of first data monitored by a first sensor, second data monitored by a second sensor and third data monitored by a third sensor, the wearable device reminds the user to perform electrocardiogram measurement. And in response to a user input instruction, the wearable device acquires electrocardiogram data of the user through an electrocardiogram sensor. Then, the wearable device divides the risk of the heart activity in combination with the first data and / or the third data and the electrocardiogram data of the user.

Description

Technical Field

[0001] This application relates to the field of terminal technology, and in particular to a method for monitoring heart activity and a wearable device. Background Technology

[0002] Coronary atherosclerotic heart disease (CAD) is one of the leading causes of death worldwide. CAD can be divided into acute coronary syndrome and chronic stable coronary artery disease. Currently, there are various methods for detecting CAD, such as coronary angiography, which is considered the gold standard for diagnosis. However, coronary angiography is an invasive procedure and carries certain post-operative risks.

[0003] To address this, a coronary heart disease screening device is proposed. This device acquires heart sound signals, pulse wave signals, and electrocardiogram signals through a microphone, pulse wave sensor, and electrocardiogram sensor. It then combines these signals with the user's medical history and basic physiological parameters to obtain feature vectors, and uses a machine learning model to screen for coronary heart disease.

[0004] Although the aforementioned device can perform coronary heart disease screening, it cannot determine the type of coronary heart disease the user suffers from, such as whether the user has chronic stable coronary heart disease, which may result in missed screenings. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a method and wearable device for monitoring cardiac activity. The technical solution provided in this application monitors a user's cardiac activity using a wearable device, determines whether the user is in various preset scenarios that could easily trigger coronary heart disease, identifies (or classifies) the risks associated with the user's cardiac activity, such as identifying whether the user has coronary heart disease and the type of disease, thereby achieving more timely early warning of coronary heart disease and avoiding missed diagnoses.

[0006] To achieve the above-mentioned technical objectives, the embodiments of this application provide the following technical solutions:

[0007] Firstly, a method for monitoring cardiac activity is provided, applied to a wearable device. The method includes: determining that a user is in a preset scenario based on at least one of the following data: first data monitored by a first sensor, second data monitored by a second sensor, and third data monitored by a third sensor; reminding the user to perform an electrocardiogram (ECG) measurement; receiving a user input command and acquiring fourth data, which is ECG data, via an ECG sensor; and classifying the risk of cardiac activity based on the first and / or third data, and the fourth data.

[0008] In some embodiments, the preset scenario includes one or more of the following: a sports scenario, an abnormal ambient temperature scenario, and an abnormal heart rate variability scenario.

[0009] In some embodiments, the first sensor is a motion sensor, the second sensor is a temperature sensor, and the third sensor is a photoplethysmography (PPG) sensor. The motion sensor, for example, is an accelerometer.

[0010] In some embodiments, the wearable device can determine exercise intensity based on measurements from a motion sensor and a photoplethysmography (PPG) sensor. Then, by combining the first and / or third data to determine the user's exercise intensity with a fourth data point (i.e., electrocardiogram data), the wearable device can classify the risk associated with the user's cardiac activity.

[0011] Therefore, compared to existing coronary heart disease screening devices that can only screen for coronary heart disease when the user is at rest and cannot promptly identify chronic stable coronary heart disease induced by special factors such as exercise, the cardiac activity monitoring method provided in this application can determine whether the user has the disease and identify different types of coronary heart disease by judging various preset scenarios that are likely to induce coronary heart disease, thus achieving more timely early warning of coronary heart disease and avoiding missed diagnoses. Secondly, since this method is applied to wearable devices, users can carry the wearable device with them. The wearable device can identify whether the user is in the preset scenario in real time and perform coronary heart disease screening based on the user's operation, preventing the user from missing the diagnosis time due to the presence of the disease.

[0012] According to the first aspect, or any implementation of the first aspect above, the risk includes a first risk and a second risk; the method further includes: if the risk of cardiac activity is classified as a first risk, displaying a first prompt message, the first prompt message being used to remind the user to seek medical attention at a hospital in a timely manner. Alternatively, if the risk of cardiac activity is classified as a second risk, displaying a second prompt message, the second prompt message being used to remind the user to schedule a time to seek medical attention at a hospital.

[0013] In some embodiments, different types of coronary artery disease have different requirements for the timing of medical visits. For example, acute coronary syndrome requires immediate medical attention, while chronic stable coronary artery disease has a more flexible timeframe, allowing patients to schedule their visits to the hospital. Therefore, the risk of cardiac activity can be categorized, providing different information to meet the different time requirements for medical visits for different types of coronary artery disease.

[0014] In this way, by classifying different risks that may exist in cardiac activity according to the type of coronary heart disease and providing separate reminders for different situations, it ensures that patients with acute coronary syndrome who have a strong need for timely medical attention can receive timely treatment, thereby reducing user risks.

[0015] According to the first aspect, or any implementation of the first aspect above, determining that a user is in a preset scenario based on at least one of the first data monitored by the first sensor, the second data monitored by the second sensor, and the third data monitored by the third sensor includes: determining that the user is in an exercise scenario if the exercise intensity meets a first preset condition based on the first data monitored by the first sensor and / or the third data monitored by the third sensor; or determining that the user is in an abnormal ambient temperature scenario if the second data monitored by the second sensor is less than a second threshold within a first preset time period, or if the variance of the second data within the second preset time period is greater than a third threshold; or determining that the user is in an abnormal heart rate variability scenario if the third data monitored by the third sensor meets a second preset condition.

[0016] In some embodiments, the wearable device may determine the user’s exercise intensity based on first data and / or third data, and then determine whether the exercise intensity meets a first preset condition.

[0017] For example, the first data is the acceleration waveform data detected by the motion sensor. Based on the waveform data, the wearable device determines that the user's movement type is mapped to high-intensity exercise, and if the duration of the high-intensity exercise meets the preset time requirement, the exercise intensity can be determined to meet the first preset condition.

[0018] For example, the third data is the PPG signal. Wearable devices determine the user's real-time heart rate based on the PPG signal. If it is determined that the user's average heart rate exceeds a preset heart rate threshold within a preset time, it can be determined that the user's exercise intensity meets the first preset condition.

[0019] In some embodiments, the second preset condition is used to determine HRV abnormality. For example, based on the PPG signal, the wearable device determines during HRV analysis that the high-frequency power within a preset time period is greater than or equal to a preset frequency threshold, and determines that the coefficient of variation is greater than or equal to a preset coefficient of variation threshold (i.e., the first preset condition is met), which can determine that the user has an HRV abnormality and may be at risk of coronary heart disease.

[0020] In this way, different judgment criteria are set for different preset scenarios, ensuring that wearable devices can correctly identify preset scenarios that are likely to induce coronary heart disease. This effectively avoids missed screening of coronary heart disease and improves the user experience.

[0021] According to the first aspect, or any implementation of the first aspect above, the system reminds the user to perform an electrocardiogram (ECG) measurement, receives user input instructions, and acquires fourth data through an ECG sensor, including: displaying a first interface for displaying the ECG measurement reminder, the ECG measurement reminder including ECG measurement posture information; receiving user input instructions; and acquiring fourth data measured by the user according to the ECG measurement posture through an ECG sensor.

[0022] In some embodiments, the methods for reminding users to perform ECG measurements include displaying a measurement reminder interface, voice reminders, vibration reminders, and other methods.

[0023] Optionally, the wearable device can also send instructions to the electronic device, instructing the electronic device to display an ECG measurement interface to prompt the user to perform an ECG measurement.

[0024] In this way, by displaying ECG measurement posture information, users can avoid being unfamiliar with the ECG measurement posture. This ensures the accuracy of ECG measurement results and, consequently, improves the accuracy of cardiac activity risk classification.

[0025] According to the first aspect, or any implementation of the first aspect above, before classifying the risk of cardiac activity based on the first data and / or the third data, and the fourth data, the method further includes: displaying a second interface. Receiving fifth data input and / or selected by the user on the second interface, the fifth data including pain location, pain type, pain duration, accompanying symptoms, and one or more of the activities within the most recent preset time period.

[0026] According to the first aspect, or any implementation of the first aspect above, before classifying the risk of cardiac activity based on the first data and / or the third data, and the fourth data, the method further includes: displaying a third interface. Receiving sixth data input and / or selected by the user on the third interface, the sixth data including one or more of the following: gender, age, history of smoking, history of hypertension, family history of cardiovascular disease, and history of coronary heart disease.

[0027] Based on the first aspect, or any implementation of the first aspect above, the risk of cardiac activity is classified based on the first data and / or the third data, and the fourth data, including: classifying the risk of cardiac activity based on the first data, and / or the third data, and the fourth, fifth, and sixth data.

[0028] In some embodiments, the fifth data is user symptom data, and the sixth data is user basic information. The fifth and sixth data are collected before the risk assessment of cardiac activity; they can be collected separately or together. For example, they can be collected after the ECG measurement is completed, after an ECG measurement reminder, or after the wearable device is first activated to collect user basic information.

[0029] In this way, combining user symptom data and basic user information to classify the risks of cardiac activity can effectively improve the accuracy of risk classification, enhance the effectiveness of coronary heart disease screening, and improve the user experience.

[0030] Secondly, a wearable device is provided. The wearable device includes a processor, a memory, and a computer program, wherein the computer program is stored in the memory. When the computer program is executed by the processor, the wearable device performs the following actions: determining that the user is in a preset scenario based on at least one of the following data: first data monitored by a first sensor, second data monitored by a second sensor, and third data monitored by a third sensor; reminding the user to perform an electrocardiogram (ECG) measurement; receiving a user input command and acquiring fourth data, which is ECG data, via an ECG sensor; and classifying the risk of cardiac activity based on the first and / or third data, and the fourth data.

[0031] According to the second aspect, the preset scenarios include one or more of the following: sports scenarios, abnormal ambient temperature scenarios, and abnormal heart rate variability scenarios.

[0032] According to the second aspect, or any implementation of the second aspect above, the first sensor is a motion sensor, the second sensor is a temperature sensor, and the third sensor is a photoplethysmography (PPG) sensor.

[0033] According to the second aspect, or any implementation of the second aspect above, the risk includes a first risk and a second risk. When the processor reads computer instructions from memory, it also causes the wearable device to perform the following operations: if the risk to cardiac activity is classified as a first risk, a first prompt message is displayed, which prompts the user to seek medical attention at a hospital in a timely manner. Alternatively, if the risk to cardiac activity is classified as a second risk, a second prompt message is displayed, which prompts the user to schedule a time to seek medical attention at a hospital.

[0034] According to the second aspect, or any implementation thereof, determining that a user is in a preset scenario based on at least one of the first data monitored by the first sensor, the second data monitored by the second sensor, and the third data monitored by the third sensor includes: determining that the user is in an exercise scenario if the exercise intensity meets a first preset condition based on the first data monitored by the first sensor and / or the third data monitored by the third sensor; or determining that the user is in an abnormal ambient temperature scenario based on the second data monitored by the second sensor if the second data is less than a second threshold within a first preset time period, or if the variance of the second data within the second preset time period is greater than a third threshold; or determining that the user is in an abnormal heart rate variability scenario based on the third data monitored by the third sensor if the third data meets a second preset condition.

[0035] According to the second aspect, or any implementation of the second aspect above, the system reminds the user to perform an electrocardiogram (ECG) measurement, receives user input instructions, and acquires fourth data through an ECG sensor, including: displaying a first interface for displaying the ECG measurement reminder, which includes ECG measurement posture information; receiving user input instructions; and acquiring fourth data measured by the user according to the ECG measurement posture through an ECG sensor.

[0036] According to the second aspect, or any implementation of the second aspect above, when the processor reads computer instructions from memory, it also causes the wearable device to perform the following operations: display a second interface; receive fifth data input and / or selected by the user on the second interface, the fifth data including one or more of the following: pain location, pain type, pain duration, accompanying symptoms, and activities within the most recent preset time period.

[0037] According to the second aspect, or any implementation of the second aspect above, when the processor reads computer instructions from memory, it also causes the wearable device to perform the following operations: display a third interface; receive sixth data input and / or selected by the user on the third interface, the sixth data including one or more of the following: gender, age, history of smoking, history of hypertension, family history of cardiovascular disease, and history of coronary heart disease.

[0038] According to the second aspect, or any implementation of the second aspect above, the risk of cardiac activity is classified based on the first data and / or the third data, and the fourth data, including: classifying the risk of cardiac activity based on the first data, and / or the third data, and the fourth, fifth, and sixth data.

[0039] For the technical effects of the second aspect and any implementation thereof, please refer to the technical effects of the first aspect and any implementation thereof, which will not be repeated here.

[0040] Thirdly, embodiments of this application provide a wearable device that has the function of implementing the cardiac activity monitoring method as described in the first aspect and any of its possible implementations. This function can be implemented in hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the aforementioned function.

[0041] For the technical effects of the third aspect and any of its implementation methods, please refer to the technical effects of the first aspect and any of its implementation methods mentioned above, which will not be repeated here.

[0042] Fourthly, a computer-readable storage medium is provided. The computer-readable storage medium is non-volatile and stores a computer program (also referred to as instructions or code) that, when executed by a wearable device, causes the wearable device to perform the method of the first aspect or any embodiment of the first aspect.

[0043] For the technical effects of the fourth aspect and any of its implementation methods, please refer to the technical effects of the first aspect and any of its implementation methods mentioned above, which will not be repeated here.

[0044] Fifthly, embodiments of this application provide a computer program product that, when run on a wearable device, causes the wearable device to perform the method of the first aspect or any one of the embodiments of the first aspect.

[0045] The technical effects of the fifth aspect and any of its implementations can be found in the first aspect and any of its implementations, and will not be repeated here.

[0046] In a sixth aspect, embodiments of this application provide a circuit system including a processing circuit configured to perform the method of the first aspect or any of the embodiments of the first aspect.

[0047] For the technical effects of the sixth aspect and any implementation thereof, please refer to the technical effects of the first aspect and any implementation thereof, which will not be repeated here.

[0048] In a seventh aspect, embodiments of this application provide a chip system including at least one processor and at least one interface circuit. The at least one interface circuit is used to perform transceiver functions and send instructions to the at least one processor. When the at least one processor executes the instructions, the at least one processor performs the method of the first aspect or any one of the embodiments of the first aspect.

[0049] The technical effects of the seventh aspect and any implementation thereof can be found in the first aspect and any implementation thereof, and will not be repeated here. Attached Figure Description

[0050] Figure 1 A schematic diagram of the communication system used in the cardiac activity monitoring method provided in the embodiments of this application;

[0051] Figure 2A This is a schematic diagram of the hardware structure of a wearable device provided in an embodiment of this application;

[0052] Figure 2B This is a schematic diagram of the electrode positions of a smartwatch provided in an embodiment of this application;

[0053] Figure 3 A schematic flowchart of the cardiac activity monitoring method provided in the embodiments of this application. Figure 1 ;

[0054] Figure 4 Interface illustration provided for embodiments of this application Figure 1 ;

[0055] Figure 5 A second schematic diagram of the interface provided for an embodiment of this application;

[0056] Figure 6 Interface illustration provided for embodiments of this application Figure 3 ;

[0057] Figure 7 Interface illustration provided for embodiments of this application Figure 4 ;

[0058] Figure 8 Interface illustration provided for embodiments of this application Figure 5 ;

[0059] Figure 9 Interface illustration provided for embodiments of this application Figure 6 ;

[0060] Figure 10 This is a schematic diagram illustrating the risk classification of coronary heart disease provided in an embodiment of this application;

[0061] Figure 11 Interface illustration provided for embodiments of this application Figure 7 ;

[0062] Figure 12 Interface illustration provided for embodiments of this application Figure 8 ;

[0063] Figure 13Interface illustration provided for embodiments of this application Figure 9 ;

[0064] Figure 14 Interface illustration provided for embodiments of this application Figure 10 ;

[0065] Figure 15 Flowchart 2 of the cardiac activity monitoring method provided in the embodiments of this application;

[0066] Figure 16 This is a schematic diagram of the structure of a wearable device provided in an embodiment of this application. Detailed Implementation

[0067] The technical solutions of the embodiments of this application are described below with reference to the accompanying drawings. In the description of the embodiments of this application, the terminology used in the following embodiments is for the purpose of describing specific embodiments only and is not intended to be a limitation 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 include expressions such as “one or more,” unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of this application, “at least one” and “one or more” refer to one or more (including two).

[0068] References to "one embodiment" or "some embodiments" 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. The term "connection" includes direct connections and indirect connections, unless otherwise stated. "First" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0069] In the embodiments of this application, the words "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of the words "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0070] Figure 1 This is a schematic diagram of a communication system used in a cardiac activity monitoring method according to an embodiment of this application. Figure 1 As shown, the communication system includes a wearable device 100 and an electronic device 200.

[0071] Wearable device 100 can establish a wireless communication connection with electronic device 200 through wireless communication technology. This wireless communication technology includes, but is not limited to, at least one of the following: near field communication (NFC), Bluetooth (BT) (e.g., classic Bluetooth or Bluetooth Low Energy (BLE)), wireless local area networks (WLAN) (e.g., wireless fidelity (Wi-Fi) networks), Zigbee, frequency modulation (FM), infrared (IR), etc.

[0072] In some embodiments, both the wearable device 100 and the electronic device 200 support proximity detection. For example, when the wearable device 100 approaches the electronic device 200, the two devices can discover each other and then establish wireless communication connections such as Wi-Fi peer-to-peer (P2P) connections or Bluetooth connections. After establishing the wireless communication connection, the wearable device 100 and the electronic device 200 can interact via the wireless communication connection.

[0073] In some embodiments, the wearable device 100 and the electronic device 200 establish a wireless communication connection via a local area network. For example, both the wearable device 100 and the electronic device 200 are connected to the same router.

[0074] In some embodiments, the wearable device 100 and the electronic device 200 establish a wireless communication connection via a cellular network, the Internet, or the like. For example, the electronic device 200 accesses the Internet via a router, and the wearable device 100 accesses the Internet via a cellular network; thus, the wearable device 100 and the electronic device 200 establish a wireless communication connection.

[0075] Optionally, the wearable device 100 may be a smartwatch, smart bracelet, or other terminal device with heart activity monitoring capabilities. The operating system installed on the wearable device 100 may include, but is not limited to, [other operating systems]. Alternatively, other operating systems may be used. In some embodiments, the wearable device 100 may be a fixed device or a portable device. This application does not limit the specific type of wearable device 100 or the operating system installed on it.

[0076] Optionally, the electronic device 200 may be, for example, a mobile phone, a personal computer (PC), a tablet computer, a laptop computer, a desktop computer, a computer with transceiver capabilities, a wearable device, an in-vehicle device, an artificial intelligence (AI) device, or other terminal device. The operating system installed on the electronic device 200 may include, but is not limited to, […]. Alternatively, other operating systems may be used. In some embodiments, the electronic device 200 may be a fixed device or a portable device. This application does not limit the specific type of the electronic device 200 or the operating system installed on it.

[0077] In some embodiments, when the wearable device 100 detects at least one of the following: abnormal heart rate variability, abnormal ambient temperature, or exercise intensity meeting preset conditions, it determines that the current scenario is likely to induce symptoms of myocardial ischemia in patients with coronary heart disease, indicating a risk of coronary heart disease, and may send a warning message to the wirelessly connected electronic device 200. Abnormal ambient temperature includes cold environments or drastic temperature fluctuations. Meeting preset conditions for exercise intensity indicates that the wearable device 100, through measurements from sensors such as motion sensors, has determined that the user has engaged in strenuous exercise.

[0078] Subsequently, the electronic device 200 prompts the user to perform coronary heart disease detection through the wearable device 100, based on the provided information. The wearable device 100 detects the user's input, such as activating the coronary heart disease detection function, collects the user's electrocardiogram (ECG) data, and sends the ECG data along with historical cardiac monitoring data to the electronic device 200. Based on the received data, the electronic device 200 determines whether the user is at risk of developing coronary heart disease and outputs a prediction result (e.g., displayed on a screen). This ensures that the user can promptly detect any risk of developing coronary heart disease.

[0079] Optionally, the electronic device 200 may have an application installed to monitor the user's heart activity. Upon receiving a prompt from the wearable device 100, the electronic device 200 will launch the application, displaying a coronary heart disease detection prompt, and prompting the user to undergo coronary heart disease testing through the wearable device 100. The application for monitoring the user's heart activity may be, for example, a coronary heart disease screening application.

[0080] In other embodiments, the communication system described above may not include the electronic device 200. After detecting a scenario that could easily induce coronary heart disease, the wearable device 100 directly outputs a prompt message (e.g., displaying the prompt message on its own screen or broadcasting the prompt message through a speaker) to prompt the user to undergo coronary heart disease testing. Subsequently, based on the test results, it outputs a predicted result (e.g., displaying a prompt message on its own screen or broadcasting the predicted result through a speaker). Optionally, the wearable device 100 may also install an application for monitoring the user's cardiac activity, such as a coronary heart disease screening application.

[0081] Figure 2A A schematic diagram of the structure of the wearable device 100 is shown.

[0082] Wearable device 100 may include a processor 110, a memory 120, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna, a wireless communication module 160, an audio module 170, a sensor module 180, a button 190, a motor 191, an indicator 192, a camera 193, and a display screen 194, etc. The sensor module 180 may include a photoplethysmography (PPG) sensor 180A, an accelerometer 180B, a temperature sensor 180C, an electrocardiogram (ECG) sensor 180D, a touch sensor 180E, etc.

[0083] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the wearable device 100. In other embodiments of this application, the wearable device 100 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, the wearable device 100 may not include the camera 193, that is, the wearable device 100 may not have a camera function.

[0084] Processor 110 may include one or more processing units, such as application processors (APs), modem processors, graphics processing units (GPUs), image signal processors (ISPs), controllers, video codecs, digital signal processors (DSPs), baseband processors, and / or neural network processing units (NPUs). These different processing units may be independent devices or integrated into one or more processors.

[0085] The controller can generate operation control signals based on the instruction opcode and timing signals to complete the control of instruction fetching and execution.

[0086] The processor 110 may also include a memory for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. This memory can store instructions or data that the processor 110 has just used or that are used repeatedly. If the processor 110 needs to use the instruction or data again, it can retrieve it directly from the memory. This avoids repeated accesses, reduces the waiting time of the processor 110, and thus improves the efficiency of the system.

[0087] In some embodiments, the processor 110 may include one or more interfaces. Interfaces may include an inter-integrated circuit (I2C) interface, an inter-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 subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0088] The I2C interface is a bidirectional synchronous serial bus, including a serial data line (SDA) and a serial clock line (SCL). In some embodiments, the processor 110 may include multiple I2C buses. The processor 110 can couple to the touch sensor 180E, charger, flash, camera 193, etc., through different I2C bus interfaces. For example, the processor 110 can couple to the touch sensor 180E through the I2C interface, enabling the processor 110 and the touch sensor 180E to communicate through the I2C bus interface, thereby realizing the touch function of the wearable device 100.

[0089] The MIPI interface can be used to connect the processor 110 to peripheral devices such as the display screen 194 and the camera 193. The MIPI interface includes a camera serial interface (CSI) and a display serial interface (DSI). In some embodiments, the processor 110 and the camera 193 communicate via the CSI interface to enable the wearing device 100 to take pictures. The processor 110 and the display screen 194 communicate via the DSI interface to enable the wearing device 100 to display.

[0090] USB interface 130 is an interface that conforms to the USB standard specification, specifically it can be a Mini USB interface, Micro USB interface, USB Type C interface, etc. USB interface 130 can be used to connect a charger to charge wearable device 100, and can also be used for data transfer between wearable device 100 and peripheral devices.

[0091] 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 wearable device 100. In other embodiments of this application, the wearable device 100 may also employ different interface connection methods or combinations of multiple interface connection methods as described in the above embodiments.

[0092] The charging management module 140 receives charging input from a charger. The charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 receives charging input from the wired charger via the USB interface 130. In some wireless charging embodiments, the charging management module 140 receives wireless charging input via the wireless charging coil of the wearable device 100. While charging the battery 142, the charging management module 140 can also supply power to the wearable device via the power management module 141.

[0093] The power management module 141 connects the battery 142, the charging management module 140, and the processor 110. The power management module 141 receives input from the battery 142 and / or the charging management module 140, providing power to the processor 110, memory 120, display screen 194, camera 193, and wireless communication module 160, etc. The power management module 141 can also monitor parameters such as battery capacity, battery cycle count, and battery health status (leakage current, impedance). In some other embodiments, the power management module 141 may also be located within the processor 110. In other embodiments, the power management module 141 and the charging management module 140 may be located in the same device.

[0094] The wireless communication function of the wearable device 100 can be achieved through an antenna, a wireless communication module 160, etc.

[0095] Antennas are used to transmit and receive electromagnetic wave signals. Each antenna in wearable device 100 can be used to cover one or more communication frequency bands. Different antennas can also be reused to improve antenna utilization. In some embodiments, antennas can be used in conjunction with tuning switches.

[0096] The wireless communication module 160 can provide solutions for wireless communication applications on the wearable device 100, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies. The wireless communication module 160 can be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via an antenna, performs frequency modulation and filtering of the electromagnetic wave signals, and sends the processed signal to the processor 110. The wireless communication module 160 can also receive signals to be transmitted from the processor 110, perform frequency modulation and amplification, and convert them into electromagnetic waves for radiation via the antenna.

[0097] In some embodiments, the antenna of the wearable device 100 is coupled to the wireless communication module 160, enabling the wearable device 100 to communicate with networks and other devices via wireless communication technologies. The wireless communication technologies may include BT, GNSS, WLAN, NFC, FM, and / or IR technologies, etc. The GNSS may include the Global Positioning System (GPS), the Global Navigation Satellite System (GLONASS), the BeiDou Navigation Satellite System (BDS), the Quasi-Zenith Satellite System (QZSS), and / or satellite-based augmentation systems (SBAS).

[0098] The wearable device 100 implements display functions through a GPU, a display screen 194, and an application processor. The GPU is a microprocessor for image processing, connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor 110 may include one or more GPUs, which execute program instructions to generate or modify display information.

[0099] Display screen 194 is used to display images, videos, etc. Display screen 194 includes a display panel. The display panel can be manufactured using a liquid crystal display (LCD), such as an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), a mini-LED, a micro-LED, a micro-OLED, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the wearable device 100 may include one or N displays 194, where N is a positive integer greater than 1.

[0100] Camera 193 is used to capture still images or videos. In some embodiments, wearable device 100 may include one or N cameras 193, where N is a positive integer greater than 1.

[0101] The memory 120 can be used to store computer executable program code, which includes instructions. The memory 120 may include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback, image playback, etc.), etc. The data storage area may store data created during the use of the wearable device 100 (such as audio data, phonebook, etc.). Furthermore, the memory 120 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc. The processor 110 executes various functional applications and data processing of the wearable device 100 by running instructions stored in the memory 120 and / or instructions stored in memory disposed within the processor.

[0102] The wearable device 100 can implement audio functions, such as music playback and recording, through the audio module 170 and application processor.

[0103] The audio module 170 is used to convert digital audio information into analog audio signals for output, and also to convert analog audio input into digital audio signals. The audio module 170 can also be used for encoding and decoding audio signals. In some embodiments, the audio module 170 may be located in the processor 110, or some functional modules of the audio module 170 may be located in the processor 110. The wearable device 100 can use the audio module 170 for functions such as music playback and recording. The audio module 170 may include a speaker, receiver, microphone, and application processor to implement audio functions.

[0104] The sensor module 180 includes a photoplethysmography (PPG) pulse wave sensor 180A, an acceleration (ACC) sensor 180B, a temperature sensor 180C, an electrocardiogram (ECG) sensor 180D, a touch sensor 180E, and other sensors.

[0105] The 180A photoplethysmography (PPG) sensor, based on an LED light source and detector, measures attenuated light reflected and absorbed by blood vessels and tissues, recording the pulsation state of blood vessels and measuring PPG signals. PPG signals contain a wealth of cardiovascular physiological and pathological information, making them an important means of real-time monitoring of cardiovascular conditions such as blood pressure and vascular elasticity.

[0106] In some embodiments, the wearable device 100 performs heart rate variability (HRV) analysis using PPG signals to assess the condition and prevent cardiovascular diseases. HRV is a valuable indicator for predicting sudden cardiac death and arrhythmic events. Therefore, the wearable device 100 can use PPG signals to determine whether the user has experienced HRV abnormalities, thereby determining whether the user is in a high-risk environment for coronary heart disease and whether coronary heart disease risk screening is necessary.

[0107] In other embodiments, the wearable device 100 can determine the user's real-time heart rate via PPG signals. The user's exercise intensity can then be measured based on the real-time heart rate, thereby determining whether the user is engaging in strenuous exercise. Strenuous exercise is prone to triggering coronary heart disease, thus allowing for coronary heart disease risk screening of the user.

[0108] For example, if the wearable device 100 determines, based on the PPG signal, that the user's average heart rate within a preset time period exceeds a preset heart rate threshold, it can determine that the user is in an exercise scenario. Optionally, the preset heart rate threshold can be determined based on factors such as the user's age; for example, the preset heart rate threshold = (220 – age – resting heart rate) * 60% + resting heart rate. The preset time period is, for example, 1 hour. The accelerometer 180B can be used to monitor the exercise intensity of the user wearing the wearable device 100. In some embodiments, the wearable device 100 can determine whether the user is in an exercise scenario based on the measurement results of the accelerometer 180B. If the user is in an exercise scenario, the device can prompt the user to undergo coronary heart disease risk screening.

[0109] For example, wearable device 100 can determine the type of movement based on waveform data measured by accelerometer 180B. Different types of movement can be mapped to different intensities. For instance, movement types include stationary, walking, running, climbing stairs, and jumping. Among these, running, climbing stairs, and jumping can be mapped to high-intensity exercise. Therefore, if wearable device 100 determines that the user's movement type can be mapped to high-intensity exercise, and the duration of high-intensity exercise exceeds a preset time, it can determine that the user is in an exercise scenario and prompt the user to undergo coronary heart disease risk screening.

[0110] Optionally, the intensity of motion can also be determined by measurements from motion sensors other than the accelerometer 180B.

[0111] A temperature sensor 180C is used to monitor ambient temperature. In some embodiments, the wearable device 100 can determine whether the current environment is cold or whether the ambient temperature fluctuates drastically based on the measurement results of the temperature sensor 180C. For example, if the wearable device 100 determines that the ambient temperature measured by the temperature sensor 180C is lower than a preset temperature threshold 1 for a preset time period (e.g., 1 hour), it can determine that the current environment is cold, and that the user is in a scenario prone to coronary heart disease, requiring coronary heart disease risk screening. Alternatively, if the wearable device 100 determines, based on the ambient temperature measured by the temperature sensor 180C, that the variance of the ambient temperature fluctuation is greater than a preset temperature threshold 2 within a preset time period (e.g., half an hour), it can determine that the user is in a scenario prone to coronary heart disease, requiring coronary heart disease risk screening.

[0112] The ECG sensor 180D can be used to perform six-lead ECG measurements of the limbs to obtain electrocardiogram (ECG) data. Optionally, the wearable device 100 is equipped with multiple electrodes corresponding to the ECG sensor 180D for performing six-lead ECG measurements of the limbs.

[0113] For example, such as Figure 2B As shown, taking a smartwatch as an example of a wearable device 100, the distribution of electrodes is explained. Figure 2B As shown, the smartwatch includes a watch face and a watch band. The smartwatch is equipped with three electrodes: electrode A, located on the inside of the watch band opposite the watch face; electrode B, located on the side of the watch face in a user-accessible position; and electrode C, located on the outside of the watch band. Specifically, this application embodiment does not specifically limit the electrode positions, but the positions of electrodes A, B, and C can at least meet the requirements of at least one of the following six-lead electrocardiogram measurement procedures for limbs.

[0114] For example, if a user wears a smartwatch on their left wrist, they can place their right index finger on electrode B and electrode C on the abdomen about 5cm to the left of the navel, and hold it for a preset time (e.g., 30 seconds) to perform a six-lead electrocardiogram measurement of the limbs.

[0115] For example, if a user wears a smartwatch on their left wrist, they can place their right index finger on electrode B and electrode C on their left ankle, and hold for a preset time (e.g., 30 seconds) to perform a six-lead ECG measurement of the limb.

[0116] For example, if a user wears a smartwatch on their left wrist, they can place their right index finger on electrode B and electrode C on the middle of their left thigh, hold for a preset time (e.g., 30 seconds), and perform a six-lead ECG measurement of the limb.

[0117] Touch sensor 180E, also known as a "touch device," can be located on display screen 194. The touch sensor 180E and display screen 194 together form a touchscreen, also known as a "touchscreen." Touch sensor 180E detects touch operations applied to or near it. Touch sensor 180E can transmit the detected touch operation to the application processor to determine the type of touch event. Visual output related to the touch operation can be provided through display screen 194. In other embodiments, touch sensor 180E may also be located on the surface of wearable device 100, in a different position than display screen 194.

[0118] Button 190, including a power button, etc. Button 190 can be a mechanical button or a touch button. Wearable device 100 can receive button input and generate key signal inputs related to user settings and function control of wearable device 100.

[0119] Motor 191 can generate vibration alerts. Motor 191 can be used for incoming call vibration alerts or for touch vibration feedback. For example, different application scenarios (such as time reminders, receiving messages, alarm clocks, etc.) can correspond to different vibration feedback effects. Touch vibration feedback effects can also be customized.

[0120] Indicator 192 can be an indicator light, used to indicate charging status, power changes, or to indicate messages, missed calls, notifications, etc.

[0121] In some embodiments, coronary artery disease (CAD) is a heart disease caused by narrowing or blockage of the coronary arteries due to atherosclerosis, resulting in myocardial ischemia, hypoxia, or necrosis. Generally, based on its pathogenesis, CAD can be divided into acute coronary syndrome and chronic stable CAD. Acute coronary syndrome includes ST-segment elevation myocardial infarction, non-ST-segment elevation myocardial infarction, and unstable angina; chronic stable CAD includes stable exertional angina and asymptomatic myocardial ischemia.

[0122] It should be noted that the wearable device 100 or electronic device 200 can determine the amplitude and variation of the ST segment based on ECG data, and determine whether there is ST segment elevation or non-ST segment elevation. The ST segment corresponds to the slow repolarization process of the central ventricle in an electrocardiogram. The specific meaning and determination method of the ST segment can be found in existing technologies, and this application embodiment does not provide specific details.

[0123] In some embodiments, certain scenarios can easily trigger coronary heart disease. Therefore, wearable devices 100 can monitor the user's physiological signs and environment to determine whether the user is in a scenario that is likely to trigger coronary heart disease. For example, strenuous exercise, cold, drastic temperature fluctuations, and emotional excitement can all induce myocardial ischemia, leading to acute coronary syndrome (such as unstable angina) or chronic stable coronary heart disease (such as stable exertional angina).

[0124] For example, such as Figure 3 As shown, the wearable device 100 is implemented as described above. Figure 2A After detecting measurement data, the sensor module 180 can determine whether the user is in a preset scenario where coronary heart disease is likely to occur. If the wearable device 100 determines that the user is in a preset scenario, it can remind the user to perform an electrocardiogram (ECG) measurement for coronary heart disease screening.

[0125] Optionally, the aforementioned preset scenarios may include one or more of the following: exercise scenarios, abnormal ambient temperature scenarios, abnormal heart rate variability scenarios, and preset reminder time scenarios.

[0126] In some embodiments, the wearable device 100 is as described above. Figure 2A The photoplethysmography (PPG) sensor 180A and accelerometer 180B shown monitor the activity intensity of a user wearing the wearable device 100 to determine if the user is engaged in strenuous exercise. Strenuous exercise can easily induce symptoms of myocardial ischemia in patients with coronary heart disease, posing a risk of coronary heart disease to the user, thus requiring reminders to the user. For example, ... Figure 3 As shown, the system reminds users to take ECG measurements.

[0127] For example, wearable device 100 can obtain PPG signals through photoplethysmography (PPG) sensor 180A to determine the user's real-time heart rate. If wearable device 100 determines that the user's average heart rate within a preset time period exceeds a preset heart rate threshold, it can determine that the user is in an exercise scenario. Optionally, the preset heart rate threshold can be determined based on factors such as the user's age; for example, preset heart rate threshold = (220 – age – resting heart rate) * 60% + resting heart rate. The preset time period is, for example, 1 hour. If wearable device 100 determines, based on the PPG signal, that the user's average heart rate within one hour exceeds the preset heart rate threshold, it can determine to remind the user to perform an electrocardiogram (ECG) measurement.

[0128] For example, the wearable device 100 can determine the user's movement type based on waveform data measured by the accelerometer 180B. Different movement types can be mapped to different movement intensities, thereby determining whether the movement intensity meets preset conditions. Optionally, movement types include stationary, walking, running, climbing stairs, jumping, etc. Among them, running, climbing stairs, and jumping can be mapped to high-intensity exercise. Then, if the wearable device 100 determines that the user's movement type is mapped to high-intensity exercise for more than a preset time (e.g., half an hour), it can determine that the user is in an exercise scenario and can determine to remind the user to perform electrocardiogram (ECG) measurement.

[0129] For another example, the wearable device 100 can also combine the measurement results of the photoplethysmography (PPG) sensor 180A and the accelerometer 180B to comprehensively determine the user's exercise intensity. This allows it to determine whether the user is in an exercise scenario and whether an ECG measurement reminder is needed. For instance, based on the waveform data measured by the accelerometer 180B, if the wearable device 100 determines that the user has been running, climbing stairs, or jumping for more than a preset time 2 (e.g., half an hour), then based on the PPG signal, the wearable device 100 determines that the user's average heart rate within a preset time 1 (e.g., 1 hour) exceeds a preset heart rate threshold. Therefore, the wearable device 100 can determine that the user's exercise intensity meets the preset conditions, indicating an exercise scenario, and that an ECG measurement reminder is needed. This ensures the accuracy of the exercise intensity judgment and avoids misjudgments caused by relying on a single condition.

[0130] Optionally, the wearable device 100 may remind the user to perform an electrocardiogram (ECG) measurement after the user has finished exercising. Alternatively, it may remind the user to perform an ECG measurement during the user's exercise.

[0131] For example, wearable device 100, as described above Figure 2A The accelerometer 180B shown monitors the user's movement and can remind the user to perform an electrocardiogram (ECG) measurement 1-2 minutes after the user stops exercising. This avoids the influence of exercise on the sensor's measurement data, such as the influence of exercise on the measurement results of the photoplethysmography (HRV) sensor 180A, which could lead to inaccurate HRV analysis and misjudgments.

[0132] For example, wearable device 100, through the above... Figure 2A The accelerometer 180B shown monitors the user's movement. During the movement, the photoplethysmography (PPG) sensor 180A can detect abnormal HRV and directly remind the user to perform ECG measurements to avoid further harm to the user from continued exercise.

[0133] The methods for reminding users of ECG measurements include displaying a measurement reminder interface, voice reminders, vibration reminders, and so on. This application does not specifically limit the methods used in this embodiment.

[0134] For example, wearable device 100 determines that the user is currently in a preset exercise scenario and can send instruction information to electronic device 200. Upon receiving the instruction information, electronic device 200 can display, for example... Figure 4 The measurement reminder interface 401 shown displays a prompt message 41 to remind the user to perform an electrocardiogram (ECG) measurement.

[0135] For example, such as Figure 5 As shown, the wearable device 100 determines that the user is currently in a preset exercise scenario and can directly display the measurement reminder interface 501 to prompt the user to perform an electrocardiogram (ECG) measurement. This direct display of the reminder information by the wearable device 100 avoids the problem of users not being able to view the reminder information in a timely manner due to not carrying the electronic device 200, thus reducing risk.

[0136] It should be noted that the prompt information can be displayed on the wearable device 100, on the electronic device 200, or jointly by the wearable device 100 and the electronic device 200. This will not be explained further below.

[0137] In other embodiments, the wearable device 100 is implemented as described above. Figure 2A The temperature sensor 180°C shown monitors the ambient temperature. If the wearable device 100 determines, based on the measured ambient temperature, that the ambient temperature is below a preset temperature threshold, it can determine that the user is currently in a cold environment. Then, the wearable device 100 can start timing. If, after a first preset time, it still determines that the user is in a cold environment, it can determine that the user is in a scenario prone to coronary heart disease. Alternatively, if the wearable device 100 determines, based on the measured ambient temperature, that the variance of the ambient temperature fluctuation within a second preset time period is greater than a preset temperature threshold of 2, it can determine that the user is currently in a scenario prone to coronary heart disease. Then, if... Figure 3 As shown, the wearable device 100 can remind the user to take an electrocardiogram (ECG) measurement. For example, if the wearable device 100 determines that the user has been in a cold environment for more than one hour based on the temperature sensor 180°C measurement, it can remind the user to take an ECG measurement. Alternatively, if it determines that the variance of the ambient temperature fluctuation in the user's environment within half an hour is greater than a preset temperature threshold of 2, it can remind the user to take an ECG measurement.

[0138] For example, wearable device 100 determines that the user is in a preset scenario of a cold environment with abnormal ambient temperature, and can send an instruction message to electronic device 200. Upon receiving the instruction message, electronic device 200 can display, as shown below... Figure 6The measurement reminder interface 601 shown displays a prompt message 61 to remind the user to perform an electrocardiogram (ECG) measurement.

[0139] In other embodiments, the wearable device 100 is implemented as described above. Figure 2A The photoplethysmography (PPG) sensor 180A shown measures the PPG signal, and heart rate variability (HRV) analysis is performed using the PPG signal. A normal heart is innervated by the sympathetic and vagus nerves. Sympathetic nerve excitation increases the heart rate, while vagus nerve excitation decreases the heart rate. HRV analysis measures whether the innervation of the heart by the sympathetic and vagus nerves is in balance, quantitatively assessing the tone and balance of the cardiac sympathetic and vagus nerves, and reflecting the activity of the autonomic nervous system. This allows for the assessment of cardiovascular disease conditions and prevention. HRV is a valuable indicator for predicting sudden cardiac death and arrhythmic events.

[0140] For example, based on the PPG signal, during HRV analysis, the wearable device 100 determines that the high-frequency (HF) power within a preset time period is greater than or equal to a preset frequency threshold, and that the coefficient of variability (CV) is greater than or equal to a preset CV threshold. This indicates that the user has an abnormal HRV and may be at risk of coronary heart disease. For instance, the wearable device 100 determines that within the most recent five-minute period, the HF power is greater than the preset frequency threshold, and the CV is greater than the CV threshold. Then, the wearable device 100 can send an indication message to the electronic device 200. Upon receiving the indication message, the electronic device 200 can display, as shown below... Figure 7 The measurement reminder interface 701 shown displays a prompt message 71 to remind the user to perform an electrocardiogram (ECG) measurement.

[0141] The coefficient of variation (CV) represents the standard deviation of heart rate intervals divided by the mean heart rate intervals, reflecting the degree of fluctuation in heart rate intervals. High-frequency (HF) power represents the sum of power in the frequency band of (0.15-0.4) Hz, reflecting the regulatory capacity of the heart's parasympathetic nervous system.

[0142] In some embodiments, there may be a high risk of coronary heart disease during certain specific time periods. Therefore, a preset reminder time can be set to remind users to perform electrocardiogram (ECG) measurements at that time, thus reducing the risk. For example, after waking up in the morning, a user's metabolic activity increases, heart rate rises, and myocardial load increases. For some users, this may lead to symptoms of myocardial ischemia. Therefore, these users can be reminded to perform ECG measurements within half an hour of waking up to screen for coronary heart disease. These users may include those with coronary artery stenosis, a history of smoking, a history of hypertension, a family history of cardiovascular disease, or those over 60 years of age.

[0143] For example, a preset reminder time (e.g., 7:00 AM) is configured in wearable device 100; or, wearable device 100 is configured according to... Figure 2A The sensor module 180, as shown, measures data to determine when the user wakes up. The wearable device 100 can then send an instruction to the electronic device 200. Upon receiving the instruction, the electronic device 200 can display, as shown... Figure 8 The measurement reminder interface 801 shown displays a prompt message 81 to remind the user to perform an electrocardiogram (ECG) measurement.

[0144] Optionally, a night mode can be configured in the wearable device 100 to prevent the wearable device 100 from prompting the user to perform ECG measurements at night, thus affecting the user's sleep. For example, in night mode, the wearable device 100 automatically turns off the measurement reminder function at night (e.g., 10:00 PM to 6:00 AM the next day). Optionally, in night mode, the wearable device 100 can also pause measurements from some sensors (such as accelerometers, temperature sensors, etc.) to reduce the power consumption of the wearable device 100. Optionally, the wearable device 100 can be equipped with a first application for monitoring the user's heart activity, and the user can turn the night mode on or off in the first application. This first application could be, for example, a coronary heart disease screening application.

[0145] In some embodiments, when a user first uses the wearable device 100, the wearable device 100 may default to enabling the photoplethysmography (PPG) sensor to monitor the user's heart rate variability (HRV) to prevent the user from having a risk of coronary heart disease. Optionally, the user may also actively disable the HRV monitoring function in the first application. Optionally, while disabling the HRV monitoring function, the user may also actively screen for coronary heart disease using the wearable device 100. Further, if the wearable device 100 determines that the user's active screening results in an abnormal electrocardiogram or that the number of suspected coronary heart disease cases exceeds a preset number (e.g., 3 times), the wearable device 100 may actively activate the HRV monitoring function.

[0146] In some embodiments, the above-mentioned ECG measurement reminder methods may include other reminder methods besides displaying prompt information. For example, the wearable device 100 may remind the user to perform ECG measurements through vibration reminders, sound reminders, etc., and this application embodiment does not specifically limit this.

[0147] Therefore, compared to existing coronary heart disease screening devices that can only screen for coronary heart disease when the user is at rest and cannot promptly identify chronic stable coronary heart disease induced by special factors such as exercise, the cardiac activity monitoring method provided in this application can achieve more timely early warning of coronary heart disease by judging various preset scenarios that are likely to induce the onset of coronary heart disease, thus avoiding missed diagnoses.

[0148] In some embodiments, such as Figure 3 As stated above, after the ECG measurement reminder is received, if it is confirmed that the user is performing an ECG measurement, the wearable device 100 and / or electronic device 200 can perform coronary heart disease screening based on the measurement results.

[0149] For example, such as Figure 4 As shown, after the electronic device 200 detects the user's click on the confirmation control 42, it determines that the user allows the ECG measurement and can then launch a second application for coronary heart disease screening. Figure 9 As shown in (a), after the electronic device 200 launches the second application, it displays interface 901. On interface 901, the electronic device 200 displays prompt information 91, prompting the user to perform ECG measurements using the wearable device 100 (such as a smartwatch) according to the ECG measurement posture. Optionally, it may also prompt the user that the ECG measurement posture can be obtained from the instruction manual of the wearable device 100, or directly display the ECG measurement posture on interface 901. Figure 9 (not shown in (a)). After detecting that the user clicks the start measurement control 92, the electronic device 200 can determine that the user is ready to start the electrocardiogram (ECG) measurement. Optionally, the electronic device 200 can send an ECG measurement signal to the wearable device 100, instructing the wearable device 100 to perform the user's ECG measurement.

[0150] It should be noted that the first application installed in the wearable device 100 and the second application installed in the electronic device 200 can be different versions of the same application, such as a coronary heart disease screening application for a smartwatch and a mobile phone, respectively. Alternatively, the first application installed in the wearable device 100 and the second application installed in the electronic device 200 can be different applications for coronary heart disease screening. This application does not specifically limit the scope of the embodiments in this regard.

[0151] In addition, Figures 6-8 In the scenario shown, electronic device 200 can also respond to the user's click of the "OK" control and launch a second application for coronary heart disease screening. This process can be referenced. Figure 9 The relevant content shown will not be repeated in the embodiments of this application.

[0152] In some embodiments, the electrocardiogram (ECG) measurement may be, for example, a six-lead ECG measurement of the limbs, such as... Figure 2B As shown, assuming the wearable device 100 is a smartwatch, the ECG measurement posture could include, for example, the user wearing the smartwatch on their left wrist, placing their right index finger on the B electrode, and attaching the C electrode approximately 5cm to the left of the navel, holding this position for a preset time (e.g., 30 seconds); or, the user wearing the smartwatch on their left wrist, placing their right index finger on the B electrode, and attaching the C electrode to their left ankle, holding this position for a preset time (e.g., 30 seconds); or, the user wearing the smartwatch on their left wrist, placing their right index finger on the B electrode, and attaching the C electrode to the mid-thigh of their left leg, holding this position for a preset time (e.g., 30 seconds). After determining that the user is in the ECG measurement posture, or after receiving an ECG measurement signal from the electronic device 200, the wearable device 100 begins ECG measurement and determines the ECG data.

[0153] In some embodiments, the wearable device 100 may send an ECG measurement completion signal to the electronic device 200 after determining that the ECG measurement is complete. Upon receiving the ECG measurement completion signal, the electronic device 200 may display, as shown below... Figure 9 The interface 902 shown in (b) prompts the user that the ECG measurement is complete.

[0154] Optionally, to ensure the accuracy of coronary heart disease screening results, the electronic device 200 can also collect the user's symptom data. For example, ... Figure 9 As shown in (b), after the electronic device 200 detects the user clicking the next control 93 on the interface 902, it displays the following... Figure 9 Interface 903 is shown in (c). On interface 903, electronic device 200 can collect symptom data entered and / or selected by the user, as shown by reference numeral 94 in the attached diagram, such as pain location, pain type, pain duration, accompanying symptoms, and other activities within the past hour. After detecting that the user clicks the next step control 95 shown on interface 903, electronic device 200 determines that the user has completed the input of symptom data.

[0155] In some embodiments, the electronic device 200 can acquire ECG data, PPG data, and acceleration data sent by the wearable device 100. The electronic device 200 can perform coronary heart disease screening based on one or more of the following data: ECG data, PPG data, acceleration data, symptom data, and basic user information. The basic user information may include, for example, one or more of the following: gender, age, smoking history, history of hypertension, family history of cardiovascular disease, and history of coronary heart disease.

[0156] Optionally, after detecting that the user has launched the application for monitoring the user's heart activity for the first time, the electronic device 200 may display a user basic information collection page and receive the user basic information selected or filled in by the user on the page. Alternatively, the electronic device 200 may display the user basic information collection page and receive the user basic information selected or filled in by the user on the page before performing the user's electrocardiogram measurement.

[0157] For example, after performing coronary heart disease screening based on the above data, electronic device 200 can proceed as follows: Figure 10 The illustrated process categorizes potential risks of coronary artery disease (CAD). For example, CAD risk is categorized as normal, intermediate risk, and high risk. Normal risk indicates the user has no risk of CAD; intermediate risk indicates the user has abnormal electrocardiogram (ECG); and high risk indicates the user has a risk of acute coronary syndrome or chronic stable CAD. Figure 10 As shown, the process of classifying the risk of coronary heart disease may include the following steps.

[0158] S1001. Electronic device 200 determines that the user is suspected of having coronary heart disease. If yes, step S1002 can be executed; if no, step S1004 can be executed.

[0159] In some embodiments, the electronic device 200 can determine whether a user is suspected of having coronary heart disease based on one or more of the following data: ECG data, PPG data, acceleration data, symptom data, and user basic information.

[0160] For example, if electronic device 200 determines that the data in the EGG data used to determine whether someone has coronary heart disease (such as the ST segment) is abnormal, it can determine that the user is suspected of having coronary heart disease. Alternatively, if electronic device 200 determines that the data in the EGG data used to determine whether someone has coronary heart disease is normal, and the symptom data is also normal (such as the user having no pain symptoms or accompanying symptoms), it can determine that the user does not have coronary heart disease.

[0161] It should be noted that other methods for determining whether a user is suspected of having coronary heart disease by combining the above data can refer to existing technologies, and will not be elaborated here.

[0162] S1002, Electronic device 200 determines ST segment elevation in the electrocardiogram data. If yes, it determines the risk of acute coronary syndrome; if not, proceed to step S1003.

[0163] In some embodiments, if the electronic device 200 determines that a user is suspected of having coronary artery disease, it can determine the ST segment amplitude value based on ECG data, and then determine whether the ST segment is elevated. If the ST segment is elevated, it is suspected to be acute ST-segment elevation myocardial infarction (STEMI), and the user can be identified as having a risk of acute coronary syndrome. If the ST segment is not elevated, the electronic device 200 can further determine the user's risk of coronary artery disease.

[0164] For example, such as Figure 3 As shown, after the coronary heart disease screening is completed, the screening results can be displayed so that users can understand their own situation. Figure 11 As shown on interface 1101, after determining that the user has a risk of acute coronary syndrome, the electronic device 200 can identify it as a high-risk scenario for coronary heart disease and recommend that the user go to the hospital for treatment in a timely manner.

[0165] In this way, based on the type of coronary heart disease, different reminders are given for different types of coronary heart disease, ensuring that patients with acute coronary syndrome who have a strong need for timely medical attention can receive timely treatment and improve treatment outcomes.

[0166] It should be noted that the method by which the electronic device 200 determines the ST segment amplitude value based on ECG data can refer to the prior art, and this application embodiment will not describe it in detail.

[0167] S1003, Electronic device 200 determines that the exercise intensity meets preset conditions. If yes, it determines that there is a risk of developing chronic stable coronary artery disease; if no, it determines that there is a risk of developing acute coronary syndrome.

[0168] In some embodiments, if the electronic device 200 determines that the ST segment is not raised, it can determine the user's motion intensity based on acceleration data and / or PPG signals, and then determine whether the motion intensity meets preset conditions.

[0169] For example, electronic device 200 can receive the PPG signal sent by wearable device 100 (the wearable device 100 measures the signal through photoplethysmography sensor 180A), determine the user's real-time heart rate based on the PPG signal, and determine the user's exercise intensity based on the real-time heart rate. If it is determined that the user's average heart rate within a preset time period exceeds a preset heart rate threshold, it can be determined that the user's exercise intensity meets the preset conditions. Optionally, the preset heart rate threshold can be determined based on factors such as the user's age. For example, preset heart rate threshold = (220 – age – resting heart rate) * 60% + resting heart rate. The preset time period is, for example, 1 hour.

[0170] For example, electronic device 200 can obtain acceleration data sent by wearable device 100 (data measured by motion sensors such as accelerometer 180B by wearable device 100) and determine that the user's exercise type is mapped to high-intensity exercise. If the duration exceeds a preset time, it can be determined that the user's exercise intensity meets preset conditions. Optionally, electronic device 200 can determine the exercise type based on the waveform data of the acceleration data. Different exercise types correspond to different exercise intensities. For example, exercise types include standing still, walking, running, climbing stairs, jumping, etc. Among them, running, climbing stairs, and jumping are classified as high-intensity exercise.

[0171] For example, the electronic device 200 determines that the exercise intensity meets the preset conditions only after the exercise intensity determined by the PPG signal meets the corresponding preset conditions (such as the average heart rate exceeding a preset heart rate threshold within a preset time 1) and the exercise intensity determined by the acceleration data meets the corresponding preset conditions (such as the type of exercise meeting the requirements within a preset time 2). That is, it determines that the user has engaged in strenuous exercise.

[0172] In some embodiments, after determining whether the motion intensity meets preset conditions, the electronic device 200 can classify the corresponding risks.

[0173] For example, if electronic device 200 determines that the exercise intensity meets preset conditions, it can identify a risk of developing chronic stable coronary artery disease. Chronic stable coronary artery disease is also a high-risk disease, requiring timely notification to the user; electronic device 200 could display something like this. Figure 12 The interface shown is 1201. Chronic coronary artery disease requires medical attention, but compared to acute coronary syndrome, the timing requirements for consultations are more lenient for chronic stable coronary artery disease. Users can be advised to schedule a visit to the hospital. Optionally, users can also be advised to avoid strenuous physical activity and to manage their emotions, avoiding excessive excitement or anger.

[0174] For example, if an electronic device determines that the exercise intensity does not meet the preset conditions, it can indicate a risk of acute coronary syndrome, and the user should be advised to seek medical attention promptly.

[0175] In some embodiments, in step S1003, the electronic device 200 may also combine the pain duration in the acquired symptom data to determine the user's risk of coronary heart disease.

[0176] For example, if electronic device 200 determines that the exercise intensity meets preset conditions and the pain duration is less than a pain duration threshold, it can determine the risk of developing chronic stable coronary artery disease. As another example, if electronic device 200 determines that the exercise intensity is less than an exercise intensity threshold and the pain duration is greater than a pain duration threshold, it can determine the risk of developing acute coronary syndrome.

[0177] In this way, reminders are sent separately according to the type of coronary heart disease, meeting the different needs of different types of coronary heart disease for timely medical treatment and ensuring the safety of users' lives.

[0178] It should be noted that the preset conditions for judging exercise intensity used by the electronic device 200 or wearable device 100 in determining that the user is in a preset exercise scenario can be the same as the preset conditions for judging the exercise scenario used in classifying disease risk. Therefore, if the electronic device 200 or wearable device 100 performs an electrocardiogram (ECG) measurement after determining that the user is in an exercise scenario, then during the risk classification process in step S1003, it is not necessary to judge the user's exercise intensity again; it can be directly determined that the user's exercise intensity meets the preset conditions.

[0179] S1004, Electronic device 200 confirms that the user's ECG data is normal. If yes, confirm the user is normal. If no, confirm the user does not have coronary heart disease but has an abnormal ECG.

[0180] In some embodiments, after determining in step S1001 above that the user is not suspected of having coronary heart disease, the electronic device 200 may further determine whether there are other abnormalities in the user's ECG data.

[0181] For example, such as Figure 13 On interface 1301, the electronic device 200 determines that the user's ECG data, excluding data used to determine whether they have coronary heart disease (such as ST segment abnormalities), is normal. It can then display a normal coronary heart disease screening result to alleviate the user's anxiety. If the electronic device determines that the user's ECG data, excluding data used to determine whether they have coronary heart disease (such as ST segment abnormalities), contains abnormalities, it can determine that the user is at medium risk and indicate that they do not have coronary heart disease, but that an ECG abnormality exists.

[0182] Thus, through the steps S1001-S1004 described above, the electronic device 200 completes the classification of coronary heart disease risk, enabling it to provide different reminders to users with different risk profiles, thereby meeting the needs of different users.

[0183] In some embodiments, the wearable device 100 can also perform the coronary heart disease screening and screening result display performed by the electronic device 200. For example, the wearable device 100 can acquire symptom data sent by the electronic device 200, or the wearable device 100 can also display a symptom data collection page to collect the user's symptom data. Then, the wearable device 100 performs coronary heart disease screening and displays the screening results based on ECG data, PPG data, acceleration data, symptom data, user basic information, and other data. The specific process can be referred to the process of the electronic device 200 performing coronary heart disease screening and displaying screening results, which will not be elaborated further in this embodiment.

[0184] In some scenarios, for patients with a history of myocardial infarction, wearable devices can be used to monitor their PPG data over a long period. For example, on a daily basis, the standard deviation of the time interval between successive normal heart beats (SDNN) and the percentage of interval differences of successive RR-intervals greater than 50ms (pNN50) in a continuous 24-hour HRV analysis can be calculated. The trends of SDNN and pNN50 can then be analyzed over a 7-day period to determine whether the user needs to seek medical attention.

[0185] SDNN is a time-domain indicator of heart rate variability, representing the standard deviation of consecutive R-peak intervals on an electrocardiogram (ECG). PNN50 is also a time-domain indicator of heart rate variability, representing the proportion of heartbeat intervals with a difference of more than 50 ms between adjacent heartbeats. If a user's SDNN and pNN50 indicators in heart rate variability continue to decrease, the patient's risk of death increases.

[0186] For example, wearable device 100 can send PPG data to electronic device 200. During PPG data monitoring, if electronic device 200 determines that the SDNN and pNN50 in heart rate variability within a preset period continuously decrease and fall below normal values, it can display as follows: Figure 14 Interface 1401 is shown in (a). In interface 1401, electronic device 200 can prompt the user to seek medical attention promptly to ensure the user's safety. After detecting the user's click on control 141, electronic device 200 can display the following... Figure 14 The interface 1402 shown in (b) displays detailed HRV trend statistics to the user so that the user can understand their own physical condition.

[0187] It should be noted that the wearable device 100 can also directly monitor heart rate variability and display HRV trend statistics. The specific execution method can be referred to the process of heart rate variability monitoring performed by the electronic device 200 mentioned above, which will not be repeated in this embodiment.

[0188] Thus, for users who have already suffered myocardial infarction, targeted and continuous monitoring of their heart rate variability indicators can provide timely risk warnings for the prognosis of myocardial infarction patients and enable outpatient management of patients.

[0189] In other scenarios, for patients with a prognosis of myocardial infarction or those without, wearable device 100 can be used for long-term monitoring of their PPG data. During PPG data monitoring, electronic device 200 can obtain PPG data sent by wearable device 100, determine if the SDNN and pNN50 in heart rate variability continuously decrease and fall below normal values ​​within a preset period, and determine if the user is suspected of having coronary heart disease. This can be achieved through the methods described above. Figure 10 The method described above categorizes the risk of coronary heart disease and then prompts users to take corresponding measures. For details, please refer to the above. Figure 10 The relevant content shown will not be repeated here.

[0190] Optionally, during long-term PPG data monitoring, the wearable device 100 can also directly access data as described above. Figure 10 The method shown classifies the risk of coronary heart disease and prompts users to take corresponding measures.

[0191] For example, Figure 15 This is a schematic flowchart illustrating a cardiac activity monitoring method provided in an embodiment of this application. The method is applied to a wearable device and includes the following steps.

[0192] S1501. Determine that the user is in a preset scenario by using at least one of the first data monitored by the first sensor, the second data monitored by the second sensor, and the third data monitored by the third sensor.

[0193] In some embodiments, the first sensor is a motion sensor (such as an accelerometer), the second sensor is a temperature sensor, and the third sensor is a photoplethysmography (PPG) sensor. The preset scenarios include one or more of the following: motion scenarios, abnormal ambient temperature scenarios, and abnormal heart rate variability scenarios. The preset scenario is one that is likely to induce coronary heart disease.

[0194] For example, the wearable device determines that the user is in an exercise scenario if the exercise intensity meets a first preset condition based on first data monitored by a first sensor and / or third data monitored by a third sensor. Alternatively, it determines that the user is in an abnormal ambient temperature scenario if the second data monitored by a second sensor is less than a second threshold within a first preset time period, or if the variance of the second data within a second preset time period is greater than a third threshold. Alternatively, it determines that the user is in an abnormal heart rate variability scenario if the third data monitored by a third sensor meets a second preset condition.

[0195] Optionally, the wearable device can determine the user's exercise intensity based on the first data and / or the third data, and then determine whether the exercise intensity meets the first preset condition.

[0196] For example, the first data is the acceleration waveform data detected by the motion sensor. Based on the waveform data, the wearable device determines that the user's movement type is mapped to high-intensity exercise, and if the duration of the high-intensity exercise meets a preset time requirement, the exercise intensity can be determined to meet a first preset condition. Optionally, the movement type includes stationary, walking, running, climbing stairs, jumping, etc. Among these, running, climbing stairs, and jumping can be mapped to high-intensity exercise.

[0197] For example, the third data is the PPG signal. The wearable device determines the user's real-time heart rate based on the PPG signal. If it determines that the user's average heart rate exceeds a preset heart rate threshold within a preset time, it can be determined that the user's exercise intensity meets the first preset condition. Optionally, the preset heart rate threshold = (220 – age – resting heart rate) * 60% + resting heart rate.

[0198] Optionally, a second preset condition is used to determine HRV abnormalities. For example, based on the PPG signal, during HRV analysis, the wearable device determines that the high-frequency power within a preset time period is greater than or equal to a preset frequency threshold, and that the coefficient of variation is greater than or equal to a preset coefficient of variation threshold (i.e., the first preset condition is met). This can determine that the user has an HRV abnormality and may be at risk of coronary heart disease.

[0199] S1502, Remind the user to perform an electrocardiogram (ECG) measurement.

[0200] In some embodiments, when the wearable device determines that the user is in a preset scenario, it prompts the user to perform an electrocardiogram (ECG) measurement. The methods for prompting the user to perform an ECG measurement include displaying a measurement reminder interface, voice prompts, vibration prompts, and other methods, which are not specifically limited in this embodiment.

[0201] For example, such as Figure 5 As shown, the wearable device displays an ECG measurement interface 501 to prompt the user to perform an ECG measurement. Alternatively, the wearable device sends an instruction message to an electronic device with which it has established a paired connection, instructing the electronic device to display, as shown... Figure 4 ,or Figure 6 ,or Figure 7 The ECG measurement interface shown prompts the user to perform an ECG measurement.

[0202] S1503: Receive user input instructions and acquire fourth data through the electrocardiogram sensor.

[0203] The fourth data point is electrocardiogram (ECG) data.

[0204] In some embodiments, after receiving an input instruction from the user, the wearable device may determine to begin measuring the user's electrocardiogram (ECG) data using an ECG sensor.

[0205] For example, the wearable device displays a first interface for displaying an ECG measurement reminder, which includes ECG measurement posture information. The wearable device receives user input commands and acquires fourth data measured by the user in the ECG measurement posture using an ECG sensor.

[0206] For example, such as Figure 9 As shown in (a), the electronic device displays a prompt message 91 on interface 901, prompting the user to perform an ECG measurement using the wearable device according to the ECG measurement posture. After detecting the user's click on the start measurement control 92, the electronic device determines that the user is ready to begin the ECG measurement. Then, the electronic device sends an ECG measurement instruction signal to the wearable device, instructing it to perform the user's ECG measurement. Accordingly, the wearable device begins the ECG measurement based on the instruction signal.

[0207] S1504. Based on the first and / or third data, and the fourth data, classify the risk of cardiac activity.

[0208] In some embodiments, the wearable device can classify the risk of cardiac activity based on the user's exercise intensity and electrocardiogram (ECG) data. Exercise intensity can be determined based on measurements from a motion sensor and a photoplethysmography (PPG) sensor. In other words, the wearable device determines the user's exercise intensity based on first and / or third data, and then combines this with fourth data (i.e., ECG data) to classify the risk of cardiac activity.

[0209] In some embodiments, different types of coronary artery disease have different requirements for the timing of medical visits. For example, acute coronary syndrome requires immediate medical attention, while chronic stable coronary artery disease has a more flexible timeframe, allowing patients to schedule their visits to the hospital. Therefore, the risk of cardiac activity can be categorized, providing different information to meet the time requirements for medical visits in coronary artery disease.

[0210] For example, the risks associated with cardiac activity can be categorized into a primary risk (such as the risk of developing acute coronary syndrome) and a secondary risk (such as the risk of developing chronic stable coronary artery disease). Both risks fall under the high-risk category for coronary artery disease warnings, requiring users to seek medical attention. If the wearable device classifies the risk of cardiac activity as a primary risk, it will display a primary alert message to remind the user to seek medical attention promptly. Alternatively, if the wearable device classifies the risk of cardiac activity as a secondary risk, it will display a secondary alert message to remind the user to schedule a medical visit.

[0211] In some embodiments, the wearable device may display a second interface before classifying the risk of cardiac activity based on the first and / or third and fourth data. It may also receive fifth data input and / or selected by the user on the second interface. The fifth data includes one or more of the following: pain location, pain type, pain duration, accompanying symptoms, and activities within a recent preset time period. The fifth data is user symptom data.

[0212] In some embodiments, the wearable device may also display a third interface before classifying the risk of cardiac activity based on the first data and / or the third data, and the fourth data. It may also receive sixth data input and / or selected by the user on the third interface, which includes one or more of the following: gender, age, history of smoking, history of hypertension, family history of cardiovascular disease, and history of coronary heart disease. The sixth data represents basic user information.

[0213] In some embodiments, the wearable device can also classify the risk of cardiac activity by combining user symptom data and basic user information. For example, the wearable device can classify the risk of cardiac activity based on first data, and / or third data, as well as fourth, fifth, and sixth data.

[0214] By combining user symptom data and basic user information to classify the risks associated with cardiac activity, the accuracy of risk classification can be effectively improved, the effectiveness of coronary heart disease screening can be enhanced, and the user experience can be improved.

[0215] Optionally, the fifth and sixth data points can be collected before the risk assessment of cardiac activity. They can be collected separately or together. For example, they can be collected after the ECG measurement is completed, after the ECG measurement reminder, or after the wearable device is first activated to collect the user's basic information.

[0216] Therefore, compared to existing coronary heart disease screening devices that can only screen for coronary heart disease when the user is at rest and cannot promptly identify chronic stable coronary heart disease induced by special factors such as exercise, the cardiac activity monitoring method provided in this application can achieve more timely early warning of coronary heart disease by judging various preset scenarios that are likely to induce the onset of coronary heart disease, thus avoiding missed diagnoses.

[0217] Furthermore, by classifying different risks associated with cardiac activity based on the type of coronary heart disease and providing separate reminders for different situations, it ensures that patients with acute coronary syndrome who have strict requirements for timely medical attention can receive timely treatment, thereby improving treatment outcomes.

[0218] Optionally, the wearable device can also perform the steps and functions performed by the wearable device 100 in the above embodiments, thereby realizing the cardiac activity monitoring method provided in the above embodiments.

[0219] The above combination Figures 3-15 The cardiac activity monitoring method provided in the embodiments of this application is described in detail below. Figure 16 This application provides a detailed description of the cardiac activity monitoring device provided in its embodiments.

[0220] In one possible design, Figure 16 This is a schematic diagram of the structure of a wearable device provided in an embodiment of this application. Figure 16 As shown, the wearable device 1600 may include a transceiver unit 1601 and a processing unit 1602. The wearable device 1600 can be used to implement the functions of the wearable device 100 involved in the above method embodiments.

[0221] Optionally, the transceiver unit 1601 is used to support the wearable device 1600 in performing operations. Figure 15 S1502 and S1503 in the example.

[0222] Optionally, the processing unit 1602 is used to support the wearable device 1600 in performing operations. Figure 15 S1501 and S1504 in the example.

[0223] The transceiver unit may include a receiving unit and a transmitting unit, and may be implemented by a transceiver or transceiver-related circuit components, and may be a transceiver or transceiver module. The operation and / or function of each unit in the wearable device 1600 are respectively to implement the corresponding process of the cardiac activity monitoring method described in the above method embodiments. All relevant content of each step involved in the above method embodiments can be referred to the functional description of the corresponding functional unit, and for the sake of brevity, it will not be repeated here.

[0224] Optionally, Figure 16 The wearable device 1600 shown may also include a storage unit ( Figure 16 (not shown in the image), this storage unit stores a program or instruction. When the transceiver unit 1601 and the processing unit 1602 execute the program or instruction, it causes... Figure 16 The wearable device 1600 shown can perform the cardiac activity monitoring method described in the above method embodiments.

[0225] Figure 16 The technical effects of the wearable device 1600 shown can be referred to the technical effects of the cardiac activity monitoring method described in the above method embodiments, and will not be repeated here.

[0226] In addition to being in the form of a wearable device 1600, the technical solutions provided in this application can also be functional units or chips in a wearable device, or devices used in conjunction with a wearable device.

[0227] This application also provides a chip system, including: a processor coupled to a memory, the memory being used to store programs or instructions, wherein when the program or instructions are executed by the processor, the chip system implements the methods in any of the above method embodiments.

[0228] Optionally, the chip system may contain one or more processors. These processors can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor, implemented by reading software code stored in memory.

[0229] Optionally, the chip system may contain one or more memories. The memory may be integrated with the processor or disposed separately from it; this application embodiment does not limit this. For example, the memory may be a non-transient processor, such as a read-only memory (ROM), which may be integrated with the processor on the same chip or disposed separately on different chips. This application embodiment does not specifically limit the type of memory or the arrangement of the memory and processor.

[0230] For example, the chip system may be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processor (CPU), a network processor (NP), a digital signal processor (DSP), a micro controller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0231] It should be understood that each step in the above method embodiments can be completed by integrated logic circuits in the processor hardware or by instructions in software form. The method steps disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0232] This application also provides a computer-readable storage medium storing a computer program. When the computer program is run on a computer, it causes the computer to perform the aforementioned steps to implement the cardiac activity monitoring method described in the above embodiments.

[0233] This application also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the cardiac activity monitoring method described in the above embodiments.

[0234] Additionally, this application also provides an apparatus. Specifically, the apparatus may be a component or module, and may include one or more processors and a memory connected together. The memory stores a computer program. When the computer program is executed by one or more processors, the apparatus performs the cardiac activity monitoring method described in the above-described method embodiments.

[0235] The apparatus, computer-readable storage medium, computer program product, or chip provided in the embodiments of this application are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0236] The steps of the methods or algorithms described in conjunction with the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an application-specific integrated circuit (ASIC).

[0237] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, the division of the above functional modules is only used as an example. In practical applications, the above functions can be assigned to different functional modules as needed; that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0238] In the several embodiments provided in this application, it should be understood that the disclosed methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation; for example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of modules or units may be electrical, mechanical or other forms.

[0239] Furthermore, 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. The integrated unit can be implemented in hardware or as a software functional unit.

[0240] Computer-readable storage media include, but are not limited to, any of the following: USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media capable of storing program code.

[0241] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope 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 wearable device, characterized in that, include: A processor and a memory, the memory being coupled to the processor, the memory being used to store computer-readable instructions, which, when the processor reads the computer-readable instructions from the memory, enable the wearable device to execute: determining that the user is in a preset scenario based on at least one of first data monitored by a first sensor, second data monitored by a second sensor, and third data monitored by a third sensor; Remind users to perform electrocardiogram (ECG) measurements; Receive user input instructions and acquire fourth data through an electrocardiogram (ECG) sensor, wherein the fourth data is ECG data; Based on the first data and / or the third data, and the fourth data, it is determined that the user is currently in a preset first risk scenario, and a first prompt message is displayed. The first prompt message is used to remind the user to go to the hospital for treatment in a timely manner. Alternatively, if the user is identified as being in a preset second risk scenario, a second prompt message will be displayed, which will prompt the user to schedule a time to visit the hospital. The preset scenarios include sports scenarios, abnormal ambient temperature scenarios, and abnormal heart rate variability scenarios. Determining that the user is in a preset scenario based on at least one of the first data monitored by the first sensor, the second data monitored by the second sensor, and the third data monitored by the third sensor includes: If the user is in a motion scenario, based on the first data monitored by the first sensor and / or the third data monitored by the third sensor, and it is determined that the motion intensity meets the first preset condition, the user is in a motion scenario. If the second data monitored by the second sensor is less than the second threshold within a first preset time, or the fluctuation variance of the second data within a second preset time is greater than the third threshold, it is determined that the user is in an abnormal ambient temperature scenario. If the third data monitored by the third sensor meets the second preset condition, it is determined that the user is in a heart rate variability abnormality scenario.

2. The wearable device according to claim 1, characterized in that, The first sensor is a motion sensor, the second sensor is a temperature sensor, and the third sensor is a photoplethysmography (PPG) sensor.

3. The wearable device according to claim 1 or 2, characterized in that, The process of reminding the user to perform an electrocardiogram (ECG) measurement, receiving user input commands, and acquiring fourth data through an ECG sensor includes: The first interface is displayed, which is used to display ECG measurement reminders, including ECG measurement posture information. The system receives user input commands and acquires the fourth data measured by the user in the posture required for ECG measurement via an ECG sensor.

4. The wearable device according to claim 1 or 2, characterized in that, Before classifying the risk of cardiac activity based on the first data and / or the third data, and the fourth data, the wearable device also performs: Display the second interface; The system receives fifth data input and / or selected by the user on the second interface. The fifth data includes one or more of the following: pain location, pain type, pain duration, accompanying symptoms, and activities within the most recent preset time period.

5. The wearable device according to claim 1 or 2, characterized in that, Before classifying the risk of cardiac activity based on the first data and / or the third data, and the fourth data, the wearable device also performs: Display the third interface; The system receives sixth data input and / or selected by the user on the third interface. The sixth data includes one or more of the following: gender, age, history of smoking, history of hypertension, family history of cardiovascular disease, and history of coronary heart disease.

6. The wearable device according to claim 4, characterized in that, The step of classifying the risk of cardiac activity based on the first data and / or the third data, and the fourth data, includes: Based on the first data, and / or the third data, as well as the fourth, fifth, and sixth data, the risk of cardiac activity is classified.

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