WO2021052362A1 - 数据显示方法及电子设备 - Google Patents

数据显示方法及电子设备 Download PDF

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Publication number
WO2021052362A1
WO2021052362A1 PCT/CN2020/115575 CN2020115575W WO2021052362A1 WO 2021052362 A1 WO2021052362 A1 WO 2021052362A1 CN 2020115575 W CN2020115575 W CN 2020115575W WO 2021052362 A1 WO2021052362 A1 WO 2021052362A1
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WIPO (PCT)
Prior art keywords
data
physiological
pulse data
pulse
duration
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PCT/CN2020/115575
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English (en)
French (fr)
Inventor
张�杰
李宏宝
杨斌
李靖
陈宜欣
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华为技术有限公司
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Priority to EP20865942.5A priority Critical patent/EP3981326B1/en
Publication of WO2021052362A1 publication Critical patent/WO2021052362A1/zh

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A61B5/681—Wristwatch-type devices
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
    • A61B3/113—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for determining or recording eye movement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02405—Determining heart rate variability
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02416—Detecting, measuring or recording pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02438—Detecting, measuring or recording pulse rate or heart rate with portable devices, e.g. worn by the patient
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/0245—Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4812—Detecting sleep stages or cycles
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4815—Sleep quality

Definitions

  • This application relates to the field of electronic technology, and in particular to a data display method and electronic equipment.
  • Wearable devices can detect physiological data of the human body such as sleep staging data, pressure index data, and blood pressure.
  • sleep staging data as an example, the sleep state of the human body can be controlled by the autonomic nerve of the human body, and the autonomic nerve function of the human body can be reflected by the pulse data of the human body.
  • the wearable device can detect and display the sleep staging data of the human body, for example, by detecting the pulse data of the human body.
  • arrhythmia also affects the pulse data, and the corresponding pulse data during the arrhythmia is not controlled by the autonomic nervous system. Therefore, if the user has arrhythmia, the sleep staging data detected and displayed by the wearable device is wrong data.
  • This application provides a data display method and electronic equipment, which can solve the problem that the displayed data is wrong data.
  • the present application provides a data display method, including: collecting pulse data in a first time period, where the length of the first time period is a first time period; determining that the pulse data includes abnormal pulse data, The abnormal pulse data refers to the pulse data corresponding to arrhythmia; the ratio of the second duration to the first duration is determined, and the second duration is the cumulative duration of the abnormal pulse data; when the ratio is greater than the first
  • the preset value is used, the first physiological data is displayed, the first preset value is a value greater than 0 and less than 1, and the first physiological data includes the first physiological characteristic parameter.
  • the ratio of the second duration to the first duration is determined.
  • the first duration is the length of the first time period
  • the second duration is the cumulative duration of the abnormal pulse data.
  • the electronic device can display as many physiological characteristic parameters as possible on the basis of ensuring that the correct physiological data is displayed, thereby improving user experience.
  • third physiological data when the ratio is less than or equal to the first preset value, third physiological data is displayed, and the third physiological data includes the first physiological characteristic parameter and the second physiological characteristic parameter And the third physiological characteristic parameter.
  • the electronic device can display more physiological characteristic parameters of the user. This not only ensures the correctness of the displayed physiological data, but also makes the displayed physiological data more detailed.
  • the method before displaying the first physiological data, the method further includes: analyzing the pulse data by using an acceleration ACC detection method to obtain the first physiological data.
  • an acceleration ACC detection method to analyze the pulse data so as to obtain the correct physiological data.
  • the method before displaying the first physiological data and the second physiological data, the method further includes: analyzing the abnormal pulse data in the pulse data by using an acceleration ACC detection method to obtain the first 1. Physiological data; using a cardiopulmonary coupling CPC detection method to analyze data in the pulse data other than the abnormal pulse data to obtain the second physiological data.
  • the electronic device may use the ACC detection method to analyze abnormal pulse data in the pulse data to obtain the first physiological data, and use CPC to detect Methods Analyze the non-abnormal pulse data in the pulse data to obtain the second physiological data, so that as many physiological characteristic parameters as possible can be displayed on the basis of ensuring that the correct physiological data is displayed.
  • the method before displaying the third physiological data, further includes: processing the abnormal pulse data in the pulse data to obtain reconstructed pulse data; and analyzing the reconstructed pulse by using a CPC detection method Data to obtain the third physiological data.
  • the electronic device when there are relatively few abnormal pulse data in the pulse data (the ratio is less than or equal to the first preset value), the electronic device can obtain the third physiological data based on most of the non-abnormal pulse data. Based on this, the electronic device can process the abnormal pulse data in the pulse data to obtain reconstructed pulse data, and the reconstructed pulse data does not include the abnormal pulse data. Furthermore, the electronic device uses the CPC detection method to analyze the reconstructed pulse data to obtain the third physiological data, so that accurate and detailed physiological data can be obtained.
  • the processing the abnormal pulse data in the pulse data to obtain reconstructed pulse data includes: deleting the abnormal pulse data from the pulse data to obtain the reconstructed pulse Data; or, modify the abnormal pulse data according to the non-abnormal pulse data adjacent to the abnormal pulse data to obtain the reconstructed pulse data.
  • the electronic device can obtain data that does not contain abnormal pulse data based on the pulse data, and furthermore, can use the CPC detection method to generate third physiological data.
  • the first physiological characteristic parameter includes deep sleep duration and light sleep duration; the second physiological characteristic parameter includes rapid eye movement duration; and the third physiological characteristic parameter includes deep sleep ratio, shallow sleep At least one of sleep ratio, rapid eye movement ratio, deep sleep continuity score, sleep quality score, and breathing quality score. It can be seen that the electronic device can display different physiological characteristic parameters according to the relative amount of abnormal pulse data, thereby ensuring the correctness of the displayed physiological data.
  • the present application provides an electronic device that has the function of implementing the behavior of the electronic device in the foregoing method.
  • the function can be realized by hardware, or by hardware executing corresponding software.
  • the hardware or software includes one or more modules corresponding to the above-mentioned functions.
  • the structure of the foregoing electronic device includes a sensor, a processor, and a display, and the processor is configured to process the electronic device to perform corresponding functions in the foregoing method.
  • the sensor is used to realize the collection of pulse data by the above-mentioned electronic device.
  • the display is used to realize the display of the physiological data by the above-mentioned electronic device.
  • the electronic device may further include a memory, which is used for coupling with the processor and stores necessary program instructions and data of the electronic device.
  • the present application provides a computer storage medium that stores instructions in the computer storage medium.
  • the computer executes the first aspect and various possible implementations of the first aspect.
  • the data shows part or all of the steps of the method.
  • this application provides a computer program product that, when running on a computer, causes the computer to execute part or all of the steps of the data display method in the first aspect and various possible implementations of the first aspect .
  • the electronic device calculates the ratio of the cumulative duration of the abnormal pulse data to the total duration of the pulse data, and further determines whether the ratio is Is greater than the first preset value, and when the ratio is greater than the first preset value, the electronic device displays the user's first physiological data, where the first physiological data includes the first physiological characteristic parameter, thereby ensuring that the physiological data displayed to the user is Correctly, improve the user experience.
  • FIG. 1A is a schematic diagram of an exemplary structure of a smart watch 10 provided by this application.
  • FIG. 1B is a schematic diagram of an exemplary structure of an electronic device 100 provided by this application.
  • FIG. 1C is an exemplary schematic diagram of the display screen in FIG. 1A provided by this application.
  • FIG. 2A is a schematic diagram of a first implementation manner of a user interface provided by this application.
  • 2B is a schematic diagram of a second implementation manner of the user interface provided by this application.
  • 2C is a schematic diagram of a third implementation manner of the user interface provided by this application.
  • 2D is a schematic diagram of a fourth implementation manner of the user interface provided by this application.
  • 2E is a schematic diagram of a fifth implementation manner of the user interface provided by this application.
  • FIG. 3 is an exemplary method flowchart of the data display method 10 provided by this application.
  • FIG. 4A is a schematic diagram of an exemplary structure of an electronic device 40 provided by this application.
  • FIG. 4B is a schematic diagram of an exemplary structure of the electronic device 41 provided by this application.
  • the following embodiments introduce an electronic device and an embodiment of a data display method related to the electronic device.
  • the electronic device described in this application may be a wearable device, and the wearable device may be any device with a function of detecting physiological data of a user.
  • the wearable device may be supported by the wrist, such as smart watches, smart bracelets, and wristbands, or may be supported by the feet, such as smart shoes, socks, or other products worn on the legs in the future, etc.
  • the head for example, smart glasses, smart helmets, headbands, etc.
  • the electronic device described in this application may be other devices for detecting the physiological parameters of the user.
  • the electronic device is a device that uses radar waves to detect the pulse wave of the user; for another example, the electronic device uses a mattress to collect data.
  • a device for the user's ballistic cardiogram signal; another example, an electronic device is a device that detects pulse waves and the like by using a mobile phone camera to take a picture of the user's bare skin. This application does not restrict this.
  • UI user interface
  • UI is a medium interface for interaction and information exchange between applications or operating systems and users. It realizes the internal form of information and users can Accept the conversion between forms.
  • the user interface of the application is the source code written in a specific computer language such as java, extensible markup language (XML), etc.
  • the interface source code is parsed and rendered on the terminal device, and finally presented as content that can be recognized by the user.
  • Control also called widget, is the basic element of the user interface. Typical controls include toolbar, menu bar, text box, button, and scroll bar. (scrollbar), pictures and text.
  • the attributes and content of the controls in the interface are defined by tags or nodes.
  • XML specifies the controls contained in the interface through nodes such as ⁇ Textview>, ⁇ ImgView>, and ⁇ VideoView>.
  • a node corresponds to a control or attribute in the interface. After the node is parsed and rendered, it is presented as user-visible content.
  • applications such as hybrid applications, usually include web pages in their interfaces.
  • a webpage also called a page, can be understood as a special control embedded in the application program interface.
  • the webpage is source code written in a specific computer language, such as hypertext markup language (HTML), cascading style Tables (cascading style sheets, CSS), java scripts (JavaScript, JS), etc.
  • web page source code can be loaded and displayed as user-recognizable content by a browser or a web page display component with similar functions to the browser.
  • the specific content contained in a web page is also defined by tags or nodes in the source code of the web page. For example, HTML defines the elements and attributes of the web page through ⁇ p>, ⁇ img>, ⁇ video>, and ⁇ canvas>.
  • part of the human body's physiological functions such as sleep, breathing, etc. are controlled by the autonomic nerves, and the role of the autonomic nerves can be reflected by the human heart rhythm.
  • the blood volume in the blood vessels increases under the action of heart contraction, and the blood volume in the blood vessels decreases under the action of heart dilation.
  • the human body’s heart rhythm can be reflected on the human body’s pulse.
  • electronic devices can By collecting and detecting the user’s pulse data (also known as pulse wave data or photoplethysmography (PPG) data), the user’s blood pressure, blood oxygen, cerebral oxygen, muscle oxygen, blood sugar, and pulse can be detected Physiological data such as rate and respiration rate.
  • PPG photoplethysmography
  • arrhythmia such as atrial fibrillation
  • arrhythmia is not controlled by the autonomic nervous system. If the user has symptoms of arrhythmia, the pulse data collected by the electronic device will include the pulse data corresponding to the arrhythmia, resulting in the user's physiological data analyzed and displayed by the electronic device it's wrong.
  • This application provides a data display method and electronic device. Even if the user using the electronic device has symptoms of arrhythmia, the electronic device can still analyze and display the user's correct physiological data based on the collected pulse data, thereby improving the user Experience.
  • the following describes an exemplary electronic device 100 involved in an embodiment of the present application.
  • the electronic device 100 involved in the present application may be, for example, the smart watch 10 shown in FIG. 1A, and the smart watch 10 includes a watch body 11 and a wristband 12.
  • the wristband 12 is used to enable the user to wear the smart watch 10
  • the watch body 11 is used to implement some or all of the embodiments of the data display method involved in the present application.
  • the smart watch 10 illustrated in FIG. 1A does not constitute a specific limitation on the electronic device 100.
  • the electronic device 100 may be a smart bracelet, smart glasses, smart helmet, etc., which will not be described in detail here.
  • FIG. 1B shows a schematic diagram of the structure of the electronic device 100.
  • the devices illustrated in FIG. 1B may be arranged in the watch body 11 of the smart watch 10 illustrated in FIG. 1A.
  • the electronic device 100 may include a processor 110, a sensor 120, a memory 130, a wireless communication module 140, a microphone (MIC) 150, a power supply 160, a power management module 170, and so on.
  • the wireless communication module 140 may include a wireless fidelity (Wi-Fi) module 141, a Bluetooth module 142, a near field communication (NFC) module 143, and so on.
  • the sensor 120 may include a photoelectric sensor 121 for detecting physiological signals of the human body, a motion sensor 122 and an ambient light sensor 123.
  • the structure illustrated in this application does not constitute a specific limitation on the electronic device 100.
  • the electronic device 100 may include more or fewer components (such as a display screen, a gyroscope, etc.) than those shown in the figure, or combine certain components, or split certain components, or different components. Component arrangement.
  • the illustrated components can be implemented in hardware, software, or a combination of software and hardware.
  • the processor 110 is used to perform system scheduling, such as calling the WiFi module 141, the Bluetooth module 142, and the NFC module 143, supporting the processing of the microphone 150, and so on.
  • the processor 110 is also configured to detect whether the user has arrhythmia according to the data input by the sensor 120, and then use corresponding detection methods to detect the physiological data of the user according to different scenes.
  • the photoelectric sensor 121 for detecting physiological signals of the human body may be, for example, a PPG sensor or an electrocardiogram (ECG) sensor.
  • the photoelectric sensor 121 for detecting physiological signals of the human body may be set as the side of the electronic device 100 that is in contact with the user's skin when the electronic device 100 is worn.
  • the photoelectric sensor 121 for detecting physiological signals of the human body can be used to detect changes in light intensity, and convert the detected changes in light intensity into digital electrical signals, such as PPG signals.
  • the photoelectric sensor 121 for detecting human physiological signals can also be configured to detect human electrocardiographic signals, vibration signals, bio-impedance signals, and the like.
  • the photoelectric sensor 121 for detecting human physiological signals is also used for the detected signals, such as PPG signals, electrocardiogram signals, vibration signals, bio-impedance signals, etc., to be sent to the processor 110, so that the processor 110 can read the corresponding signals
  • the pulse data of the user is acquired, and further, the physiological data of the user is measured according to the acquired pulse data.
  • the motion sensor 122 is, for example, an acceleration sensor.
  • the motion sensor 122 is used to detect the magnitude of the acceleration of the electronic device 100 in various directions (generally three-axis).
  • the ambient light sensor 123 is used to detect the intensity of the ambient light and convert the intensity of the ambient light into a data electrical signal.
  • the PPG sensor or ECG sensor may be arranged on the inner side of the watch body 11. After the user wears the smart watch 10, the PPG sensor or ECG sensor may be in contact with the user's body.
  • the inner side of the watch body 11 refers to the side in contact with the user's body when the user wears the smart watch 10.
  • the memory 130 is used to store software programs and data.
  • the processor 110 executes each embodiment of the data display method of the present application by running the software program.
  • the data is used to provide support for the processor 110 to run software programs.
  • the memory 130 may include a storage program area and a storage data area, where the storage program area can store an operating system and at least one application program required by at least one function (for example, data display function, sound playback function, etc.); The data created by the device 100 and the algorithm required by the processor 110 to perform physiological data detection, etc.
  • the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.
  • the wireless communication module 140 can provide applications on the electronic device 100 including wireless local area networks (WLAN) (such as Wi-Fi networks), Bluetooth (BT), global navigation satellite system (GNSS) ), NFC, infrared technology (infrared, IR) and other wireless communication solutions.
  • the wireless communication module 140 may be one or more devices integrating at least one communication processing module.
  • the wireless communication module 140 receives electromagnetic waves via an antenna, modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 110.
  • the wireless communication module 140 may also receive the signal to be sent from the processor 110, perform frequency modulation, amplify, and convert it into electromagnetic waves to radiate through the antenna.
  • the wireless communication solution provided by the wireless communication module 140 can enable the electronic device 100 to interact with other electronic devices (such as mobile phones, tablet computers, etc.), for example, the physiological data of the user can be displayed through other electronic devices.
  • the microphone 150 also known as a microphone, can convert the collected sound signals into electrical signals, which are received by the audio circuit and converted into audio data; the audio circuit can also convert audio data into electrical signals, transmitted to the speaker, and converted into sound by the speaker Signal output.
  • the power management module 170 is used to connect the power supply 160 and the processor 110.
  • the power management module 170 receives the input of the power supply 160 and supplies power to the processor 110, the sensor 120, the memory 130, the wireless communication module 140, the microphone 150 and other components.
  • the power management module 170 can also be used to monitor the capacity of the power supply 160, the number of cycles, and the state of health (leakage, impedance) and other parameters.
  • the electronic device 100 may include a display screen, which is used to display controls, data, images, and the like.
  • the display screen includes a display panel.
  • the display panel can use liquid crystal display (LCD), organic light-emitting diode (OLED), active matrix organic light-emitting diode or active-matrix organic light-emitting diode (active-matrix organic light-emitting diode).
  • LCD liquid crystal display
  • OLED organic light-emitting diode
  • active matrix organic light-emitting diode active-matrix organic light-emitting diode
  • active-matrix organic light-emitting diode active-matrix organic light-emitting diode
  • AMOLED flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, quantum dot light-emitting diode (QLED), etc.
  • the display screen 13 of the smart watch 10 may be as shown in FIG. 1C.
  • the smart watch 10 may display the physiological data involved in the present application on the display screen 13.
  • the electronic device 100 exemplarily shown in FIGS. 1A to 1C can obtain the pulse data of the user through the sensor 120, and then the processor 110 parses the pulse data to obtain the physiological data of the user. Furthermore, in some embodiments, the display screen of the electronic device 100 displays various user interfaces described in the following embodiments. In other embodiments, the processor 110 calls the wireless communication module 140 to send the physiological data of the user to other electronic devices, so as to display various user interfaces described in the following embodiments through the other electronic devices.
  • the electronic device 100 can display relatively more physiological data of the user. If the user has symptoms of arrhythmia and the accumulated duration of the arrhythmia is relatively long, the electronic device 100 can display relatively little physiological data of the user.
  • the present application may use the ratio of the accumulated duration of the arrhythmia to the total duration of the collected pulse data as the basis for determining the accumulated duration of the arrhythmia. Based on this, “the cumulative duration of arrhythmia is relatively short”, for example, is that the corresponding ratio is less than or equal to the first preset value, and “the cumulative duration of arrhythmia is relatively long”, for example, the corresponding ratio is greater than the first preset value.
  • the first preset value is a value greater than 0 and less than 1, for example, 5%. This application does not impose restrictions on this.
  • this application summarizes the physiological data of the user into the first physiological data, the second physiological data, and the third physiological data.
  • the first physiological data includes a first physiological characteristic parameter
  • the second physiological data includes a second physiological characteristic parameter
  • the third physiological data includes a first physiological characteristic parameter, a second physiological characteristic parameter, and a third physiological characteristic parameter.
  • the following takes the display of the user's sleep staging data on a smart phone as an example to introduce the user interface involved in this application.
  • the electronic device 100 determines the sleep staging data of the user, for example, the sleep staging data is sent to a smart phone, and the smart phone then presents the following user interface on the display screen of the smart phone.
  • the user’s sleep can be divided into two states: non-rapid eye movement sleep (NREM sleep) and rapid eye movement sleep (REM sleep).
  • NREM sleep non-rapid eye movement sleep
  • REM sleep rapid eye movement sleep
  • the fourth period for example, the first period is the sleep-onset period, the second period is the light sleep period, the third period is the moderate sleep period, and the fourth period is the deep sleep period.
  • the first physiological characteristic parameter includes, for example, the length of deep sleep and the length of light sleep.
  • the second physiological characteristic parameter includes, for example, the duration of rapid eye movement.
  • the third physiological characteristic parameter includes, for example, at least one of a deep sleep ratio, a light sleep ratio, a rapid eye movement ratio, a deep sleep continuity score, a sleep quality score, and a breathing quality score.
  • the first physiological data includes deep sleep duration and light sleep duration;
  • the second physiological data includes rapid eye movement duration;
  • the third physiological data includes deep sleep ratio, light sleep ratio, rapid eye movement ratio, deep sleep continuity score, At least one of the sleep quality score and the respiratory quality score, the duration of deep sleep, the duration of light sleep, and the duration of rapid eye movement.
  • the exemplary user interface illustrated in FIG. 2A presents third physiological data.
  • the current date is June 21, 2018, the user fell asleep at 22:20 on June 20, 2018, woke up at 6:50 on June 21, 2018, the night’s sleep duration was 8 hours and 40 minutes, and the period The curve of sleep staging in each period.
  • the third physiological data also includes that the user’s night sleep score calculated by the electronic device 100 is 81 points and is accompanied by an evaluation.
  • the user’s deep sleep duration is 1 hour and 40 minutes, and the light sleep duration is 3 hours and 40 minutes.
  • the duration of rapid eye movement is 3 hours and 20 minutes, and the proportion of the total duration of each sleep stage in the total sleep duration, and the normal range and analysis results of the total duration of each sleep stage, for example, the proportion of deep sleep 20 %, normal; the proportion of rapid eye movement is 38%, which is low.
  • the third physiological data may even include the time period and duration of the user's sporadic naps the day before the current date, and the total sleep duration of sporadic naps during the day plus night sleep. This application will not be described one by one here.
  • the user interface shown in FIG. 2A presents the physiological data when the user has no arrhythmia symptoms. If the user has symptoms of arrhythmia and the corresponding ratio is less than or equal to the first preset value, the user interface may display reminder information in addition to the third physiological data. For example, the start time corresponding to the sleep data collected by the electronic device 100 is 22:30 on June 20, 2018, and the deadline is 7:00 on June 21, 2018, and the total duration of the collected sleep data is 8 hours and 30 minutes. , Where the total duration of night sleep is, for example, 8 hours and 20 minutes (including deep sleep, light sleep, and rapid eye movement), and the total duration of arrhythmia is, for example, 10 minutes. In this scenario, the user interface may also present a reminder message such as "the user has a slight arrhythmia". Other data in the user interface in this scenario are similar to those in FIG. 2A, and will not be repeated here.
  • the exemplary user interface illustrated in FIG. 2B presents the first physiological data.
  • the current date is June 21, 2018.
  • the night’s sleep duration was 8 hours and 40 minutes.
  • the total amount of arrhythmia The duration is 2 hours, and the staging curve of deep sleep, light sleep and wakefulness in each period is shown in Figure 2B, and the duration of deep sleep is 3 hours and 20 minutes, and the duration of light sleep is 5 hours and 20 minutes.
  • the electronic device 100 can display the user's first physiological characteristic parameter, second physiological characteristic parameter, and third physiological characteristic parameter.
  • the electronic device 100 only displays the user's first physiological characteristic parameter, thereby ensuring that the displayed physiological data is correct.
  • the electronic device may also display the first physiological data and the second physiological data of the user.
  • the second preset value is greater than the first preset value and less than 1, and the second preset value is, for example, 30%.
  • the exemplary user interface illustrated in FIG. 2C presents the first physiological data and the second physiological data.
  • the current date is June 21, 2018.
  • the night’s sleep duration is 8 hours and 40 minutes.
  • the first Physiological data includes: the length of deep sleep is 2 hours, and the length of light sleep is 3 hours and 10 minutes.
  • the second physiological data includes: the REM duration is 3 hours and 30 minutes.
  • the electronic device 100 can display as many physiological characteristic parameters as possible on the basis of ensuring that the correct physiological data is displayed, thereby improving the user experience.
  • the "June 21, 2018" displayed on the user interface illustrated in FIGS. 2A to 2C is only a schematic description, and does not limit the technical solution of the present application.
  • the user triggers the arrow on the left or right side of "June 21, 2018", and the sleep staging data before or after "June 21, 2018” can be displayed on the user interface.
  • the user interface can display sleep staging data on June 20, 2018; for another example, if "June 21, 2018” is not the current time, The user clicks the arrow on the right side of "June 21, 2018", and the sleep staging data on June 22, 2018 can be displayed on the user interface.
  • the electronic device 100 can also generate and display the user's heart rhythm statistics.
  • the user can view his own heart rhythm statistics for one day by triggering "day" on the user interface, or can select to view his own heart rhythm for a week by triggering "week” on the user interface.
  • the heart rhythm statistics may include, for example, whether the heart rhythm is abnormal, the change curve of the heart rhythm, the irregular heart rhythm and the time when the irregular heart rhythm occurs, and so on.
  • FIG. 2D illustrates a user interface of an exemplary heart rhythm statistical data.
  • the heart rhythm statistical data is, for example, the user's heart rhythm statistical data on March 1, 2019.
  • the heart rhythm statistics include the statistical results "no abnormality”, “average heart rhythm: 68 beats/minute”, the user's heart rhythm change curve on March 1, 2019, “irregular heart rhythm: 4 times”, “no abnormality” Abnormal: 54 times”, “Irregular heart rhythm is 70bmp, the time of appearance is 03:31:12 on March 1, 2019", “No abnormal heart rhythm 65bmp” and other data. This application will not be explained one by one.
  • FIG. 2E illustrates a user interface of exemplary heart rhythm statistics.
  • the heart rhythm statistics are, for example, heart rhythm statistics for a week from February 25, 2019 to March 3, 2019.
  • the heart rhythm statistics for the week include "irregular heart rhythm ratio: 1/58", “histogram and graph of heart rhythm every day of the week”, “irregular heart rhythm: 10 times”, and “no abnormality seen:” 260 times” and other data. This application will not be explained one by one.
  • the electronic device 100 can also count and display the heart rhythm of the user at various stages, thereby reminding the user of the health status at any time, and improving the user experience.
  • FIGS. 2A to 2E are only schematic descriptions, and do not limit the protection scope of the embodiments of the present application.
  • the data presented in the user interface may also be other physiological data of the user, and the expression form of the data presented in the user interface may also be other forms, which will not be described in detail here.
  • the user interface illustrated in FIGS. 2A to 2E is a user interface of a smart phone as an example.
  • the electronic device 100 may also display the above-mentioned physiological data on the display screen of the electronic device 100. It should be understood that the area of the display screen of different devices may be different, so that the amount of data displayed on the user interface of different electronic devices is different, which is not limited in this application.
  • Fig. 3 illustrates a data display method 10.
  • Data display method 10 (hereinafter referred to as method 10) includes the following steps:
  • Step S11 collecting pulse data in the first time period.
  • the first time period may be any time period in a day, for example, from 11 o'clock to 13 o'clock in one day, and for example, from 22 o'clock in one day to 7 o'clock in the next day.
  • the length of the first time period is the first time length.
  • the shortest first time period may be, for example, 1 minute, and the longest period may be, for example, 24 hours, which is not limited in this application.
  • the pulse data is PPG data, for example.
  • the sensor 121 for detecting physiological signals of the human body is, for example, a photoelectric sensor, which can collect pulse data of the user.
  • the photoelectric sensor is, for example, a PPG sensor.
  • the light intensity detected by the PPG sensor is relatively small; When the heart relaxes, the blood volume of the blood vessel decreases, and the light intensity detected by the PPG sensor is greater.
  • the PPG sensor can detect the pulsating change of light intensity, and then the PPG sensor can convert the light intensity change signal into a digital electrical signal, which can be called a PPG signal Or PPG data, that is, the pulse data described in this program.
  • the pulse data collected by the PPG sensor is only a schematic description, and does not limit the technical solutions of the embodiments of the present application.
  • the electronic device 100 may also collect pulse data through an ECG sensor. This application will not be detailed here.
  • step S12 it is determined that the pulse data includes abnormal pulse data.
  • abnormal pulse data refers to pulse data corresponding to arrhythmia.
  • the photoelectric sensor After collecting the pulse data, the photoelectric sensor transmits the pulse data to the processor 110.
  • the processor 110 detects whether there is arrhythmia pulse data in the pulse data, and if there is arrhythmia pulse data in the pulse data, the processor 110 can count the cumulative duration of the user's arrhythmia to obtain the second duration.
  • the processor 110 may preset an arrhythmia preliminary screening algorithm, and then use the arrhythmia preliminary screening algorithm to detect whether there is abnormal pulse data in the pulse data.
  • the arrhythmia preliminary screening algorithm is, for example, a support vector machine (SVM) algorithm, a threshold method, a machine learning method, a deep learning method, and any algorithm that can screen for arrhythmia from pulse data. This application does not impose restrictions on this.
  • SVM support vector machine
  • the processor 110 divides the PPG data in the first time period into k data segments, where k is greater than or equal to 1, wherein the duration corresponding to each data segment is the third duration, the third duration is less than the second duration, and the first Three hours is, for example, one minute. Then, the processor 110 uses the SVM algorithm to separately detect whether each data segment is abnormal data.
  • the SVM algorithm determines the detection result by calculating the distance between each data segment in the feature space and the decision plane.
  • y t (w ⁇ x t -b) ⁇ 1 refers to the corresponding relationship between the conditions that each data segment should meet.
  • w and b when y t (w ⁇ x t -b) ⁇ 1 reaches the minimum value.
  • w refers to the normal vector of the support vector (consisting of the vector closest to the decision plane)
  • b refers to the deviation value of the decision plane (in the feature space, the plane divides the sample into two parts, corresponding to the arrhythmia sample and the normal sample)
  • x t refers to the t-th data segment among k data segments, such as heart rhythm variability characteristics, entropy characteristics, and so on.
  • y t refers to the pulse index data indicating whether the arrhythmia is abnormal.
  • y t is the pulse index data corresponding to the arrhythmia
  • y t is the pulse index data corresponding to a normal heart rhythm
  • y t is the pulse index data corresponding to a normal heart rhythm
  • the processor 110 may calculate the cumulative duration of the abnormal pulse data to obtain the second duration. Furthermore, the processor 110 may determine whether the ratio of the second duration to the first duration is greater than a preset value, and then execute the following steps according to the determination result.
  • Step S13 Determine the ratio of the second duration to the first duration.
  • Step S14 when the ratio is greater than the first preset value, display the first physiological data.
  • the first preset value is a value greater than 0 and less than 1, for example, 5%, or can also be expressed as 0.05.
  • the first physiological data includes the first physiological characteristic parameter, which is exemplary, as described in the embodiment corresponding to FIG. 2B, and will not be described in detail here.
  • the processor 110 may use an acceleration (ACC) detection method to analyze the pulse data to obtain the user's first physiological data. It can be seen that by adopting this implementation method, the accuracy of the obtained physiological data can be guaranteed.
  • ACC acceleration
  • the processor 110 may use the ACC detection method to analyze the pulse data
  • the first physiological data is obtained from the abnormal pulse data in the middle of the pulse data; and the cardiopulmonary coupling (CPC) detection method or the heart rate variability (HRV) detection method is used to analyze the data other than the abnormal pulse data in the pulse data to obtain The second physiological data, wherein the second physiological data includes a second physiological characteristic parameter.
  • the first physiological data and the second physiological data are displayed on the display.
  • the second preset value is greater than the first preset value and less than 1.
  • the second preset value is, for example, 30%, or expressed as 0.3.
  • the display examples of the first physiological data and the second physiological data are, for example, as described in the embodiment corresponding to FIG. 2C, which will not be described in detail here.
  • the processor 110 may process the pulse data to obtain reconstructed pulse data, and then generate third physiological data, where the third physiological data includes The first physiological characteristic parameter, the second physiological characteristic parameter, and the third physiological characteristic parameter.
  • the processor 110 may process the pulse data to obtain reconstructed pulse data, and then generate third physiological data, where the third physiological data includes The first physiological characteristic parameter, the second physiological characteristic parameter, and the third physiological characteristic parameter.
  • the third physiological data includes The first physiological characteristic parameter, the second physiological characteristic parameter, and the third physiological characteristic parameter.
  • the processor 110 may delete abnormal pulse data from the pulse data to obtain reconstructed pulse data. In other embodiments, the processor 110 may modify the corresponding abnormal pulse data according to non-abnormal pulse data adjacent to the abnormal pulse data to obtain reconstructed pulse data.
  • the following takes the CPC detection method as an example to exemplarily describe the implementation process of the electronic device 100 obtaining the third physiological data.
  • the pulse data described in step S11 is PPG data.
  • the specific process of the processor generating the third physiological data includes the following steps:
  • the processor 110 may extract a heartbeat interval (R peak to R peak interval, RR) sequence from the PPG data, and then the processor 110 may extract a respiratory signal sequence from the RR sequence.
  • a heartbeat interval R peak to R peak interval, RR
  • ECG-derived respiratory, EDR ECG-derived respiratory
  • Step 2 The processor 110 uses the sliding window to delete abnormal sample points in the RR sequence to obtain a reconstructed RR sequence.
  • Step 3 The processor 110 uses the empirical mode decomposition algorithm to decompose the reconstructed RR sequence and the EDR signal sequence, and calculates the coherence and cross power spectrum of the decomposed RR sequence and the decomposed EDR signal sequence.
  • Step 4 The processor 110 calculates the third physiological data according to the coherent and cross power spectrum results.
  • the processor 110 may also obtain the user's heart rhythm statistics for different time periods according to the statistics of the user's heart rhythm data.
  • the heart rhythm statistics are as shown in the embodiment illustrated in Fig. 2D or 2E.
  • the processor 110 executes the display function and displays the physiological data involved in the method 10 through the display screen 13 to obtain data of the user interface illustrated in any one of FIGS. 2A to 2E. .
  • the processor 110 may send the physiological data involved in the method 10 to another electronic device through the wireless communication module 140, and the other electronic device may display the corresponding physiological data to obtain any one of FIGS. 2A to 2E. Schematic user interface.
  • CPC detection method ACC detection method
  • sleep staging data etc. are all schematic descriptions, and do not constitute a limitation to the technical solution of the present application.
  • the technical solution of the present application can also detect the user's pressure data. Accordingly, HRV detection methods, scale questioning methods, behavior observation methods, etc. can be used. This application will not be detailed here.
  • the electronic device determines that the collected pulse data contains abnormal pulse data, it calculates the ratio of the cumulative duration of the abnormal pulse data to the total duration of the pulse data, and further determines whether the ratio is greater than the first prediction.
  • the electronic device displays the user's first physiological data, where the first physiological data includes the first physiological characteristic parameter, so as to ensure that the physiological data displayed to the user is correct and improve User experience.
  • the aforementioned electronic device 100 may implement the aforementioned corresponding functions in the form of functional modules.
  • the electronic device may include an acquisition module, a processing module, and a display module.
  • the collection module can be used to perform the collection of pulse data in any of the embodiments shown in FIG. 3 above.
  • the display module may be used to perform the display of physiological data in any of the embodiments illustrated in FIG. 3 above.
  • the processing module may be used to perform operations other than the collection of pulse data and the display of physiological data in any embodiment illustrated in FIG. 3 above.
  • the division of the above modules is only a division of logical functions.
  • the functions of the acquisition module can be integrated into the sensor for implementation, and the functions of the processing module can be integrated into the processor for implementation.
  • the function of the display module can be integrated into the display.
  • the electronic device 40 includes a sensor 401, a processor 402, and a display 403.
  • the sensor 401 can perform the collection of pulse data in any of the embodiments shown in FIG. 3 above.
  • the display 403 may be used to perform the display of physiological data in any of the embodiments illustrated in FIG. 3 above.
  • the processor 402 may be used to perform operations other than the collection of pulse data and the display of physiological data in any embodiment illustrated in FIG. 3 above.
  • the sensor 401 may collect pulse data in a first time period, and the length of the first time period is the first time period.
  • the processor 402 may be used to determine that the pulse data contains abnormal pulse data, the abnormal pulse data refers to pulse data corresponding to arrhythmia, and to determine the ratio of the second duration to the first duration, the first The second duration is the accumulated duration of the abnormal pulse data.
  • the display 403 may be used to display first physiological data when the ratio is greater than a first preset value.
  • the first preset value is a value greater than 0 and less than 1, and the first physiological data includes the first physiological data. Characteristic Parameters.
  • Fig. 4A is a description of the electronic device of the present application from the perspective of an independent functional entity.
  • each independently running functional entity can be integrated into one hardware entity.
  • the electronic device 41 can include a processor 411, a sensor 412, and a memory 413.
  • the memory 413 may be used to store programs/codes pre-installed in the electronic device 41, and may also store codes used for execution by the processor 411, and the like.
  • the electronic device 41 of the present application may correspond to the electronic device in the embodiment corresponding to FIG. 3 of the present application, where the sensor 412 is used to perform pulse data collection in any embodiment illustrated in FIG. 3, and the processor 411 is used to perform The electronic device in any of the embodiments shown in FIG. 3 performs other processing besides the collection of pulse data. I won't repeat them here.
  • the present application also provides a computer storage medium, wherein the computer storage medium set in any device can store a program, and when the program is executed, it can implement data display methods including the data display method provided in FIG. 3 Part or all of the steps in each embodiment.
  • the storage medium in any device can be a magnetic disk, an optical disc, a read-only memory (ROM) or a random access memory (RAM), etc.
  • the processor may be a central processing unit (CPU), a network processor (NP), or a combination of CPU and NP.
  • the processor may further include a hardware chip.
  • the above-mentioned hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof.
  • the above-mentioned PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof.
  • the memory may include volatile memory (volatile memory), such as random-access memory (RAM); the memory may also include non-volatile memory (non-volatile memory), such as read-only memory (read-only memory).
  • ROM read-only memory
  • flash memory flash memory
  • HDD hard disk drive
  • SSD solid-state drive
  • Sensors can include sensors that detect changes in the user’s body shape, such as ACC sensors and motion sensors; sensors can also include photoelectric sensors, such as PPG sensors, ambient light sensors, and ECG sensors; sensors can also include other high-sensitivity bioelectrical sensors. Sensors, such as bio-impedance sensors, etc.
  • FIG. 4B may also include a bus interface.
  • the bus interface may include any number of interconnected buses and bridges. Specifically, one or more processors represented by the processor and various circuits of the memory represented by the memory are linked together. The bus interface can also link various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., which are all known in the art, and therefore, will not be further described herein.
  • the bus interface provides the interface.
  • the sensor provides a unit for collecting user biological data.
  • the processor is responsible for managing the bus architecture and general processing, and the memory can store messages used by the processor when performing operations.
  • the various illustrative logic units and circuits described in the embodiments of this application can be implemented by general-purpose processors, digital signal processors, application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, Discrete gates or transistor logic, discrete hardware components, or any combination of the above are designed to implement or operate the described functions.
  • the general-purpose processor may be a microprocessor.
  • the general-purpose processor may also be any traditional processor, controller, microcontroller, or state machine.
  • the processor can also be implemented by a combination of computing devices, such as a digital signal processor and a microprocessor, multiple microprocessors, one or more microprocessors combined with a digital signal processor core, or any other similar configuration. achieve.
  • the steps of the method or algorithm described in the embodiments of the present application can be directly embedded in hardware, a software unit executed by a processor, or a combination of the two.
  • the software unit can be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM or any other storage medium in the art.
  • the storage medium may be connected to the processor, so that the processor can read information from the storage medium, and can store and write information to the storage medium.
  • the storage medium may also be integrated into the processor.
  • the processor and the storage medium may be set in the ASIC, and the ASIC may be set in the UE.
  • the processor and the storage medium may also be provided in different components in the UE.
  • the size of the sequence number of each process does not mean the order of execution.
  • the execution order of each process should be determined by its function and internal logic, rather than the implementation process of the embodiment. Constitute any limitation.
  • the computer may be implemented in whole or in part by software, hardware, firmware, or any combination thereof.
  • software it can be implemented in the form of a computer program product in whole or in part.
  • the computer program product includes one or more computer instructions.
  • the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.
  • the computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.
  • the computer instructions may be transmitted from a website, computer, server, or message.
  • the center transmits to another website, computer, server, or message center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.).
  • the computer-readable storage medium may be any available medium that can be accessed by a computer or a message storage device such as a server or a message center integrated with one or more available media.
  • the usable medium may be a magnetic medium (for example, a floppy disk, a hard disk, and a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)).

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Abstract

一种数据显示方法及电子设备（100），电子设备（100）采集第一时间段内的脉搏数据（S11），并在确定脉搏数据中包含异常脉搏数据（S12）之后，确定第二时长与第一时长的比值（S13），其中，第一时长是第一时间段的长度，第二时长是异常脉博数据的累计时长。当比值大于第一预设值时，显示第一生理数据（S14），第一生理数据包括第一生理特征参数，从而能够保证向用户显示的生理数据是正确的，提高用户的使用体验。

Description

数据显示方法及电子设备 技术领域
本申请涉及电子技术领域，尤其涉及一种数据显示方法及电子设备。
背景技术
可穿戴设备(例如，智能手表和智能手环)可以检测睡眠分期数据、压力指标数据、血压等人体的生理数据。以睡眠分期数据为例，人体的睡眠状态可以由人体的自主神经控制，人体的自主神经功能可以通过人体的脉搏数据体现。基于此，可穿戴设备例如可以通过检测人体的脉搏数据检测并显示人体的睡眠分期数据。
然而，心律失常也会作用到脉搏数据上，而心律失常期间对应的脉搏数据并非自主神经系统控制。因此，若用户存在心律失常的情况，可穿戴设备检测及显示的睡眠分期数据是错误的数据。
发明内容
本申请提供了一种数据显示方法及电子设备，能够解决所显示的数据是错误数据的问题。
第一方面，本申请提供了一种数据显示方法，包括：采集第一时间段内的脉搏数据，所述第一时间段的长度是第一时长；确定所述脉搏数据中包含异常脉搏数据，所述异常脉搏数据是指对应心律失常的脉搏数据；确定第二时长与所述第一时长的比值，所述第二时长是所述异常脉博数据的累计时长；当所述比值大于第一预设值时，显示第一生理数据，所述第一预设值是大于0小于1的值，所述第一生理数据包括第一生理特征参数。
其中，电子设备确定所采集的脉搏数据中包含异常脉搏数据之后，为了量化心律失常累计时长的相对长度，确定第二时长与第一时长的比值。其中，第一时长是第一时间段的长度，第二时长是异常脉搏数据的累计时长。当比值大于第一预设值时，可以认为心律失常的累计时长相对较长，电子设备可以显示用户的第一生理数据，第一生理数据包括第一生理特征参数。这样能够在用户存在一定程度心律失常症状时，显示正确的生理数据。
在一种可能的设计中，当所述比值大于所述第一预设值且小于或者等于第二预设值时，显示第一生理数据和第二生理数据，所述第二预设值大于所述第一预设值小于1，所述第二生理数据包括第二生理特征参数。采用本实现方式，电子设备能够在保证显示正确的生理数据的基础上，显示尽量多的生理特征参数，从而提高用户体验。
在一种可能的设计中，当所述比值小于或者等于所述第一预设值时，显示第三生理数据，所述第三生理数据包括所述第一生理特征参数、第二生理特征参数和第三生理特征参数。其中，当比值小于或者等于第一预设值时，说明用户心律失常的累计时长相对较短，相应的，电子设备可以显示用户较多的生理特征参数。这样不仅能够保证所显示的生理数据的正确性，还能够使得所显示的生理数据较为详细。
在一种可能的设计中，所述显示所述第一生理数据之前，还包括：采用加速度ACC检测方法解析所述脉搏数据，得到所述第一生理数据。本申请中，当脉搏数据中存在一定量 (比值大于第一预设值)的异常脉搏数据时，采用心肺耦合(cardiopulmonary coupling，CPC)检测方法或者心律变异性(heart rate variability，HRV)检测方法解析脉搏数据，无法得到正确的生理数据，所以，电子设备采用加速度(acceleration，ACC)检测方法解析脉搏数据，从而能够得到正确的生理数据。
在一种可能的设计中，所述显示所述第一生理数据和所述第二生理数据之前，还包括：采用加速度ACC检测方法解析所述脉搏数据中所述异常脉搏数据，得到所述第一生理数据；采用心肺耦合CPC检测方法解析所述脉搏数据中除所述异常脉搏数据之外的数据，得到所述第二生理数据。一些实施例中，当比值大于第一预设值且小于或者等于第二预设值时，电子设备可以使用ACC检测方法解析脉搏数据中的异常脉搏数据，得到第一生理数据，并使用CPC检测方法解析脉搏数据中非异常的脉搏数据，得到第二生理数据，从而能够在保证显示正确的生理数据的基础上，显示尽量多的生理特征参数。
在一种可能的设计中，所述显示所述第三生理数据之前，还包括：处理所述脉搏数据中的所述异常脉搏数据得到重建的脉搏数据；使用CPC检测方法解析所述重建的脉搏数据，得到所述第三生理数据。本申请中，当脉搏数据中所存在的异常脉搏数据相对较少(比值小于或者等于第一预设值)时，电子设备可以根据大部分非异常脉搏数据得到第三生理数据。基于此，电子设备可以处理脉搏数据中的异常脉搏数据，得到重建的脉搏数据，重建的脉搏数据中不包含异常脉搏数据。进而，电子设备使用CPC检测方法解析重建的脉搏数据，得到第三生理数据，从而能够得到正确、详细的生理数据。
在一种可能的设计中，所述处理所述脉搏数据中的所述异常脉搏数据得到重建的脉搏数据，包括：将所述异常脉搏数据从所述脉搏数据中删除，得到所述重建的脉搏数据；或者，根据所述异常脉搏数据相邻的非异常脉搏数据修改所述异常脉搏数据，得到所述重建的脉搏数据。采用本实现方式，电子设备能够根据脉搏数据得到不包含异常脉搏数据的数据，进而，能够使用CPC检测方法生成第三生理数据。
在一种可能的设计中，确定所述脉搏数据中包含异常脉搏数据，包括：将所述脉搏数据划分为k个数据段，k大于等于1，所述k个数据段中每个数据段对应的市场是第三时长，所述第三时长小于所述第二时长；确定所述k个数据段中每个数据段是否满足对应关系y t(w×x t-b)≥1，其中，t＝1,2,3……k，w是指支持向量的法向量，b是指决策平面的偏差值，x t是所述k个数据段中的第t个数据段，y t是指指示心律是否失常的对应的脉搏指标数据；若所述y t是心律失常对应的脉搏指标数据，当数据段满足对应关系y t(w×x t-b)≥1时，确定所述数据段是所述异常脉搏数据。采用本实现方式，电子设备能够确定脉搏数据中是否包含异常脉搏数据，进而，电子设备可以根据所包含的异常脉搏数据的情况，显示不同的生理数据。
在一种可能的设计中，所述第一生理特征参数包括深睡时长和浅睡时长；所述第二生理特征参数包括快速眼动时长；所述第三生理特征参数包括深睡比例、浅睡比例、快速眼动比例、深睡连续性得分、睡眠质量得分以及呼吸质量得分中的至少一个。可见，电子设备能够根据异常脉搏数据的相对多少，显示不同的生理特征参数，从而保证所显示的生理数据的正确性。
第二方面，本申请提供了一种电子设备，该电子设备具有实现上述方法中电子设备行为的功能。所述功能可以通过硬件实现，也可以通过硬件执行相应的软件实现。所述硬件 或软件包括一个或多个与上述功能相对应的模块。在一个可能的设计中，上述电子设备的结构中包括传感器，处理器和显示器，所述处理器被配置为处理该电子设备执行上述方法中相应的功能。所述传感器用于实现上述电子设备对脉搏数据的采集。所述显示器用于实现上述电子设备对生理数据的显示。所述电子设备还可以包括存储器，所述存储器用于与处理器耦合，其保存该电子设备必要的程序指令和数据。
第三方面，本申请提供了一种计算机存储介质，该计算机存储介质中存储有指令，当所述指令在计算机上运行时，使得计算机执行第一方面及第一方面各种可能的实现方式中的数据显示方法的部分或全部步骤。
第四方面，本申请提供了一种计算机程序产品，该计算机程序产品在计算机上运行时，使得计算机执行第一方面及第一方面各种可能的实现方式中的数据显示方法的部分或全部步骤。
为解决现有技术的问题，本申请技术方案中，电子设备确定所采集的脉搏数据中包含异常脉搏数据之后，计算异常脉搏数据的累积时长与脉搏数据总时长的比值，进而，确定该比值是否大于第一预设值，当比值大于第一预设值时，电子设备显示用户的第一生理数据，其中，第一生理数据包括第一生理特征参数，从而能够保证向用户显示的生理数据是正确的，提高用户的使用体验。
附图说明
图1A为本申请提供的智能手表10的示例性结构示意图；
图1B为本申请提供的电子设备100的示例性结构示意图；
图1C为本申请提供的图1A中显示屏的示例性示意图；
图2A为本申请提供的用户界面的第一种实施方式的示意图；
图2B为本申请提供的用户界面的第二种实施方式的示意图；
图2C为本申请提供的用户界面的第三种实施方式的示意图；
图2D为本申请提供的用户界面的第四种实施方式的示意图；
图2E为本申请提供的用户界面的第五种实施方式的示意图；
图3为本申请提供的数据显示方法10的示例性方法流程图；
图4A为本申请提供的电子设备40的示例性结构示意图；
图4B为本申请提供的电子设备41的示例性结构示意图。
具体实施方式
下面将结合本申请中的附图，对本申请的技术方案进行清楚地描述。
本申请以下实施例中所使用的术语只是为了描述特定实施例的目的，而并非旨在作为对本申请的限制。如在本申请的说明书和所附权利要求书中所使用的那样，单数表达形式“一个”、“一种”、“所述”、“上述”、“该”和“这一”旨在也包括复数表达形式，除非其上下文中明确地有相反指示。还应当理解，尽管在以下实施例中可能采用术语第一、第二等来描述某一类对象，但所述对象不应限于这些术语。这些术语仅用来将该类对象的具体对象进行区分。例如，以下实施例中可能采用术语第一、第二等来描述时长，但时长不应限于这些术语。这些术语仅用来将不同的时间长度进行区分。以下实施例中可能采用术语第一、第二等来描述的其他类对象同理，此处不再赘述。另外，本申请中使用的术语“和 /或”是指并包含一个或多个所列出项目的任何或所有可能组合。
以下实施例介绍了电子设备，以及与该电子设备相关的数据显示方法的实施例。
一些实施例中，本申请所述的电子设备可以是可穿戴设备，该可穿戴设备可以是具备检测用户生理数据功能的任意设备。其中，该可穿戴设备可以为以手腕为支撑，例如，智能手表、智能手环和腕带等，也可以为以脚为支撑，例如，智能鞋、袜子或者将来的其他腿上佩戴的产品等，还可以为以头部为支撑，例如，智能眼镜、智能头盔、头带等。另一些实施例中，本申请所述的电子设备可以是其他用于检测用户生理参数的设备，例如，电子设备是利用雷达波检测用户脉搏波的设备；再如，电子设备是利用床垫采集用户心冲击图信号的设备；再如，电子设备是通过使用手机摄像头拍用户的裸露皮肤来检测脉搏波等的设备等。本申请对此不作限制。
本申请涉及的用于显示数据的是“用户界面(user interface，UI)”，UI是应用程序或操作系统与用户之间进行交互和信息交换的介质接口，它实现信息的内部形式与用户可以接受形式之间的转换。应用程序的用户界面是通过java、可扩展标记语言(extensible markup language，XML)等特定计算机语言编写的源代码，界面源代码在终端设备上经过解析，渲染，最终呈现为用户可以识别的内容，比如图片、文字、按钮等控件。控件(control)也称为部件(widget)，是用户界面的基本元素，典型的控件有工具栏(toolbar)、菜单栏(menu bar)、文本框(text box)、按钮(button)、滚动条(scrollbar)、图片和文本。界面中的控件的属性和内容是通过标签或者节点来定义的，比如XML通过<Textview>、<ImgView>、<VideoView>等节点来规定界面所包含的控件。一个节点对应界面中一个控件或属性，节点经过解析和渲染之后呈现为用户可视的内容。此外，很多应用程序，比如混合应用(hybrid application)的界面中通常还包含有网页。网页，也称为页面，可以理解为内嵌在应用程序界面中的一个特殊的控件，网页是通过特定计算机语言编写的源代码，例如超文本标记语言(hyper text markup language，HTML)，层叠样式表(cascading style sheets，CSS)，java脚本(JavaScript，JS)等，网页源代码可以由浏览器或与浏览器功能类似的网页显示组件加载和显示为用户可识别的内容。网页所包含的具体内容也是通过网页源代码中的标签或者节点来定义的，比如HTML通过<p>、<img>、<video>、<canvas>来定义网页的元素和属性。
其中，人体的部分生理机能例如睡眠、呼吸等由自主神经控制，自主神经的作用可以通过人体的心律体现。通常心脏收缩作用下，血管内的血液容积增多，心脏舒张作用下，血管内的血液容积减少，基于此，人体的心律可以反映到人体的脉搏跳动上，进而，在实际操作中，电子设备可以通过采集、检测用户的脉搏数据(也可以称为脉搏波数据或者光电容积脉搏波描记法(photo plethysmo graphy，PPG)数据)，检测用户的血压、血氧、脑氧、肌氧、血糖、脉率和呼吸率等生理数据。然而，心律失常(例如房颤)并非自主神经系统控制，若用户存在心律失常的症状，电子设备所采集的脉搏数据将包括心律失常对应的脉搏数据，从而导致电子设备解析和显示的用户生理数据是错误的。
本申请提供了一种数据显示方法及电子设备，即使使用该电子设备的用户存在心律失常的症状，该电子设备依然能够根据所采集的脉搏数据解析并显示用户正确的生理数据，从而能够提高用户的使用体验。
以下介绍本申请实施例涉及的示例性电子设备100。
本申请涉及的电子设备100例如可以是图1A示意的智能手表10，智能手表10包括表体11和腕带12。其中，腕带12用于使用户佩戴该智能手表10，表体11用于执行本申请涉及的数据显示方法的部分或全部实施例。
可以理解的是，图1A示意的智能手表10并不构成对电子设备100的具体限定。在本申请另一些实施例中，电子设备100可以是智能手环、智能眼镜、智能头盔等，此处不再详述。
图1B示出了电子设备100的结构示意图。图1B示意的各器件可以设置在图1A示意的智能手表10的表体11中。
电子设备100可以包括处理器110，传感器120，存储器130，无线通信模块140，麦克风(microphone，MIC)150，电源160以及电源管理模块170等。无线通信模块140可以包括无线保真(wireless fidelity，Wi-Fi)模块141，蓝牙模块142和近距离无线通信技术(near field communication，NFC)模块143等。传感器120可以包括用于检测人体生理信号的光电传感器121，运动传感器122以及环境光传感器123。
可以理解的是，本申请示意的结构并不构成对电子设备100的具体限定。在本申请另一些实施例中，电子设备100可以包括比图示更多或更少的部件(例如显示屏，陀螺仪等)，或者组合某些部件，或者拆分某些部件，或者不同的部件布置。图示的部件可以以硬件，软件或软件和硬件的组合实现。
处理器110用于进行系统调度，例如调用WiFi模块141，蓝牙模块142和NFC模块143，支持处理麦克风150等。处理器110还用于根据传感器120输入的数据，检测用户是否存在心律失常，然后，对应不同的场景分别采用相应的检测方法检测用户的生理数据。
用于检测人体生理信号的光电传感器121例如可以是PPG传感器或者心电图(electrocardiogram，ECG)传感器。用于检测人体生理信号的光电传感器121可以被设置为电子设备100被佩戴时与用户皮肤接触的一侧。用于检测人体生理信号的光电传感器121例如可以用于检测光强度的变化，并将所检测到的光强度变化转化成数字电信号，该数字电信号例如是PPG信号。用于检测人体生理信号的光电传感器121还可以被设置为检测人体心电信号、振动信号、生物阻抗信号等。进而，用于检测人体生理信号的光电传感器121还用于所检测的信号，例如PPG信号、心电信号、振动信号、生物阻抗信号等发送到处理器110，以使处理器110从相应信号中获取用户的脉搏数据，进而，根据所获取的脉搏数据测量用户的生理数据。
运动传感器122例如是加速度传感器。运动传感器122用于检测电子设备100在各个方向上(一般为三轴)加速度的大小。
环境光传感器123用于检测环境光的强度，并将环境光的强度转换为数据电信号。
结合图1A，PPG传感器或者ECG传感器可以设置在表体11的内侧，当用户佩戴智能手表10之后，PPG传感器或者ECG传感器可以与用户的身体接触。表体11的内侧是指当用户佩戴智能手表10时，与用户身体接触的一侧。
存储器130用于存储软件程序以及数据。处理器110通过运行该软件程序执行本申请数据显示方法的各实施例。该数据用于为处理器110运行软件程序提供支持。存储器130可以包括存储程序区以及存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序(例如数据显示功能，声音播放功能等)；存储数据区可以存储根据使用 电子设备100所创建的数据以及处理器110执行生理数据检测所需的算法等。此外，存储器可以包括高速随机存取存储器，还可以包括非易失存储器，例如磁盘存储器件、闪存器件或其他易失性固态存储器件。
无线通信模块140可以提供应用在电子设备100上的包括无线局域网(wireless local area networks，WLAN)(如Wi-Fi网络)，蓝牙(bluetooth，BT)，全球导航卫星系统(global navigation satellite system，GNSS)，NFC，红外技术(infrared，IR)等无线通信的解决方案。无线通信模块140可以是集成至少一个通信处理模块的一个或多个器件。无线通信模块140经由天线接收电磁波，将电磁波信号调频以及滤波处理，将处理后的信号发送到处理器110。无线通信模块140还可以从处理器110接收待发送的信号，对其进行调频，放大，经天线转为电磁波辐射出去。在一些实施例中，无线通信模块140提供的无线通信的解决方案可使得电子设备100与其他电子设备(如手机、平板电脑等)交互数据，例如可以通过其他电子设备显示用户的生理数据。
麦克风150，也称为传声器，可以将收集的声音信号转换为电信号，由音频电路接收后转换为音频数据；音频电路也可以将音频数据转换为电信号，传输到扬声器，由扬声器转换为声音信号输出。
电源管理模块170用于连接电源160和处理器110。电源管理模块170接收电源160的输入，为处理器110，传感器120，存储器130，无线通信模块140，麦克风150等各部件供电。电源管理模块170还可以用于监测电源160的容量，循环次数，健康状态(漏电，阻抗)等参数。
电子设备100可以包括显示屏，该显示屏用于显示控件，数据、图像等。显示屏包括显示面板。显示面板可以采用液晶显示屏(liquid crystal display，LCD)，有机发光二极管(organic light-emitting diode，OLED)，有源矩阵有机发光二极体或主动矩阵有机发光二极体(active-matrix organic light emitting diode的，AMOLED)，柔性发光二极管(flex light-emitting diode，FLED)，Miniled，MicroLed，Micro-oLed，量子点发光二极管(quantum dot light emitting diodes，QLED)等。
示例性的，智能手表10的显示屏13可以如图1C所示。一些实施例中，智能手表10可以在显示屏13上显示本申请涉及的生理数据。
图1A至图1C示例性所示的电子设备100可以通过传感器120获取用户的脉搏数据，然后，处理器110解析脉搏数据得到用户的生理数据。进而，一些实施例中，电子设备100的显示屏显示下述实施例所描述的各个用户界面。另一些实施例中，处理器110调用无线通信模块140将用户的生理数据发送至其他电子设备，以通过其他电子设备显示下述实施例所描述的各个用户界面。
以下介绍本申请不同场景下的示例性用户界面。
本申请中，若用户完全无心律失常的症状，或者，用户有心律失常的症状，但心律失常的累计时长相对较短，电子设备100可以显示用户相对较多的生理数据。若用户有心律失常的症状，且心律失常的累计时长相对较长，电子设备100可以显示用户相对较少的生理数据。
示例性的，为了量化心律失常累计时长的相对长度，本申请可以以心律失常的累计时长与采集脉搏数据的总时长的比值，作为确定心律失常累计时长的依据。基于此，“心律 失常的累计时长相对较短”例如是相应比值小于或者等于第一预设值，“心律失常的累计时长相对较长”例如是相应比值大于第一预设值。第一预设值是大于0小于1的数值，例如是5％。本申请对此不做限制。
此外，为了便于描述，本申请将用户的生理数据归纳为第一生理数据、第二生理数据和第三生理数据。其中，第一生理数据包括第一生理特征参数；第二生理数据包括第二生理特征参数；第三生理数据包括第一生理特征参数、第二生理特征参数和第三生理特征参数。
示例性的，以下以在智能手机显示用户的睡眠分期数据为例，对本申请中涉及的用户界面进行介绍。其中，电子设备100确定用户的睡眠分期数据之后，例如将该睡眠分期数据发送到智能手机，进而该智能手机在该智能手机的显示屏上呈现以下用户界面。
用户的睡眠可以分为：非快动眼睡眠(non-rapid eye movement sleep，NREM sleep)和快动眼睡眠(rapid eye movement sleep，REM sleep)两个状态，其中，NREM sleep阶段又可以分为四期，例如，第一期为入睡期，第二期为浅睡期，第三期为中度睡眠期，第四期为深度睡眠期。
以睡眠分期数据为例，第一生理特征参数例如包括深睡时长和浅睡时长。第二生理特征参数例如包括快速眼动时长。第三生理特征参数例如包括深睡比例、浅睡比例、快速眼动比例、深睡连续性得分、睡眠质量得分以及呼吸质量得分中的至少一个。相应的，第一生理数据包括深睡时长和浅睡时长；第二生理数据包括快速眼动时长；第三生理数据包括深睡比例、浅睡比例、快速眼动比例、深睡连续性得分、睡眠质量得分以及呼吸质量得分中的至少一个，深睡时长，浅睡时长和快速眼动时长。
图2A示意的示例性用户界面呈现出第三生理数据。其中，当前日期是2018年6月21日，用户2018年6月20日22点20分入睡，2018年6月21日6点50分醒来，夜间的睡眠时长是8小时40分钟，以及期间各时段睡眠分期的曲线。本实施例中，第三生理数据还包括电子设备100计算得到的用户夜间的睡眠得分是81分并附有评价，用户深睡的时长是1小时40分钟，浅睡的时长是3小时40分钟，快速眼动的时长是3小时20分钟，以及各睡眠分期的总时长在总睡眠时长中的占比，以及各睡眠分期的总时长占比的正常范围和分析结果，例如，深睡比例20％，正常；快速眼动比例38％，偏低等。第三生理数据甚至还可以包括用户在当前日期前一天的零星小睡的时间段和时长，以及白天零星小睡加夜间睡眠的总睡眠时长。本申请此处不再一一描述。
需要指出的是，图2A示意的用户界面呈现的是用户无心律失常症状时的生理数据。若用户有心律失常的症状，且对应的比值小于或者等于第一预设值的场景下，用户界面除呈现第三生理数据外，还可显示提醒信息。例如，电子设备100采集的睡眠数据对应的起始时刻是2018年6月20日22点30分，截止时刻是2018年6月21日7点整，采集睡眠数据的总时长是8小时30分钟，其中夜间睡眠总时长例如是8小时20分钟(包括深睡、浅睡和快速眼动)，心律失常的总时长例如是10分钟。该场景下用户界面还可以呈现例如“用户存在轻微心律失常”的提醒信息。该场景下用户界面中的其他数据与图2A类似，此处不赘述。
图2B示意的示例性用户界面呈现出第一生理数据。其中，当前日期是2018年6月21日，用户2018年6月20日20点入睡，2018年6月21日9点32分醒来，夜间的睡眠时 长是8小时40分钟，心律失常的总时长是2小时，期间各时段深睡、浅睡和清醒的分期曲线如图2B所示，以及深睡时长是3小时20分钟，浅睡的时长是5小时20分钟。
可见，当用户不存在心律失常的症状，或者存在相对较短时长的心律失常的症状时，电子设备100能够显示用户的第一生理特征参数、第二生理特征参数以及第三生理特征参数。当用户存在相对较长时长的心律失常的症状时，电子设备100只显示用户的第一生理特征参数，从而保证所显示的生理数据是正确的。
在另一些实施例中，上述比值大于第一预设值且小于第二预设值时，电子设备还可以显示用户的第一生理数据和第二生理数据。第二预设值大于第一预设值且小于1，第二预设值例如是30％。
图2C示意的示例性用户界面呈现出第一生理数据和第二生理数据。当前日期是2018年6月21日，用户2018年6月20日22点10分入睡，2018年6月21日8点30分醒来，夜间的睡眠时长是8小时40分钟，其中，第一生理数据包括：深睡时长是2小时，浅睡的时长是3小时10分钟。第二生理数据包括：快速眼动时长是3小时30分钟。
采用本实现方式，电子设备100能够在保证显示正确的生理数据的基础上，显示尽量多的生理特征参数，从而提高用户体验。
可以理解的是，图2A至图2C示意的用户界面显示的“2018年6月21日”仅仅是示意性描述，对本申请的技术方案不构成限制。在实际使用中，用户触发“2018年6月21日”左侧或者右侧的箭头，用户界面上可以显示“2018年6月21日”之前或者之后的睡眠分期数据。例如，用户单击“2018年6月21日”左侧的箭头，用户界面上可以显示2018年6月20日的睡眠分期数据；再如，若“2018年6月21日”不是当前时间，用户单击“2018年6月21日”右侧的箭头，用户界面上可以显示2018年6月22日的睡眠分期数据。
进一步的，一些实施例中，电子设备100还可以生成和显示用户的心律统计数据。示例性的，结合图2A至图2C示意的用户界面，用户可以通过触发用户界面上的“日”查看自己一天心律统计数据，也可以通过触发用户界面上的“周”选择查看自己一周的心律统计数据。心律统计数据例如可以包括心律是否异常，心律的变化曲线，不规则心律及不规则心律出现的时间等。
图2D示意了一种示例性心律统计数据的用户界面，该心律统计数据例如是2019年3月1日用户的心律统计数据。其中，该心律统计数据包括统计结果“未见异常”，“平均心律：68次/分钟”，用户在2019年3月1日的心律变化曲线，“不规则心律：4次”，“未见异常：54次”，“不规则心律是70bmp，出现的时间是2019年3月1日03:31:12”，“未见异常的心律65bmp”等数据。本申请不再一一说明。
图2E示意了一种示例性心律统计数据的用户界面，该心律统计数据例如是2019年2月25日至2019年3月3日的一周的心律统计数据。其中，该一周的心律统计数据包括“不规则心律比例：1/58”，“本周中每天的心律情况的柱状图和曲线图”，“不规则心律：10次”，“未见异常：260次”等数据。本申请不再一一说明。
可见，采用本实现方式，电子设备100还能够统计并显示用户各阶段的心律，从而随时提醒用户的健康状况，提高用户的使用体验。
可以理解的是，图2A至图2E仅仅是示意性描述，对本申请实施例的保护范围不构成限制。在另一些实施例中，用户界面中呈现的数据也可以是用户的其他生理数据，并且， 用户界面中呈现的数据的表达形式也可以是其他形式，此处不再详述。
此外，图2A至图2E示意的用户界面是以智能手机为例的用户界面，另一些实施例中，电子设备100还可以在该电子设备100的显示屏上显示上述生理数据。应理解，不同设备的显示屏面积可能不同，使得不同电子设备的用户界面上显示的数据的数量不同，本申请对此不限制。
以下从电子设备100的角度对本申请的数据显示方法进行示例性描述。
如图3所示，图3示意了一种数据显示方法10。数据显示方法10(以下简称方法10)包括以下步骤：
步骤S11，采集第一时间段内的脉搏数据。
其中，第一时间段可以是一天当中的任意时间段，例如，一天中的11点到13点，再如，一天的22点到第二天的7点。第一时间段的长度是第一时长。示例性的，第一时长最短例如可以是1分钟，最长例如可以是24小时，本申请对此不限制。
示例性的，脉搏数据例如是PPG数据。结合图1B示意的实施例，用于检测人体生理信号的传感器121例如是光电传感器，光电传感器可以采集用户的脉搏数据。一些实施例中，光电传感器例如是PPG传感器，例如，当用户的心脏收缩时，血管的血容量增多，光吸收量随之增加，相应的，PPG传感器检测到的光强度较小；当用户的心脏舒张时，血管的血容量减少，PPG传感器检测到的光强度较大。基于此，随着用户的心脏跳动，PPG传感器可以检测到呈脉动性变化的光强度，进而，PPG传感器可以将该光强度变化信号转化成数字电信号，该数字电信号可以被称为PPG信号或者PPG数据，也即本方案所述的脉搏数据。
可以理解的是，PPG传感器采集脉搏数据仅是一种示意性描述，对本申请实施例的技术方案不构成限制。在另一些实施例中，电子设备100还可以通过ECG传感器采集脉搏数据。本申请此处不再详述。
步骤S12，确定脉搏数据中包含异常脉搏数据。
其中，异常脉搏数据是指对应心律失常的脉搏数据。
光电传感器在采集到脉搏数据之后，将脉搏数据传输到处理器110。处理器110检测脉搏数据中是否存在心律失常的脉搏数据，若脉搏数据中存在心律失常的脉搏数据，处理器110可以统计用户心律失常的累计时长，得到第二时长。示例性的，处理器110中可以预置心律失常初筛算法，然后，利用该心律失常初筛算法检测脉搏数据中的是否存在异常脉搏数据。该心律失常初筛算法例如是支持向量机(support vector machine，SVM)算法、阈值法、机器学习方法、深度学习方法等任何可以从脉搏数据中筛查心律失常的算法。本申请对此不做限制。
示例性的，处理器110将第一时间段内的PPG数据划分为k个数据段，k大于等于1，其中每个数据段对应的时长是第三时长，第三时长小于第二时长，第三时长例如是一分钟。然后，处理器110使用SVM算法分别检测每个数据段是否是异常数据。
其中，SVM算法通过计算各数据段在特征空间中与决策平面的距离确定检测结果，例如，y t(w×x t-b)≥1是指各数据段应当满足的条件对应关系，
Figure PCTCN2020115575-appb-000001
定义y t(w×x t-b)≥1达到最小值时的w和b的取值。其中，w是指支持向量(由距离决策平面最近的向量构成)的法向量，
Figure PCTCN2020115575-appb-000002
b是指决策平面(在特征空间中，该平 面将样本分为两部分，分别对应心律失常样本与正常样本)的偏差值，
Figure PCTCN2020115575-appb-000003
(w×x t-y t)。x t是指k个数据段中的第t个数据段，如心律变异性特征、熵值特征等。y t是指指示心律是否失常的脉搏指标数据，一些实施例中，若检测数据段是否是异常脉搏数据，则y t是指示心律失常对应的指标数据；另一些实施例中，若检测数据段是否是正常脉搏数据，则y t是指示心律正常对应的指标数据，y t可以是医学领域的数据，t＝1,2,3……k，是指k个数据段中每个数据段的编号，a t为拉格朗日乘子，m表示支持向量(由距离决策平面最近的向量构成)的个数。示例性的，若y t是心律失常对应的脉搏指标数据，当数据段x t满足对应关系y t(w×x t-b)≥1时，确定该数据段x t是异常脉搏数据。同理，若y t是心律正常对应的脉搏指标数据，当数据段x t满足对应关系y t(w×x t-b)≥1时，确定该数据段x t是正常脉搏数据。
进一步的，处理器110确定脉搏数据中的异常脉搏数据之后，可以统计异常脉搏数据的累计时长，得到第二时长。进而，处理器110可以判断第二时长与第一时长的比值是否大于预设值，然后，根据判断结果执行下述步骤。
步骤S13，确定第二时长与第一时长的比值。
步骤S14，当比值大于第一预设值时，显示第一生理数据。
其中，第一预设值是大于0小于1的值，例如5％，或者也可以表示为0.05。第一生理数据包括第一生理特征参数，示例性的，如图2B对应的实施例所述，此处不再详述。
一些实施例中，当步骤S13示意的比值大于第一预设值时，处理器110可以采用加速度(acceleration，ACC)检测方法解析脉搏数据，得到用户的第一生理数据。可见，采用本实现方法，能够保证所得到的生理数据的准确性。
另一些实施例中，当步骤S13示意的比值大于第一预设值且小于或者等于第二预设值时，为了尽量多的呈现用户的生理数据，处理器110可以采用ACC检测方法解析脉搏数据中异常脉搏数据，得到第一生理数据；并采用心肺耦合(cardiopulmonary coupling，CPC)检测方法或者心律变异性(heart rate variability，HRV)检测方法解析脉搏数据中除异常脉搏数据之外的数据，得到第二生理数据，其中，第二生理数据包括第二生理特征参数。然后，在显示器上显示第一生理数据和第二生理数据。第二预设值大于第一预设值且小于1，第二预设值例如是30％，或者表示为0.3。第一生理数据和第二生理数据的显示实例例如如图2C对应的实施例所述，此处不再详述。
再一些实施例中，当步骤S13示意的比值小于或者等于第一预设值时，处理器110可以处理脉搏数据得到重建的脉搏数据，然后，生成第三生理数据，其中，第三生理数据包括第一生理特征参数、第二生理特征参数和第三生理特征参数。第三生理数据的显示实例可以参考图2A对应的实施例所述，此处不再详述。
一些实施例中，处理器110可以将脉搏数据中异常脉搏数据删除，得到重建的脉搏数据。另一些实施例中，处理器110可以根据异常脉搏数据相邻的非异常脉搏数据修改相应异常脉搏数据，得到重建的脉搏数据。
以下以CPC检测方法为例，对电子设备100得到第三生理数据的实施过程进行示例性描述。
本实施例中，步骤S11所述的脉搏数据即为PPG数据。基于此，处理器生成第三生理数据的具体过程包括以下步骤：
步骤1，处理器110可以从PPG数据中提取心跳间隔(R peak to R peak interval，RR)序列，然后，处理器110可以从RR序列中提取呼吸信号序列。
其中，从RR序列中提取呼吸信号序列的算法被称为基于心电信号提取呼吸信号(ECG-derived respiratory，EDR)的算法，所以，呼吸信号序列也被称为EDR信号序列。
步骤2，处理器110使用滑动窗删除RR序列中的异常样本点，得到重建的RR序列。
步骤3，处理器110使用经验模式分解算法对重建的RR序列和EDR信号序列进行分解，并计算分解后的RR序列和分解后的EDR信号序列的相干及互功率谱。
步骤4，处理器110根据相干及互功率谱结果计算得到第三生理数据。
上述步骤1至步骤4中涉及的序列以及各种算法，均是本领域技术人员所熟知的技术，此处不再详述。
此外，若脉搏数据中包含异常脉搏数据，处理器110在生成上述生理数据的过程中，还可以根据统计用户的心律数据，得到用户不同时间段的心律统计数据。心律统计数据如图2D或2E示意的实施例所述。
进一步的，结合图1A至图1C示意的实施例，处理器110执行显示功能并通过显示屏13显示方法10涉及的生理数据，得到图2A至图2E中任一图示示意的用户界面的数据。另一些实施例中，处理器110可以将方法10涉及的生理数据通过无线通信模块140发送到另一台电子设备，另一台电子设备可以显示相应生理数据，得到图2A至图2E中任一图示示意的用户界面。
可以理解的是，上述CPC检测方法，ACC检测方法，以及睡眠分期数据等均是示意性描述，对本申请的技术方案不构成限制。在另一些实施例中，本申请的技术方案也可以检测用户的压力数据，相应的，可以采用HRV检测方法、量表问券方法、行为观察方法等。本申请此处不再详述。
综上，采用本申请的实现方式，电子设备确定所采集的脉搏数据中包含异常脉搏数据之后，计算异常脉搏数据的累积时长与脉搏数据总时长的比值，进而，确定该比值是否大于第一预设值，当比值大于第一预设值时，电子设备显示用户的第一生理数据，其中，第一生理数据包括第一生理特征参数，从而能够保证向用户显示的生理数据是正确的，提高用户的使用体验。
上述实施例从电子设备的硬件结构，以及各软、硬件所执行的动作的角度对本申请提供的数据显示方法的各方案进行了介绍。本领域技术人员应该很容易意识到，结合本文中所公开的实施例描述的脉搏数据的采集、异常脉搏数据的确定、比值的计算以及生理数据的生成等的处理步骤，本申请不仅能够以硬件或硬件和计算机软件的结合形式来实现。某个功能究竟以硬件还是计算机软件驱动硬件的方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请实施例的范围。
例如，上述电子设备100可以通过功能模块的形式来实现上述相应的功能。一些实施例中，电子设备可以包括采集模块、处理模块和显示模块。采集模块可以用于执行上述图3示意的任意实施例中脉搏数据的采集。显示模块可以用于执行上述图3示意的任意实施例中生理数据的显示。处理模块可以用于执行上述图3示意的任意实施例中除了脉搏数据的采集以及生理数据的显示之外的操作。具体内容可以参考图3对应的实施例的相关描述， 此处不再赘述。
可以理解的是，以上各个模块的划分仅仅是一种逻辑功能的划分，实际实现时，所述采集模块的功能可以集成到传感器实现，所述处理模块的功能可以集成到处理器实现，所述显示模块的功能可以集成到显示器实现。如图4A所示，电子设备40包括传感器401，处理器402和显示器403。所述传感器401可以执行上述图3示意的任意实施例中脉搏数据的采集。所述显示器403可以用于执行上述图3示意的任意实施例中生理数据的显示。所述处理器402可以用于执行上述图3示意的任意实施例中除了脉搏数据的采集以及生理数据的显示之外的操作。
例如，所述传感器401可以采集第一时间段内的脉搏数据，所述第一时间段的长度是第一时长。所述处理器402可以用于确定所述脉搏数据中包含异常脉搏数据，所述异常脉搏数据是指对应心律失常的脉搏数据，以及确定第二时长与所述第一时长的比值，所述第二时长是所述异常脉博数据的累计时长。所述显示器403可以用于当所述比值大于第一预设值时，显示第一生理数据，所述第一预设值是大于0小于1的值，所述第一生理数据包括第一生理特征参数。
具体内容可以参考图3对应的实施例中电子设备相关的描述，此处不再赘述。
图4A是从独立功能实体的角度对本申请的电子设备进行描述。在另一种实施场景中，各独立运行的功能实体可以集成在一个硬件实体中，相应的，如图4B所示，本实施场景中，电子设备41可以包括处理器411、传感器412和存储器413。其中，存储器413可以用于存储电子设备41预装的程序/代码，也可以存储用于处理器411执行时的代码等。
应理解，本申请的电子设备41可对应于本申请图3对应的实施例中的电子设备，其中传感器412用于执行图3示意的任意实施例中脉搏数据的采集，处理器411用于执行上述图3示意的任意实施例中电子设备除了脉搏数据的采集之外的其它处理。在此不再赘述。
具体内容可以参考图3对应的实施例中电子设备相关的描述，此处不再赘述。
具体实现中，对应电子设备，本申请还提供一种计算机存储介质，其中，设置在任意设备中的计算机存储介质可存储有程序，该程序执行时，可实施包括图3提供的数据显示方法的各实施例中的部分或全部步骤。任意设备中的存储介质均可为磁碟、光盘、只读存储记忆体(read-only memory，ROM)或随机存储记忆体(random access memory，RAM)等。
本申请中，处理器可以是中央处理器(central processing unit，CPU)，网络处理器(network processor，NP)或者CPU和NP的组合。处理器还可以进一步包括硬件芯片。上述硬件芯片可以是专用集成电路(application-specific integrated circuit，ASIC)，可编程逻辑器件(programmable logic device，PLD)或其组合。上述PLD可以是复杂可编程逻辑器件(complex programmable logic device，CPLD)，现场可编程逻辑门阵列(field-programmable gate array，FPGA)，通用阵列逻辑(generic array logic，GAL)或其任意组合。存储器可以包括易失性存储器(volatile memory)，例如随机存取存储器(random-access memory，RAM)；存储器也可以包括非易失性存储器(non-volatile memory)，例如只读存储器(read-only memory，ROM)，快闪存储器(flash memory)，硬盘(hard disk drive，HDD)或固态硬盘(solid-state drive，SSD)；存储器还可以包括上述种类的存储器的组合。传感器可以包括检测用户身体形态变化的传感器，例如，ACC 传感器和运动传感器等；传感器还可以包括光电传感器，例如，PPG传感器、环境光传感器以及ECG传感器等；传感器还可以包括其他高灵敏度的生物电传感器，例如生物阻抗传感器等。
图4B中还可以包括总线接口，总线接口可以包括任意数量的互联的总线和桥，具体由处理器代表的一个或多个处理器和存储器代表的存储器的各种电路链接在一起。总线接口还可以将诸如外围设备、稳压器和功率管理电路等之类的各种其他电路链接在一起，这些都是本领域所公知的，因此，本文不再对其进行进一步描述。总线接口提供接口。传感器提供用于采集用户生物数据的单元。处理器负责管理总线架构和通常的处理，存储器可以存储处理器在执行操作时所使用的报文。
本领域技术任何还可以了解到本申请实施例列出的各种说明性逻辑块(illustrative logical block)和步骤(step)可以通过电子硬件、电脑软件，或两者的结合进行实现。这样的功能是通过硬件还是软件来实现取决于特定的应用和整个系统的设计要求。对于每种特定的应用，本领域技术人员可以使用各种方法实现所述的功能，但这种实现不应被理解为超出本申请实施例保护的范围。
本申请实施例中所描述的各种说明性的逻辑单元和电路可以通过通用处理器，数字信号处理器，专用集成电路(ASIC)，现场可编程门阵列(FPGA)或其它可编程逻辑装置，离散门或晶体管逻辑，离散硬件部件，或上述任何组合的设计来实现或操作所描述的功能。通用处理器可以为微处理器，可选地，该通用处理器也可以为任何传统的处理器、控制器、微控制器或状态机。处理器也可以通过计算装置的组合来实现，例如数字信号处理器和微处理器，多个微处理器，一个或多个微处理器联合一个数字信号处理器核，或任何其它类似的配置来实现。
本申请实施例中所描述的方法或算法的步骤可以直接嵌入硬件、处理器执行的软件单元、或者这两者的结合。软件单元可以存储于RAM存储器、闪存、ROM存储器、EPROM存储器、EEPROM存储器、寄存器、硬盘、可移动磁盘、CD-ROM或本领域中其它任意形式的存储媒介中。示例性地，存储媒介可以与处理器连接，以使得处理器可以从存储媒介中读取信息，并可以向存储媒介存写信息。可选地，存储媒介还可以集成到处理器中。处理器和存储媒介可以设置于ASIC中，ASIC可以设置于UE中。可选地，处理器和存储媒介也可以设置于UE中的不同的部件中。
应理解，在本申请的各种实施例中，各过程的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对实施例的实施过程构成任何限定。
在上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行所述计算机程序指令时，全部或部分地产生按照本申请所述的流程或功能。所述计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。所述计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，所述计算机指令可以从一个网站站点、计算机、服务器或报文中心通过有线(例如同轴电缆、光纤、数字用户线(DSL))或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或报文中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介 质或者是包含一个或多个可用介质集成的服务器、报文中心等报文存储设备。所述可用介质可以是磁性介质，(例如，软盘、硬盘、磁带)、光介质(例如，DVD)、或者半导体介质(例如固态硬盘(solid state disk，SSD))等。
本说明书的各个部分均采用递进的方式进行描述，各个实施例之间相同相似的部分互相参见即可，每个实施例重点介绍的都是与其他实施例不同之处。尤其，对于装置和系统实施例而言，由于其基本相似于方法实施例，所以描述的比较简单，相关之处参见方法实施例部分的说明即可。
尽管已描述了本申请的优选实施例，但本领域内的技术人员一旦得知了基本创造性概念，则可对这些实施例作出另外的变更和修改。所以，所附权利要求意欲解释为包括优选实施例以及落入本申请范围的所有变更和修改。
显然，本领域的技术人员可以对本申请进行各种改动和变型而不脱离本申请的精神和范围。这样，倘若本申请的这些修改和变型属于本申请权利要求及其等同技术的范围之内，则本申请也意图包含这些改动和变型在内。

Claims (20)

  1. 一种数据显示方法，其特征在于，包括：
    采集第一时间段内的脉搏数据，所述第一时间段的长度是第一时长；
    确定所述脉搏数据中包含异常脉搏数据，所述异常脉搏数据是指对应心律失常的脉搏数据；
    确定第二时长与所述第一时长的比值，所述第二时长是所述异常脉博数据的累计时长；
    当所述比值大于第一预设值时，显示第一生理数据，所述第一预设值是大于0小于1的值，所述第一生理数据包括第一生理特征参数。
  2. 如权利要求1所述的方法，其特征在于，还包括：
    当所述比值大于所述第一预设值且小于或者等于第二预设值时，显示第一生理数据和第二生理数据，所述第二预设值大于所述第一预设值且小于1，所述第二生理数据包括第二生理特征参数。
  3. 如权利要求1所述的方法，其特征在于，还包括：
    当所述比值小于或者等于所述第一预设值时，显示第三生理数据，所述第三生理数据包括所述第一生理特征参数、第二生理特征参数和第三生理特征参数。
  4. 如权利要求1所述的方法，其特征在于，所述显示所述第一生理数据之前，还包括：
    采用加速度ACC检测方法解析所述脉搏数据，得到所述第一生理数据。
  5. 如权利要求2所述的方法，其特征在于，所述显示所述第一生理数据和所述第二生理数据之前，还包括：
    采用加速度ACC检测方法解析所述脉搏数据中所述异常脉搏数据，得到所述第一生理数据；
    采用心肺耦合CPC检测方法解析所述脉搏数据中除所述异常脉搏数据之外的数据，得到所述第二生理数据。
  6. 如权利要求3所述的方法，其特征在于，所述显示所述第三生理数据之前，还包括：
    处理所述脉搏数据中的所述异常脉搏数据得到重建的脉搏数据；
    使用CPC检测方法解析所述重建的脉搏数据，得到所述第三生理数据。
  7. 如权利要求6所述的方法，其特征在于，所述处理所述脉搏数据中的所述异常脉搏数据得到重建的脉搏数据，包括：
    将所述异常脉搏数据从所述脉搏数据中删除，得到所述重建的脉搏数据；或者，
    根据所述异常脉搏数据相邻的非异常脉搏数据修改所述异常脉搏数据，得到所述重建的脉搏数据。
  8. 如权利要求1至7中任一项所述的方法，其特征在于，确定所述脉搏数据中包含异常脉搏数据，包括：
    将所述脉搏数据划分为k个数据段，k大于等于1，所述k个数据段中每个数据段对应的市场是第三时长，所述第三时长小于所述第二时长；
    确定所述k个数据段中每个数据段是否满足对应关系y t(w×x t-b)≥1，其中，t＝1,2,3……k，w是指支持向量的法向量，b是指决策平面的偏差值，x t是所述k个数据段 中的第t个数据段，y t是指指示心律是否失常的对应的脉搏指标数据；
    若所述y t是心律失常对应的脉搏指标数据，当数据段满足对应关系y t(w×x t-b)≥1时，确定所述数据段是所述异常脉搏数据。
  9. 如权利要求3所述的方法，其特征在于，
    所述第一生理特征参数包括深睡时长和浅睡时长；
    所述第二生理特征参数包括快速眼动时长；
    所述第三生理特征参数包括深睡比例、浅睡比例、快速眼动比例、深睡连续性得分、睡眠质量得分以及呼吸质量得分中的至少一个。
  10. 一种电子设备，其特征在于，包括：传感器，处理器和显示器，其中，
    所述传感器，用于采集第一时间段内的脉搏数据，所述第一时间段的长度是第一时长；
    所述处理器，用于确定所述脉搏数据中包含异常脉搏数据，所述异常脉搏数据是指对应心律失常的脉搏数据；
    所述处理器，还用于确定第二时长与所述第一时长的比值，所述第二时长是所述异常脉博数据的累计时长；
    所述显示器，用于当所述比值大于第一预设值时，显示第一生理数据，所述第一预设值是大于0小于1的值，所述第一生理数据包括第一生理特征参数。
  11. 如权利要求10所述的电子设备，其特征在于，
    所述显示器，还用于当所述比值大于所述第一预设值且小于或者等于第二预设值时，显示第一生理数据和第二生理数据，所述第二预设值大于所述第一预设值且小于1，所述第二生理数据包括第二生理特征参数。
  12. 如权利要求10或11所述的电子设备，其特征在于，
    所述显示器，还用于当所述比值小于或者等于所述第一预设值时，显示第三生理数据，所述第三生理数据包括所述第一生理特征参数、第二生理特征参数和第三生理特征参数。
  13. 如权利要求10所述的电子设备，其特征在于，
    所述处理器，还用于采用加速度ACC检测方法解析所述脉搏数据，得到所述第一生理数据。
  14. 如权利要求11所述的电子设备，其特征在于，
    所述处理器，还用于采用加速度ACC检测方法解析所述脉搏数据中所述异常脉搏数据，得到所述第一生理数据，并采用心肺耦合CPC检测方法解析所述脉搏数据中除所述异常脉搏数据之外的数据，得到所述第二生理数据。
  15. 如权利要求12所述的电子设备，其特征在于，
    所述处理器，还用于处理所述脉搏数据中的所述异常脉搏数据得到重建的脉搏数据，并使用CPC检测方法解析所述重建的脉搏数据，得到所述第三生理数据。
  16. 如权利要求15所述的电子设备，其特征在于，
    所述处理器，还用于将所述异常脉搏数据从所述脉搏数据中删除，得到所述重建的脉搏数据；
    所述处理器，还用于根据所述异常脉搏数据相邻的非异常脉搏数据修改所述异常脉搏数据，得到所述重建的脉搏数据。
  17. 如权利要求10至16中任一项所述的电子设备，其特征在于，
    所述处理器，还用于将所述脉搏数据划分为k个数据段，k大于等于1，所述k个数据段中每个数据段对应的市场是第三时长，所述第三时长小于所述第二时长，然后，确定所述k个数据段中每个数据段是否满足对应关系y t(w×x t-b)≥1，其中，t＝1,2,3……k，w是指支持向量的法向量，b是指决策平面的偏差值，x t是所述k个数据段中的第t个数据段，y t是指指示心律是否失常的对应的脉搏指标数据；
    所述处理器，还用于在所述y t是心律失常对应的脉搏指标数据，并当数据段满足对应关系y t(w×x t-b)≥1时，确定所述数据段是所述异常脉搏数据。
  18. 如权利要求12所述的电子设备，其特征在于，
    所述第一生理特征参数包括深睡时长和浅睡时长；
    所述第二生理特征参数包括快速眼动时长；
    所述第三生理特征参数包括深睡比例、浅睡比例、快速眼动比例、深睡连续性得分、睡眠质量得分以及呼吸质量得分中的至少一个。
  19. 一种计算机可读存储介质，其特征在于，包括计算机程序，当所述计算机程序在计算机上运行时，使得所述计算机执行如权利要求1至9中任一项所述的方法。
  20. 一种计算机程序产品，其特征在于，包括指令，当所述指令在计算机上运行时，使得所述计算机执行如权利要求1至9中任一项所述的方法。
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