WO2024032084A9 - Procédé de détection de port et dispositif à porter sur soi - Google Patents

Procédé de détection de port et dispositif à porter sur soi Download PDF

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Publication number
WO2024032084A9
WO2024032084A9 PCT/CN2023/095987 CN2023095987W WO2024032084A9 WO 2024032084 A9 WO2024032084 A9 WO 2024032084A9 CN 2023095987 W CN2023095987 W CN 2023095987W WO 2024032084 A9 WO2024032084 A9 WO 2024032084A9
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WO
WIPO (PCT)
Prior art keywords
wearable device
data
wearing
green light
detection result
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PCT/CN2023/095987
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English (en)
Chinese (zh)
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WO2024032084A1 (fr
Inventor
曹垚
张�成
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荣耀终端有限公司
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Publication of WO2024032084A1 publication Critical patent/WO2024032084A1/fr
Publication of WO2024032084A9 publication Critical patent/WO2024032084A9/fr

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Classifications

    • 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
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/0816—Measuring devices for examining respiratory frequency
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14542—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring blood gases
    • 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
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K13/00—Thermometers specially adapted for specific purposes
    • G01K13/20—Clinical contact thermometers for use with humans or animals
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K3/00—Thermometers giving results other than momentary value of temperature
    • G01K3/02—Thermometers giving results other than momentary value of temperature giving means values; giving integrated values

Definitions

  • the present application relates to the field of terminal technology, and in particular to a wearing detection method and a wearable device.
  • terminal devices have become a part of people's work and life.
  • more terminal devices can support users to monitor human body data.
  • users can use wearable devices to detect human body data.
  • the wearable device can start measuring the user's heart rate, breathing rate or blood oxygen and other human characteristics when the wearing detection passes.
  • the wearing detection is used to detect whether the wearable device is worn by a living organism with life characteristics.
  • wearable devices can perform wearing detection based on infrared signals.
  • the wearable device can use infrared signals to detect the distance between the wearable device and human skin, and determine that the wearable device is being worn by the user when the distance is close, or determine that the wearable device is not being worn by the user when the distance is far.
  • the accuracy of the above-mentioned wearing detection method using infrared signals is low.
  • the embodiments of the present application provide a wearing detection method and a wearable device, so that the wearable device can obtain green light data for indicating the heart rate condition detected when the wearable device is worn, and obtain a characteristic value for characterizing the wearing state of the wearable device through feature extraction of the green light data, and then input the characteristic value into a preset model to obtain a more accurate wearing detection result.
  • the embodiment of the present application provides a wearing detection method, which includes: the wearable device collects target data; the target data includes green light data, and the green light data is used to indicate the heart rate detected when the wearable device is worn; the wearable device extracts features from the target data to obtain feature values related to the wearing state; the wearable device inputs the feature values into a preset model to obtain a first wearing detection result; the first wearing detection result is used to indicate whether the wearable device is in a wearing state.
  • the wearable device can obtain green light data for indicating the heart rate detected when the wearable device is worn, and obtain feature values for characterizing the wearing state of the wearable device through feature extraction of the green light data, and then input the feature values into the preset model to obtain a more accurate wearing detection result.
  • the characteristic value includes: a first characteristic value obtained based on green light data
  • the first characteristic value includes one or more of the following: green light AC component, green light DC component, green light time domain autocorrelation coefficient, green light frequency domain maximum, mean of green light adjacent peak ordinate difference, standard deviation of green light adjacent peak ordinate difference, mean of green light adjacent peak abscissa difference, standard deviation of green light adjacent peak abscissa difference, mean of green light peak ordinate, or number of green light time domain peaks.
  • the wearable device can simulate the heart rate characteristics detected when the user wears the wearable device through the first characteristic value, and then the wearable device can realize the extraction of the first characteristic value for different scenes. Accurate detection of wearing status.
  • the target data further includes one or more of the following: infrared light data, temperature data, or acceleration data.
  • the eigenvalue also includes one or more of the following: a second eigenvalue obtained based on infrared light data, a third eigenvalue obtained based on temperature data, or a fourth eigenvalue obtained based on acceleration data; wherein the second eigenvalue includes one or more of the following: an infrared light AC component, an infrared light DC component, or an infrared light time domain autocorrelation coefficient; the third eigenvalue includes: a temperature mean; and the fourth eigenvalue includes: a combined velocity mean.
  • the wearable device can distinguish whether the wearable device is worn by the user or by other objects during the wearing detection process through the second eigenvalue, take the influence of temperature on the heart rate during the wearing detection process into account through the third eigenvalue, and take the influence of exercise on the heart rate during the wearing detection process into account through the fourth eigenvalue.
  • the wearable device can realize accurate detection of the wearing status in different scenarios based on the extraction of the eigenvalue.
  • the wearable device extracts features from the target data, including: when the wearable device determines that the mean value of the ambient light data is less than or equal to the first threshold, the temperature data meets the preset temperature range, and/or the mean value of the infrared light data is less than or equal to the second threshold, the wearable device extracts features from the target data.
  • the wearable device can also use infrared light data, ambient light data, and temperature data for wearing detection, excluding various scenarios that do not meet the wearing requirements, such as the wearable device not being in contact with the human body, the wearable device being placed on an object, and the wearable device having a loose strap, thereby improving the accuracy of the wearing recognition method.
  • the method further includes: when the wearable device determines that the first target service and/or the second target service is not detected, the wearable device determines the first wearing detection result as the second wearing detection result; wherein the first target service is a task executed when the wearable device is worn, and the second target task is a task executed when the wearable device is not worn.
  • the wearable device can improve the stability of the wearing detection method based on service detection.
  • the first target service includes one or more of the following: heart rate detection service, blood oxygen detection service, respiratory rate detection service, service for monitoring motion status, or service for monitoring sleep status;
  • the second target service includes one or more of the following: charging service, or service for indicating the ejection of a wristband.
  • the wearable device determines that the first target service and/or the second target service are not detected, including: the wearable device determines that the first target service is not detected, the second target service is not detected, and/or detects that the wearable device is not in motion. In this way, the wearable device can increase the stability of the wearing detection method based on the detection of the service and the detection of the motion state.
  • the method further includes: when the wearable device determines that the first target task is detected, the wearable device determines that the second wearing detection result is that the wearable device is in a wearing state; and/or, when the wearable device determines that the second target task is detected, the wearable device determines that the second wearing detection result is that the wearable device is in a non-wearing state. In this way, the wearable device can improve the stability of the wearing detection method based on the detection of the service.
  • the method further includes: when the second wearing detection result is that the wearable device is in a wearing state, the wearable device starts the target function. In this way, the wearable device can continue to execute the target function when it is detected that the wearing state is satisfied, thereby improving the accuracy of the wearable device in detecting the target function.
  • the preset model includes: a first preset model and a second preset model, the first preset model is different from the second preset model, the wearable device inputs the characteristic value into the preset model, and obtains a first wearing detection result, including: the wearable device inputs the characteristic value into the first preset model and the second preset model respectively, and obtains a first detection result corresponding to the first preset model and a second detection result corresponding to the second preset model; the wearable device Based on the first detection result and the second detection result, a first wearing detection result is obtained.
  • the wearable device can distinguish whether the wearable device is worn by a user or another object through a preset model, and use the two preset models to improve the stability and accuracy of the wearing detection method.
  • an embodiment of the present application provides a wearing detection device, including a collection unit for collecting target data; the target data includes green light data, and the green light data is used to indicate the heart rate detected when the wearable device is worn; a processing unit is used to extract features of the target data to obtain feature values related to the wearing state; the processing unit is also used to input the feature values into a preset model to obtain a first wearing detection result; the first wearing detection result is used to indicate whether the wearable device shown is in a wearing state.
  • the characteristic value includes: a first characteristic value obtained based on green light data, the first characteristic value includes one or more of the following: a green light AC component, a green light DC component, a green light time domain autocorrelation coefficient, a green light frequency domain maximum, a mean of the vertical coordinate difference of adjacent green light peaks, a standard deviation of the vertical coordinate difference of adjacent green light peaks, a mean of the horizontal coordinate difference of adjacent green light peaks, a standard deviation of the horizontal coordinate difference of adjacent green light peaks, a mean of the vertical coordinate of the green light peaks, or the number of green light time domain peaks.
  • the target data further includes one or more of the following: infrared light data, temperature data, or acceleration data.
  • the eigenvalue also includes one or more of the following: a second eigenvalue obtained based on infrared light data, a third eigenvalue obtained based on temperature data, or a fourth eigenvalue obtained based on acceleration data; wherein the second eigenvalue includes one or more of the following: an infrared light AC component, an infrared light DC component, or an infrared light time domain autocorrelation coefficient; the third eigenvalue includes: a temperature mean; and the fourth eigenvalue includes: a combined velocity mean.
  • the wearable device performs feature extraction on the target data, including: when the wearable device determines that the mean of the ambient light data is less than or equal to a first threshold, the temperature data satisfies a preset temperature range, and/or the mean of the infrared light data is less than or equal to a second threshold, the wearable device performs feature extraction on the target data.
  • the processing unit when the wearable device determines that the first target business and/or the second target business is not detected, the processing unit is also used to determine the first wearing detection result as the second wearing detection result; wherein the first target business is a task performed when the wearable device is worn, and the second target task is a task performed when the wearable device is not worn.
  • the first target service includes one or more of the following: heart rate detection service, blood oxygen detection service, respiratory rate detection service, service for monitoring motion status, or service for monitoring sleep status;
  • the second target service includes one or more of the following: charging service, or service for indicating the ejection of a wristband.
  • the wearable device determines that the first target service and/or the second target service are not detected, including: the wearable device determines that the first target service is not detected, the second target service is not detected, and/or detects that the wearable device is not in motion.
  • the processing unit when the wearable device determines that the first target task is detected, the processing unit is further used to determine that the second wearing detection result is that the wearable device is in a worn state; and/or, when the wearable device determines that the second target task is detected, the processing unit is further used to determine that the second wearing detection result is that the wearable device is in a non-worn state.
  • the processing unit is further configured to start the target function.
  • the preset model includes: a first preset model and a second preset model, the first preset model is different from the second preset model, and the processing unit is further used to input the feature values into the first preset module respectively. and the second preset model, obtaining a first detection result corresponding to the first preset model and a second detection result corresponding to the second preset model; the processing unit is also used to obtain a first wearing detection result based on the first detection result and the second detection result.
  • an embodiment of the present application provides a wearable device, comprising a processor and a memory, the memory being used to store code instructions; the processor being used to run the code instructions so that the wearable device executes the wearing detection method described in the first aspect or any one of the implementations of the first aspect.
  • an embodiment of the present application provides a computer-readable storage medium, which stores instructions. When the instructions are executed, the computer executes the wearing detection method described in the first aspect or any implementation of the first aspect.
  • a computer program product includes a computer program.
  • the computer program executes the wearing detection method as described in the first aspect or any one of the implementations of the first aspect.
  • FIG1 is a schematic diagram of a scenario provided in an embodiment of the present application.
  • FIG2 is a schematic diagram of the principle of wearing detection based on a PPG module provided in an embodiment of the present application
  • FIG3 is a schematic diagram of a PPG module structure based on 2LED+8PD provided in an embodiment of the present application
  • FIG4 is a schematic diagram of the structure of a wearable device provided in an embodiment of the present application.
  • FIG5 is a schematic diagram of the architecture of a wearing detection method provided in an embodiment of the present application.
  • FIG6 is a flow chart of a wearing detection method provided in an embodiment of the present application.
  • FIG7 is a flow chart of another wearing detection method provided in an embodiment of the present application.
  • FIG8 is a flow chart of another wearing detection method provided in an embodiment of the present application.
  • FIG9 is a schematic diagram of the structure of a wearing detection device provided in an embodiment of the present application.
  • FIG10 is a schematic diagram of the hardware structure of another wearable device provided in an embodiment of the present application.
  • words such as “first” and “second” are used to distinguish between identical or similar items with substantially the same functions and effects.
  • the first value and the second value are only used to distinguish different values, and their order is not limited.
  • words such as “first” and “second” do not limit the quantity and execution order, and words such as “first” and “second” do not necessarily limit them to be different.
  • At least one means one or more, and “more” means two or more.
  • “And/or” describes the association relationship of associated objects, indicating that there can be three relationships.
  • a and/or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural.
  • the character "/” Generally, it means that the objects before and after are in an “or” relationship.
  • “At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items.
  • At least one of a, b, or c can mean: a, b, c, a and b, a and c, b and c, or a, b and c, where a, b, c can be single or multiple.
  • terminal devices have become a part of people's work and life.
  • many terminal devices can support users to monitor human body data.
  • wearable devices can start measuring the user's heart rate, respiratory rate or blood oxygen and other human characteristics when the wearing test passes.
  • Figure 1 is a schematic diagram of a scenario provided in an embodiment of the present application. It is understandable that in the embodiment of the present application, the wearable device is a smart watch as an example for illustration, and the example does not constitute a limitation on the embodiment of the present application.
  • the user can use the smart watch to measure the user's human body characteristics during exercise.
  • the smart watch can perform a wearing test, and measure the user's heart rate after the wearing test passes, and then the smart watch can display the test results in the interface shown in b in Figure 1.
  • the interface may include: a curve for indicating heart rate changes, and a heart rate value, such as the heart rate value can be 108 beats/minute.
  • the interface can also display: the highest heart rate is 158 beats/minute, the lowest heart rate is 62 beats/minute, and the resting heart rate can be 67 beats/minute.
  • Other content can also be displayed in the interface, which is not limited in the embodiments of the present application.
  • the wearable device when the wearable device receives an operation of the user taking off the wearable device, the wearable device may display the interface shown in c in FIG.
  • the interface shown in c in FIG. 1 may indicate that the heart rate cannot be detected at present, and other contents displayed in the interface may be similar to the interface shown in b in FIG. 1, and will not be described in detail here.
  • a wearable device can perform wearing detection based on a PPG module in the wearable device.
  • FIG2 is a schematic diagram of a principle of wearing detection based on a PPG module provided in an embodiment of the present application.
  • PPG can be understood as a detection method for detecting changes in blood volume in living tissue by means of photoelectric means.
  • the PPG module 204 may include at least one PD, such as PD203, and at least one LED, such as LED202.
  • the wearable device can use the LED 202 in the PPG module 204 to emit a light signal corresponding to a preset current value, and the light signal is irradiated to the skin tissue (or understood as blood or blood vessels in the skin tissue) 201.
  • PD203 is used to receive the light signal reflected back through the skin tissue 201.
  • PD203 converts the light signal into an electrical signal, and then converts the electrical signal into a digital signal (or called a PPG signal) that can be used by the wearable device through analog to digital conversion (A/D).
  • the wearing detection method is described by taking the PPG signal as an infrared signal as an example. For example, when the wearable device detects that the received infrared signal is greater than the infrared signal threshold, the distance between the wearable device and the human skin is relatively close, and the wearable device is in a state of being worn by the user; or, when the wearable device detects that the received infrared signal is less than or equal to the infrared signal threshold, the distance between the wearable device and the human skin is relatively far, and the wearable device is in a state of not being worn by the user.
  • the user's skin depth, hair coverage, and the tightness with which the user wears the wearable device may affect the accuracy of the above-mentioned wearing detection method.
  • an embodiment of the present application provides a wearing detection method, wherein a wearable device collects target data; the target data includes green light data, and the green light data is used to indicate the heart rate detected when the wearable device is worn; the wearable device can obtain a feature value for accurately characterizing the wearing state of the wearable device by extracting features of the target data; further, the wearable device inputs the feature value into a preset model to obtain a more accurate wearing detection result; the wearing detection result is used to indicate whether the wearable device shown is in a wearing state.
  • the wearable device includes a PPG module
  • the PPG module may include at least one PD and at least one LED
  • the LED may be a three-color LED of red light, green light and infrared light.
  • the PPG module described in the embodiment of the present application may include 2 LEDs and 8 PDs.
  • FIG3 is a schematic diagram of a PPG module structure based on 2LED+8PD provided in an embodiment of the present application.
  • a circular PPG module may be provided in a wearable device, and the circular PPG module may include: 2 three-color-in-one LEDs and 8 PDs.
  • the innermost side of the PPG module is two three-color-in-one LEDs, and the two three-color-in-one LEDs can be used to emit light signals, for example, red light, green light, and infrared light, etc.; 8 PDs in an enclosing structure are arranged on the outside of the two three-color-in-one LEDs.
  • the two three-color-in-one LEDs may include: LED1 and LED2.
  • the 8 PDs in an enclosing structure may include: PD1, PD2, PD3, PD4, PD5, PD6, PD7, and PD8.
  • At least one of the two LEDs can emit a light signal, and at least one of the eight PDs can obtain the light signal reflected back through the skin tissue, and then the wearable device can perform wearing detection based on the light signal obtained by at least one of the eight PDs.
  • the wearable device can use one of the 8 PDs to obtain the optical signal, or use a pair of PDs (for example, two PDs) among the 8 PDs to obtain the optical signal, or use all of the 8 PDs to obtain the optical signal, which is not limited in the embodiments of the present application.
  • the wearable device may also perform wearing detection based on an optical signal obtained by at least one of the eight PDs, data detected by other sensors, and/or business conditions performed by the wearable device.
  • the structure of the PPG module described in Figure 3 is only an example.
  • the structure of the PPG module can also be 2LED+4PD or 3LED+6PD, etc., which is not limited in the embodiments of the present application.
  • the wearable device in the embodiment of the present application may include: a smart watch, a smart bracelet, a smart glove, or a smart belt, etc.
  • the specific technology and specific device form used by the wearable device in the embodiment of the present application are not limited.
  • FIG4 is a schematic diagram of the structure of a wearable device provided in the embodiment of the present application.
  • the wearable device may include a processor 110, an internal memory 121, a universal serial bus (USB) interface, a charging management module 140, a power management module 141, an antenna, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a sensor module 180, a button 190, an indicator 192, a camera 193, and a display screen 194.
  • the sensor module 180 may include a gyroscope sensor 180B, a barometer 180C, an acceleration sensor 180E, a proximity light sensor 180G, a temperature sensor 180J, a touch sensor 180K, and an ambient light sensor 180L.
  • the wearable device may include more or fewer components than shown in the figure, or combine certain components. Or some components may be separated, or different component arrangements may be made.
  • the components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.
  • the processor 110 may include one or more processing units. Different processing units may be independent devices or integrated into one or more processors.
  • the processor 110 may also be provided with a memory for storing instructions and data.
  • the charging management module 140 is used to receive charging input from a charger, which may be a wireless charger or a wired charger.
  • the power management module 141 is used to connect the charging management module 140 to the processor 110 .
  • the wireless communication function of the wearable device can be implemented through an antenna, a mobile communication module 150, a wireless communication module 160, a modem processor, and a baseband processor.
  • Antennas are used to transmit and receive electromagnetic wave signals.
  • Antennas in wearable devices can be used to cover single or multiple communication frequency bands. Different antennas can also be reused to improve antenna utilization.
  • the mobile communication module 150 can provide solutions for wireless communications including 2G/3G/4G/5G, etc., applied to wearable devices.
  • the mobile communication module 150 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc.
  • the mobile communication module 150 can receive electromagnetic waves through an antenna, filter, amplify, etc. the received electromagnetic waves, and transmit them to a modulation and demodulation processor for demodulation.
  • the wireless communication module 160 can provide wireless communication solutions for wearable devices, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), etc.
  • WLAN wireless local area networks
  • BT Bluetooth
  • GNSS global navigation satellite system
  • FM frequency modulation
  • the wearable device implements display functions through a GPU, a display screen 194, and an application processor.
  • the GPU is a microprocessor for image processing, connecting the display screen 194 and the application processor.
  • the GPU is used to perform mathematical and geometric calculations for graphics rendering.
  • the display screen 194 is used to display images, videos, etc.
  • the display screen 194 includes a display panel.
  • the wearable device may include 1 or N display screens 194, where N is a positive integer greater than 1.
  • the wearable device can realize the shooting function through an ISP, a camera 193, a video codec, a GPU, a display 194, and an application processor.
  • the camera 193 is used to capture static images or videos.
  • the wearable device may include 1 or N cameras 193, where N is a positive integer greater than 1.
  • the internal memory 121 can be used to store computer executable program codes, and the executable program codes include instructions.
  • the internal memory 121 can include a program storage area and a data storage area.
  • the wearable device can implement audio functions such as music playing and recording through the audio module 170, the speaker 170A, the receiver 170B, and the application processor.
  • the audio module 170 is used to convert digital audio information into analog audio signal output, and is also used to convert analog audio input into digital audio signals.
  • the speaker 170A also known as the "speaker” is used to convert audio electrical signals into sound signals.
  • the wearable device can listen to music or listen to hands-free calls through the speaker 170A.
  • the receiver 170B also known as the "earpiece”, is used to convert audio electrical signals into sound signals. When the wearable device answers a call or voice message, the voice can be answered by placing the receiver 170B close to the human ear.
  • the gyro sensor 180B can be used to determine the motion posture of the wearable device.
  • the gyro sensor 180B and the acceleration sensor 180E can be used together to detect the motion state of the wearable device.
  • the barometer 180C is used to measure air pressure.
  • the wearable device can calculate the altitude through the air pressure value measured by the barometer 180C to assist positioning and navigation.
  • the acceleration sensor 180E can detect the magnitude of the acceleration of the wearable device in various directions (generally three axes). In the embodiment of the present application, the acceleration sensor 180E is used to detect whether the wearable device is in motion.
  • the three axes can be the X-axis, the Y-axis, and the Z-axis.
  • the proximity light sensor 180G may include a light emitting diode (LED) and a light detector, for example, the light detector may be a photodiode (PD).
  • the LED may be a three-color LED, which may emit red light, green light, infrared light and other light sources; the PD may be used to receive light signals and process the light signals into electrical signals.
  • the PD may receive light signals reflected back from skin tissue.
  • the ambient light sensor 180L is used to sense the ambient light brightness.
  • the temperature sensor 180J is used to detect the temperature of the wearable device. In the embodiment of the present application, the temperature sensor is used to detect the temperature of the environment in which the wearable device is located.
  • the touch sensor 180K is also called a “touch control device.”
  • the touch sensor 180K may be disposed on the display screen 194 , and the touch sensor 180K and the display screen 194 form a touch screen, also called a “touch control screen.”
  • the button 190 includes a power button, a volume button, etc.
  • the button 190 may be a mechanical button. It may also be a touch button.
  • the wearable device may receive the button input and generate a key signal input related to the user settings and function control of the wearable device.
  • the indicator 192 may be an indicator light, which may be used to indicate the charging status, power change, message, missed call, notification, etc.
  • FIG5 is a schematic diagram of the architecture of a wearing detection method provided in an embodiment of the present application.
  • the architecture of the wearing detection method may include multiple functional modules, such as: a sensor data acquisition module 501, a primary wearing detection module 502, a feature extraction module 503, a secondary wearing detection module 504, and a wearing state correction module (or also referred to as a tertiary wearing detection module) 505.
  • the wearable device can use various types of sensors to collect data respectively.
  • the wearable device can use an acceleration sensor to detect acceleration data, use a gyroscope sensor to detect gyroscope data (or angular acceleration data), use a temperature sensor to detect temperature data, and use a proximity light sensor to detect infrared light data, green light data, and ambient light data.
  • the ambient light data may be data detected by the PD when the LED in the proximity light sensor is not emitting light; or, the ambient light data may also be collected using a sensor such as an ambient light sensor.
  • the method for obtaining ambient light data is not limited in the embodiments of the present application.
  • the wearable device can use infrared light data and green light data to perform primary wearing detection, and use temperature data to filter out situations where the user is not wearing the wearable device during the primary wearing detection process.
  • the wearable device may perform signal filtering on the collected data to filter out noise; further, the wearable device may perform feature extraction on the filtered data to obtain a feature value.
  • the characteristic value may include one or more of the following: green light AC component F1, green light DC component F2, red light The external light AC component F3, the infrared light DC component F4, the green light time domain autocorrelation coefficient F5, the infrared light time domain autocorrelation coefficient F6, the green light frequency domain maximum value F7, the temperature mean F8, the mean value F9 of the green light adjacent peak ordinate difference, the standard deviation F10 of the green light adjacent peak ordinate difference, the mean value F11 of the green light adjacent peak abscissa difference, the standard deviation F12 of the green light adjacent peak abscissa difference, the mean value F13 of the green light peak ordinate, the number of green light time domain peaks F14, or the combined velocity mean F15, etc.
  • the description of the above-mentioned characteristic values can refer to the embodiment corresponding to FIG7, which will not be repeated here.
  • the 15 characteristic values provided in the embodiment of the present application are only used as an example, and the characteristic values may also include other parameter values, which are not limited in the embodiment of the present application.
  • the wearable device can input the feature values into the decision tree classification model and the logistic regression (LR) classification model respectively to obtain the detection results corresponding to the two classification models respectively, and obtain the secondary wearing detection results based on the detection results corresponding to the two classification models respectively.
  • LR logistic regression
  • the wearable device can obtain a final wearing detection result by detecting the current service state, detecting the service state transition, and the secondary wearing detection result.
  • the embodiments of the present application do not limit the order of the primary wearing detection, the secondary wearing detection, and the wearing state correction.
  • the wearable device may also first perform the wearing state correction process, and then perform the primary wearing detection and the secondary wearing detection, which is not limited in the embodiments of the present application.
  • the wearable device can perform wearing detection based on one or more of the multi-level wearing detection methods shown in Figure 5, so that the wearing detection method can be used in various scenarios and improve the accuracy of the wearing detection method.
  • the method for performing wearing detection by the primary wearing detection module 502 in the wearable device can refer to the embodiment corresponding to FIG. 6 .
  • Fig. 6 is a flowchart of a wearing detection method provided in an embodiment of the present application. As shown in Fig. 6, the wearing detection method may include the following steps:
  • the wearable device obtains infrared light data, ambient light data, and temperature data within a first time period.
  • the first time period may be 1 second.
  • the wearable device may obtain N infrared light data, N ambient light data, and N temperature data within 1 second. Further, the wearable device may obtain the infrared light mean value corresponding to the N infrared light data and the ambient light mean value corresponding to the N ambient light data.
  • the wearable device may execute the step shown in S601 when detecting that the user turns on the target function; or the wearable device may periodically execute the step shown in S601 according to a pre-set instruction.
  • the target function may be: a function for monitoring heart rate, a function for monitoring blood oxygen, a function for detecting respiratory rate, a function for recording sleep status, a function for recording exercise status, etc.
  • the wearable device determines whether the temperature data meets a preset temperature range.
  • the wearable device when the wearable device determines that N temperature data all meet the preset range, the wearable device can execute the step shown in S603; or, when the wearable device determines that at least one temperature data among the N temperature data does not meet the preset range, the wearable device can execute the step shown in S606.
  • the wearable device can detect the body temperature data of the human body, so the wearable device can use the temperature data to exclude the scene when the wearable device is not in contact with the human body.
  • the preset temperature range can be the body temperature range of the human body under normal environment.
  • S603 The wearable device determines whether the ambient light average is greater than the ambient light threshold.
  • the wearable device when the wearable device determines that the average value of the ambient light is greater than the ambient light threshold, the wearable device may execute the step shown in S606; or, when the wearable device determines that the average value of the ambient light is less than or equal to the ambient light threshold, The wearable device may execute the steps shown in S604, wherein the ambient light mean value and the ambient light threshold value may both be current values.
  • the wearable device can exclude the scene when the wearable device is not normally worn on the user's wrist by acquiring the ambient light data.
  • the wearable device can exclude the scene where the wearable device is placed on an object or the strap is loose, etc., where the ambient light is strong because the wearable device is not close to the human body.
  • the wearable device determines whether the infrared light average is greater than the infrared light threshold.
  • the wearable device when the wearable device determines that the infrared light average value is greater than the infrared light threshold value, the wearable device may execute the step shown in S605; or, when the wearable device determines that the infrared light average value is less than or equal to the infrared light threshold value, the wearable device may execute the step shown in S606.
  • the infrared light threshold value may be a value when the infrared signal is reflected back to the wearable device by the human body under normal circumstances, and the infrared light average value and the infrared light threshold value may both be current values.
  • wearable devices can use infrared light data to exclude scenarios where wearable devices are placed on objects.
  • the wearable device determines that the first-level wearing detection result is in a wearing state.
  • the wearable device may continue to perform the secondary wearing detection based on the embodiment corresponding to FIG. 7 .
  • the wearable device determines that the first-level wearing detection result is in a not-worn state.
  • the wearable device when the wearable device determines that it is in a non-worn state based on the primary wearing detection, the wearable device may perform a secondary wearing detection based on the embodiment corresponding to FIG. 7 .
  • the wearable device when the wearable device determines that it is not worn based on the first-level wearing detection, the wearable device can end the wearing detection process and display the interface shown in c in Figure 1. At this time, the wearable device may not detect heart rate or blood oxygen, etc., to reduce the power consumption of the wearable device.
  • the wearable device may also display a prompt message or vibrate or ring a bell when it is determined that the wearable device is not being worn, indicating that the user is not currently wearing the wearable device properly.
  • the wearable device can perform wearing detection based on one or more judgment logics in S602, S603 and S604, which is not limited in the embodiments of the present application.
  • wearable devices can use infrared light data, ambient light data and temperature data to perform initial wearing detection, eliminating various scenarios that do not meet the wearing requirements, such as the wearable device not being in contact with the human body, the wearable device being placed on an object, and the wearable device having a loose strap, thereby improving the accuracy of the wearing recognition method.
  • the method for performing wearing detection by the secondary wearing detection module 504 may refer to the embodiment corresponding to FIG. 7 .
  • Fig. 7 is a flowchart of another wearing detection method provided in an embodiment of the present application. As shown in Fig. 7, the wearing detection method may include the following steps:
  • the wearable device obtains green light data, infrared light data, temperature data, and acceleration data within a second time period.
  • the wearable device can obtain at least one of green light data, infrared light data, temperature data, or acceleration data within the second time period.
  • the wearable device when the target data includes green light data, the wearable device can The green light data executes the steps shown in S702-S706 below to obtain the secondary wearing detection result. Further, when the target data also includes: at least one of infrared light data, temperature data, or acceleration data, the wearable device can execute the steps shown in S702-S706 below on at least one of the infrared light data, temperature data, or acceleration data to obtain the secondary wearing detection result.
  • the second time period may be 5 seconds.
  • the wearable device may obtain M green light data, M infrared light data, M temperature data, and M acceleration data within 5 seconds.
  • the wearable device may also acquire M gyroscope data while acquiring M acceleration data, wherein the acceleration data and/or gyroscope data may be used to detect the motion state of the wearable device.
  • the wearable device processes the green light data, the infrared light data, the temperature data, and the acceleration data.
  • the data processing method may include: filtering processing, Fourier transform processing, etc.
  • the wearable device may perform bandpass filtering processing on the green light data, infrared light data, temperature data, and acceleration data to filter noise; and perform Fourier transform on the filtered green light data and infrared light data to obtain green light data in the frequency domain and infrared light data in the frequency domain.
  • the wearable device can store the green light data in the time domain before Fourier transform and the infrared light data in the time domain before Fourier transform, so that the wearable device can perform feature extraction on the green light data in the time domain and the infrared light data in the time domain.
  • S703 The wearable device performs feature extraction on the processed green light data, infrared light data, temperature data, and acceleration data to obtain feature values (F1-F15).
  • the wearable device can perform feature extraction on at least one of the green light data, infrared light data, temperature data, or acceleration data after data processing in the step shown in S702 to obtain a feature value corresponding to the at least one data.
  • the characteristic value (or first characteristic value) obtained based on the green light data may include one or more of the following: green light AC component F1, green light DC component F2, green light time domain autocorrelation coefficient F5, green light frequency domain maximum F7, mean F9 of the vertical coordinate difference of adjacent green light peaks, standard deviation F10 of the vertical coordinate difference of adjacent green light peaks, mean F11 of the horizontal coordinate difference of adjacent green light peaks, standard deviation F12 of the horizontal coordinate difference of adjacent green light peaks, mean F13 of the vertical coordinate of the green light peaks, or number F14 of green light time domain peaks;
  • the characteristic value (or second characteristic value) obtained based on the infrared light data may include one or more of the following: infrared light AC component F3, infrared light DC component F4, infrared light time domain autocorrelation coefficient F6;
  • the characteristic value (or third characteristic value) obtained based on the temperature data may include: temperature mean F8; the characteristic value (or fourth characteristic value) obtained based on the acceleration data
  • the green light frequency domain maximum value F7 can be: the maximum value of the frequency in the frequency domain coordinate;
  • the mean value F9 of the vertical coordinate difference of adjacent green light peaks can be: the average value of the vertical coordinate difference between two adjacent peaks of green light in the time domain coordinate;
  • the standard deviation F10 of the vertical coordinate difference of adjacent green light peaks can be: the standard deviation of the vertical coordinate difference between two adjacent peaks of green light in the time domain coordinate;
  • the mean value F11 of the horizontal coordinate difference of adjacent green light peaks can be: the average value of the horizontal coordinate difference between two adjacent peaks of green light in the time domain coordinate;
  • the standard deviation F12 of the horizontal coordinate difference of adjacent green light peaks can be: the standard deviation of the horizontal coordinate difference between two adjacent peaks of green light in the time domain coordinate;
  • the mean value F13 of the vertical coordinate of the green light peak can be: the mean value of the vertical coordinate corresponding to each peak of green light in the time domain coordinate system;
  • the number F14 of the green light time domain peaks can be:
  • the wearable device can simulate the user's heart rate characteristics by obtaining characteristic values related to green light; distinguish whether the wearable device is worn by the user or other objects during the wearing detection process by obtaining characteristic values related to infrared light; take into account the impact of temperature on heart rate during the wearing detection process by obtaining characteristic values related to temperature; and take into account the impact of movement on heart rate during the wearing detection process by obtaining characteristic values related to acceleration.
  • the characteristic value may also include characteristic values related to gyroscope data, such as the mean value of gyroscope data, so that the wearable device can accurately identify various motion states of the wearable device through characteristic values related to acceleration and/or characteristic values related to gyroscope data.
  • characteristic values related to gyroscope data such as the mean value of gyroscope data
  • the wearable device inputs the feature value into a decision tree classification model to obtain a first detection result.
  • the decision tree classification module may be obtained through training based on sample feature data; the first detection result may include: a probability value P1 for indicating being in a worn state, and a probability value 1-P1 for indicating being in a not worn state.
  • the wearable device inputs the feature value into the LR classification model to obtain a second detection result.
  • the LR classification module can also be obtained based on training of sample feature data; the second detection result can include: a probability value P2 for indicating being in a wearing state, and a probability value 1-P2 for indicating being in a not-wearing state.
  • the wearable device can also input the feature value into other machine learning modules for multiple detections, and the modules for wearing monitoring may not be limited to the above-mentioned decision tree classification model and LR classification model.
  • the wearable device can input the feature value into at least 3 or 4 different models for wearing detection, which is not limited in the embodiments of the present application.
  • the wearable device obtains a secondary wearing detection result based on the first detection result and the second detection result.
  • the wearable device when the wearable device detects that P1+P2 is greater than 2-P1-P2, the wearable device can determine that the secondary wearing detection result is in the wearing state; or, when the wearable device detects that P1+P2 is less than or equal to 2-P1-P2, the wearable device can determine that the secondary wearing detection result is in the not-worn state.
  • the wearable device determines that it is in the wearing state based on the secondary wearing detection, the wearable device can continue to perform wearing correction based on the embodiment corresponding to FIG. 8 .
  • the wearable device can continue to perform wearing correction based on the embodiment corresponding to FIG8.
  • the wearable device can end the wearing detection process and display the interface shown in FIG1c. At this time, the wearable device may not detect heart rate or blood oxygen, etc., to reduce the power consumption of the wearable device.
  • wearable devices can distinguish whether users are wearing wearable devices or other objects are wearing wearable devices through feature extraction of green light data, infrared light data, temperature data, and acceleration data, as well as accurate recognition through machine learning modules. It can also take into account the impact of temperature on heart rate and the impact of exercise on heart rate during the wearing detection process, thereby significantly improving the accuracy of the wearing detection method.
  • the method for the wearing state correction module 505 to perform wearing detection can refer to the embodiment corresponding to FIG. 8 .
  • Fig. 8 is a flowchart of another wearing detection method provided in an embodiment of the present application. As shown in Fig. 8, the wearing detection method may include the following steps:
  • the wearable device determines whether a non-wearing service is satisfied.
  • the non-wearing service may include one or more of the following, for example: charging Specifically, when the wearable device determines that the not-worn service is satisfied, the wearable device can execute the steps shown in S807; or, when the wearable device determines that the not-worn service is not satisfied, the wearable device can execute the steps shown in S802.
  • the wearable device when the wearable device detects that the not-worn service is satisfied, the wearable device can directly output the final wearing result as being in a not-worn state.
  • S802 The wearable device determines whether the wearing service is satisfied.
  • the wearing service may include one or more of the following, for example: heart rate detection service, blood oxygen detection service, respiratory rate detection service, services corresponding to each exercise mode, or services corresponding to sleep mode, etc., which are not limited in the embodiments of the present application.
  • the wearable device may execute the steps shown in S806; or, when the wearable device determines that the wearing service is not satisfied, the wearable device may execute the steps shown in S803.
  • the wearable device when the wearable device detects that the wearing service is satisfied, the wearable device can directly output the final wearing result as being in the wearing state.
  • S803 The wearable device determines whether the duration during which the combined speed is greater than the combined speed threshold exceeds a duration threshold.
  • the wearable device may execute the step shown in S804. It is understandable that the wearable device may be in motion in this scenario.
  • the wearable device may execute the step shown in S805. It is understandable that in this scenario, the wearable device may be in a non-moving state.
  • the wearable device when the wearable device determines that the wearable device is in motion based on the step shown in S803, the wearable device can further determine whether an interactive service is detected.
  • the interactive service can be understood as a service that interacts with the user. For example, when the wearable device detects any trigger operation of the user on the wearable device, the wearable device can determine that an interactive service is detected and execute the step shown in S806.
  • the wearable device determines the secondary wearing detection result as the final wearing detection result.
  • the wearable device when the wearable device determines that one or more of the following conditions are met, the wearable device can determine the secondary wearing detection result as the final wearing detection result.
  • the wearable device determines that: the non-wearing service is not met (based on the step shown in S801 for detection), the non-wearing service is not met (based on the step shown in S802 for detection), the motion state is not met (based on the step shown in S803 for detection), or the interactive service is not detected (based on the step shown in S804 for detection), the wearable device can determine the secondary wearing detection result as the final wearing detection result.
  • S806 The wearable device determines that the final wearing detection result is in the wearing state.
  • the wearable device when the wearable device determines that the final wearing detection result is in the wearing state, the wearable device can continue to use the wearable device to detect human characteristics such as heart rate, blood oxygen and/or respiratory rate.
  • the wearable device determines that the final wearing detection result is in a not-worn state.
  • the wearable device when the wearable device determines that the final wearing detection result is in a not-worn state, the wearable device can end the wearing detection process and display an interface as shown in c in FIG. 1 .
  • the wearable device can improve the stability of the wearing detection method based on the detection of the service. It can be understood that based on the wearing detection process described in the embodiments corresponding to Figures 6 to 8, the wearable device can achieve the accuracy of wearing detection in various scenarios.
  • Figure 9 is a schematic diagram of the structure of a wear detection device provided in the embodiment of the present application.
  • the wear detection device can be a wearable device in the embodiment of the present application, or a chip or chip system in a wearable device.
  • a wear detection device 90 can be used in a communication device, a circuit, a hardware component or a chip, and the wear detection device includes: a collection unit 901 and a processing unit 902.
  • the collection unit 901 is used to support the wear detection device 90 to perform data collection steps
  • the processing unit 902 is used to support the wear detection device 90 to perform data processing steps.
  • an embodiment of the present application provides a wearing detection device 90, an acquisition unit 901, for acquiring target data; the target data includes green light data, and the green light data is used to indicate the heart rate detected when the wearable device is worn; a processing unit 902 is used to extract features of the target data to obtain feature values related to the wearing state; the processing unit 902 is also used to input the feature values into a preset model to obtain a first wearing detection result; the first wearing detection result is used to indicate whether the wearable device shown is in a wearing state.
  • the wearing detection device 90 may also include a communication unit 903.
  • the communication unit 903 is used to support the wearing detection device 90 to perform the steps of sending and receiving data.
  • the communication unit 903 may be an input or output interface, a pin or a circuit.
  • the wearing detection device 90 may further include: a storage unit 904.
  • the processing unit 902 and the storage unit 904 are connected via a line.
  • the storage unit 904 may include one or more memories, and the memory may be a device used to store programs or data in one or more devices or circuits.
  • the storage unit 904 may exist independently and be connected to the processing unit 902 of the wearing detection device via a communication line.
  • the storage unit 904 may also be integrated with the processing unit 902.
  • the storage unit 904 may store computer-executable instructions of the method in the wearable device so that the processing unit 902 executes the method in the above embodiment.
  • the storage unit 904 may be a register, a cache, or a RAM, etc., and the storage unit 904 may be integrated with the processing unit 902.
  • the storage unit 904 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, and the storage unit 904 may be independent of the processing unit 902.
  • FIG 10 is a schematic diagram of the hardware structure of another wearable device provided in an embodiment of the present application.
  • the wearable device includes a processor 1001, a communication line 1004 and at least one communication interface (the communication interface 1003 is used as an example in Figure 10).
  • Processor 1001 can be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present application.
  • CPU central processing unit
  • ASIC application-specific integrated circuit
  • Communications link 1004 may include circuitry to transmit information between the above-described components.
  • the communication interface 1003 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, wireless local area networks (WLAN), etc.
  • the wearable device may further include a memory 1002 .
  • the memory 1002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.
  • the memory may be independent and connected to the processor via the communication line 1004. The memory may also be integrated with the processor.
  • the memory 1002 is used to store computer-executable instructions for executing the solution of the present application, and the execution is controlled by the processor 1001.
  • the processor 1001 is used to execute the computer-executable instructions stored in the memory 1002, thereby realizing the wearing detection method provided in the embodiment of the present application.
  • the computer-executable instructions in the embodiments of the present application may also be referred to as application code, and the embodiments of the present application do not specifically limit this.
  • the processor 1001 may include one or more CPUs, such as CPU0 and CPU1 in FIG. 10 .
  • the wearable device may include multiple processors, such as processor 1001 and processor 1005 in FIG. 10.
  • processors may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor.
  • the processor here may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).
  • a computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part.
  • the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.
  • 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. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.
  • wired e.g., coaxial cable, optical fiber, digital subscriber line (DSL)
  • wireless e.g., infrared, wireless, microwave, etc.
  • the computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrated.
  • available media may include magnetic media (e.g., floppy disks, hard disks, or tapes), optical media (e.g., digital versatile discs (DVD)), or semiconductor media (e.g., solid-state drives (SSD)), etc.
  • Computer-readable media may include computer storage media and communication media, and may also include any medium that can transfer a computer program from one place to another.
  • the storage medium may be any target medium that can be accessed by a computer.
  • the computer readable medium may include a compact disc read-only memory (CD-ROM), RAM, ROM, EEPROM or other optical disk storage; the computer readable medium may include a magnetic disk storage or other magnetic disk storage device.
  • any connecting line may also be appropriately referred to as a computer.
  • Computer readable medium For example, if the software is transmitted from a website, server or other remote source using coaxial cable, fiber optic cable, twisted pair, DSL or wireless technologies such as infrared, radio and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL or wireless technologies such as infrared, radio and microwave are included in the definition of medium.
  • Disk and disc as used herein include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers.

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Abstract

L'invention concerne un procédé de détection de port et un dispositif à porter sur soi, qui se rapportent au domaine technique des terminaux. Le procédé comprend les étapes suivantes : un dispositif à porter sur soi collecte des données cibles, les données cibles comprenant des données de lumière verte, et les données de lumière verte étant utilisées pour indiquer une condition de fréquence cardiaque détectée lorsque le dispositif à porter sur soi est porté ; le dispositif à porter sur soi effectue une extraction de caractéristique sur les données cibles pour obtenir une valeur de caractéristique associée à un état de port ; et le dispositif à porter sur soi entre la valeur de caractéristique dans un modèle prédéfini pour obtenir un premier résultat de détection de port, le premier résultat de détection de port étant utilisé pour indiquer si le dispositif à porter sur soi se trouve dans un état porté ou non. De cette manière, le dispositif à porter sur soi peut acquérir les données de lumière verte utilisées pour indiquer la condition de fréquence cardiaque détectée lorsque le dispositif à porter sur soi est porté, effectue une extraction de caractéristique sur les données de lumière verte pour obtenir une valeur de caractéristique utilisée pour représenter l'état de port du dispositif à porter sur soi, puis entre la valeur de caractéristique dans le modèle prédéfini, de telle sorte qu'un résultat de détection de port relativement précis puisse être obtenu.
PCT/CN2023/095987 2022-08-08 2023-05-24 Procédé de détection de port et dispositif à porter sur soi WO2024032084A1 (fr)

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CN202210946262.1 2022-08-08
CN202210946262.1A CN117562522A (zh) 2022-08-08 2022-08-08 佩戴检测方法和可穿戴设备

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WO2024032084A1 WO2024032084A1 (fr) 2024-02-15
WO2024032084A9 true WO2024032084A9 (fr) 2024-05-23

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