WO2019101222A2

WO2019101222A2 - Method for updating data sampling interval, and method and device for collecting data on basis of sampling interval

Info

Publication number: WO2019101222A2
Application number: PCT/CN2018/125849
Authority: WO
Inventors: 李宏宝; 杨斌; 张�杰; 黄曦; 彭家辉; 陈宜欣
Original assignee: 华为技术有限公司
Priority date: 2018-09-26
Filing date: 2018-12-29
Publication date: 2019-05-31
Also published as: WO2019101222A3; CN113242712B; CN113242712A

Abstract

A method and device for determining sampling intervals are disclosed in embodiments of the present invention. The method comprises: a terminal device acquires sampling data in respect of a user for a first duration of time, and obtains a first sampling interval on the basis of said sampling data; on the basis of the first sampling interval, a wearable device acquires sampling data in respect of the user for a second duration of time; the terminal device receives from the wearable device the sampling data of the second duration of time, and obtains a second sampling interval on the basis of the sampling data of the second duration of time; and, on the basis of the second sampling interval, the wearable device acquires new sampling data in respect of the user for a third duration of time. Because the data collected in respect of different users is different, adjusted sampling intervals better correspond to each individual user. Further, because different sampling intervals are better suited to different users, accurate data collection is ensured and power consumption is reduced.

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 collecting data according to a sampling interval, and an apparatus therefor.

Background technique

In 2015, among the main causes of death of Chinese residents, cardiovascular diseases accounted for more than 42%. Arrhythmia is the most common heart disease, and central fibrillation (ie, atrial fibrillation) is one of the most common types of arrhythmia. One in every five Chinese adults is at risk of developing atrial fibrillation. Atrial fibrillation increases the risk of stroke by 5 times. It is clear that atrial fibrillation is the leading cause of stroke. According to statistics, the incidence rate of people over 35 years old is about 1%, and the incidence rate is increasing year by year, and there is a trend of rejuvenation.

Early atrial fibrillation is mostly paroxysmal, and early detection of treatment can avoid its continuous development. Single ECG (electrocardiography) measurements are difficult to find. The 72-hour Holter (dynamic electrocardiogram) detection rate is about 72%, but Holter is not easy to carry; based on PPG (photoplethysmography, Photoplethysmography) wearable device, user experience is good, the detection rate of atrial fibrillation can be as high as 90% the above.

Currently, there are two main ways to collect PPG data:

– The PPG sensor is normally open, which reduces the standby time of the wearable device and affects the user experience.

– The PPG sensor is opened at a fixed frequency. This method will cause unnecessary waste of power consumption for people with low risk of arrhythmia; for high-risk people with arrhythmia, it is not enough to meet the requirements of testing.

Summary of the invention

Embodiments of the present invention provide a method for updating a data sampling interval, a method for collecting data according to a sampling interval, and a device thereof, which can adjust a data sampling interval according to different users.

In a first aspect, the embodiment of the present invention provides a method for updating a data sampling interval, including: acquiring, by a terminal device, sampling data of a user in a first time, and obtaining a first sampling interval based on sampling data of the user in the first time period. Transmitting, by the terminal device, the first sampling interval to the wearable device, so that the wearable device acquires sampling data of the user in a second time according to the first sampling interval; the terminal device receives the The sampling data of the user in the second time period sent by the wearable device, and obtaining a second sampling interval based on the sampling data of the user in the second time; the terminal device sending the second sampling interval to the The device is worn to enable the wearable device to acquire new sampling data of the user in a third time according to the second sampling interval.

In the method for updating the data sampling interval in the embodiment of the present invention, the terminal device acquires sampling data from the wearable device, and adjusts the sampling interval according to the sampling data, so that the wearable device can collect data based on the adjusted sampling interval. Since the data collected by different users is different, the adjusted sampling interval is suitable for the corresponding user. Further, since different sampling intervals are suitable for different users, both accurate data collection and power consumption can be saved.

In a possible implementation manner, the sampling data of the user in the second time period includes first sampling data, where the first sampling data is when the wearable device arrives at a sampling time point, according to the first duration Sampling data collected. In this way, the terminal device can determine the sampling interval according to the first sampled data.

In a possible implementation manner, the sampling data of the user in the second time period includes first and second sampling data, wherein the first sampling data is when the wearable device arrives at a sampling time point, according to Sampling data collected by the first duration; the second sampling data is sampling data collected by the wearable device according to the second duration after the first sampling data meets the set condition, and the second duration is greater than the first duration . In this way, the terminal device can determine the sampling interval based on the first and second sample data. Since the second data is data collected under the set condition and the acquisition duration is longer, the terminal device determines that the sampling interval is more reasonable than when only the first sample data is collected.

In a possible implementation manner, the sampling data includes PPG data, and the first sampling data satisfies a setting condition that the first sampling data includes heart rate irregularity information. The data collection in the embodiment of the present invention can be applied to detect the PPG signal collection when the user has a risk of arrhythmia.

In a possible implementation manner, the heartbeat information is specifically atrial fibrillation information.

In a possible implementation manner, the terminal device displays a visualization graph drawn based on the PPG data. The embodiment of the invention can display the sampling result by visualizing the graphic, and can more intuitively reflect the physical condition of the user.

In a possible implementation manner, the first or second sampling interval is obtained according to the following formula:

I=LI+(HI-LI)×(1-prob _AF ) where HI, LI is used to characterize the reference sampling interval range [LI, HI]; Prob _AF is the probability of atrial fibrillation, which is based on the first or The user's sampled data in the second time is obtained by the logistic regression algorithm.

A second aspect, a method for updating a data sampling interval, the method comprising: acquiring, by a terminal device, sampling data and motion data of a user in a fourth time, and obtaining a third sampling interval based on the sampling data and the motion data; Transmitting, by the terminal device, the third sampling interval to the wearable device, so that the wearable device acquires sampling data of the user in a fifth time according to the third sampling interval; a motion data of the user in five time; the terminal device obtains a fourth sampling interval based on sampling data and motion data of the user in the fifth time; the terminal device sends the fourth sampling interval to the wearable And a device, so that the wearable device acquires new sampling data of the user in a sixth time according to the fourth sampling interval.

It can be seen that, in the method for updating the data sampling interval in the embodiment of the present invention, when determining the sampling interval, both the sampling data and the motion data should be considered, and the adjusted sampling interval is not only suitable for the corresponding user, but also more reasonable. Further, since different sampling intervals are suitable for different users, both accurate data collection and power consumption can be saved.

In a possible implementation manner, the sampled data includes PPG data. The data collection in the embodiment of the present invention can be applied to detect the PPG signal collection when the user has a risk of arrhythmia.

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

According to the method of the embodiment of the present invention, the wearable device acquires sampling data, adjusts the sampling interval according to the sampling data, and the wearable device can collect data based on the adjusted sampling interval. Since the data collected by different users is different, the adjusted sampling interval is suitable for the corresponding user. Further, since different sampling intervals are suitable for different users, both accurate data collection and power consumption can be saved.

In a possible implementation manner, the sampling data of the user in the second time period includes first sampling data, where the first sampling data is when the wearable device arrives at a sampling time point, according to the first duration Sampling data collected. In this way, the wearable device can determine the sampling interval based on the first sampled data.

In a possible implementation manner, the sampling data of the user in the second time period includes first and second sampling data, wherein the first sampling data is when the wearable device arrives at a sampling time point, according to The sample data collected by the first time length; the second data is sample data collected by the wearable device according to the second time length after the first sample data meets the set condition, and the second time length is greater than the first time length. In this way, the wearable device can determine the sampling interval based on the first and second sampled data. Since the second data is data acquired under the set conditions, and the duration of the acquisition is longer, the wearable device determines that the sampling interval is more reasonable than when only the first sampled data is collected.

In a possible implementation manner, the sampling data includes PPG data, and the first sampling data satisfies a setting condition that the first sampling data includes heart rate irregularity information. The data collection in this embodiment can be applied to detect whether the user has a PPG signal collection when the heart rate is not aligned.

In a possible implementation manner, the method further includes: the wearable device outputs a visualization graph drawn based on the PPG data. In this way, the wearable device can directly display the graphic, or project the image, or send it to other devices to display the graphic, and can display the sampling result through the visual graphic, thereby more intuitively reflecting the physical condition of the user.

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

It can be seen that, in the embodiment of the present invention, data is collected according to the sampling interval. When determining the sampling interval, both the sampling data and the motion data should be considered. The adjusted sampling interval is not only suitable for the corresponding user, but also more reasonable. Further, since different sampling intervals are suitable for different users, both accurate data collection and power consumption can be saved.

In a possible implementation manner, the sampled data includes PPG data. The data collection in the embodiment of the present invention can be applied to detect the acquisition of a PPG signal when the user has a risk of arrhythmia.

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, where The one or more programs include instructions that, when executed by the terminal device, cause the terminal device to perform the methods of the first aspect and/or the second aspect of the embodiments of the present 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 include instructions that, when executed by the wearable device, cause the wearable device to perform a method as in the third and/or fourth aspects of embodiments of the present invention.

The description of the technical features, technical solutions, advantages, or similar language in this application is not intended to suggest that all features and advantages may be realized in any single embodiment. Rather, it is to be understood that a description of a feature or benefit is meant to include a particular technical feature, technical solution, or benefit in at least one embodiment. Therefore, descriptions of technical features, technical solutions, or advantageous effects in the present specification are not necessarily referring to the same embodiments. Further, the technical features, technical solutions, and advantageous effects described in the embodiment can also be combined in any suitable manner. Those skilled in the art will appreciate that embodiments may be implemented without one or more specific technical features, technical solutions, or advantages of the specific embodiments. In other embodiments, additional technical features and benefits may also be identified in a particular embodiment that does not embody all embodiments.

DRAWINGS

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

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 present invention;

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

3 is a schematic diagram showing the hardware structure of a mobile phone according to an embodiment of the present invention;

4 is a flowchart of a method for updating a data collection interval according to Embodiment 1 of the present invention;

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

6 is a schematic diagram of a visualization graph based on PPG sampling data in an embodiment of the present invention;

7 is another schematic diagram of a visualization graph based on PPG sampling data in an embodiment of the present invention;

8 is a flowchart of a method for updating a data collection interval according to Embodiment 2 of the present invention;

9 is a flowchart of a method for updating a data collection interval according to Embodiment 3 of the present invention;

10 is a flowchart of a method for updating a data collection interval according to Embodiment 4 of the present invention;

11 is a flowchart of a method for collecting data according to a sampling interval according to Embodiment 5 of the present invention;

12 is a flowchart of a method for collecting data according to a sampling interval according to Embodiment 6 of the present invention;

FIG. 13 is a flowchart of a method for collecting data according to a sampling interval according to Embodiment 7 of the present invention.

Detailed ways

The terminology used in the description of the present invention is for the purpose of describing particular embodiments and is not intended to limit the invention. The singular expression "a", "an", "sai", "said", "the", "the" and "the" are intended to be used in the description of the invention and the appended claims. The plural expressions are included unless they are expressly indicated to the contrary. 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 a smart watch as an example and a terminal device as a mobile phone as an example, this embodiment provides a schematic diagram of a network system, wherein the smart watch 200 can wirelessly communicate with the wireless communication base station 100 or Wireless network communication with the mobile phone 300. For example, the smart watch 200 can transmit a wireless signal to the base station 100 through the wireless communication link L1 through its own radio frequency circuit and antenna, and then request the base station 100 to perform wireless network service processing on the specific service requirements of the smart watch 200; for example, a smart watch. The 200 can be matched with the mobile phone 30 through its own Bluetooth. After the matching is successful, the data communication with the mobile phone through the Bluetooth communication link L2, and of course, the data communication with the mobile phone through other wireless communication methods, such as radio frequency identification technology, short-range wireless communication Technology, etc. The smart watch 200 can also detect data of various environments by its own various sensors.

As shown in FIG. 2, the smart watch 200 may specifically include a body and a wristband (not shown in FIG. 2) connected to each other, wherein the watch body may include a touch screen 215 and a NFC (Near-field communication) device 212. The processor 214, the memory 204, the microphone 206, the ambient light sensor 213, the Bluetooth device 211, the positioning device 205, the power management system 203 (including the power supply), the Wi-Fi (Wireless Fidelity) device 201, and the time system 202 , motion sensor 209, PPG sensor 210, and the like. Although not shown, the smart watch 200 may also include an antenna, a speaker, an accelerometer, a gyroscope, 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 can be overlaid on the display panel 208 . The touch panel 207 can collect a touch operation on or near a user of the smart watch (such as a user using a finger, a stylus, or the like, any suitable object or accessory on or near the touch panel), and according to A preset program drives the connected connection device. The touch panel 207 can include two parts of a touch detection device and a touch controller. Wherein, the touch detection device detects the touch orientation of the user, and detects a signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts the touch information into contact coordinates, and sends the touch information. The processor 214 is provided and can receive commands from the processor 214 and execute them. In addition, touch panels can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic waves. A display panel (generally referred to as a display screen) can be used to display information entered by the user or information provided to the user as well as various menus of the smart watch. Optionally, 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 can be overlaid on the display screen. When the touch panel detects a touch operation on or near it, the touch panel transmits to the processor 214 to determine the type of the touch event, and then the processor 214 displays the type according to the type of the touch event. A corresponding visual output is provided on the screen. Although in FIG. 2, the touch panel and the display screen are two independent components to implement the input and output functions of the smart watch, in some embodiments, the touch panel and the display can be integrated to implement the smart watch. 200 input and output functions.

The NFC device 212 is used to provide an NFC function to the smart watch 200, which can have three application modes, namely a card reader mode, a peer-to-peer mode, and a card emulation mode. In some embodiments, the NFC device 212 can include an NFC controller, an NFC radio frequency circuit, a Secure Element. The NFC controller is respectively connected to the NFC radio frequency circuit and the security unit, and is mainly used for modulation and demodulation of the contactless communication signal, controlling the input and output of data in the NFC device, and performing data interaction with the processor 214; the NFC radio frequency circuit and The NFC controller is connected to realize the transmission and reception of the 13.56 MHz RF signal, and can be composed of an EMC (Electromagnetic Compatibility) filter circuit, a matching circuit, a receiving circuit, and an NFC antenna. The security unit may include a memory, one or more processors, and the main function of the security unit is to implement secure storage of applications and data, and provide secure computing services externally. The security module also communicates with external devices through the NFC controller to achieve data storage and transaction security. It should be noted that the security unit can be a tamper-proof component used in mobile devices to provide security, confidentiality, and to support various application environments. The security unit can exist in various shapes. For example, the security unit can be integrated in a Universal Integrated Circuit Card (UICC), such as a Subscriber Identity Module (SIM) card, and an embedded security unit (a circuit located in a mobile device). Board), Secure Digital (SecureDigital SD) card, micro SD card, etc. Further, the security unit may also include one or more applications executing in the context of the security unit, such as in an operating system of the security unit/or in a Java runtime environment running on the security unit. Additionally, the one or more applications can include one or more payment applications that can be saved in the memory 204. The security unit supports application secure transactions and secure data storage, supports downloading, installing, deleting, updating, etc. of multiple applications. The security unit also supports secure isolation of application data. For security, the security unit may not allow different applications. Free access; the security unit also provides symmetric, asymmetric encryption algorithms and certificate capabilities for a variety of payment needs, provides a program interface for secure transaction application access, and supports two-way communication with the NFC controller or processor 214.

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

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

Operating system, etc.

The positioning device 205 is configured to provide a geographic location for the smart watch 200. It can be understood that the positioning device 205 can be specifically a receiver of a positioning system such as a Global Positioning System (GPS) or a Beidou satellite navigation system or a Russian GLONASS. After receiving the geographical location transmitted by the positioning system, the positioning device 205 sends the information to the processor 214 for processing, or sends it to the memory 204 for storage. In some other embodiments, the positioning device 205 can be an Assisted GPS (AGPS) receiver, and the AGPS is an operation mode for performing GPS positioning with certain assistance, which can utilize the base station. The signal, in conjunction with the GPS satellite signal, allows the smart watch 200 to be positioned faster; in the AGPS system, the positioning device 205 can obtain positioning assistance by communicating with an auxiliary positioning server (eg, a mobile phone location server). The AGPS system assists the positioning device 205 in performing the ranging and positioning services by acting as a secondary server, in which case the secondary positioning server communicates with the mobile device via the wireless communication network (eg, the smart watch 200, the positioning device 340 of the handset 300 (ie, GPS reception) Providing location assistance in communication. In still other embodiments, the location device 205 may also be a Wi-Fi access point based location technology. Since each Wi-Fi access point has a globally unique MAC Address, the mobile device can scan and collect the broadcast signal of the surrounding Wi-Fi access point when Wi-Fi is turned on, so the MAC (Media Access Control) broadcasted by the Wi-Fi access point can be obtained. Controlling the address; the mobile device sends the data (eg, MAC address) capable of indicating the Wi-Fi access point to the location server through the wireless communication network, and the location server retrieves the geographic location of each Wi-Fi access point, and In conjunction with the strength of the Wi-Fi broadcast signal, the geographic location of the mobile device is calculated and sent to the location device 205 of the mobile device.

The Wi-Fi device 201 is configured to provide Wi-Fi network access for the smart watch 200, and the smart watch 200 can access the Wi-Fi access point through the Wi-Fi device 201, thereby helping the user to send and receive emails, browse web pages, and Access to streaming media, etc., it provides users with wireless broadband Internet access. In still other embodiments, the Wi-Fi device 201 can also act as a Wi-Fi access point to provide Wi-Fi network access to other mobile devices.

The smart watch also includes a power source (such as a battery) that supplies power to various components. The power source can be logically coupled to the processor 214 through the power management system 203 to manage functions such as charging, discharging, and power management through the power management system 203. The microphone 206 can convert the collected sound signal into an electrical signal, which is received by the audio circuit and converted into audio data; the Bluetooth device 211, the smart watch can exchange information with other electronic devices (such as the mobile phone 300) through Bluetooth, and through the above The electronic device is connected to the network, connected to the server, and handles functions such as voice recognition.

The smart watch 200 can also include a time system 202 that provides an indication of time for the smart watch 200.

The smart watch 200 may further include a motion sensor 209, which may include an acceleration sensor, a gyroscope, etc., wherein the acceleration sensor determines whether the device moves by measuring the direction and the acceleration force, thereby achieving the purpose of the step counting, and matching by the collected data. The type of exercise the user is performing, which in turn monitors the user's walking number, calorie consumption, etc., to achieve the most basic functions of the smart watch.

The smart watch 200 can also include a PPG sensor 210 that can measure heart rate and other biometric indicators using photoplethysmography (PPG). PPG is a method of illuminating light into the skin and measuring light scattering due to blood flow. This method is more commonly used. When the blood flow force changes, for example, when the blood pulse rate (heart rate) or the blood volume (cardiac output) changes, the light entering the human body will be foreseeable scattering.

As shown in FIG. 3, the mobile phone 300 includes an RF (Radio Frequency) circuit 310, a memory 320, a touch screen 330, a positioning device 340, an NFC device 302, a sensor 350, an audio circuit 360, a Wi-Fi device 370, and a processor 380. , Bluetooth device 381 and power system 390 and other components. It will be understood by those skilled in the art that the structure of the handset shown in FIG. 3 does not constitute a limitation to the handset, and may include more or less components than those illustrated, or some components may be combined, or different component arrangements.

The components of the mobile phone 300 will be specifically described below with reference to FIG. 3:

The RF circuit 310 can be used to transmit and receive information and receive and transmit signals during a call. Specifically, the RF circuit 310 receives the downlink data of the base station and then processes it to the processor 380; in addition, transmits data related to the uplink to the base station. Generally, RF circuits include, but are 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 circuitry 310 can also communicate with other devices over a wireless communication network. The wireless communication network may use any communication standard or protocol including, but not limited to, global mobile communication systems, general packet radio services, code division multiple access, wideband code division multiple access, long term evolution, email, short message service, and the like.

The handset 300 can 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 and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel of the touch screen 330 according to the brightness of the ambient light, and the proximity sensor may close the display panel when the mobile phone 300 moves to the ear. Power supply. As a kind of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in all directions (usually three axes). When it is stationary, it can detect the magnitude and direction of gravity. It can be used to identify the gesture of the mobile phone (such as horizontal and vertical screen switching, related Game, magnetometer attitude calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for the mobile phone 300 can also be configured with gyroscopes, barometers, hygrometers, thermometers, infrared sensors and other sensors, here Let me repeat.

The audio circuit 360, the speaker 361, and the microphone 362 can provide an audio interface between the user and the handset 300. The audio circuit 360 can transmit the converted electrical data of the received audio data to the speaker 361 for conversion to the sound signal output by the speaker 361; on the other hand, the microphone 362 converts the collected sound signal into an electrical signal, by the audio circuit 360. After receiving, it is converted into audio data, and the audio data is output to the RF circuit 310 for transmission to, for example, another mobile phone, or the audio data is output to the memory 320 for further processing.

The processor 380 is a control center of the mobile phone 300, and connects various parts of the mobile phone using various interfaces and lines, and executes each of the mobile phones 300 by running or executing an application stored in the memory 320 and calling data stored in the memory 320. The function and processing of data to monitor the phone as a whole. In some embodiments, processor 380 can include one or more processing units; processor 380 can also integrate an application processor and a modem processor, where the application processor primarily processes operating systems, user interfaces, applications, and the like The modem processor primarily handles wireless communications. It will be appreciated that the above described modem processor may also not be integrated into the processor 380.

The handset 300 also includes a power system 390 (including a battery and power management chip) that powers the various components. The battery can be logically coupled to the processor 380 via a power management chip to manage functions such as charging, discharging, and power consumption 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 other input/output devices), and the like, and details are not described herein.

In some embodiments, the functions and functions of the components 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 those in the smart watch 200 of the above embodiment. The functions and functions 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 are the same or similar, and the above components are not described too much herein.

Embodiment 1

4 is a flowchart of a method for updating a data sampling interval according to Embodiment 1 of the present invention. For convenience of understanding, the method shown in FIG. 4 is an example in which a user uses a smart watch and a terminal device to collect sampling data for the first time. Of course, if the user has used the smart watch and the terminal device to collect the sampled data, but the interval is used again after a long time, for the convenience of description, it is regarded as the first use. In the specific description, the embodiment of the present invention uses a mobile phone as a terminal device, and the smart watch is a wearable device as an example, but it can be understood that this is not limited thereto.

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

The sampling interval is used to collect user data, such as the user's PPG data, from a smart watch that is connected to the mobile phone. When the sampling interval is determined by the mobile phone, the sampling time point is also determined, so that by the sampling time point, the smart watch will collect the sampling data.

Taking the scenario of the embodiment of the present invention as a case of atrial fibrillation, the user information of the user may include at least one of the following information: basic information of the user, including gender, age, smoking, drinking; user's past medical history information, including whether There are heart disease, coronary heart disease, hyperthyroidism, etc.; user's existing heart rate information, including heart rate, whether there is atrial fibrillation, the proportion of atrial fibrillation.

It can be understood that the embodiment of the present invention and other embodiments of the present invention are described by taking atrial fibrillation as an example, but can also be used for arrhythmia, which is not limited herein.

As shown in FIG. 5, when the user first uses the mobile phone and the smart watch to collect the PPG data, the mobile phone can display a questionnaire as shown in the example, and let the user input the user information. It can be understood that the survey questions listed in FIG. 5 are merely examples and are not limited thereto.

When the user uses the method of the embodiment of the present invention for the first time, when the user inputs the user information, the mobile phone estimates an approximate sampling interval.

After the third-party company counts the user information of a large number of users in advance, it obtains common estimation results of multiple sampling intervals, such as user gender, age 30-39 years old, smoking, drinking, no past medical history, arrhythmia, etc., corresponding to a sampling interval For example, the user gender male, age 30-39 years old, smoking, not drinking, no past medical history, no arrhythmia, etc., corresponding to another sampling interval, and so on. The mobile phone can store the above-mentioned multiple sampling intervals, so that after the user inputs the user information, the mobile phone can find the corresponding sampling interval.

Of course, the mobile phone can also use the general computing model of the mobile phone to perform calculations. For details, refer to the content of step S405. It can be understood that the determination of the sampling interval in the embodiment of the present invention is a loop iterative process, which is a process of continuous correction, so when the user first uses the mobile phone and the smart watch to collect the PPG data, the first is determined based on the user information. A sampling interval does not need to be as precise as it is.

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.

Step S403, the wearable device acquires sampling data of the user in the first time according to the first sampling interval.

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

Alternatively, the first time can be 24 hours.

According to an example of the embodiment of the present invention, the sampled data may be the user's PPG data. Of course, those skilled in the art can understand that the data is not limited to this type of data.

Optionally, before collecting the sampling data, the smart watch may first determine whether the user is in a state suitable for collecting sampling data, for example, the user's body is relaxed and quiet, so that the collected data is more accurate.

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

For example, when the sampling time point arrives, the smart watch collects PPG data for 1 minute duration. If there is no atrial fibrillation information in the PPG data, the smart watch stops collecting, and waits for the next sampling time point to arrive, and then collects the PPG data for 1 minute.

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

For example, in the embodiment of the present invention, PPG data collection for atrial fibrillation includes the PPG data including atrial fibrillation information. For example, when the sampling time point arrives, the smart watch collects PPG data for 1 minute duration. If the PPG data contains atrial fibrillation information, the smart watch will then collect PPG data for a further 2 minutes. Obviously, the multi-acquisition of PPG data will help the mobile phone to determine a more reasonable sampling interval based on the sampling data of the smart watch in the future.

In the embodiment of the present invention, how to collect PPG data for a smart watch belongs to the prior art, and is not described in detail in the embodiment of the present invention. For example, the blood vessel is irradiated by red and green light, the blood flow velocity is detected, and the heart rate is indirectly obtained.

In the above example, the setting conditions for the judgment are mentioned that the PPG data contains atrial fibrillation information, and as an undefined example, how to judge the PPG data to include atrial fibrillation information is described below.

After collecting the first duration (for example, 1 minute) of the PPG data, applying the atrial fibrillation preliminary screening algorithm to determine whether the data contains atrial fibrillation information can be expressed by the following formula:

y=f(PPG)

Where y represents the test result (ie, normal or atrial fibrillation), and f represents the atrial fibrillation primary screening algorithm, which may be a support vector machine (SVM) or other classification algorithm. The following takes the SVM model algorithm as an example to describe how to calculate the atrial fibrillation information contained in the collected PPG data.

The SVM model determines the classification result by calculating the distance of the sample from the decision plane in the feature space. The following formula describes the linear SVM model. The optimization problem proposed by the linear SVM model is:

At the same time

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), and b is the decision plane (in the feature space, the plane divides the sample into two parts, corresponding to the atrial fibrillation sample and the normal sample respectively) Distance, x _t is a sensitive feature of atrial fibrillation calculated according to the PPG signal, such as heart rate variability characteristics, entropy characteristics, etc., y _t is the result (atrial fibrillation or normal), and t is the sample number.

When calculating, according to the above

And y _t (w·x _t -b) ≥ 1

Can get:

Where αt is a Lagrangian multiplier, n is the number of samples, and m is the number of support vectors (consisting of vectors closest to the decision plane).

Based on the above w, b, and the existing x _t , the value of the piece of data can be judged according to y _t =sign(wx _t -b), where sign represents a symbol operation, and if the symbol is a positive sign, the line is indicated The data is normal data. If the symbol is a negative sign, the data is a sample of atrial fibrillation.

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

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

Optionally, the smart watch can send the sampled data to the mobile phone via a Bluetooth connection.

According to the setting, the smart watch can uniformly send the collected sample data to the mobile phone at a fixed time, such as 8:00 every morning. Of course, the sampling data can be sent to the mobile phone every time the sampling data is collected, which is not limited.

Optionally, the smart watch can also synchronize the sampled data to the cloud server and then send it to the mobile phone through the cloud server, which is not limited.

Step S405, the terminal device obtains a second sampling interval based on the sampled data.

The sampling data includes first sampling data collected according to the first duration, or the sampling data includes first sampling data and second sampling data collected according to the second duration, the second sampling interval is that the terminal device is based on the first sampling data and/or Determined by the second sampled data.

Optionally, based on the sampled data, the mobile phone can calculate the proportion of the user's atrial fibrillation. For example, there are 100 sampled data collected, and 60 sample data containing atrial fibrillation information, and the proportion of atrial fibrillation is 0.6. How to identify the atrial fibrillation data and how to calculate it is a prior art and will not be described here. With the user's ratio of atrial fibrillation, the phone can get a second sampling interval.

Optionally, the terminal device may determine the second sampling interval based on the sampled data at a set time, such as after 8:00 every morning.

Optionally, step S405 can also be replaced by:

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

With respect to step S405, step S405' also considers the user's personal information more, which is obviously more reasonable and comprehensive, and is particularly suitable for the case where the user's personal information changes. For example, the user's age is increasing, the user is abstaining from alcohol, etc. Obviously, when calculating the second sampling interval, these factors are considered to make the calculation of the sampling interval more accurate.

Specifically, the terminal determines, according to the sampled data, that the sampling interval can be sampled as follows:

First, the range of the data reference sampling interval can be set to [LI, HI] according to the power consumption capability of the wearable device and the doctor's suggestion, or according to the collected big data; for example, LI is 5 min and HI is 20 min.

The application model models the user-associated data (input) and the probability of atrial fibrillation prob _AF (referring to the probability of atrial fibrillation in different users). The results are as follows:

Prob _AF =model(input)

Where model can be a logistic regression, and the following formula represents a model using logistic regression:

The input may be user-associated data, including the user's personal information, the proportion of atrial fibrillation obtained based on the sampled data, and the like. It can be understood that, in the specific calculation, it is possible to set only the proportion of atrial fibrillation obtained based on the sampled data (corresponding to step S405), and also consider the personal information of the user and the proportion of atrial fibrillation obtained based on the sampled data (corresponding to step S405') .

Representing model parameters (θ is a one-dimensional vector representing the magnitude of importance corresponding to each input value), determined for input, such as for user-associated data. θ reflects the importance of the input value, which can be set according to experience, or according to the big data setting, which is not limited here.

T represents the transpose in mathematical calculations.

Specifically, in the embodiment of the present invention, the user association data includes personal information of the user and a proportion of atrial fibrillation obtained based on the sampled data. For example, the user's personal information includes, male, age 56, and the proportion of atrial fibrillation based on the sampled data is 0.6, then the input vector for constructing 4 input values can be: [age, gender, atrial fibrillation ratio, exercise data], Considering the need of the operation, the age and motion data are characterized by normalized age and normalized motion data, and correspondingly brought in [56/100,1,0.6,0] ^T can be obtained. Among them, since the motion data data is not considered, the input value corresponding to "motion data" is 0. It can be understood that the embodiment of the present invention is exemplified by four input values, and in practice, the parameter values can be increased or decreased as needed. For example, the user's personal information can also increase alcoholism, heart disease history and so on.

θ can be [0.1, 0.2, 0.3, 0.4] ^T . It can be seen that the four input values of age, gender, atrial fibrillation ratio and exercise data correspond to the importance of 0.1, 0.2, 0.3, 0.4, respectively. It can be understood that θ is a value obtained according to experience or a result of big data collection, and can be adjusted according to requirements in practice.

Bring input and θ into

Calculated prob _AF =0.5637

Then obtain the second sampling interval according to the following formula

I=LI+(HI-LI)×(1-prob _AF )

I=5+(20-5)*(1-0.5637)=11.5

Finally, the second sampling interval is 11.5 min.

It is easy to understand that the above 11.5 min is a second sampling interval obtained based on the sampled data and the personal information of the user (corresponding to step S405 ′). If the second sampling interval only considers the sampled data (corresponding to step S405), the input can be constructed as [0,0,0.6,0] ^T , the second sampling interval can be calculated.

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

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

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

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

It can be understood by those skilled in the art that after acquiring the new sampling data of the user in step S407, the wearable device can send it to the terminal device, so that the terminal device can update the sampling interval according to the new sampling data. This cycle, so the determination of the sampling interval will be more reasonable.

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

Figures 6 and 7 show the visual graphics that the mobile phone draws and displays based on the received PPG data. Obviously, the visualized graphics are used to display the sampling results, which can more intuitively reflect the user's physical condition.

Embodiment 2

Referring to the description of the first embodiment, after the new sampling data of the user is acquired in step S407 of the method in the first embodiment, the wearable device can send the data to the terminal device, so that the terminal device can be updated according to the new sampling data. sampling interval.

Therefore, if the smart watch already has sampled data before determining the first sampling interval, the mobile phone can determine the first sampling interval based on the existing sampling data.

Specifically, as shown in FIG. 8 , the flow of the method for updating the data sampling interval involved in the second embodiment is as follows: It can be understood that the method steps involved in the second embodiment are the same as or similar to the method steps involved in the first embodiment. The description of the embodiments of the present invention will not be repeated. In addition, some terms involved in the first embodiment continue to be used in the embodiment of the invention, such as the first time, the second time, and the like.

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

For example, the mobile phone obtains the sampling data of the user within 24 hours of the previous day at 8:00 every morning, and obtains the sampling interval based on the sampling data of the user within the first 24 hours.

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

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

Step S803, the wearable device acquires sampling data of the user in the first time according to the first sampling interval.

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

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

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

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

Similarly, after step S807, the wearable device can send the new sampling data of the user to the terminal device in a second time, so that the terminal device can update the sampling interval according to the new sampling data. This cycle, so the determination of the sampling interval will be more reasonable.

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

Embodiment 3

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

As shown in Figure 9,

In step S901, the terminal device acquires user information and/or updated sampling data.

If it is used for the first time, the mobile phone obtains the user information of the user. If there is already PPG sampling data before use, the mobile phone will update regularly, for example, every 8 o'clock in the morning to get the previous time period, such as PPG sampling data in the first 24 hours.

Those skilled in the art can understand that the above user information and/or updated sampling data belong to data associated with the user.

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

For the method of determining the sampling interval, refer to the corresponding content of the first or second embodiment, and details are not described herein again.

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

Step S904, the wearable device determines whether the user is in a quiet state. If yes, step S905 is performed, otherwise step S903 is performed, and waits for the next sampling time point to arrive.

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

Step S906, the terminal device uses the atrial fibrillation preliminary screening algorithm for the PPG data.

For the specific algorithm, refer to the corresponding content of the first or second embodiment, and details are not described herein again.

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

Step S908, the wearable device continuously collects PPG data of 2 minutes and sends the PPG data to the terminal device.

Step S909, the terminal device records the collected PPG data, and tags and time stamp.

PPG data tagging can be, there is atrial fibrillation, normal and so on.

Next, after the sampling time point arrives, steps S903-S909 are cycled, so that by 8:00 the next morning, the terminal device acquires the PPG data in the first 24 hours, and loops through steps S901-S909.

Embodiment 4

In order to make the determination of the sampling interval more reasonable, the fourth embodiment has further improvement on the basis of the second embodiment. In short, the determination of the sampling interval also considers the motion data of the user.

Specifically, as shown in FIG. 10, the flow of the method for updating the data sampling interval involved in the fourth embodiment is as follows: It can be understood that the method steps involved in the fourth embodiment are the same as the method steps involved in the first to third embodiments. The details of the embodiments of the present invention are not described again. In addition, for ease of understanding, when calculating the sampling interval in the embodiment of the present invention, some of the values follow the values exemplified before.

Step S1001: The terminal device acquires sampling data of the user and motion data 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 user's exercise data includes: exercise data such as walking, running, swimming, and the like. These data can be recorded on the wearable device, of course, it is also possible to record 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 8:00 every morning, and obtains a sampling interval based on the sampling data and motion data of the user within 24 hours of the previous day.

The algorithm described in the first embodiment is still used when determining the sampling interval. Specifically, the difference is that when the input value is determined, the input value can be represented by [0, 0, 0.6, 6000/10000]. Among them, 6000/10000, used to characterize the user to walk 6000 steps within 24 hours of the previous day.

In step S1002, the terminal device sends the third sampling interval to the wearable device.

Step S1003: The wearable device acquires sampling data of the user in a fifth time according to the third sampling interval.

Step S1004: The wearable device sends the sampling data of the user in the fifth time to the terminal device.

Step S1005: The wearable device sends the motion data of the user in the fifth time to the terminal device.

In the fifth time that the smart watch collects the user's sample data, the smart watch also collects the user's motion data.

It can be understood that when the smart watch sends the motion data, it can be sent to the mobile phone every time the user's motion data is collected, or can be unified at the set time, for example, sent to the mobile phone at 8 o'clock every morning, which is not limited herein.

The method of sending can be sent directly to the mobile phone by the smart watch, or it can be uploaded to the cloud server by the smart watch, and then synchronized to the mobile phone, which is not limited herein.

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.

For details, how to obtain the sampling interval according to the sampled data, refer to step S405 in the first embodiment, and the above step S1001.

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

Step S1008: The wearable device acquires new sampling data of the user in the sixth time according to the fourth sampling interval.

Similarly, after step S1008, the wearable device can send the acquired new sampling data of the user to the terminal device, so that the terminal device can update the sampling interval according to the new sampling data and the motion data of the user that is updated again. This cycle, so the determination of the sampling interval will be more reasonable.

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 in the fourth time, and the personal information of the user; in step S1006, the terminal device may be based on the user in the fifth time. The sampled data and motion data, as well as the user's personal information, are given a fourth sampling interval.

It can be understood that the above steps are not strictly sequential.

Embodiment 5

As the hardware configuration of the wearable terminal becomes higher and higher, the wearable terminal can have powerful storage capacity and computing power in addition to data collection. That is to say, in the first to fourth embodiments, the calculation sampling interval of the terminal device, etc., can also be placed on the wearable device.

As shown in FIG. 11 , corresponding to the first embodiment, a method for collecting data according to a sampling interval in the fifth embodiment is described as follows: It can be understood that the method steps involved in the fifth embodiment are related to the methods involved in the first to fourth embodiments. The method steps are the same or similar, and the embodiments of the present invention are not described again. For convenience of understanding, the method shown in FIG. 11 is an example in which the user uses the smart watch to collect sampling data for the first time. Of course, if the user has used the smart watch to collect the sampling data, but after a long time interval, it is used again for convenience of description. , considered as the first use. In the specific description, the embodiment of the present invention takes a smart watch as a wearable device as an example, but it can be understood that this is not limited thereto.

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 in the first time according to the first sampling interval.

Step S1103: The wearable device obtains a second sampling interval based on the sampled data.

Step S1104: The wearable device acquires new sampling data of the user in the second time according to the second sampling interval.

For the same reason, the method for calculating the sampling interval in step S1101 and step S1103 is referred to the corresponding description of the foregoing embodiment.

Similarly, after step S1104, the wearable device can store the new sampling data of the user in the second time, so that the wearable device can update the sampling interval according to the new sampling data. This cycle, so the determination of the sampling interval will be more reasonable.

The sampling data may include first sampling data, or the sampling data includes first sampling data and second sampling data. The first sampling data is sampling data collected according to the first duration when the wearable device arrives at the sampling time point; and the second data is that the wearable device meets the setting condition in the first sampling data. Then, according to the sampling data collected by the second duration, the second duration is greater than the first duration.

Optionally, the sampling data includes PPG data, and the first sampling data satisfies the setting condition that the first sampling data includes arrhythmia information, in particular atrial fibrillation information.

Optionally, the wearable device can also output visual graphic data based on the PPG data. In this way, the wearable device can display the visualized graphic by itself, or can display the visualized graphic by projection, or can output to the other device to display the visualized graphic.

Embodiment 6

As shown in FIG. 12, corresponding to the second embodiment, one of the sixth embodiments is described according to the method for collecting data according to the sampling interval. It can be understood that the method steps involved in the sixth embodiment are related to the first to fifth embodiments. The method steps are the same or similar, and the embodiments of the present invention are not described again. For ease of understanding, the method illustrated in FIG. 12 has the previous sampled data before the wearable device determines the first sampling interval. In addition, some terms involved in the fifth embodiment continue to be used in the embodiment of the invention, such as the first time, the second time, and the like.

Step S1201: The wearable device acquires sampling data of the user in the third time, and obtains a first sampling interval based on the sampling data of the user in the third time, wherein the third time is before the first time.

Step S1202: The wearable device acquires sampling data of the user in the first time according to the first sampling interval.

Step S1203: The wearable device obtains a second sampling interval based on the sampling data of the user in the first time.

Step S1204: The wearable device acquires new sampling data of the user in the second time according to the second sampling interval.

Similarly, after step S1204, the wearable device can save the user's new sampling data for a second time, so that the wearable device can update the sampling interval according to the new sampling data. This cycle, so the determination of the sampling interval will be more reasonable.

Example 7

As shown in FIG. 13 , corresponding to the fourth embodiment, one of the seventh embodiments is described according to the method for collecting data according to the sampling interval as follows: It can be understood that the method steps involved in the seventh embodiment are related to the first to sixth embodiments. The method steps are the same or similar, and the embodiments of the present invention are not described again.

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

Step S1302: The wearable device acquires 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 sampling data and motion data of the user in the fifth time period;

Step S1305: The wearable device acquires new sampling data of the user in a sixth time according to the fourth sampling interval.

The sampling data includes PPG sampling data.

It should be noted that those skilled in the art can understand that all or part of the process of implementing the foregoing embodiments can be completed by a computer program to instruct related hardware, and the program can be stored in a computer readable storage. In the medium, the program, when executed, may include the flow of an embodiment of the methods as described above. The storage medium may be a magnetic disk, an optical disk, a read only memory or a random access memory.

For the purposes of explanation, the foregoing description has been described by reference to the specific embodiments. However, the above exemplary discussion is not intended to be exhaustive or to limit the invention to the precise form disclosed. Many modifications and variations are possible in light of the above teachings. The embodiments were chosen and described in order to explain the principles of the embodiments of the invention, An embodiment.

Claims

A method for updating a data sampling interval, the method comprising:

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

Transmitting, by the terminal device, the first sampling interval to the wearable device, so that the wearable device acquires sampling data of the user in the second time according to the first sampling interval;

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

The terminal device sends the second sampling interval to the wearable device, so that the wearable device acquires new sampling data of the user in a third time according to the second sampling interval.
The method according to claim 1, wherein the sampling data of the user in the second time comprises first sampling data;

The first sampling data is sampling data collected by the wearable device according to the first duration when the sampling time point arrives.
The method according to claim 1, wherein the sampling data of the user in the second time comprises first and second sampling data;

The first sampling data is sampling data collected by the wearable device according to the first duration when the sampling time point arrives;

The second sampling data is sampling data collected by the wearable device according to the second duration after the first sampling data meets the setting condition, and the second duration is greater than the first duration.
The method according to any one of claims 1 to 3, wherein the sampling data includes PPG data, and the first sampling data satisfies setting conditions including:

The first sampled data includes heart rate irregularity information.
The method according to claim 4, wherein the heart rate irregularity information is specifically atrial fibrillation information.
The method according to claim 4, wherein said terminal device displays a visualization graph drawn based on said PPG data.
The method according to claim 5, wherein the first or second sampling interval is obtained based on the following formula:

I=LI+(HI-LI)×(1-prob AF )

Where HI, LI are used to characterize the reference sampling interval range [LI, HI];

Prob AF is the probability of atrial fibrillation, which is based on the sampling data of the user in the first or second time, and is obtained by the logistic regression algorithm.
A method for updating a data sampling interval, the method comprising:

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

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

The terminal device acquires motion data of the user in the fifth time period;

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

The terminal device sends the fourth sampling interval to the wearable device, so that the wearable device acquires new sampling data of the user in a sixth time according to the fourth sampling interval.
The method of claim 8 wherein said sampled data comprises PPG data.
A method for collecting data according to a sampling interval, the method comprising:

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

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

The wearable device acquires new sampling data of the user in a third time according to the second sampling interval.
The method according to claim 10, wherein the sampling data of the user in the second time comprises first sampling data;

The first sampling data is sampling data collected by the wearable device according to the first duration when the sampling time point arrives.
The method according to claim 10, wherein the sampling data of the user in the second time comprises first and second sampling data;

The first sampling data is sampling data collected by the wearable device according to the first duration when the sampling time point arrives;

The second data is sampling data collected by the wearable device according to the second duration after the first sampling data meets the set condition, and the second duration is greater than the first duration.
The method according to any one of claims 10 to 12, wherein the sampled data comprises PPG data, and the first sampled data satisfies a set condition comprising:

The first sampled data includes heart rate irregularity information.
The method according to claim 13, wherein the heart rate irregularity information is specifically atrial fibrillation information.
The method of claim 13 further comprising:

The wearable device outputs a visualization graph drawn based on the PPG data.
The method of claim 14, wherein the first or second sampling interval is obtained based on the following formula:

I=LI+(HI-LI)×(1-prob AF )

Where HI, LI are used to characterize the reference sampling interval range [LI, HI];

Probability Prob AF is atrial fibrillation, the atrial fibrillation is based on the probability that the first user or the second time sample data, using lo g istics regression algorithm.
A method for collecting data according to a sampling interval, the method comprising:

The wearable device acquires sampling data and motion data of the user in the 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 in 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 sampling data and motion data of the user in the fifth time period;

The wearable device acquires new sampling data of the user in a sixth time according to the fourth sampling interval.
The method of claim 17 wherein said sampled data comprises PPG data.
A terminal device, comprising:

One or more processors;

Memory

And one or more programs, wherein the one or more programs are stored in the memory, the one or more programs comprising instructions that, when executed by the terminal device, cause the terminal device Performing the method of any of claims 1-9.
A wearable device, comprising:

One or more processors;

Memory

And one or more programs, wherein the one or more programs are stored in the memory, the one or more programs comprising instructions that, when executed by the wearable device, cause the The wearable device performs the method of any of claims 10-18.