CN113242712B

CN113242712B - Method for updating data sampling interval, method for collecting data according to sampling interval and device thereof

Info

Publication number: CN113242712B
Application number: CN201880097951.1A
Authority: CN
Inventors: 李宏宝; 杨斌; 张�杰; 黄曦; 彭家辉; 陈宜欣
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2018-09-26
Filing date: 2018-12-29
Publication date: 2022-08-26
Anticipated expiration: 2038-12-29
Also published as: CN113242712A; WO2019101222A3; WO2019101222A2

Abstract

A method and apparatus for determining a sampling interval. The method comprises the following steps: the method comprises the steps that the terminal equipment obtains sampling data of a user in a first time, and obtains a first sampling interval based on the sampling data of the user in the first time (S801); the wearable device acquires sampling data of the user in a second time according to the first sampling interval (S803); the terminal device receives the sampling data in the second time sent by the wearable device, and obtains a second sampling interval based on the sampling data of the user in the second time (S805); the wearable device acquires new sampling data of the user in a third time according to the second sampling interval (S807). Since different users collect different data, the adjusted sampling interval is suitable for the corresponding user. Furthermore, different sampling intervals are suitable for different users, so that accurate data acquisition is guaranteed, and power consumption can be saved.

Description

Method for updating data sampling interval, method for collecting data according to sampling interval and device thereof

Technical Field

The present invention relates to the field of communications, and in particular, to a method for updating a data sampling interval, a method for acquiring data according to the sampling interval, and an apparatus thereof.

Background

In 2015, the main cause of death of diseases of Chinese residents accounts for more than 42 percent of cardiovascular diseases. Cardiac arrhythmias are the most common cardiac disorders, with atrial fibrillation (i.e., atrial fibrillation) being one of the most common cardiac arrhythmias. In 1 out of 5 adults in china, there is a risk of atrial fibrillation. Atrial fibrillation increases the risk of stroke by 5 times, and obviously is the primary reason for stroke. According to statistics, the incidence rate of people over 35 years old is about 1%, and the incidence rate increases year by year and has a trend of being young.

Early stage atrial fibrillation is usually paroxysmal, and the treatment can be found as early as possible to avoid the sustainable development. Single ECG (electrocardiography) measurements are difficult to find. The detection rate of the Holter (dynamic electrocardiogram) in 72 hours is about 72 percent, but the Holter is not easy to carry; the wearable equipment based on PPG (Photoplethysmography) has good user experience, and the atrial fibrillation detection rate can reach over 90%.

At present, there are two main ways for PPG data acquisition:

the PPG sensor is always on, which reduces the standby time of the wearable device, affecting the user experience.

PPG sensors are switched on at a fixed frequency, which causes unnecessary power consumption waste for arrhythmia low risk groups; for the people with high risk of arrhythmia, the requirement of detection is not enough.

Disclosure of Invention

The embodiment of the invention provides a method for updating a data sampling interval, a method for acquiring data according to the sampling interval and a device thereof, which can adjust the data sampling interval according to different users.

In a first aspect, an embodiment of the present invention provides a method for updating a data sampling interval, including: the method comprises the steps that terminal equipment obtains sampling data of a user in a first time, and a first sampling interval is obtained based on the sampling data of the user in the first time; the terminal device sends the first sampling interval to a wearable device, so that the wearable device obtains sampling data of a user in a second time according to the first sampling interval; the terminal equipment receives the sampling data of the user in the second time sent by the wearable equipment, and obtains a second sampling interval based on the sampling data of the user in the second time; and the terminal equipment sends the second sampling interval to the wearable equipment so that the wearable equipment acquires new sampling data of the user in a third time according to the second sampling interval.

According to the method for updating the data sampling interval, the terminal equipment acquires the sampling data from the wearable equipment, and the sampling interval is adjusted according to the sampling data, so that the wearable equipment can acquire the data based on the adjusted sampling interval. Since different users collect different data, the adjusted sampling interval is suitable for the corresponding user. Furthermore, different sampling intervals are suitable for different users, so that accurate data acquisition is guaranteed, and power consumption can be saved.

In one possible embodiment, the sampled data of the user in the second time includes first sampled data; wherein the first sampled data is sampled data acquired by the wearable device according to a first duration when a sampling time point arrives. In this way, the terminal device can determine the sampling interval from the first sample data.

In one possible embodiment, the sampled data of the user in the second time includes first and second sampled data; wherein the first sampled data is sampled data acquired by the wearable device according to a first duration when a sampling time point arrives; the second sampling data is sampling data acquired by the wearable device according to a second duration after the first sampling data meets a set condition, and the second duration is longer than the first duration. In this way, the terminal device can determine the sampling interval from the first and second sample data. Because the second data is the data acquired under the condition that the set condition is met and the acquisition time is longer, compared with the condition that only the first sampling data is acquired, the terminal equipment determines the sampling interval more reasonably.

In a possible embodiment, the sampling data includes PPG data, and the first sampling data satisfying a set condition includes: the first sample data includes arrhythmia information. The data acquisition in the embodiment of the invention can be applied to acquisition of the PPG signal when detecting whether the user is at risk of arrhythmia.

In a possible embodiment, the arrhythmia information is in particular atrial fibrillation information.

In one possible embodiment, the terminal device displays a visual graphic rendered based on the PPG data. According to the embodiment of the invention, the sampling result can be displayed through the visual graph, and the physical condition of the user can be reflected more intuitively.

In one possible embodiment, the first or second sampling interval is obtained based on the following formula:

I＝LI+(HI-LI)×(1-prob _AF ) Wherein the HI, LI are used to characterize a reference sampling interval range [ LI, HI]；Prob _AF And obtaining the atrial fibrillation probability by using a logistic regression algorithm based on the sampling data of the user in the first time or the second time.

In a second aspect, a method of updating a data sampling interval, the method comprising: the terminal equipment acquires sampling data and motion data of the user in a fourth time, and obtains a third sampling interval based on the sampling data and the motion data; the terminal device sends the third sampling interval to the wearable device, so that the wearable device obtains sampling data of a user in a fifth time according to the third sampling interval; the terminal equipment acquires the motion data of the user in the fifth time; the terminal equipment obtains a fourth sampling interval based on the sampling data and the motion data of the user in the fifth time; and the terminal equipment sends the fourth sampling interval to the wearable equipment so that the wearable equipment can acquire new sampling data of the user in sixth time according to the fourth sampling interval.

Therefore, in the method for updating the data sampling interval in the embodiment of the invention, when the sampling interval is determined, both the sampling data and the motion data need to be considered, and the adjusted sampling interval is not only suitable for the corresponding user, but also more reasonable. Furthermore, different sampling intervals are suitable for different users, so that accurate data acquisition is guaranteed, and power consumption can be saved.

In one possible embodiment, the sampled data comprises PPG data. The data acquisition in the embodiment of the invention can be applied to acquisition of the PPG signal when detecting whether the user is at risk of arrhythmia.

In a third aspect, an embodiment of the present invention provides a method for acquiring data according to a sampling interval, where the method includes: the wearable device obtains sampling data of a user in a first time, and obtains a first sampling interval based on the sampling data of the user in the first time; the wearable device acquires sampling data of the user in a second time according to the first sampling interval; the wearable device obtains a second sampling interval based on the sampling data of the user in the second time; and the wearable equipment acquires new sampling data of the user in a third time according to the second sampling interval.

According to the method provided by the embodiment of the invention, the wearable device acquires the sampling data, the sampling interval is adjusted according to the sampling data, and the wearable device can acquire the data based on the adjusted sampling interval. Since different users collect different data, the adjusted sampling interval is suitable for the corresponding user. Furthermore, different sampling intervals are suitable for different users, so that accurate data acquisition is guaranteed, and power consumption can be saved.

In one possible embodiment, the sampled data of the user in the second time includes first sampled data; wherein the first sampled data is sampled data acquired by the wearable device according to a first duration when a sampling time point arrives. In this way, the wearable device may determine a sampling interval from the first sampling data.

In one possible embodiment, the sampled data of the user in the second time includes first and second sampled data; wherein the first sampled data is sampled data acquired by the wearable device according to a first duration when a sampling time point arrives; the second data is sampling data acquired by the wearable device according to a second duration after the first sampling data meets a set condition, and the second duration is longer than the first duration. In this way, the wearable device may determine a sampling interval from the first and second sampling data. Since the second data is data acquired under the condition that the set condition is satisfied, and the acquisition time is longer, the wearable device determines the sampling interval more reasonably than the case of acquiring only the first sampling data.

In a possible embodiment, the sampling data includes PPG data, and the first sampling data satisfying a set condition includes: the first sample data includes arrhythmia information. The data acquisition in this embodiment can be applied to the acquisition of the PPG signal when detecting whether the user has an arrhythmia.

In one possible embodiment, the method further comprises: the wearable device outputs a visualization graph rendered based on the PPG data. Therefore, the wearable device can directly display the graph, or project the graph, or send the graph to other devices to display the graph, and the sampling result can be displayed through the visual graph, so that the physical condition of the user can be reflected more intuitively.

In one possible implementation, the first or second sampling interval is obtained based on the following formula:

I＝LI+(HI-LI)×(1-prob _AF ) Wherein the HI, LI are used to characterize a reference sampling interval range [ LI, HI]；Prob _AF Is the probability of atrial fibrillation based on the sampling of the user at the first or second timeData were obtained using a logistic regression algorithm.

In a fourth aspect, an embodiment of the present invention provides a method for acquiring data according to a sampling interval, where the method includes: the wearable device acquires sampling data and motion data of the user within a fourth time, and obtains a third sampling interval based on the sampling data and the motion data; the wearable device acquires sampling data of the user within a fifth time according to the third sampling interval; the wearable device acquires motion data of the user in the fifth time; the wearable device obtains a fourth sampling interval based on the sampling data and the motion data of the user within the fifth time; and the wearable device acquires new sampling data of the user in a sixth time according to the fourth sampling interval.

Therefore, in the embodiment of the invention, data are collected according to the sampling interval, when the sampling interval is determined, both the sampling data and the motion data need to be considered, and the adjusted sampling interval is not only suitable for the corresponding user, but also more reasonable. Furthermore, different sampling intervals are suitable for different users, so that accurate data acquisition is guaranteed, and power consumption can be saved.

In a fifth aspect, an embodiment of the present invention provides a terminal device, including: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory, the one or more programs comprising instructions which, when executed by the terminal device, cause the terminal device to perform the method of the first and/or second aspect of an embodiment of the invention.

In a fourth aspect, an embodiment of the present invention provides a wearable device, including: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory, the one or more programs comprising instructions which, when executed by the wearable device, cause the wearable device to perform a method according to the third and/or fourth aspects of embodiments of the present invention.

It should be appreciated that the description of technical features, solutions, benefits, or similar language in this application does not imply that all of the features and advantages may be realized in any single embodiment. Rather, it is to be understood that the description of a feature or advantage is intended to include the specific features, aspects or advantages in at least one embodiment. Therefore, the descriptions of technical features, technical solutions or advantages in the present specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions and advantages described in the present embodiments may also be combined in any suitable manner. One skilled in the relevant art will recognize that an embodiment may be practiced without one or more of the specific features, aspects, or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below.

Fig. 1 is a schematic diagram of a network system in which a mobile phone and a smart watch are located in an embodiment of the invention;

fig. 2 is a schematic diagram of a hardware structure of a smart watch according to an embodiment of the present invention;

FIG. 3 is a diagram illustrating a hardware structure of a mobile phone according to an embodiment of the present invention;

FIG. 4 is a flow chart of a method for updating a data acquisition interval according to an embodiment of the present invention;

FIG. 5 is a schematic illustration of a questionnaire involved in an embodiment of the present invention;

fig. 6 is a schematic diagram of a visualization graph based on PPG sample data in an embodiment of the invention;

fig. 7 is another schematic diagram of a visualization graph based on PPG sample data in an embodiment of the invention;

FIG. 8 is a flowchart of a method for updating a data collection interval according to a second embodiment of the present invention;

FIG. 9 is a flowchart of a method for updating a data collection interval according to a third embodiment of the present invention;

FIG. 10 is a flow chart of a method of updating a data collection interval according to a fourth embodiment of the present invention;

fig. 11 is a flow chart of a method for collecting data according to a sampling interval according to a fifth embodiment of the present invention;

FIG. 12 is a flow chart of a method of collecting data according to a sampling interval in accordance with a sixth embodiment of the present invention;

fig. 13 is a flowchart of a method for acquiring data according to a sampling interval according to a seventh embodiment of the present invention.

Detailed Description

The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the invention and the appended claims, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

As shown in fig. 1, taking a wearable device as an example of a smart watch and a terminal device as an example of a mobile phone, the present embodiment provides a schematic diagram of a network system, where a smart watch 200 may wirelessly communicate with a wireless communication base station 100 or a mobile phone 300. For example, the smart watch 200 may send a wireless signal to the base station 100 through the radio frequency circuit and the antenna thereof and through the wireless communication link L1, so as to request the base station 100 to perform a wireless network service to process a specific service requirement of the smart watch 200; for another example, the smart watch 200 may match the mobile phone 30 through its own bluetooth, and perform data communication with the mobile phone through the bluetooth communication link L2 after the matching is successful, or certainly perform data communication with the mobile phone through other wireless communication methods, such as a radio frequency identification technology, a short-range wireless communication technology, and the like. The smart watch 200 may also detect data of various environments through various sensors thereof.

As shown in fig. 2, the smart watch 200 may specifically include a watch body and a wrist band (not shown in fig. 2) connected to each other, where the watch body may include a touch screen 215, an NFC (Near-field communication) device 212, a processor 214, a memory 204, a microphone 206, an ambient light sensor 213, a bluetooth device 211, a positioning device 205, a power management system 203 (including a power supply), a Wi-Fi (Wireless Fidelity) device 201, a time system 202, a motion sensor 209, a PPG sensor 210, and the like. Although not shown, the smart watch 200 may also include an antenna, speakers, accelerometers, gyroscopes, and the like.

The following describes each functional component of the smart watch 200:

the touch screen 215 includes a touch panel 207 and a display panel 208, and the touch panel 207 may be overlaid on the display panel 208. The touch panel 207 may capture touch operations of a user of the smart watch on or near the touch panel (e.g., operations of the user on or near the touch panel using a finger, a stylus, or any other suitable object or accessory), and drive a responsive connection device according to a predetermined program. The touch panel 207 may include two parts of a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts it to touch point coordinates, and sends the touch point coordinates to the processor 214, and can receive and execute commands sent by the processor 214. In addition, the touch panel may be implemented in various types such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. A display panel (commonly referred to as a display screen) may be used to display information entered by or provided to the user as well as various menus of the smart watch. Alternatively, the display panel may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The touch panel may be overlaid on the display screen, and when the touch panel detects a touch operation thereon or nearby, the touch panel transmits the touch operation to the processor 214 to determine the type of the touch event, and then the processor 214 provides a corresponding visual output on the display screen according to the type of the touch event. Although in fig. 2, the touch panel and the display screen are two separate components to implement the input and output functions of the smart watch, in some embodiments, the touch panel and the display screen may be integrated to implement the input and output functions of the smart watch 200.

The NFC device 212 is used to provide the smart watch 200 with an NFC function, and may have three application modes, i.e., a card reader mode, a peer-to-peer mode, and a card emulation mode. In some embodiments, the NFC device 212 may include an NFC controller, an NFC radio circuit, and a Secure Element (Secure Element). The NFC controller is connected to the NFC rf circuit and the security unit, and is mainly used for modulating and demodulating a non-contact communication signal, controlling input and output of data in the NFC device, and performing data interaction with the processor 214; the NFC radio frequency circuit is connected to the NFC controller to transmit and receive a 13.56MHz radio frequency signal, and may be composed of an EMC (Electromagnetic Compatibility) filter circuit, a matching circuit, a receiving circuit, and an NFC antenna. The safety unit can comprise a memory and one or more processors, and the main function of the safety unit is to realize the safe storage of application programs and data and provide safe operation service for the outside. The safety module is communicated with external equipment through the NFC controller, and safety of data storage and transaction processes is achieved. It is noted that the security element may be a tamper-resistant component in the mobile device for providing security, confidentiality, and for supporting various application environments. The security element may exist in a variety of shapes, for example, the security element may be Integrated in a Universal Integrated Circuit Card (UICC), such as a subscriber Identity module sim (subscriber Identity module) Card, an embedded security element (located on a Circuit board of the mobile device), a secure digital (secure digital SD) Card, a micro SD Card, and the like. In addition, the secure element may also include one or more applications that execute in the environment of the secure element (such as in the operating system of the secure element and/or in a Java runtime environment running on the secure element). Further, the one or more applications may include one or more payment applications, which may be stored in memory 204. The safety unit supports application program safe transaction and safe data storage, supports downloading, installation, deletion, updating and the like of multiple application programs, also supports safe isolation of application program data, and can not allow free access among different application programs for safety; the security unit also provides symmetric and asymmetric encryption algorithms and certificate capabilities for various payment needs, provides a program interface for secure transaction application access, and supports bi-directional communication with the NFC controller or processor 214.

The processor 214 is the control center of the smart watch 200, connects various parts of the watch using various interfaces and lines, and performs various functions of the smart watch 200 and processes data by running or executing applications stored in the memory 204 and calling up data stored in the memory 204. In some embodiments, processor 214 may include one or more processing units; the processor 214 may also integrate an application processor, which primarily handles operating systems, user interfaces, applications, etc., and a modem processor, which primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 214. For example, the processor 214 may be an kylin 960 chip manufactured by Huanti technologies, Inc.

The memory 204 is used for storing application programs and data, and the processor 214 executes various functions and data processing of the smart watch 200 by operating the application programs and data stored in the memory. The memory 204 mainly includes a program storage area and a data storage area, wherein the program storage area can store an operating system and application programs (such as a sound playing function and an image playing function) required by at least one function; the stored data area may store data (such as audio data, a phone book, etc.) created from use of the smart watch. In addition, the memory may include a high speed random access memory, andnon-volatile memory may be included, such as a magnetic disk storage device, flash memory device, or other volatile solid state storage device. The memory 204 may store an operating system that enables the smart Watch to run, such as the Watch operating system developed by apple Inc., Android developed by Google Inc

An operating system, etc.

A positioning device 205 for providing a geographic location for the smart watch 200. It can be understood that the Positioning device 205 can be a receiver of a Positioning System such as a Global Positioning System (GPS) or a beidou satellite navigation System, russian GLONASS, etc. After receiving the geographic location transmitted by the positioning system, the positioning device 205 transmits the information to the processor 214 for processing or to the memory 204 for storage. In other embodiments, the positioning device 205 may be an Assisted Global Positioning System (AGPS) receiver, wherein AGPS is an operation mode for performing GPS positioning with certain assistance, and it can utilize signals of a base station in cooperation with GPS satellite signals to enable the smart watch 200 to perform positioning faster; in AGPS systems, the positioning device 205 may obtain positioning assistance through communication with an assisted positioning server (e.g., a cell phone positioning server). The AGPS system assists the positioning device 205 in performing ranging and positioning services by acting as an assistance server, in which case the assistance server provides positioning assistance by communicating with a mobile device (e.g., the smart watch 200, the positioning device 340 (i.e., GPS receiver) of the handset 300) via a wireless communication network, in other embodiments, the positioning device 205 may also be based on positioning technology of Wi-Fi Access points, since each Wi-Fi Access point has a globally unique MAC address, the mobile device can scan and collect broadcast signals of surrounding Wi-Fi Access points when the Wi-Fi is turned on, and thus can acquire a Media Access Control (MAC) address broadcasted by the Wi-Fi Access points, the mobile device sends data (e.g., MAC address) identifying the Wi-Fi Access points to the location server via the wireless communication network, the location server retrieves the geographical location of each Wi-Fi access point, and calculates the geographical location of the mobile device according to the strength of the Wi-Fi broadcast signal, and sends the geographical location of the mobile device to the positioning apparatus 205 of the mobile device.

The Wi-Fi device 201 is used for providing Wi-Fi network access for the smart watch 200, the smart watch 200 can access a Wi-Fi access point through the Wi-Fi device 201, and therefore a user is helped to receive and send e-mails, browse webpages, access streaming media and the like, and wireless broadband internet access is provided for the user. In some other embodiments, the Wi-Fi apparatus 201 may also serve as a Wi-Fi access point to provide Wi-Fi network access for other mobile devices.

The smart watch may also include a power source (e.g., a battery) for powering the various components, which may be logically coupled to the processor 214 via the power management system 203 to manage charging, discharging, and power consumption via the power management system 203. A microphone 206 for converting the collected sound signal into an electrical signal, which is received by an audio circuit and converted into audio data; the bluetooth device 211 and the smart watch may interact information with other electronic devices (such as the mobile phone 300) by using bluetooth, and may be connected to a network through the electronic devices, and connected to a server to process functions such as voice recognition.

The smart watch 200 may also include a time system 202, the time system 202 providing an indication of time to the smart watch 200.

The smart watch 200 may further include a motion sensor 209, and the motion sensor 209 may include an acceleration sensor, a gyroscope, etc., where the acceleration sensor determines whether the device is moving by measuring a direction and acceleration force, so as to achieve a purpose of counting steps, and matches a type of motion being performed by the user through the collected data, thereby monitoring a number of steps, a calorie consumption amount, etc. of the user, and implementing the most basic functions of the smart watch.

The smart watch 200 may also include a PPG sensor 210, and the smart watch 200 may employ photoplethysmography (PPG) to measure heart rate and other biometric indicators. PPG is a method of shining light into the skin and measuring the light scattering due to blood flow. This method is commonly used, when the blood flow dynamics change, for example the blood pulse rate (heart rate) or the blood volume (cardiac output) changes, a predictable scattering of the light entering the body occurs.

As shown in fig. 3, the mobile phone 300 includes: RF (Radio Frequency) circuitry 310, memory 320, touch screen 330, pointing device 340, NFC device 302, sensor 350, audio circuitry 360, Wi-Fi device 370, processor 380, bluetooth device 381, and power system 390. Those skilled in the art will appreciate that the handset configuration shown in fig. 3 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.

The following specifically describes each constituent component of the mobile phone 300 with reference to fig. 3:

RF circuitry 310 may be used for receiving and transmitting signals during a message or call. Specifically, the RF circuit 310 receives downlink data from the base station and then processes the received downlink data to the processor 380; in addition, data relating to uplink is transmitted to the base station. Typically, the RF circuitry includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, RF circuit 310 may also communicate with other devices via a wireless communication network. The wireless communication network may use any communication standard or protocol including, but not limited to, global system for mobile communications, general packet radio service, code division multiple access, wideband code division multiple access, long term evolution, email, short message service, etc.

The cell phone 300 may also include at least one sensor 350, such as a light sensor, motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor that adjusts the brightness of the display panel of the touch screen 330 according to the brightness of ambient light, and a proximity sensor that turns off the power of the display panel when the mobile phone 300 moves to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), detect the magnitude and direction of gravity when stationary, and can be used for applications of recognizing gestures of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometers and taps), and the like; as for other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which may be further configured on the mobile phone 300, further description is omitted here.

Audio circuitry 360, speaker 361, and microphone 362 may provide an audio interface between a user and the handset 300. The audio circuit 360 may transmit the electrical signal converted from the received audio data to the speaker 361, and the audio signal is converted by the speaker 361 and output; on the other hand, the microphone 362 converts collected sound signals into electrical signals, which are received by the audio circuit 360 and converted into audio data, which are then output to the RF circuit 310 for transmission to, for example, another cell phone, or to the memory 320 for further processing.

The processor 380 is a control center of the mobile phone 300, connects various parts of the mobile phone by using various interfaces and lines, and performs various functions of the mobile phone 300 and processes data by running or executing an application program stored in the memory 320 and calling data stored in the memory 320, thereby performing overall monitoring of the mobile phone. In some embodiments, processor 380 may include one or more processing units; processor 380 may also integrate an application processor, which primarily handles operating systems, user interfaces, applications, etc., and a modem processor, which primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into processor 380.

The handset 300 also includes a power system 390 (including a battery and a power management chip) that provides power to the various components. The battery may be logically connected to the processor 380 through a power management chip to manage charging, discharging, and power consumption functions through the power system 390. Although not shown, the mobile phone 300 may further include a camera, a Subscriber Identity Module (SIM) card slot, a peripheral interface (for connecting to other input/output devices), and the like, which are not described herein again.

In some embodiments, the functions and roles of the memory 320, the touch screen 330, the positioning device 340, the NFC device 302, the Wi-Fi device 370, the bluetooth device 381, and the like in the mobile phone 300 may be the same as or similar to the functions and roles of the memory 205, the touch screen 201, the positioning device 209, the NFC device 202, the Wi-Fi device 214, and the bluetooth device 208 in the smart watch 200 of the above embodiments, and the above components will not be described in detail.

Example one

Fig. 4 shows a flowchart of a method for updating a data sampling interval according to an embodiment of the present invention, and for convenience of understanding, the method shown in fig. 4 is taken as an example where a user uses a smart watch and a terminal device to collect sampling data for the first time, and certainly if the user uses the smart watch and the terminal device to collect sampling data, the user uses the smart watch and the terminal device again after a long time interval, and the method is taken as the first time for convenience of description. In the specific description, the mobile phone is taken as the terminal device and the smart watch is taken as the wearable device as an example, but it is understood that this does not limit the invention.

Step S401, the terminal device obtains user information of a user, and obtains a first sampling interval based on the user information of the user.

The sampling interval is used for a smart watch in communication connection with the handset to collect user data, such as PPG data of the user. After the sampling interval is determined by the mobile phone, the sampling time point is also determined, and thus, the smart watch can acquire sampling data when the sampling time point is reached.

Taking the case that the method of the embodiment of the present invention is used in a scene of atrial fibrillation, the user information of the user may include at least one of the following information: basic information of the user, including sex, age, whether smoking or not, drinking; the past medical history information of the user comprises whether heart disease, coronary heart disease, hyperthyroidism and the like exist; the existing heart rate information of the user comprises the heart rate, whether atrial fibrillation exists or not, the atrial fibrillation proportion and the like.

It is understood that the embodiments of the present invention and other embodiments described below are described in terms of atrial fibrillation, but the present invention may also be used for arrhythmia, and is not limited thereto.

As shown in fig. 5, the first time the user uses the cell phone and smart watch to collect PPG data, the cell phone may display a questionnaire as illustrated in the figure, allowing the user to input user information. It is to be understood that the survey questions listed in FIG. 5 are merely exemplary and not limiting.

When a user first uses the method of the embodiment of the invention, after the user inputs user information, the mobile phone estimates an approximate sampling interval.

After the third-party company counts user information of a large number of users in advance, common estimation results of a plurality of sampling intervals are obtained, for example, the users are male in sex, age is 30-39 years old, smoking is carried out, wine is drunk, no past medical history and arrhythmia are generated, the method corresponds to one sampling interval, for example, the users are male in sex, age is 30-39 years old, smoking is carried out, wine is not drunk, no past medical history and arrhythmia are generated, the method corresponds to another sampling interval, and the like. The mobile phone may store the above-mentioned commonly used multiple sampling intervals, so that after the user inputs the user information, the mobile phone may find the corresponding sampling interval.

Of course, the mobile phone may also perform calculation by using a general calculation model of the mobile phone, and the specific content may refer to the content in the following step S405. It can be understood that the determination of the sampling interval in the embodiment of the present invention is a loop iteration process, which is a continuously modified process, so that when a user uses a mobile phone and a smart watch to acquire PPG data for the first time, the first sampling interval determined based on the user information does not need to be as accurate, which is approximately enough.

Step S402, the terminal device sends the first sampling interval to the wearable device.

Optionally, the mobile phone establishes a bluetooth connection with the smart watch, and then the mobile phone sends the first sampling interval to the smart watch through the bluetooth connection.

In step S403, the wearable device acquires the sampling data of the user within a first time according to a first sampling interval.

Taking the first sampling interval as 20 minutes as an example, every 20 minutes is the sampling time point of the smart watch.

Alternatively, the first time may be 24 hours.

According to an example of embodiment of the present invention, the sampled data may be PPG data of the user, although those skilled in the art will appreciate that it is not limited to such data.

Optionally, before the smart watch collects the sampling data, it may be determined whether the user is in a state suitable for collecting the sampling data, for example, the user is relaxed and quiet, so that the collected data is more accurate.

Optionally, the sampling data includes first sampling data acquired by the wearable device according to the first duration when the sampling time point arrives.

For example, when a sampling time point arrives, the smart watch collects PPG data for a 1 minute period. And if no atrial fibrillation information exists in the PPG data, the intelligent watch stops collecting the PPG data, and the PPG data with the duration of 1 minute is collected when the next sampling time point arrives.

Optionally, the sampling data further includes the second sampling data, where the second sampling data is sampling data that is acquired by the wearable device according to a second duration after the first sampling data meets the set condition, and the second duration is greater than the first duration.

Taking the PPG data acquisition for atrial fibrillation according to the embodiment of the present invention as an example, the set condition includes that the PPG data includes atrial fibrillation information. For example, when a sampling time point arrives, the smart watch collects PPG data for a 1 minute period. If the PPG data contains atrial fibrillation information, the smart watch may then acquire additional PPG data for a duration of 2 minutes. Obviously, the multiple collected PPG data is helpful for the future mobile phone to determine a more reasonable sampling interval based on the sampled data of the smart watch.

In the embodiment of the present invention, how the smart watch acquires PPG data belongs to the prior art, and the embodiment of the present invention is not described in detail, for example, a heart rate is indirectly acquired by irradiating a blood vessel with red and green light and detecting a blood flow velocity.

In the above example, it is mentioned that the setting condition for judgment includes that the PPG data contains atrial fibrillation information, and as a non-limiting example, how to judge that the PPG data contains atrial fibrillation information is described below.

After collecting the PPG data of a first duration (e.g., 1 minute), the application of the atrial fibrillation prescreening algorithm to determine whether the data contains atrial fibrillation information can be expressed by the following formula:

y＝f(PPG)

wherein y represents a detection result (i.e., normal or atrial fibrillation), and f represents an atrial fibrillation prescreening algorithm, which may be a Support Vector Machine (SVM) or other classification algorithm. In the following, an SVM model algorithm is taken as an example to describe how to calculate that the acquired PPG data includes atrial fibrillation information therein.

The SVM model determines a classification result by calculating the distance between the sample and the decision plane in the feature space. The following formula describes a linear SVM model. The optimization problem proposed by the linear SVM model is as follows:

at the same time satisfy

y _t (w·x _t -b)≥1

Where w is the normal vector of the support vector (consisting of the vector closest to the decision plane), b is the intercept of the decision plane (in feature space, this plane divides the sample into two parts, corresponding to the atrial fibrillation sample and the normal sample, respectively), x _t Is obtained by calculating atrial fibrillation sensitivity characteristics such as heart rate variability characteristics, entropy value characteristics and the like according to PPG signals _t As a result (atrial fibrillation or normal), t represents the sample number.

When calculating specifically, according to the above

And y _t (w·x _t -b)≥1

It is possible to obtain:

where α t is a lagrange multiplier, n represents the number of samples, and m represents the number of support vectors (consisting of vectors closest to the decision plane).

Based on the above w, b, and the existing x _t Can be according to y _t ＝sign(wx _t -b) determining the value of the piece of data, wherein sign represents a sign operation, and if the sign is a positive sign, the piece of data is determined to be normal data, and if the sign is a negative sign, the piece of data is determined to be atrial fibrillation sample.

It can be understood that, the above determining whether the PPG data includes atrial fibrillation information may be determined by a terminal device, or may be determined by a wearable device, which is not limited herein.

Step S404, the wearable device sends the sampling data to the terminal device.

Optionally, the smart watch may send the sampled data to the mobile phone through a bluetooth connection.

According to the setting, the smart watch can uniformly send the collected multiple sampling data to the mobile phone at a fixed time, such as 8 am every morning. Of course, the sampling data may be sent to the mobile phone every time the sampling data is collected, which is not limited to this.

Optionally, the smart watch may also synchronize the sampling data to a cloud server, and then send the sampling data to the mobile phone through the cloud server, which is not limited to this.

In step S405, the terminal device obtains a second sampling interval based on the sampling data.

The sampling data comprises first sampling data acquired according to a first time length, or the sampling data comprises the first sampling data and second sampling data acquired according to a second time length, and the second sampling interval is determined by the terminal device based on the first sampling data and/or the second sampling data.

Alternatively, based on these sample data, the mobile phone may calculate the atrial fibrillation ratio of the user, for example, 100 pieces of sample data are collected, 60 pieces of sample data containing atrial fibrillation information are collected, and then the atrial fibrillation ratio is 0.6. How to identify atrial fibrillation data and how to calculate the atrial fibrillation data belong to the prior art, and are not described herein. With the atrial fibrillation ratio of the user, the handset can obtain a second sampling interval.

Alternatively, the terminal device may determine the second sampling interval based on the sampled data at a set time, such as 8 am each.

Optionally, step S405 may also be replaced by:

step S405', the terminal device obtains a second sampling interval based on the sampling data and the personal information of the user.

Compared with step S405, step S405' also considers the personal information of the user, which is obviously more reasonable and comprehensive, and is particularly suitable for the situation that the personal information of the user changes. Such as the user's age, the user's abstinence from alcohol, etc., it is clear that these factors are taken into account when calculating the second sampling interval, so that the calculation of the sampling interval is more accurate.

Specifically, the terminal may determine the sampling interval based on the sampling data in the following manner:

firstly, setting a range of a data reference sampling interval as [ LI, HI ] according to the power consumption capability of the wearable device and doctor suggestion or according to acquired big data; for example, LI is 5min and HI is 20 min.

Application model to user correlation data (expressed in input) and atrial fibrillation occurrence probability prob _AF (referring to the magnitude of the probability of atrial fibrillation occurring for different users) the modeling was performed, with the following results:

prob _AF ＝model(input)

where the model can be a logistic regression, the following equation represents modeling using logistic regression:

the input may be user-related data, including personal information of the user, atrial fibrillation ratio obtained based on the sampled data, and the like. It is understood that, in the specific calculation, only the atrial fibrillation ratio obtained based on the sample data may be considered (corresponding to step S405), or personal information of the user and the atrial fibrillation ratio obtained based on the sample data may be considered (corresponding to step S405').

Theta represents model parameters (theta is a one-dimensional vector representing the importance of each input value), and is determined for input, such as data associated with a user. θ reflects the importance of input value, and can be set empirically or based on big data, and is not limited herein.

T represents the transpose in the mathematical calculation.

Specifically, in the embodiment of the present invention, the user-related data includes personal information of the user and an atrial fibrillation ratio obtained based on the sampled data. For example, if the personal information of the user includes male, age 56, and the atrial fibrillation ratio obtained based on the sampled data is 0.6, then the input vector for constructing 4 input values may be: [ age, sex, atrial fibrillation ratio, exercise data]Considering the need of calculation, the age and exercise data are represented by normalized age and normalized exercise data, and the corresponding substitution can obtain [56/100, 1, 0.6, 0 [ ]] ^T . Here, since the motion data is not considered, the input value corresponding to the "motion data" is 0. It will be appreciated that embodiments of the invention are illustrated with 4 input values, and in practice it is entirely possible to increase or decrease the parameter values as required. For example, the personal information of the user can also increase the alcohol consumption, the heart disease history and the like.

Theta can be [0.1, 0.2, 0.3, 0.4 ]] ^T . It can be seen that the importance of the four input values of age, sex, atrial fibrillation ratio and motion data is 0.1, 0.2, 0.3 and 0.4 respectively, which are increased in sequence. It will be appreciated that θ is an empirical, or large data acquisition result, value and may be adjusted as desired in practice.

Bring in input and θ

Calculated to obtain prob _AF ＝0.5637

Then, the second sampling interval is obtained according to the following formula

I＝LI+(HI-LI)×(1-prob _AF )

I＝5+(20-5)*(1-0.5637)＝11.5

The resulting second sampling interval was 11.5 min.

It is easily understood that the above 11.5min is a second sampling interval (corresponding to step S405') obtained based on the sample data and the personal information of the user, and if the second sampling interval considers only the sample data (corresponding to step S405), the input may be constructed as [0, 0, 0.6, 0]] ^T The second sampling interval is calculated.

Similarly, returning to step S401, when the first sampling interval is obtained only from the personal information of the user, input may be constructed as [56/100, 1, 0] ^T The first sampling interval is calculated.

Step S406, the terminal device sends the second sampling interval to the wearable device.

In step S407, the wearable device acquires new sampling data of the user in a second time according to a second sampling interval.

The second time may be the same as or different from the first time, and is not limited herein. For example, the second time may be 24 hours.

As will be understood by those skilled in the art, after acquiring the new sampling data of the user in step S407, the wearable device may send the new sampling data to the terminal device, so that the terminal device may update the sampling interval according to the new sampling data. The sampling interval can be determined more reasonably by circulating the above steps.

Optionally, in this embodiment of the present invention, the terminal device may further generate visual graphic data based on the sampling data, and display the graphic.

Fig. 6 and 7 show the visual graphics drawn and displayed by the mobile phone based on the received PPG data, and it is obvious that the physical condition of the user can be more intuitively reflected by displaying the sampling result through the visual graphics.

Example two

As can be seen from the description of the first embodiment, after acquiring new sampling data of the user in step S407 of the method in the first embodiment, the wearable device may send the new sampling data to the terminal device, so that the terminal device may update the sampling interval according to the new sampling data.

Thus, if the smart watch already has sample data before determining the first sampling interval, the handset may determine the first sampling interval based on the already-existing sample data.

Specifically, as shown in fig. 8, the flow of the method for updating the data sampling interval according to the second embodiment is as follows: it is to be understood that, where the method steps related to the second embodiment are the same as or similar to the method steps related to the first embodiment, the method steps related to the second embodiment are not described in detail herein. In addition, some terms mentioned in the first embodiment are continuously used in the embodiment of the invention, such as the first time, the second time, and the like.

Step S801, the terminal device obtains sampling data of the user in a third time, and obtains a first sampling interval based on the sampling data of the user in the third time, where the third time is before the first time.

For example, the mobile phone obtains sampling data of the user within 24 hours of the previous day at 8 am every morning, and obtains a sampling interval based on the sampling data of the user within 24 hours of the previous day

Specifically, how to obtain the sampling interval according to the sampling data, refer to step S405 in the first embodiment.

Step S802, the terminal device sends the first sampling interval to the wearable device.

In step S803, the wearable device acquires the sampling data of the user within a first time according to a first sampling interval.

Step S804, the wearable device sends the sampling data of the user in the first time to the terminal device.

In step S805, the terminal device obtains a second sampling interval based on the sampling data of the user in the first time.

Specifically, reference may be made to step S405 in the first embodiment to obtain a sampling interval according to the sampling data.

Step S806, the terminal device sends the second sampling interval to the wearable device.

In step S807, the wearable device acquires new sampling data of the user in a second time according to a second sampling interval.

Similarly, after step S807, the wearable device may send the new sampling data obtained by the user in the second time period to the terminal device, so that the terminal device may update the sampling interval according to the new sampling data. The sampling interval can be determined more reasonably by circulating the above steps.

Likewise, in step S801, the terminal device may obtain the first sampling interval based on the sampling data of the user and the personal information of the user in the third time. In step S805, the terminal device obtains a second sampling interval based on the sampling data of the user and the personal information of the user in the first time.

EXAMPLE III

In order to more fully understand the first embodiment and the second embodiment, the third embodiment takes the acquisition of PPG data as an example, and describes in detail how to update the sampling interval.

As shown in figure 9 of the drawings,

step S901, the terminal device obtains user information and/or updated sample data.

If the mobile phone is used for the first time, the mobile phone acquires the user information of the user. If the PPG sample data is available before use, the handset will update periodically, for example, 8 a.m. to update the PPG sample data acquired during the previous time period, for example, the previous 24 hours.

It will be understood by those skilled in the art that the user information and/or the updated sample data described above are data associated with the user.

Step S902, the terminal device determines a sampling interval based on the user information and/or the updated sampling data.

For a specific method for determining the sampling interval, reference is made to the corresponding contents of the first embodiment or the second embodiment, which are not described herein again.

Step S903, the wearable device determines that a sampling time point arrives according to the sampling interval.

In step S904, the wearable device determines whether the user is in a quiet state, if so, step S905 is executed, otherwise, step S903 is executed to wait for the next sampling time point to arrive.

And step S905, the wearable device collects the PPG data of 1 minute and sends the PPG data to the terminal device.

And step S906, the terminal equipment uses an atrial fibrillation prescreening algorithm aiming at the PPG data.

For a specific algorithm, reference is made to the corresponding content in the first embodiment or the second embodiment, which is not described herein again.

In step S907, the terminal device determines whether the user has atrial fibrillation. If it is judged to be atrial fibrillation, step S908 is performed. Otherwise, step S909 is executed.

And step S908, the wearable device continuously collects PPG data for 2 minutes and sends the PPG data to the terminal device.

And step S909, the terminal equipment records the collected PPG data, and tags and time stamps.

The PPG data may be tagged with atrial fibrillation, normal, etc.

Next, when the sampling time point arrives, steps S903 to S909 loop, so that the next morning at 8, the terminal device acquires the PPG data within the first 24 hours, and steps S901 to S909 loop.

Example four

In order to make the determination of the sampling interval more reasonable, the fourth embodiment is further improved on the basis of the second embodiment, and in brief, the determination of the sampling interval also needs to consider the motion data of the user.

Specifically, as shown in fig. 10, the flow of the method for updating the data sampling interval according to the fourth embodiment is as follows: it is to be understood that, where the method steps related to the fourth embodiment are the same as or similar to the method steps related to the first to third embodiments, the embodiments of the present invention are not described in detail again. In addition, for convenience of understanding, when the sampling interval is calculated in the embodiment of the present invention, some numerical values therein follow the numerical values exemplified previously.

Step S1001, the terminal device obtains the sampling data and the motion data of the user in the fourth time, and obtains a third sampling interval based on the sampling data and the motion data of the user in the fourth time.

The motion data of the user includes: walking, running, swimming and other sports data. The data may be recorded by the wearable device, but may be recorded by the terminal device.

For example, the mobile phone obtains sampling data and motion data of the user within 24 hours of the previous day at 8 am every day, and obtains a sampling interval based on the sampling data and the motion data of the user within 24 hours of the previous day.

When determining the sampling interval, the algorithm described in the first embodiment is still used. Specifically, there is a difference in that, when determining the input value, the input value can be represented by [0, 0, 0.6, 6000/10000 ]. 6000/10000, for the user to take 6000 steps within 24 hours of the previous day.

And step S1002, the terminal equipment sends the third sampling interval to the wearable equipment.

In step S1003, the wearable device acquires the sampling data of the user in a fifth time according to the third sampling interval.

Step S1004, the wearable device sends the sampled data of the user in the fifth time to the terminal device.

In step S1005, the wearable device sends the motion data of the user in the fifth time to the terminal device.

And in the fifth time when the intelligent watch acquires the sampling data of the user, the intelligent watch also acquires the motion data of the user.

It can be understood that when the smart watch sends the exercise data, the exercise data of the user may be sent to the mobile phone each time the user collects the exercise data, or the exercise data may be sent to the mobile phone at a set time in a unified manner, for example, 8 am every day, which is not limited herein.

The sending mode may be that the smart watch is directly sent to the mobile phone, or that the smart watch is uploaded to the cloud server and then synchronized into the mobile phone, which is not limited herein.

In step S1006, the terminal device obtains a fourth sampling interval based on the sampling data and the motion data of the user in the fifth time.

Specifically, reference may be made to step S405 in the first embodiment and step S1001 above to how to obtain the sampling interval according to the sampling data.

Step S1007, the terminal device sends the fourth sampling interval to the wearable device.

In step S1008, the wearable device obtains new sampling data of the user in the sixth time according to the fourth sampling interval.

Similarly, after step S1008, the wearable device may send the obtained new sampling data of the user to the terminal device, so that the terminal device may update the sampling interval according to the new sampling data and the motion data of the user after being updated again. The sampling interval can be determined more reasonably by circulating the above steps.

Similarly, in step S1001, the terminal device may obtain a third sampling interval based on the sampling data and the motion data of the user and the personal information of the user in the fourth time; in step S1006, the terminal device may obtain a fourth sampling interval based on the sampled data and motion data of the user and the personal information of the user in the fifth time.

It is understood that the steps described above are not in strict order of precedence.

EXAMPLE five

With the increasing hardware configuration of wearable terminals, the wearable terminals can collect data and have strong storage and computing capabilities. That is, in the first to fourth embodiments, the calculation sampling interval of the terminal device, etc. may also be put on the wearable device.

As shown in fig. 11, a method for acquiring data according to a sampling interval in the fifth embodiment corresponding to the first embodiment is described as follows: it is to be understood that, where the method steps related to the fifth embodiment are the same as or similar to the method steps related to the first to fourth embodiments, the embodiments of the present invention are not described in detail again. For convenience of understanding, the method shown in fig. 11 is exemplified by that a user uses a smart watch to collect sample data for the first time, and of course, if the user uses the smart watch to collect sample data, but uses the smart watch again after a long time, for convenience of description, the method is regarded as being used for the first time. In the specific description, the smart watch is taken as a wearable device as an example in the embodiment of the present invention, but it is to be understood that this does not limit the wearable device.

In step S1101, the wearable device acquires user information of the user, and obtains a first sampling interval based on the user information of the user.

Step S1102, the wearable device acquires sampling data of the user within a first time according to a first sampling interval.

Step S1103, the wearable device obtains a second sampling interval based on the sampling data;

in step S1104, the wearable device acquires new sampling data of the user in a second time according to the second sampling interval.

Similarly, the method for calculating the sampling interval in step S1101 and step S1103 refers to the corresponding description of the foregoing embodiments.

Similarly, after step S1104, the wearable device may store the new sampling data obtained by the user in the second time period, so that the wearable device may update the sampling interval according to the new sampling data. The sampling interval can be determined more reasonably by circulating the above steps.

Wherein the sample data may include first sample data, or the sample data includes first sample data and second sample data. The first sampling data is sampling data acquired by the wearable device according to a first duration when a sampling time point arrives; the second data is sampling data acquired by the wearable device according to a second duration after the first sampling data meets a set condition, and the second duration is longer than the first duration.

Optionally, the sampling data includes PPG data, and the first sampling data meeting the set condition includes: the first sampled data includes arrhythmia information, particularly atrial fibrillation information.

Optionally, the wearable device may also output visual graphical data based on the PPG data. Therefore, the wearable device can display the visual graph by itself, can also display the visual graph in a projection mode, and can also output the visual graph to other devices for display.

Example six

As shown in fig. 12, corresponding to the second embodiment, one of the sixth embodiments is described as follows according to the method of acquiring data according to the sampling interval: it is to be understood that, where the method steps related to the sixth embodiment are the same as or similar to the method steps related to the first to fifth embodiments, the method steps according to the sixth embodiment are not described again. For ease of understanding, fig. 12 illustrates a method in which the wearable device has had prior sampling data prior to determining the first sampling interval. In addition, some terms mentioned in the fifth embodiment are used continuously in the embodiments of the invention, such as the first time, the second time, and the like.

Step S1201, the wearable device obtains sampling data of the user within a third time, and obtains a first sampling interval based on the sampling data of the user within the third time, where the third time is before the first time.

In step S1202, the wearable device acquires sample data of the user within a first time according to a first sampling interval.

In step S1203, the wearable device obtains a second sampling interval based on the sampling data of the user in the first time.

In step S1204, the wearable device obtains new sampling data of the user in a second time according to a second sampling interval.

Similarly, after step S1204, the wearable device may save the new sampling data obtained by the user in the second time period, so that the wearable device may update the sampling interval according to the new sampling data. The sampling interval can be determined more reasonably by circulating the above steps.

EXAMPLE seven

As shown in fig. 13, one of the seventh embodiments according to the method of acquiring data according to the sampling interval is described as follows, corresponding to the fourth embodiment: it is to be understood that, where the method steps related to the seventh embodiment are the same as or similar to the method steps related to the first to sixth embodiments, the embodiments of the present invention are not described in detail again.

Step S1301, the wearable device acquires sampling data and motion data of the user in a fourth time, and obtains a third sampling interval based on the sampling data and the motion data;

step S1302, the wearable device obtains sampling data of the user in a fifth time according to the third sampling interval;

step S1303, the wearable device acquires motion data of the user in the fifth time;

step S1304, the wearable device obtains a fourth sampling interval based on the sampling data and the motion data of the user within the fifth time;

step S1305, the wearable device obtains new sampling data of the user in a sixth time according to the fourth sampling interval.

Wherein the sampled data comprises PPG sampled data.

It should be noted that, as will be understood by those skilled in the art, all or part of the processes in the methods of the above embodiments may be implemented by a computer program, which may be stored in a computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.

The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the exemplary discussions above are not intended to be exhaustive or to limit the present teachings to the precise forms disclosed. Many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the present disclosure and its practical application, to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

1. A method of updating a data sampling interval, the method comprising:

the method comprises the steps that terminal equipment obtains sampling data of a user in a first time, and a first sampling interval is obtained based on the sampling data of the user in the first time;

the terminal device sends the first sampling interval to a wearable device, so that the wearable device obtains sampling data of a user in a second time according to the first sampling interval;

the terminal equipment receives the sampling data of the user in the second time sent by the wearable equipment, and obtains a second sampling interval based on the sampling data of the user in the second time;

the terminal device sends the second sampling interval to the wearable device, so that the wearable device obtains new sampling data of a user in a third time according to the second sampling interval;

wherein the sampled data of the user in the second time period comprises first and second sampled data;

wherein the first sampled data is sampled data acquired by the wearable device according to a first duration when a sampling time point arrives;

the second sampling data is sampling data acquired by the wearable device according to a second duration after the first sampling data meets a set condition, and the second duration is longer than the first duration.

2. The method of claim 1, wherein the sampled data for the user in the second time comprises first sampled data;

wherein the first sampled data is sampled data acquired by the wearable device according to a first duration when a sampling time point arrives.

3. The method according to claim 1 or 2, wherein the sampled data comprises PPG data, and the first sampled data satisfying a set condition comprises:

the first sample data includes arrhythmia information.

4. A method according to claim 3, wherein the arrhythmia information is specifically atrial fibrillation information.

5. The method according to claim 3, characterized in that the terminal device displays a visualization graphic rendered based on the PPG data.

6. The method of claim 4, wherein the first or second sampling interval is obtained based on the following formula:

I＝LI+(HI-LI)×(1-prob _AF )

the method comprises the following steps of obtaining a reference sampling interval range [ LI, HI ];

Prob _AF and obtaining the atrial fibrillation probability by using a logistic regression algorithm based on the sampling data of the user in the first time or the second time.

7. A method of updating a data sampling interval, the method comprising:

the terminal equipment acquires sampling data and motion data of the user in a fourth time, and obtains a third sampling interval based on the sampling data and the motion data;

the terminal device sends the third sampling interval to a wearable device, so that the wearable device obtains sampling data of a user in a fifth time according to the third sampling interval;

the terminal equipment acquires the motion data of the user in the fifth time;

the terminal equipment obtains a fourth sampling interval based on the sampling data and the motion data of the user in the fifth time;

the terminal device sends the fourth sampling interval to the wearable device, so that the wearable device obtains new sampling data of a user in sixth time according to the fourth sampling interval;

wherein the sampled data of the user in the fifth time comprises first and second sampled data;

8. The method of claim 7, wherein the sampled data comprises PPG data.

9. A method of collecting data according to a sampling interval, the method comprising:

the wearable device obtains sampling data of a user in a first time, and obtains a first sampling interval based on the sampling data of the user in the first time;

the wearable device acquires sampling data of the user in a second time according to the first sampling interval;

the wearable device obtains a second sampling interval based on the sampling data of the user in the second time;

the wearable device acquires new sampling data of a user in a third time according to the second sampling interval;

wherein the sampled data of the user in the second time includes first and second sampled data;

10. The method of claim 9, wherein the sampled data for the user in the second time comprises first sampled data;

11. The method according to claim 9 or 10, wherein the sampled data comprises PPG data, and the first sampled data satisfying a set condition comprises:

the first sample data includes arrhythmia information.

12. The method of claim 11, wherein the arrhythmia information is specifically atrial fibrillation information.

13. The method of claim 11, further comprising:

the wearable device outputs a visualization graph rendered based on the PPG data.

14. The method of claim 12, wherein the first or second sampling interval is obtained based on the following formula:

I＝LI+(HI-LI)×(1-prob _AF )

the HI and the LI are used for representing a reference sampling interval range [ LI and HI ];

15. A method of collecting data according to a sampling interval, the method comprising:

the wearable device acquires sampling data and motion data of the user within a fourth time, and obtains a third sampling interval based on the sampling data and the motion data;

the wearable device acquires sampling data of the user within a fifth time according to the third sampling interval;

the wearable device acquires motion data of the user within the fifth time;

the wearable device obtains a fourth sampling interval based on the sampling data and the motion data of the user within the fifth time;

the wearable device acquires new sampling data of the user within a sixth time according to the fourth sampling interval;

the second sampling data is sampling data acquired by the wearable device according to a second time length after the first sampling data meets a set condition, and the second time length is longer than the first time length.

16. The method of claim 15, wherein the sampled data comprises PPG data.

17. A terminal device, comprising:

one or more processors;

a memory;

and one or more programs, wherein the one or more programs are stored in the memory, the one or more programs comprising instructions which, when executed by the terminal device, cause the terminal device to perform the method of any of claims 1-8.

18. A wearable device, comprising:

one or more processors;

a memory;

and one or more programs, wherein the one or more programs are stored in the memory, the one or more programs comprising instructions, which when executed by the wearable device, cause the wearable device to perform the method of any of claims 9-16.