CN117562522A

CN117562522A - Wearing detection method and wearable device

Info

Publication number: CN117562522A
Application number: CN202210946262.1A
Authority: CN
Inventors: 曹垚; 张�成
Original assignee: Honor Device Co Ltd
Current assignee: Honor Device Co Ltd
Priority date: 2022-08-08
Filing date: 2022-08-08
Publication date: 2024-02-20
Also published as: WO2024032084A9; WO2024032084A1

Abstract

The embodiment of the application provides a wearing detection method and wearable equipment, and relates to the technical field of terminals, wherein the method comprises the following steps: the wearable device collects target data; the target data includes green light data for indicating heart rate conditions detected while wearing the wearable device; the wearable device performs feature extraction on the target data to obtain a feature value related to the wearing state; the wearable device inputs the characteristic value into a preset model to obtain a first wearing detection result; the first wearing detection result is used for indicating whether the wearable device is in a wearing state. Therefore, the wearable device can acquire green light data for indicating the heart rate condition detected when the wearable device is worn, and the characteristic value for representing the wearing state of the wearable device is obtained through characteristic extraction of the green light data, so that the characteristic value is input into a preset model, and a more accurate wearing detection result can be obtained.

Description

Wearing detection method and wearable device

Technical Field

The application relates to the technical field of terminals, in particular to a wearing detection method and wearable equipment.

Background

Currently, with the development of terminal technology, terminal devices have become part of people's work and life. In order to meet the requirement of users on self health management, more terminal devices can support the users to monitor human body data. For example, a user may detect human body data using a wearable device. Specifically, the wearable device may begin measuring the heart rate, respiration rate, or blood oxygen, etc., of the user with the wear detection passing. The wearing detection is used for detecting whether the wearable device is worn by an organism with vital signs.

Typically, the wearable device may perform wear detection based on infrared signals. For example, the wearable device may detect a distance between the wearable device and human skin using infrared signals, determine that the wearable device is in a user wearing condition when the distance is closer, or determine that the wearable device is not in a user wearing condition when the distance is farther.

However, in some scenarios, the accuracy of the wearing detection method using infrared signals is low.

Disclosure of Invention

The embodiment of the application provides a wear detection method and wearable equipment, which enables the wearable equipment to acquire green light data for indicating the heart rate condition detected when the wearable equipment is worn, obtain a characteristic value for representing the wearing state of the wearable equipment through characteristic extraction of the green light data, and further input the characteristic value into a preset model to obtain a more accurate wear detection result.

In a first aspect, an embodiment of the present application provides a wear detection method, including: the wearable device collects target data; the target data includes green light data for indicating heart rate conditions detected while wearing the wearable device; the wearable device performs feature extraction on the target data to obtain a feature value related to the wearing state; the wearable device inputs the characteristic value into a preset model to obtain a first wearing detection result; the first wearing detection result is used for indicating whether the wearable device is in a wearing state. Therefore, the wearable device can acquire green light data for indicating the heart rate condition detected when the wearable device is worn, and the characteristic value for representing the wearing state of the wearable device is obtained through characteristic extraction of the green light data, so that the characteristic value is input into a preset model, and a more accurate wearing detection result can be obtained.

In one possible implementation, the feature values include: based on the first characteristic value obtained by the green light data, the first characteristic value comprises one or more of the following: the method comprises the steps of a green light alternating current component, a green light direct current component, a green light time domain autocorrelation coefficient, a green light frequency domain maximum value, a green light adjacent peak-to-vertical coordinate difference value average value, a green light adjacent peak-to-vertical coordinate difference value standard deviation, a green light adjacent peak-to-horizontal coordinate difference value average value, a green light adjacent peak-to-horizontal coordinate difference value standard deviation, a green light peak-to-vertical coordinate average value or a green light time domain peak value number. Like this for wearable equipment can pass through first eigenvalue, and the heart rate characteristic that detects when simulating the user wears wearable equipment, and then wearable equipment can be according to the extraction of first eigenvalue, realizes wearing the accurate detection of state in the different scenes.

In one possible implementation, the target data further includes one or more of the following: infrared light data, temperature data, or acceleration data.

In one possible implementation, the characteristic values further include one or more of the following: a second characteristic value obtained based on infrared light data, a third characteristic value obtained based on temperature data, or a fourth characteristic value obtained based on acceleration data; wherein the second characteristic value comprises one or more of the following: an infrared light alternating current component, an infrared light direct current component, or an infrared light time domain autocorrelation coefficient; the third characteristic value includes: a temperature average value; the fourth characteristic value includes: and (5) combining the speed average value. Like this, make wearable equipment wear through the second eigenvalue, distinguish and wear the wearable equipment of testing in-process and wear for the user or other objects etc. wear the influence of temperature to the rhythm of heart in the testing in-process through the third eigenvalue, consider the influence of wearing the testing in-process motion to the rhythm of heart through the fourth eigenvalue, and then wearable equipment can be according to the extraction of eigenvalue, realize wearing the accurate detection of state in the different scenes.

In one possible implementation, the wearable device performs feature extraction on the target data, including: and under the condition that the wearable device determines that the average value of the ambient light data is smaller than or equal to a first threshold value, the temperature data meets a preset temperature range and/or the average value of the infrared light data is smaller than or equal to a second threshold value, the wearable device performs feature extraction on the target data. Therefore, the wearable device can also utilize the infrared light data, the ambient light data and the temperature data to carry out wearing detection, so that various wearing unsatisfied scenes such as that the wearable device is not contacted with a human body, the wearable device is placed on an object, the watchband of the wearable device is loose and the like are eliminated, and the accuracy of a wearing identification method is improved.

In one possible implementation, the method further includes: when the wearable device determines that the first target service is not detected and/or the second target service is not detected, the wearable device determines the first wearing detection result as a second wearing detection result; the first target service is a task executed when the wearable device is worn, and the second target service is a task executed when the wearable device is not worn. In this way, the wearable device can improve the stability of the wearing detection method based on the detection of the service.

In one possible implementation, the first target service includes one or more of the following: heart rate detection, blood oxygen detection, respiration rate detection, monitoring of movement states, or monitoring of sleep states; the second target service includes one or more of the following: a charging service, or a service for indicating a pop-up wristband.

In one possible implementation, the wearable device determines that the first target traffic is not detected and/or the second target traffic is not detected, including: the wearable device determines that the first target traffic is not detected, the second target traffic is not detected, and/or the wearable device is detected not to be 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 movement state.

In one possible implementation, 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 an unworn state. In this way, the wearable device can improve the stability of the wearing detection method based on the detection of the service.

In one possible implementation, the method further includes: and under the condition that the wearable equipment is in the wearing state as a result of the second wearing detection, the wearable equipment starts the target function. In this way, the wearable device can continue to execute the target function under the condition that the wearing state is detected, so that the accuracy of the wearable device in detecting the target function is improved.

In one possible implementation, the preset model includes: the wearable device inputs the characteristic value into the preset model to obtain a first wearing detection result, wherein the first preset model is different from the second preset model, and the wearable device comprises: the wearable device inputs the characteristic values into a first preset module and a second preset model respectively to obtain a first detection result corresponding to the first preset model and a second detection result corresponding to the second preset model; the wearable device obtains a first wearing detection result based on the first detection result and the second detection result. In this way, the wearable device can distinguish whether the user wears the wearable device or other objects wear the wearable device through the preset model, and the stability and accuracy of the wearing detection method are improved by utilizing the two preset models.

In a second aspect, an embodiment of the present application provides a wear detection device, an acquisition unit, configured to acquire target data; the target data includes green light data for indicating heart rate conditions detected while wearing the wearable device; the processing unit is used for extracting the characteristics of the target data to obtain characteristic values related to the wearing state; the processing unit is further used for inputting the characteristic value into a preset model to obtain a first wearing detection result; the first wearing detection result is used for indicating whether the wearable device is in a wearing state.

In one possible implementation, the feature values include: based on the first characteristic value obtained by the green light data, the first characteristic value comprises one or more of the following: the method comprises the steps of a green light alternating current component, a green light direct current component, a green light time domain autocorrelation coefficient, a green light frequency domain maximum value, a green light adjacent peak-to-vertical coordinate difference value average value, a green light adjacent peak-to-vertical coordinate difference value standard deviation, a green light adjacent peak-to-horizontal coordinate difference value average value, a green light adjacent peak-to-horizontal coordinate difference value standard deviation, a green light peak-to-vertical coordinate average value or a green light time domain peak value number.

In one possible implementation, the characteristic values further include one or more of the following: a second characteristic value obtained based on infrared light data, a third characteristic value obtained based on temperature data, or a fourth characteristic value obtained based on acceleration data; wherein the second characteristic value comprises one or more of the following: an infrared light alternating current component, an infrared light direct current component, or an infrared light time domain autocorrelation coefficient; the third characteristic value includes: a temperature average value; the fourth characteristic value includes: and (5) combining the speed average value.

In one possible implementation, the wearable device performs feature extraction on the target data, including: and under the condition that the wearable device determines that the average value of the ambient light data is smaller than or equal to a first threshold value, the temperature data meets a preset temperature range and/or the average value of the infrared light data is smaller than or equal to a second threshold value, the wearable device performs feature extraction on the target data.

In a possible implementation manner, when the wearable device determines that the first target service is not detected and/or the second target service is not detected, the processing unit is further configured to determine the first wearing detection result as a second wearing detection result; the first target service is a task executed when the wearable device is worn, and the second target service is a task executed when the wearable device is not worn.

In one possible implementation, the wearable device determines that the first target traffic is not detected and/or the second target traffic is not detected, including: the wearable device determines that the first target traffic is not detected, the second target traffic is not detected, and/or the wearable device is detected not to be in motion.

In one possible implementation manner, when the wearable device determines that the first target task is detected, the processing unit is further configured to determine 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 processing unit is further configured to determine that the wearable device is in an unworn state as a result of the second wearing detection.

In one possible implementation manner, the processing unit is further configured to start the target function when the second wearing detection result indicates that the wearable device is in a wearing state.

In one possible implementation, the preset model includes: the device comprises a first preset model and a second preset model, wherein the first preset model is different from the second preset model, and the processing unit is further used for inputting characteristic values into the first preset module and the second preset model respectively to obtain 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 further used for obtaining a first wearing detection result based on the first detection result and the second detection result.

In a third aspect, embodiments of the present application provide a wearable device comprising a processor and a memory for storing code instructions; the processor is configured to execute code instructions to cause the wearable device to perform the wear detection method as described in the first aspect or any implementation of the first aspect.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing instructions that, when executed, cause a computer to perform a wear detection method as described in the first aspect or any implementation manner of the first aspect.

In a fifth aspect, a computer program product comprising a computer program which, when run, causes a computer to perform the wear detection method as described in the first aspect or any implementation of the first aspect.

It should be understood that, the third aspect to the fifth aspect of the present application correspond to the technical solutions of the first aspect of the present application, and the beneficial effects obtained by each aspect and the corresponding possible embodiments are similar, and are not repeated.

Drawings

Fig. 1 is a schematic view of a scenario provided in an embodiment of the present application;

fig. 2 is a schematic diagram of wearing detection based on a PPG module according to an embodiment of the present application;

fig. 3 is a schematic diagram of a PPG module structure based on 2led+8pd according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of a wearable device according to an embodiment of the present application;

fig. 5 is a schematic architecture diagram of a wear detection method according to an embodiment of the present application;

fig. 6 is a schematic flowchart of a wearing detection method according to an embodiment of the present application;

fig. 7 is a flowchart of another wear detection method according to an embodiment of the present application;

fig. 8 is a flowchart of another wear detection method according to an embodiment of the present application;

fig. 9 is a schematic structural diagram of a wear detection device according to an embodiment of the present application;

fig. 10 is a schematic hardware structure of another wearable device according to an embodiment of the present application.

Detailed Description

In order to clearly describe the technical solutions of the embodiments of the present application, in the embodiments of the present application, the words "first", "second", etc. are used to distinguish the same item or similar items having substantially the same function and effect. For example, the first value and the second value are merely for distinguishing between different values, and are not limited in their order. It will be appreciated by those of skill in the art that the words "first," "second," and the like do not limit the amount and order of execution, and that the words "first," "second," and the like do not necessarily differ.

In this application, the terms "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.

In the present application, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a alone, a and B together, and B alone, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b, or c may represent: a, b, c, a and b, a and c, b and c, or a, b and c, wherein a, b, c may be single or plural.

Currently, with the development of terminal technology, terminal devices have become part of people's work and life. In order to meet the requirement of users on self health management, more terminal devices can support the users to monitor human body data. Specifically, the wearable device may begin measuring the heart rate, respiration rate, or blood oxygen, etc., of the user with the wear detection passing.

Exemplary, fig. 1 is a schematic view of a scenario provided in an embodiment of the present application. It may be understood that, in the embodiments of the present application, the wearable device is taken as an example of a smart watch, and this example does not constitute a limitation of the embodiments of the present application.

As shown in fig. 1, a user may measure a user's body characteristics during exercise using a smart watch. For example, in the case where the user wears the smart watch normally as shown by a in fig. 1, the smart watch may perform wear detection and measure the heart rate of the user after the wear detection passes, and thus the smart watch may display the detection result in an interface as shown by b in fig. 1. An interface as shown in b in fig. 1, which may include: a curve for indicating heart rate variability, and a heart rate value, which may be 108 beats/minute, for example. The interface may also display: the highest heart rate is 158 times/min, the lowest heart rate is 62 times/min, the resting heart rate can be 67 times/min, and other contents can be displayed in the interface, which is not limited in the embodiment of the present application.

Further, in the case where the wearable device displays an interface as shown in b in fig. 1, when the wearable device receives an operation that the user takes off the wearable device, the wearable device may then display an interface as shown in c in fig. 1. The interface shown in c in fig. 1 may indicate that the heart rate cannot be detected currently, and other contents displayed in the interface may be similar to the interface shown in b in fig. 1, which will not be described again.

Typically, the wearable device may perform wear detection based on a PPG module in the own device. Fig. 2 is a schematic diagram of wearing detection based on a PPG module according to an embodiment of the present application. Among these, PPG is understood to be a detection method for detecting a change in blood volume in living tissue by means of an optoelectronic means. As shown in fig. 2, the PPG module 204 may include at least one PD, such as PD203, and at least one LED, such as LED202.

In the embodiment corresponding to fig. 2, the wearable device may emit an optical signal corresponding to a preset current value by using the LED202 in the PPG module 204, the optical signal irradiates the skin tissue (or understood as blood, blood vessel, etc. in the skin tissue) 201, the PD203 receives the optical signal reflected back through the skin tissue 201, the PD203 converts the optical signal into an electrical signal, and the electrical signal is converted into a digital signal (or referred as PPG signal) that can be utilized by the wearable device through analog-to-digital conversion (analogue to digital conversion, a/D).

Further, the wearing detection method is exemplified by taking the PPG signal as an infrared signal. For example, when the wearable device detects that the received infrared signal is greater than the threshold value of the infrared signal, the distance between the wearable device and the skin of the human body is relatively short, and then the wearable device is in a wearing state of the user; or when the wearable device detects that the received infrared signal is smaller than or equal to the infrared signal threshold value, the distance between the wearable device and the human skin is far, and then the wearable device is in the condition that the user does not wear the wearable device.

However, when the wearable device is placed on other objects or when the wearable device is in environments such as darker light or stronger light, the accuracy of the method for carrying out wearing detection based on the infrared signal is low, and flexible detection when the wearable device is in different scenes cannot be realized.

In a possible implementation manner, the accuracy of the wearing detection method may be affected by the skin depth, the hair coverage degree, the tightness degree of wearing the wearable device by the user, and the like.

In view of this, the embodiment of the application provides a wearing detection method, and a wearable device collects target data; the target data includes green light data for indicating heart rate conditions detected while wearing the wearable device; the wearable device can obtain a characteristic value for accurately representing the wearing state of the wearable device through extracting the characteristics of the target data; furthermore, the wearable device inputs the characteristic value into a preset model, so that a more accurate wearing detection result can be obtained; the wearing detection result is used for indicating whether the wearable device is in a wearing state.

In this embodiment of the present application, the wearable device includes a PPG module, where the PPG module may include at least one PD and at least one LED, and 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.

Fig. 3 is a schematic diagram of a PPG module structure based on 2led+8pd according to an embodiment of the present application. As shown in fig. 3, a PPG module with a circular structure may be provided in the wearable device, and the PPG module with a circular structure may include: 2 three-color-in-one LEDs and 8 PDs. Specifically, the innermost side of the PPG module is two three-color integrated LEDs, and the two three-color integrated LEDs can be used for emitting light signals, such as red light, green light, infrared light, and the like; the outside of the two three-in-one LEDs is provided with 8 PDs which are of surrounding structures. As shown in fig. 3, the two three-in-one LED may include: LED1 and LED2. The 8 PDs in the surrounding structure may include: PD1, PD2, PD3, PD4, PD5, PD6, PD7 and PD8.

Specifically, when the PPG module of 2led+8pd is used for wearing detection, at least one LED of the 2 LEDs may emit an optical signal, at least one PD of the 8 PDs may acquire the optical signal reflected back by the percutaneous tissue, and further the wearable device may perform wearing detection based on the optical signal acquired by at least one PD of the 8 PDs.

It may be understood that, in the wearing detection process, the wearable device may acquire an optical signal by using one PD of the 8 PDs, may acquire an optical signal by using a pair of PDs (e.g., two PDs) of the 8 PDs, or may acquire an optical signal by using all PDs of the 8 PDs, which is not limited in the embodiment of the present application.

In the embodiment of the present application, the wearable device may also perform wear detection based on an optical signal acquired by at least one PD of the 8 PDs, data detected by other sensors, and/or a service condition executed by the wearable device.

It is to be understood that the structure of the PPG module described in fig. 3 is only an example, and the structure of the PPG module may be 2led+4pd or 3led+6pd, etc. in a possible manner, which is not limited in the embodiment of the present application.

It may be appreciated that the wearable device in the embodiments of the present application may include: smart watches, smart bracelets, smart gloves, or smart belts. The specific technology and the specific equipment form adopted by the wearable equipment are not limited in the embodiment of the application.

In order to better understand the embodiments of the present application, the structure of the wearable device of the embodiments of the present application is described below. Fig. 4 is a schematic structural diagram of a wearable device according to an embodiment of the present application.

The wearable device may include a processor 110, an internal memory 121, a universal serial bus (universal serial bus, USB) interface, a charge 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, keys 190, an indicator 192, a camera 193, a display screen 194, and the like. Wherein the sensor module 180 may include: a gyro sensor 180B, a barometer 180C, an acceleration sensor 180E, a proximity light sensor 180G, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, and the like.

It will be appreciated that the structures illustrated in the embodiments of the present application do not constitute a specific limitation on the wearable device. In other embodiments of the present application, the wearable device may include more or fewer components than shown, or certain components may be combined, or certain components may be split, or different arrangements of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

Processor 110 may include one or more processing units. Wherein the different processing units may be separate devices or may be integrated in one or more processors. A memory may also be provided in the processor 110 for storing instructions and data.

The charge management module 140 is configured to receive a charge input from a charger. The charger can be a wireless charger or a wired charger. The power management module 141 is used for connecting the charge management module 140 and the processor 110.

The wireless communication function of the wearable device may be implemented by an antenna, the mobile communication module 150, the wireless communication module 160, a modem processor, a baseband processor, and the like.

The antenna is used for transmitting and receiving electromagnetic wave signals. Antennas in the wearable device may be used to cover single or multiple communication bands. Different antennas may also be multiplexed to improve the utilization of the antennas.

The mobile communication module 150 may provide a solution for wireless communication including 2G/3G/4G/5G for use on a wearable device. The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (low noise amplifier, LNA), etc. The mobile communication module 150 may receive electromagnetic waves from an antenna, perform processes such as filtering, amplifying, etc., on the received electromagnetic waves, and transmit the electromagnetic waves to a modem processor for demodulation.

The wireless communication module 160 may provide solutions for wireless communication including wireless local area network (wirelesslocal area networks, WLAN) (e.g., wireless fidelity (wireless fidelity, wi-Fi) network), bluetooth (BT), global navigation satellite system (global navigation satellite system, GNSS), frequency modulation (frequency modulation, FM), etc., for use on a wearable device.

The wearable device implements display functions through the GPU, the display screen 194, and the application processor, etc. The GPU is a microprocessor for image processing, and is connected to the display 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, and the like. The display 194 includes a display panel. In some embodiments, the wearable device may include 1 or N display screens 194, N being a positive integer greater than 1.

The wearable device may implement shooting functions through an ISP, a camera 193, a video codec, a GPU, a display screen 194, an application processor, and the like.

The camera 193 is used to capture still images or video. In some embodiments, the wearable device may include 1 or N cameras 193, N being a positive integer greater than 1.

The internal memory 121 may be used to store computer-executable program code that includes instructions. The internal memory 121 may include a storage program area and a storage data area.

The wearable device may implement audio functions through an audio module 170, a speaker 170A, a receiver 170B, an application processor, and the like. Such as music playing, recording, etc.

The audio module 170 is used to convert digital audio information into an analog audio signal output and also to convert an analog audio input into a digital audio signal. The speaker 170A, also referred to as a "horn," is used to convert audio electrical signals into sound signals. The wearable device may listen to music, or to hands-free conversations, through speaker 170A. A receiver 170B, also referred to as a "earpiece", is used to convert the audio electrical signal into a sound signal. When the wearable device picks up a phone call or voice message, the voice can be picked up by placing the receiver 170B close to the human ear.

The gyro sensor 180B may be used to determine a motion pose of the wearable device. In this embodiment of the present application, the gyro sensor 180B and the acceleration sensor 180E may be used together to detect the motion state of the wearable device.

Barometer 180C is used to measure air pressure. In some embodiments, the wearable device may calculate altitude, assist in positioning and navigation, by barometric pressure values measured by barometer 180C.

The acceleration sensor 180E may detect the magnitude of acceleration of the wearable device in various directions (typically three axes). In the embodiment of the present application, the acceleration sensor 180E is used to detect whether the wearable device is in a motion state. Wherein the three axes can be an X axis, a Y axis and a Z axis.

The proximity light sensor 180G may include a light emitting diode LED and a light detector, which may be a photodiode PD, for example. In the embodiment of the application, the LED may be a three-in-one LED, and the LED may emit light sources such as red light, green light, and infrared light; the PD may be configured to receive an optical signal and process the optical signal into an electrical signal. For example, in a scenario where wear detection is performed with a wearable device, the PD may receive light signals reflected back through skin tissue.

The ambient light sensor 180L is used to sense ambient light level. The temperature sensor 180J is used to detect the temperature of the wearable device. In the embodiment of the application, the temperature sensor is used for detecting the temperature condition of the environment where the wearable equipment is located.

The touch sensor 180K, also referred to as a "touch 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, which is also called a "touch screen".

The keys 190 include a power-on key, a volume key, etc. The keys 190 may be mechanical keys. Or may be a touch key. The wearable device may receive key inputs, generating key signal inputs related to user settings of the wearable device and function control. The indicator 192 may be an indicator light, may be used to indicate a state of charge, a change in charge, a message indicating a missed call, a notification, etc.

In a possible implementation manner, other hardware modules may also be provided in the wearable device, and the hardware structure provided in the embodiment of the present application is only used as an example and is not limited to the embodiment of the present application.

The following describes the technical solutions of the present application and how the technical solutions of the present application solve the above technical problems in detail with specific embodiments. The following embodiments may be implemented independently or combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

Fig. 5 is a schematic diagram of an architecture of a wear detection method according to an embodiment of the present application. As shown in fig. 5, the architecture of the wear detection method may include a plurality of functional modules, for example: a sensor data acquisition module 501, a primary wear detection module 502, a feature extraction module 503, a secondary wear detection module 504, and a wear state correction module (or may also be referred to as a tertiary wear detection module) 505.

In the sensor data acquisition module 501, the wearable device may perform data acquisition with each type of sensor, respectively. For example, the wearable device may detect acceleration data with an acceleration sensor, gyroscope data (or referred to as angular acceleration data) with a gyroscope sensor, temperature data with a temperature sensor, infrared light data, green light data, and ambient light data with a proximity light sensor, and so forth.

In a possible implementation, the ambient light data may be data detected by the PD in proximity to when the LED in the light sensor is not emitting light; alternatively, the ambient light data may be acquired by a sensor such as an ambient light sensor, and the method for acquiring the ambient light data is not limited in the embodiment of the present application.

In the first-level wearing detection module 502, the wearable device can perform first-level wearing detection by using infrared light data and green light data, and filter the situation that the user does not wear the wearable device by using temperature data in the first-level wearing detection process.

In the feature extraction module 503, the wearable device may perform signal filtering processing on the collected data to filter noise; further, the wearable device performs feature extraction on the data subjected to the filtering processing to obtain a feature value.

Wherein the characteristic value may include one or more of the following: a green light alternating current component F1, a green light direct current component F2, an infrared light alternating current component F3, an infrared light direct current component F4, a green light time domain autocorrelation coefficient F5, an infrared light time domain autocorrelation coefficient F6, a green light frequency domain maximum value F7, a temperature average value F8, a green light adjacent peak-to-vertical coordinate difference value average value F9, a green light adjacent peak-to-vertical coordinate difference value standard deviation F10, a green light adjacent peak-to-horizontal coordinate difference value average value F11, a green light adjacent peak-to-horizontal coordinate difference value standard deviation F12, a green light peak-to-vertical coordinate average value F13, a green light time domain peak value number F14, or a combination speed average value F15 and the like. The description of the above feature values may refer to the corresponding embodiment of fig. 7, and will not be repeated herein.

It should be understood that the 15 feature values provided in the embodiments of the present application are only used as an example, and other parameter values may also be included in the feature values, which are not limited in the embodiments of the present application.

In the secondary wearing detection module 504, the wearable device may input the feature values into a decision tree classification model and a logistic regression (logistic regression, LR) classification model, respectively, to obtain detection results corresponding to the two classification models, and obtain a secondary wearing detection result based on the detection results corresponding to the two classification models.

In the wearing state correction module 505, the wearable device may obtain a final wearing detection result through detection of a current service state, detection of a service state transition condition, and a secondary wearing detection result.

It can be appreciated that the embodiments of the present application do not limit the order of the first-level wear detection, the second-level wear detection, and the wear state correction. In a possible implementation manner, the wearable device may also perform the wearing state correction process first, and then perform the first-stage wearing detection and the second-stage wearing detection, which is not limited in this embodiment of the present application.

It can be appreciated that the wearable device can perform wear detection based on one or more of the multiple levels of wear detection shown in fig. 5, so that the wear detection method can be used in multiple scenarios and the accuracy of the wear detection method is improved.

Based on the embodiment corresponding to fig. 5, in a possible implementation manner, a method for performing wear detection by using the primary wear detection module 502 in the wearable device may refer to the embodiment corresponding to fig. 6.

Fig. 6 is a schematic flowchart of a wearing detection method according to an embodiment of the present application. As shown in fig. 6, the wear detection method may include the steps of:

s601, the wearable device acquires infrared light data, ambient light data and temperature data in a first time period.

In this embodiment of the present application, the first period may be 1 second. Specifically, the wearable device may acquire N pieces of infrared light data, N pieces of ambient light data, and N pieces of temperature data within 1 second. Further, the wearable device may obtain an infrared light average value corresponding to the N infrared light data and an ambient light average value corresponding to the N ambient light data.

For example, the wearable device may perform the step shown in S601 when detecting that the user turns on the target function; alternatively, the wearable device may periodically perform the steps shown in S601 according to a preset instruction. Wherein, 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 states, a function for recording movement states, and the like.

S602, the wearable device judges whether the temperature data meets a preset temperature range.

In the embodiment of the present application, when the wearable device determines that the N temperature data all satisfy the preset range, the wearable device may execute the step shown in S603; alternatively, when the wearable device determines that at least one of the N temperature data does not satisfy the preset range, the wearable device may perform the step shown in S606.

It can be appreciated that in the process of wearing the wearable device by the user, the wearable device can detect the body temperature data of the human body, so that 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 a human body in a normal environment.

And S603, the wearable device judges whether the ambient light average value is larger than an ambient light threshold value.

In the embodiment of the present application, when the wearable device determines that the ambient light average value is greater than the ambient light threshold value, the wearable device may execute the step shown in S606; alternatively, when the wearable device determines that the ambient light mean value is less than or equal to the ambient light threshold value, the wearable device may perform the step shown in S604. The ambient light average value and the ambient light threshold value may be current values.

It can be appreciated that the wearable device can exclude scenarios when the wearable device is not normally worn on the wrist of the user by the acquisition of ambient light data. For example, the wearable device may exclude scenes where the ambient light is strong due to the wearable device not being in close proximity to the human body, such as the wearable device being placed on an object, or the wristband being loose.

S604, the wearable device judges whether the average value of the infrared light is larger than an infrared light threshold value.

In the embodiment of the present application, when the wearable device determines that the average value of the infrared light is greater than the infrared light threshold, the wearable device may execute the step shown in S605; alternatively, 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 perform the step shown in S606. The infrared light threshold may be a value when an infrared signal is reflected back to the wearable device through a human body under normal conditions, and the infrared light average value and the infrared light threshold may be current values.

It can be understood that, when the infrared light irradiates the human body, blood or blood vessels in skin tissues and the like can absorb part of the infrared light, so that the infrared light data reflected by the human body has a larger difference from the infrared light data reflected by other objects when the infrared light irradiates the other objects, and therefore the wearable device can exclude a scene of the wearable device placed on the objects by utilizing the infrared light data.

S605, the wearable device determines that the primary wearing detection result is in a wearing state.

For example, in a case where the wearable device is determined to be in a worn state based on the primary wear detection, the wearable device may continue the secondary wear detection based on the corresponding embodiment of fig. 7.

S606, the wearable device determines that the primary wearing detection result is in an unworn state.

In a possible implementation, in a case where the wearable device is determined to be in an unworn state based on the primary wear detection, the wearable device may perform the secondary wear detection based on the embodiment corresponding to fig. 7.

In a possible implementation, in a case where the wearable device determines that it is in the unworn state based on the first-level wear detection, the wearable device may end the wear detection flow, and display an interface as shown by c in fig. 1. At this time, the wearable device may not detect heart rate or blood oxygen, etc., reducing power consumption of the wearable device.

In a possible implementation manner, the wearable device may also display a prompt message or perform a prompt such as vibration or ringing when determining that the wearable device is in an unworn state, so as to indicate that the user does not wear the wearable device currently.

It is understood that the wearable device may perform wear detection based on one or more of the judgment logics in S602, S603, and S604, which is not limited in the embodiment of the present application.

Based on the above, the wearable device can utilize the infrared light data, the ambient light data and the temperature data to carry out primary wearing detection, so that various wearing unsatisfied scenes such as that the wearable device is not contacted with a human body, the wearable device is placed on an object, the watchband of the wearable device is loose and the like are eliminated, and the accuracy of the wearing identification method is improved.

In a possible implementation manner, based on the embodiment corresponding to fig. 5, the method for performing wear detection by the secondary wear detection module 504 may refer to the embodiment corresponding to fig. 7.

Fig. 7 is a schematic flow chart of another wearing detection method according to an embodiment of the present application. As shown in fig. 7, the wear detection method may include the steps of:

s701, the wearable device acquires green light data, infrared light data, temperature data, and acceleration data in a second period of time.

In a possible implementation, the wearable device may acquire at least one of green light data, infrared light data, temperature data, or acceleration data over the second period of time.

In a possible implementation manner, in the case that the target data includes green light data, the wearable device may obtain the secondary wearing detection result based on performing steps as shown in S702-S706 below on the green light data. Further, when the target data further includes: when at least one of the infrared light data, the temperature data, or the acceleration data is used, the wearable device may perform the steps shown in the following S702-S706 on the at least one of the infrared light data, the temperature data, or the acceleration data, so as to obtain a secondary wearing detection result.

The second time period in the embodiment of the present application may be 5 seconds. Specifically, the wearable device may acquire M green light data, M infrared light data, M temperature data, and M acceleration data within 5 seconds.

In a possible implementation manner, the wearable device may acquire M pieces of gyroscope data at the same time as M pieces of acceleration data. Wherein the acceleration data and/or gyroscope data may be used to detect a motion state of the wearable device.

And S702, the wearable device performs data processing on the green light data, the infrared light data, the temperature data and the acceleration data.

In an embodiment of the present application, the method for processing data may include: filtering processing, fourier transform processing, and the like. For example, the wearable device may perform band-pass filtering processing on the green light data, the infrared light data, the temperature data, and the acceleration data, respectively, to filter noise; and performing Fourier transform on the filtered green light data and the infrared light data respectively to obtain green light data in a frequency domain and infrared light data in the frequency domain.

It is understood that the wearable device may store green light data of the time domain before fourier transform and infrared light data of the time domain before fourier transform, so that the wearable device may perform feature extraction on the green light data and the infrared light data of the time domain.

S703, the wearable device performs feature extraction on the green light data, the infrared light data, the temperature data and the acceleration data after the data processing to obtain feature values (F1-F15).

In a possible implementation manner, the wearable device may perform feature extraction on at least one of green light data, infrared light data, temperature data, or acceleration data after the data processing in the step shown in S702, to obtain a feature value corresponding to the at least one data.

In an embodiment of the present application, the feature value (or referred to as the first feature value) obtained based on the green light data may include one or more of the following: a green light alternating current component F1, a green light direct current component F2, a green light time domain autocorrelation coefficient F5, a green light frequency domain maximum value F7, a mean value F9 of green light adjacent peak-to-vertical coordinate differences, a standard deviation F10 of green light adjacent peak-to-vertical coordinate differences, a mean value F11 of green light adjacent peak-to-horizontal coordinate differences, a standard deviation F12 of green light adjacent peak-to-horizontal coordinate differences, a mean value F13 of green light peak-to-vertical coordinates, or a green light time domain peak number F14; the characteristic value (or referred to as a second characteristic value) derived based on the infrared light data may include one or more of the following: an infrared light alternating current component F3, an infrared light direct current component F4 and an infrared light time domain autocorrelation coefficient F6; the feature value (or referred to as a third feature value) obtained based on the temperature data may include: a temperature mean value F8; the feature value (or referred to as a fourth feature value) obtained based on the acceleration data may include: and the average value F15 of the combined speed.

Wherein, the green light frequency domain maximum F7 may be: maximum value of frequency under frequency domain coordinates; the mean value F9 of green adjacent peak-to-vertical coordinate differences may be: the green light is in the time domain coordinate, mean value of the difference value of the vertical coordinate between two adjacent peak values; the standard deviation F10 of green light adjacent peak vertical coordinate differences may be: the green light is in the time domain coordinate, the standard deviation of the difference value of the vertical coordinate between two adjacent peak values; the mean value F11 of the green adjacent peak horizontal coordinate difference value may be: the green light is in the time domain coordinate, the average value of the difference value of the horizontal coordinate between two adjacent peak values; the standard deviation F12 of the green adjacent peak horizontal coordinate difference value may be: the green light is in the time domain coordinate, the standard deviation of the horizontal coordinate difference value between two adjacent peak values; the mean value F13 of the green peak ordinate may be: the green light is in the time domain coordinate system, and the average value of the ordinate corresponding to each peak value; the number of green time domain peaks F14 may be: the number of peaks of green light under a time domain coordinate system; the sum velocity mean F15 may be: and obtaining acceleration data based on the three axes respectively corresponding to the acceleration data.

It may be appreciated that in the feature extraction module 503, the wearable device may simulate the heart rate feature of the user by acquiring the feature value related to the green light; by acquiring the characteristic value related to infrared light, distinguishing whether the wearable device is worn by a user or other objects in the wearing detection process; taking the influence of temperature on heart rate in the wearing detection process into consideration by acquiring a characteristic value related to the temperature; by acquiring the characteristic value related to the acceleration, the influence of the movement on the heart rate during the wear detection is taken into account.

In a possible implementation manner, the characteristic value may also include a characteristic value related to gyroscope data, for example, a gyroscope data average value, so that the wearable device can accurately identify various motion states of the wearable device through the characteristic value related to acceleration and/or the characteristic value related to gyroscope data.

S704, the wearable device inputs the characteristic value into the decision tree classification model to obtain a first detection result.

The decision tree classification module can be obtained through training based on sample characteristic data; the first detection result may include: a probability value P1 for indicating a worn state, and probability values 1-P1 for indicating an unworn state.

And S705, the wearable device inputs the characteristic value into the LR classification model to obtain a second detection result.

The LR classification module may be obtained by training based on sample feature data; the second detection result may include: a probability value P2 for indicating a worn state, and probability values 1-P2 for indicating an unworn state.

It can be appreciated that the wearable device may also input the feature value into other machine learning modules for multiple detections, and the module for wearing monitoring may not be limited to the decision tree classification model and the LR classification model. For example, the wearable device may input the feature values into at least 3 or 4 different models respectively for wear detection, which is not limited in the embodiment of the present application.

S706, the wearable device obtains a secondary wearing detection result based on the first detection result and the second detection result.

When the wearable device detects that P1+P2 is larger than 2-P1-P2, the wearable device can determine that the secondary wearing detection result is in a wearing state; or when the wearable device detects that the P1+P2 is less than or equal to 2-P1-P2, the wearable device can determine that the secondary wearing detection result is in an unworn state.

Further, in a case where the wearable device determines that it is in the wearing state based on the secondary wearing detection, the wearable device may continue to perform the wearing correction based on the embodiment corresponding to fig. 8.

In the event that the wearable device determines that it is in an unworn state based on the secondary wear detection, the wearable device may continue to perform wear correction based on the corresponding embodiment of fig. 8. Alternatively, in the case where the wearable device determines that it is in the unworn state based on the secondary wear detection, the wearable device may end the wear detection flow, and display an interface as shown by c in fig. 1. At this time, the wearable device may not detect heart rate or blood oxygen, etc., reducing power consumption of the wearable device.

Based on the method, the wearable device can distinguish whether a user wears the wearable device or other objects wear the wearable device through feature extraction of green light data, infrared light data, temperature data, acceleration data and the like and accurate identification of the machine learning module, and the influence of temperature on heart rate and the influence of motion on heart rate in the wearing detection process are taken into consideration, so that the accuracy of the wearing detection method is remarkably improved.

In a possible implementation manner, based on the embodiment corresponding to fig. 5, the method for performing wear detection by using the wear state correction module 505 may refer to the embodiment corresponding to fig. 8.

Fig. 8 is a schematic flow chart of still another wearing detection method according to an embodiment of the present application. As shown in fig. 8, the wear detection method may include the steps of:

s801, the wearable device judges whether unworn service is met.

Wherein the unworn service (or referred to as the second target service) may include one or more of the following, for example: the charging service, the pop-up wristband service, or the like, which is not limited in the embodiment of the present application. Specifically, when the wearable device determines that the unworn service is satisfied, the wearable device may perform the step shown in S807; alternatively, when the wearable device determines that the unworn service is not satisfied, the wearable device may perform the step shown in S802.

It can be appreciated that when the wearable device detects that the unworn service is satisfied, the wearable device may directly output the final wearing result as being in the unworn state.

S802, the wearable device judges whether wearing service is met.

Wherein the wearing service (or first target service) may include one or more of the following, for example: heart rate detection service, blood oxygen detection service, respiratory rate detection service, service corresponding to each movement mode, service corresponding to sleep mode, and the like, which are not limited in the embodiment of the present application. Specifically, when the wearable device determines that the wearing service is satisfied, the wearable device may execute the step shown in S806; alternatively, when the wearable device determines that the wearing service is not satisfied, the wearable device may perform the step shown in S803.

It can be appreciated that when the wearable device detects that the wearing service is satisfied, the wearable device may directly output the final wearing result as being in the wearing state.

S803, the wearable device judges whether the duration that the sum speed is larger than the sum speed threshold exceeds a duration threshold.

For example, when the wearable device determines that the duration in which the composite speed is greater than the composite speed threshold exceeds the duration threshold, the wearable device may perform the step shown in S804. It will be appreciated that the wearable device may be in motion in this scenario.

Alternatively, when the wearable device determines that the composite speed is less than or equal to the composite speed threshold, or the wearable device determines that the composite speed is greater than the composite speed threshold for a period of time not exceeding the length of time threshold, the wearable device may perform the step shown in S805. It will be appreciated that the wearable device may be in an unmoved state in this scenario.

S804, when the wearable device detects the interactive service, the wearable device determines that the wearable device is in a wearing state.

It is understood that, in the case where the wearable device determines that the wearable device is in a motion state based on the step shown in S803, the wearable device may further determine whether or not the interactive service is detected. The interactive service may be understood as a service for interacting with a user. For example, when the wearable device detects any trigger operation by the user for the wearable device, the wearable device may determine that the interactive service is detected and perform the step shown in S806.

And S805, the wearable device determines the secondary wearing detection result as a final wearing detection result.

In a possible implementation, when the wearable device determines that one or more of the following is satisfied, the wearable device may determine the secondary wear detection result as the final wear detection result. Illustratively, when the wearable device determines: the wearable device may determine the secondary wearing detection result as the final wearing detection result when the unworn service (detection based on the step shown in S801), the unworn service (detection based on the step shown in S802), the movement state (detection based on the step shown in S803), or the interactive service (detection based on the step shown in S804) is not satisfied.

S806, the wearable device determines that the final wearing detection result is in a wearing state.

For example, when the wearable device determines that the final wear detection result is in the worn state, the wearable device may then continue with human body feature detection of heart rate, blood oxygen, and/or respiratory rate, etc. with the wearable device.

S807, the wearable device determines that the final wearing detection result is in an unworn state.

For example, when the wearable device determines that the final wear detection result is in the unworn state, the wearable device may end the wear detection flow, and display an interface as shown by c in fig. 1.

It will be appreciated that the execution sequence of each step in the embodiment corresponding to fig. 8 in the embodiment of the present application is not specifically limited.

Based on the method, the wearable device can improve the stability of the wearing detection method based on the detection of the service. It can be appreciated that based on the wearing detection flow described in the corresponding embodiments of fig. 6-8, respectively, the wearable device can realize the accuracy of wearing detection in various scenes.

The method provided by the embodiment of the present application is described above with reference to fig. 5 to 8, and the device for performing the method provided by the embodiment of the present application is described below. As shown in fig. 9, fig. 9 is a schematic structural diagram of a wear detection device provided in an embodiment of the present application, where the wear detection device may be a wearable device in an embodiment of the present application, or may be a chip or a chip system in the wearable device.

As shown in fig. 9, the wear detection apparatus 90 may be used in a communication device, a circuit, a hardware component, or a chip, and includes: an acquisition unit 901, and a processing unit 902. Wherein, the acquisition unit 901 is used for supporting the step of wearing detection device 90 to carry out data acquisition, and the processing unit 902 is used for supporting the step of wearing detection device 90 to carry out data processing.

Specifically, the embodiment of the application provides a wearing detection device 90, an acquisition unit 901, configured to acquire target data; the target data includes green light data for indicating heart rate conditions detected while wearing the wearable device; a processing unit 902, configured to perform feature extraction on the target data to obtain a feature value related to the wearing state; the processing unit 902 is further configured to input the feature value into a preset model to obtain a first wearing detection result; the first wearing detection result is used for indicating whether the wearable device is in a wearing state.

In a possible implementation, the wear detection device 90 may also include a communication unit 903. Specifically, the communication unit 903 is configured to support the wear detection device 90 to perform the step of transmitting data and the step of receiving data. The communication unit 903 may be an input or output interface, a pin, or a circuit, among others.

In a possible embodiment, the wear detection device 90 may further include: a storage unit 904. The processing unit 902 and the storage unit 904 are connected by a line. The memory unit 904 may include one or more memories, which may be one or more devices, devices in a circuit for storing programs or data. The storage unit 904 may exist independently and is connected to the processing unit 902 provided in the wear detection device through a communication line. The memory unit 904 may also be integrated with the processing unit 902.

The storage unit 904 may store computer-executable instructions of the methods in the wearable device to cause the processing unit 902 to perform the methods in the embodiments described above. The storage unit 904 may be a register, a cache, a RAM, or the like, and the storage unit 904 may be integrated with the processing unit 902. The memory unit 904 may be a read-only memory (ROM) or other type of static storage device that may store static information and instructions, and the memory unit 904 may be separate from the processing unit 902.

Fig. 10 is a schematic hardware structure of another wearable device provided in the embodiment of the present application, as shown in fig. 10, where the wearable device includes a processor 1001, a communication line 1004, and at least one communication interface (illustrated in fig. 10 by taking the communication interface 1003 as an example).

The processor 1001 may be a general purpose central processing unit (central processing unit, CPU), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the programs of the present application.

Communication line 1004 may include circuitry to communicate information between the components described above.

Communication interface 1003 uses any transceiver-like device for communicating with other devices or communication networks, such as ethernet, wireless local area network (wireless local area networks, WLAN), etc.

Possibly, the wearable device may also comprise a memory 1002.

The memory 1002 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (electrically erasable programmable read-only memory, EEPROM), a compact disc read-only memory (compact disc read-only memory) or other optical disk storage, a compact disc storage (including compact 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 desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be implemented on its own and coupled to the processor via communication line 1004. The memory may also be integrated with the processor.

The memory 1002 is used for storing computer-executable instructions for executing the embodiments of the present application, and is controlled by the processor 1001 for execution. The processor 1001 is configured to execute computer-executable instructions stored in the memory 1002, thereby implementing the wear detection method provided in the embodiment of the present application.

Possibly, the computer-executed instructions in the embodiments of the present application may also be referred to as application program code, which is not specifically limited in the embodiments of the present application.

In a particular implementation, the processor 1001 may include one or more CPUs, such as CPU0 and CPU1 in fig. 10, as one embodiment.

In a particular implementation, as one embodiment, the wearable device may include multiple processors, such as processor 1001 and processor 1005 in fig. 10. Each of these processors may be a single-core (single-CPU) processor or may be a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).

The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. 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, for example, the 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, fiber optic, digital subscriber line (digital subscriber line, DSL), or wireless (e.g., infrared, wireless, microwave, etc.), or semiconductor medium (e.g., solid state disk, SSD)) or the like.

Embodiments of the present application also provide a computer-readable storage medium. The methods described in the above embodiments may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. Computer readable media can include computer storage media and communication media and can include any medium that can transfer a computer program from one place to another. The storage media may be any target media that is accessible by a computer.

As one possible design, the computer-readable medium may include compact disk read-only memory (CD-ROM), RAM, ROM, EEPROM, or other optical disk memory; the computer readable medium may include disk storage or other disk storage devices. Moreover, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a 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, includes Compact Disc (CD), laser disc, optical disc, digital versatile disc (digital versatile disc, DVD), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers.

Combinations of the above should also be included within the scope of computer-readable media. The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.

Claims

1. A wear detection method, the method comprising:

the wearable device collects target data; the target data includes green light data for indicating heart rate conditions detected while wearing the wearable device;

the wearable device performs feature extraction on the target data to obtain a feature value related to the wearing state;

the wearable equipment inputs the characteristic value into a preset model to obtain a first wearing detection result; the first wearing detection result is used for indicating whether the wearable device is in a wearing state.

2. The method of claim 1, wherein the characteristic value comprises: a first characteristic value obtained based on the green light data, the first characteristic value including one or more of: the method comprises the steps of a green light alternating current component, a green light direct current component, a green light time domain autocorrelation coefficient, a green light frequency domain maximum value, a green light adjacent peak-to-vertical coordinate difference value average value, a green light adjacent peak-to-vertical coordinate difference value standard deviation, a green light adjacent peak-to-horizontal coordinate difference value average value, a green light adjacent peak-to-horizontal coordinate difference value standard deviation, a green light peak-to-vertical coordinate average value or a green light time domain peak value number.

3. The method of claim 1 or 2, wherein the target data further comprises one or more of: infrared light data, temperature data, or acceleration data.

4. A method according to claim 3, wherein the characteristic values further comprise one or more of the following: a second characteristic value obtained based on the infrared light data, a third characteristic value obtained based on the temperature data, or a fourth characteristic value obtained based on the acceleration data;

wherein the second characteristic value comprises one or more of the following: an infrared light alternating current component, an infrared light direct current component, or an infrared light time domain autocorrelation coefficient; the third characteristic value includes: a temperature average value; the fourth characteristic value includes: and (5) combining the speed average value.

5. The method of claim 3 or 4, wherein the wearable device performs feature extraction on the target data, comprising:

and the wearable device performs feature extraction on the target data under the condition that the wearable device determines that the average value of the ambient light data is smaller than or equal to a first threshold value, the temperature data meets a preset temperature range and/or the average value of the infrared light data is smaller than or equal to a second threshold value.

6. The method according to any one of claims 1-5, further comprising:

when the wearable device determines that the first target service is not detected and/or the second target service is not detected, the wearable device determines the first wearing detection result as a second wearing detection result;

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.

7. The method of claim 6, wherein the first target traffic comprises one or more of: heart rate detection, blood oxygen detection, respiration rate detection, monitoring of movement states, or monitoring of sleep states; the second target service includes one or more of: a charging service, or a service for indicating a pop-up wristband.

8. The method according to claim 6 or 7, wherein the wearable device determining that the first target traffic is not detected and/or that the second target traffic is not detected comprises: the wearable device determines that the first target traffic is not detected, the second target traffic is not detected, and/or the wearable device is not in motion.

9. The method according to any one of claims 6-8, further comprising:

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 the 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 an unworn state.

10. The method according to claim 9, wherein the method further comprises:

and under the condition that the wearable equipment is in the wearing state as a result of the second wearing detection, the wearable equipment starts a target function.

11. The method according to any one of claims 1-10, wherein the predetermined model comprises: the wearable device inputs the characteristic value into a preset model to obtain a first wearing detection result, wherein the first wearing detection result comprises:

the wearable equipment respectively inputs the characteristic values into the first preset module and the second preset model to obtain a first detection result corresponding to the first preset model and a second detection result corresponding to the second preset model;

The wearable device obtains the first wearing detection result based on the first detection result and the second detection result.

12. A wearable device, comprising: a processor coupled with a memory for storing a computer program that, when invoked by the processor, causes the wearable device to perform the method of any of claims 1 to 11.

13. A computer readable storage medium storing a computer program comprising instructions for implementing the method of any one of claims 1 to 11.

14. A computer program product comprising computer program code embodied therein, which when run on a computer causes the computer to implement the method of any of claims 1 to 11.