WO2024032084A1

WO2024032084A1 - Wearing detection method and wearable device

Info

Publication number: WO2024032084A1
Application number: PCT/CN2023/095987
Authority: WO
Inventors: 曹垚; 张�成
Original assignee: 荣耀终端有限公司
Priority date: 2022-08-08
Filing date: 2023-05-24
Publication date: 2024-02-15
Also published as: CN117562522A; WO2024032084A9

Abstract

Provided are a wearing detection method and a wearable device, which relate to the technical field of terminals. The method comprises: a wearable device collecting target data, the target data comprising green light data, and the green light data being used for indicating a heart rate condition detected when the wearable device is worn; the wearable device performing feature extraction on the target data to obtain a feature value related to a wearing state; and the wearable device inputting the feature value into a preset model to obtain a first wearing detection result, the first wearing detection result being used for indicating whether the wearable device is in a worn state or not. In this way, the wearable device can acquire the green light data used for indicating the heart rate condition detected when the wearable device is worn, performs feature extraction on the green light data to obtain a feature value used for representing the wearing state of the wearable device, and then inputs the feature value into the preset model, such that a relatively accurate wearing detection result can be obtained.

Description

Wear detection methods and wearable devices

This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on August 8, 2022, with application number 202210946262.1 and the application name "Wearable Detection Method and Wearable Device", the entire content of which is incorporated herein by reference. Applying.

Technical field

The present application relates to the field of terminal technology, and in particular, to a wear detection method and a wearable device.

Background technique

At present, with the development of terminal technology, terminal equipment has become a part of people's work and life. In order to meet users' needs for their own health management, many terminal devices can support users to monitor human body data. For example, users can use wearable devices to detect human body data. Specifically, the wearable device can start to measure the user's heart rate, breathing rate or blood oxygen and other human body characteristics after passing the wearing test. Among them, wearing detection is used to detect whether the wearable device is worn by an organism with vital characteristics.

Typically, wearable devices can perform wear detection based on infrared signals. For example, a wearable device can use infrared signals to detect the distance between the wearable device and human skin, and determine that the wearable device is in a user-worn condition when the distance is close, or determine that the wearable device is not in a user-worn condition when the distance is far. .

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

Contents of the invention

Embodiments of the present application provide a wear detection method and a wearable device, so that the wearable device can obtain green light data used to indicate the heart rate detected when wearing the wearable device, and obtain the user information through feature extraction of the green light data. Characteristic values used to characterize the wearing status of the wearable device, and then inputting the characteristic values into the preset model can obtain more accurate wearing detection results.

In a first aspect, embodiments of the present application provide a wear detection method. The method includes: 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 performs feature extraction on the target data to obtain feature values related to the wearing status; the wearable device inputs the feature values into the preset model to obtain the first wearing detection result; the first wearing detection result is used to indicate the wearable Whether the device is being worn. In this way, the wearable device can obtain the green light data used to indicate the heart rate detected when wearing the wearable device, and through feature extraction of the green light data, obtain the feature value used to characterize the wearing status of the wearable device, and then Inputting the feature values into the preset model can obtain more accurate wearing detection results.

In a possible implementation, the characteristic value includes: a first characteristic value obtained based on green light data, and 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 value, the mean value of the ordinate difference between adjacent green light peaks, the standard deviation of the ordinate difference between adjacent green light peaks, the mean value of the abscissa difference between adjacent green light peaks, The standard deviation of the abscissa difference between adjacent green light peaks, the mean of the ordinate of the green light peak, or the number of green light time domain peaks. In this way, the wearable device can use the first characteristic value to simulate the heart rate characteristics detected when the user wears the wearable device, and then the wearable device can extract the first characteristic value to implement various functions in different scenarios. Accurate detection of wearing status.

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

In a possible implementation, the characteristic value also includes 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. Characteristic values; wherein, the second characteristic value includes one or more of the following: infrared light AC component, infrared light DC component, or infrared light time domain autocorrelation coefficient; the third characteristic value includes: temperature average; the fourth characteristic value Includes: average combined velocity. In this way, the wearable device can use the second characteristic value to distinguish whether the wearable device is worn by the user or other objects during the wearing detection process, and the third characteristic value can take into account the impact of temperature on the heart rate during the wearing detection process. The fourth eigenvalue takes into account the impact of movement on heart rate during the wearing detection process, and the wearable device can achieve accurate detection of the wearing status in different scenarios based on the extraction of eigenvalues.

In a possible implementation, the wearable device performs feature extraction on 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 infrared When the mean value of the light data is less than or equal to the second threshold, the wearable device performs feature extraction on the target data. In this way, the wearable device can also use infrared light data, ambient light data and temperature data for wear detection to eliminate various inaccuracies such as the wearable device not coming into contact with the human body, the wearable device being placed on an object, and the wearable device strap being loose. Meet the wearing scenarios and improve the accuracy of the wearing recognition method.

In a 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 the second Wearing detection results; where 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. In this way, wearable devices can perform business-based detection and improve the stability of the wear detection method.

In a possible implementation, the first target service includes one or more of the following: a heart rate detection service, a blood oxygen detection service, a respiratory rate detection service, a service for monitoring exercise status, or a service for monitoring sleep status. The second target service includes one or more of the following: charging service, or service for indicating ejection of the wristband.

In a possible implementation, the wearable device determines that the first target service is not detected and/or the second target service is not detected, including: the wearable device determines that the first target service is not detected, and the second target service is not detected. Target business, and/or detect that the wearable device is not in motion. In this way, wearable devices can detect business-based and motion status, increasing the stability of the wear detection method.

In a 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 first target task is detected, When the 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, wearable devices can perform business-based detection and improve the stability of the wear detection method.

In a possible implementation, the method further includes: when the second wearing detection result is that the wearable device is in a wearing state, the wearable device activates the target function. In this way, the wearable device can continue to perform the target function after detecting that the wearing status is satisfied, thereby improving the accuracy of the wearable device in detecting the target function.

In a possible implementation, 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. In the model, obtaining the first wearing detection result includes: the wearable device inputs the characteristic values into the first preset module and the second preset model respectively, and obtains the first detection result and the second preset model corresponding to the first preset model. The second detection result corresponding to the preset model; wearable device Based on the first detection result and the second detection result, a first wearing detection result is obtained. In this way, the wearable device can use the preset model to distinguish whether the user is wearing the wearable device or other objects are wearing the wearable device, and use the two preset models to improve the stability and accuracy of the wear detection method.

In the second aspect, embodiments of the present application provide a wear detection device and 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 wearing the wearable device; a processing unit , used to extract features from the target data and obtain feature values related to the wearing status; the processing unit is also used to input the feature values into the preset model to obtain the first wearing detection result; the first wearing detection result is used to indicate Whether the wearable device shown is being worn.

In a possible implementation, the characteristic value includes: a first characteristic value obtained based on green light data, and 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 value, the mean value of the ordinate difference between adjacent green light peaks, the standard deviation of the ordinate difference between adjacent green light peaks, the mean value of the abscissa difference between adjacent green light peaks, The standard deviation of the abscissa difference between adjacent green light peaks, the mean of the ordinate of the green light peak, or the number of green light time domain peaks.

In a possible implementation, the characteristic value also includes 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. Characteristic values; wherein, the second characteristic value includes one or more of the following: infrared light AC component, infrared light DC component, or infrared light time domain autocorrelation coefficient; the third characteristic value includes: temperature average; the fourth characteristic value Includes: average combined velocity.

In a possible implementation, the wearable device performs feature extraction on 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 infrared When the mean value of the light data is less than or equal to the second threshold, the wearable device performs feature extraction on the target data.

In a possible implementation, when the wearable device determines that the first target service and/or the second target service is not detected, the processing unit is also configured to determine the first wear detection result as the second wear detection result. Detection results; among them, the first target business is a task performed when wearing a wearable device, and the second target task is a task performed when a wearable device is not worn.

In a possible implementation, the wearable device determines that the first target service is not detected and/or the second target service is not detected, including: the wearable device determines that the first target service is not detected, and the second target service is not detected. Target business, and/or detect that the wearable device is not in motion.

In a possible implementation, when the wearable device determines to detect the first target task, the processing unit is also 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 When it is determined that the second target task is detected, the processing unit is also configured to determine that the second wearing detection result is that the wearable device is in an unworn state.

In a possible implementation, when the second wearing detection result is that the wearable device is in a wearing state, the processing unit is also configured to activate the target function.

In a possible implementation, 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 processing unit is also configured to separate the feature values into Input into the first preset module to and the second preset model, obtain the first detection result corresponding to the first preset model and the second detection result corresponding to the second preset model; the processing unit is also configured to, based on the first detection result and the second detection result, Get the first wearing test result.

In a third aspect, embodiments of the present application provide a wearable device, including a processor and a memory. The memory is used to store code instructions; the processor is used to run the code instructions, so that the wearable device executes the first aspect or the first aspect. The wear detection method described in any implementation.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are executed, the computer executes as in the first aspect or any implementation of the first aspect. Described wear detection method.

In a fifth aspect, a computer program product includes a computer program. When the computer program is run, the computer performs the wearing detection method as described in the first aspect or any implementation of the first aspect.

It should be understood that the third to fifth aspects of the present application correspond to the technical solution of the first aspect of the present application, and the beneficial effects achieved by each aspect and corresponding feasible implementations are similar, and will not be described again.

Description of drawings

Figure 1 is a schematic diagram of a scenario provided by an embodiment of the present application;

Figure 2 is a schematic diagram of the principle of wearing detection based on the PPG module provided by the embodiment of the present application;

Figure 3 is a schematic structural diagram of a PPG module based on 2LED+8PD provided by the embodiment of the present application;

Figure 4 is a schematic structural diagram of a wearable device provided by an embodiment of the present application;

Figure 5 is a schematic structural diagram of a wearing detection method provided by an embodiment of the present application;

Figure 6 is a schematic flow chart of a wearing detection method provided by an embodiment of the present application;

Figure 7 is a schematic flow chart of another wearing detection method provided by an embodiment of the present application;

Figure 8 is a schematic flow chart of yet another wearing detection method provided by an embodiment of the present application;

Figure 9 is a schematic structural diagram of a wearing detection device provided by an embodiment of the present application;

Figure 10 is a schematic diagram of the hardware structure of another wearable device provided by an embodiment of the present application.

Detailed ways

In order to facilitate a clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as “first” and “second” are used to distinguish the same or similar items with basically the same functions and effects. For example, the first value and the second value are only used to distinguish different values, and their order is not limited. Those skilled in the art can understand that words such as "first" and "second" do not limit the number and execution order, and words such as "first" and "second" do not limit the number and execution order.

It should be noted that in this application, words such as “exemplary” or “for example” are used to represent examples, illustrations or explanations. Any embodiment or design described herein as "exemplary" or "such as" is not intended to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the words "exemplary" or "such as" is intended to present the concept in a concrete manner.

In this application, "at least one" refers to one or more, and "plurality" refers to two or more. "And/or" describes the association of associated objects, indicating that there can be three relationships, for example, A and/or B, which can mean: A exists alone, A and B exist simultaneously, and B exists alone, where A, B can be singular or plural. character"/" Generally, it means that the related objects are in an "or" relationship. "At least one of the following" or similar expressions thereof refers to any combination of these items, including any combination of a single item (items) or a plurality of items (items). For example, at least one of a, b, or c can represent: 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.

At present, with the development of terminal technology, terminal equipment has become a part of people's work and life. In order to meet users' needs for their own health management, many terminal devices can support users to monitor human body data. Specifically, the wearable device can start to measure the user's heart rate, breathing rate or blood oxygen and other human body characteristics after passing the wearing test.

Illustratively, Figure 1 is a schematic diagram of a scenario provided by an embodiment of the present application. It can be understood that in the embodiment of the present application, the wearable device is a smart watch as an example for illustration, and this example does not constitute a limitation to the embodiment of the present application.

As shown in Figure 1, users can use smart watches to measure the user's human body characteristics during exercise. For example, when the user wears the smart watch normally as shown in a in Figure 1, the smart watch can perform wearing detection and measure the user's heart rate after passing the wearing detection, and then the smart watch can display the detection results as shown in In the interface shown in b in Figure 1. As shown in b in Figure 1, the interface may include: a curve used to indicate changes in heart rate, and a heart rate value. For example, the heart rate value may be 108 beats/minute. This interface can also display: the highest heart rate is 158 beats/min, the lowest heart rate is 62 beats/min, and the resting heart rate can be 67 beats/min. Other content can also be displayed in this interface, which is not the case in the embodiment of the present application. Make limitations.

Further, in the case where the wearable device displays the interface as shown in b in Figure 1, when the wearable device receives the user's operation of taking off the wearable device, the wearable device can display the interface as shown in c in Figure 1. interface shown. The interface shown in c in Figure 1 may indicate that the heart rate cannot be detected currently, and other content displayed in this interface may be similar to the interface shown in b in Figure 1, which will not be described again here.

Normally, wearable devices can perform wear detection based on the PPG module in their own devices. For example, FIG. 2 is a schematic diagram of the principle of wearing detection based on the PPG module provided by the embodiment of the present application. Among them, PPG can be understood as a detection method that uses photoelectric means to detect changes in blood volume in living tissues. 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 Figure 2, 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). etc.) 201, using PD203 to receive the light signal reflected back through the skin tissue 201, PD203 converts the light signal into an electrical signal, and through analogue to digital conversion (A/D), converts the electrical signal into Digital signals (also known as PPG signals) that wearable devices can utilize.

Furthermore, the wearing detection method is illustrated 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 close, and the wearable device is in the user-worn state; or, when the wearable device detects When the received infrared signal is less than or equal to the infrared signal threshold, the distance between the wearable device and human skin is far, and the wearable device is not worn by the user.

However, when the wearable device is placed on other objects, or when the wearable device is in an environment with dark or strong light, the accuracy of the above-mentioned wearing detection method based on infrared signals is low, and the wearable device cannot be implemented. Flexible detection in different scenarios.

In possible implementations, the depth of the user's skin, the degree of hair coverage, and the tightness of the wearable device worn by the user may affect the accuracy of the above wearing detection method.

In view of this, embodiments of the present application provide a wear detection method. 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 wearing the wearable device; the wearable device can Through feature extraction of the target data, feature values used to accurately characterize the wearing status of the wearable device are obtained; further, the wearable device inputs the feature values into the preset model to obtain more accurate wearing detection results; Wearing detection The result indicates whether the wearable device shown is worn.

In this embodiment of the present application, 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 this application may include 2 LEDs and 8 PDs.

For example, FIG. 3 is a schematic structural diagram of a PPG module based on 2LED+8PD provided by the embodiment of the present application. As shown in Figure 3, a wearable device can be provided with a circular structure PPG module. The circular structure PPG module can include: 2 three-color LEDs and 8 PDs. Specifically, the innermost part of the PPG module is two three-color LEDs. Both of the two three-color LEDs can be used to emit light signals, such as red light, green light, infrared light, etc.; the two three-color LEDs can be used to emit light signals. There are 8 PDs in a surrounding structure on the outside of the three-color LED. As shown in Figure 3, the two three-color LEDs may include: LED1 and LED2. The eight PDs in a surrounding structure may include: PD1, PD2, PD3, PD4, PD5, PD6, PD7 and PD8.

Specifically, when using a 2LED+8PD PPG module for wear detection, at least one of the 2 LEDs can emit a light signal, and at least one of the 8 PDs can obtain the light reflected back through the skin tissue. signal, and then the wearable device can perform wear detection based on the light signal obtained by at least one PD among the eight PDs.

It can be understood that during the wearing detection process, the wearable device can use one PD among the 8 PDs to obtain the optical signal, or can use a pair of PDs (for example, two PDs) among the 8 PDs to obtain the optical signal. , or all PDs among the eight PDs may also be used to acquire optical signals, which is not limited in the embodiments of the present application.

In the embodiment of the present application, the wearable device can also perform wear detection based on the light signal obtained by at least one PD among the eight PDs, data detected by other sensors, and/or business conditions executed by the wearable device.

It can be understood that the structure of the PPG module described in Figure 3 is only an example. In possible ways, the structure of the PPG module can also be 2LED+4PD or 3LED+6PD, etc., which is not done in the embodiment of this application. limited.

It can be understood that the wearable devices in the embodiments of the present application may include: smart watches, smart bracelets, smart gloves, or smart belts and other devices. In the embodiments of this application, there are no limitations on the specific technology and specific device form used by the wearable device.

In order to better understand the embodiments of the present application, the structure of the wearable device according to the embodiments of the present application is introduced below. Exemplarily, FIG. 4 is a schematic structural diagram of a wearable device provided by an 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, and an audio module. 170, speaker 170A, receiver 170B, sensor module 180, button 190, indicator 192, camera 193, and display screen 194, etc. Among them, 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, an ambient light sensor 180L, etc.

It can be understood that the structure illustrated in the embodiment of the present application does 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 in the figures, or some components may be combined, Or splitting some parts, or different parts arrangements. The components illustrated may be implemented in hardware, software, or a combination of software and hardware.

Processor 110 may include one or more processing units. Among them, different processing units can be independent devices or integrated in 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 the charger. Among them, the charger can be a wireless charger or a wired charger. The power management module 141 is used to connect the charging management module 140 and the processor 110 .

The wireless communication function of the wearable device can be implemented through the antenna, mobile communication module 150, wireless communication module 160, modem processor and baseband processor, etc.

Antennas are used to transmit and receive electromagnetic wave signals. Antennas in wearable devices can be used to cover single or multiple communication bands. Different antennas can also be reused to improve antenna utilization.

The mobile communication module 150 can provide wireless communication solutions including 2G/3G/4G/5G applied to wearable devices. The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves through an antenna, perform filtering, amplification, and other processing on the received electromagnetic waves, and transmit them to a modem processor for demodulation.

The wireless communication module 160 can provide applications on wearable devices including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (bluetooth, BT), and global navigation satellite systems. (global navigation satellite system, GNSS), frequency modulation (frequency modulation, FM) and other wireless communication solutions.

Wearable devices implement display functions through GPU, display 194, and application processor. The GPU is an image processing microprocessor and is connected to the display screen 194 and the application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering.

The display screen 194 is used to display images, videos, etc. Display 194 includes a display panel. In some embodiments, the wearable device may include 1 or N display screens 194, where N is a positive integer greater than 1.

Wearable devices can implement shooting functions through ISP, camera 193, video codec, GPU, display 194 and application processor.

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

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

The wearable device can implement audio functions through the audio module 170, the speaker 170A, the receiver 170B, and the application processor. Such as music playback, recording, etc.

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. Speaker 170A, also called "speaker", is used to convert audio electrical signals into sound signals. The wearable device can listen to music through speaker 170A, or listen to hands-free calls. Receiver 170B, also called "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 heard by bringing the receiver 170B close to the human ear.

The gyro sensor 180B may be used to determine the motion posture of the wearable device. In this embodiment of the present application, the gyro sensor 180B and the acceleration sensor 180E can 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 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 acceleration of the wearable device in various directions (generally three axes). In this embodiment of the present application, the acceleration sensor 180E is used to detect whether the wearable device is in motion. Among them, the three axes can be X-axis, Y-axis and 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. In the embodiment of the present application, the LED can be a three-color LED that can emit red light, green light, infrared light and other light sources; the PD can be used to receive optical signals and process the optical signals into electrical signals. . For example, in a scenario where a wearable device is used for wear detection, the PD can receive the light signal reflected back by the skin tissue.

The ambient light sensor 180L is used to sense 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 where the wearable device is located.

Touch sensor 180K, also known as "touch device". The touch sensor 180K can be disposed on the display screen 194. The touch sensor 180K and the display screen 194 form a touch screen, which is also called a "touch screen".

The buttons 190 include a power button, a volume button, etc. Key 190 may be a mechanical key. It can also be a touch button. The wearable device can receive key input and generate key signal input related to user settings and function control of the wearable device. The indicator 192 may be an indicator light, which may be used to indicate charging status, power changes, or may be used to indicate messages, missed calls, notifications, etc.

In possible implementations, other hardware modules may also be provided in the wearable device. The hardware structure provided in the embodiments of this application is only an example and does not constitute a limitation on the embodiments of this application.

The technical solution of the present application and how the technical solution of the present application solves the above technical problems will be described in detail below with specific embodiments. The following specific embodiments can be implemented independently or combined with each other. The same or similar concepts or processes may not be described again in some embodiments.

Exemplarily, FIG. 5 is an architectural schematic diagram of a wearing detection method provided by an embodiment of the present application. As shown in Figure 5, the architecture of the wearing detection method may include multiple functional modules, such as: sensor data collection module 501, primary wearing detection module 502, feature extraction module 503, secondary wearing detection module 504, and wearing status Calibration module (or can also be called a three-level wearing detection module) 505.

In the sensor data collection module 501, the wearable device can use various types of sensors to collect data respectively. For example, a wearable device can use an acceleration sensor to detect acceleration data, a gyroscope sensor to detect gyroscope data (or angular acceleration data), a temperature sensor to detect temperature data, a proximity light sensor to detect infrared light data, green light data, and Ambient light data, etc.

In a possible implementation, the ambient light data can be data detected by the PD when the LED in the proximity light sensor does not emit light; or, the ambient light data can also be collected using sensors such as ambient light sensors. This application implements In the example, there are no restrictions on the method of obtaining ambient light data.

In the first-level wearing detection module 502, the wearable device can use infrared light data and green light data to perform first-level wearing detection, and use temperature data to filter out situations where the user is not wearing the wearable device during the first-level wearing detection process.

In the feature extraction module 503, the wearable device can perform signal filtering on the collected data to filter out noise; further, the wearable device can perform feature extraction on the filtered data to obtain feature values.

Wherein, the characteristic value may include one or more of the following: green light AC component F1, green light DC component F2, red light External light AC component F3, infrared light DC component F4, green light time domain autocorrelation coefficient F5, infrared light time domain autocorrelation coefficient F6, green light frequency domain maximum value F7, temperature mean F8, green light adjacent peak ordinate The mean value of the difference F9, the standard deviation of the ordinate difference between adjacent green light peaks F10, the mean value of the abscissa difference between adjacent green light peaks F11, the standard deviation of the abscissa difference between adjacent green light peaks F12, the green light peak value The mean value of the ordinate F13, the number of peaks in the green light time domain F14, or the mean value of the combined speed F15, etc. For the description of the above characteristic values, please refer to the corresponding embodiment in Figure 7 and will not be described again here.

It can be understood that the 15 characteristic values provided in the embodiment of the present application are only used as an example. The characteristic values may also include other parameter values, which are not limited in the embodiment of the present application.

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

In the wearing status correction module 505, the wearable device can obtain the final wearing detection result by detecting the current business status, detecting the business status transfer situation, and the secondary wearing detection results.

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

It can be understood that 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 a variety of scenarios and improve the accuracy of the wearing detection method. .

Based on the embodiment corresponding to Figure 5, in a possible implementation, the method for performing wear detection by the primary wearing detection module 502 in the wearable device can refer to the embodiment corresponding to Figure 6.

Exemplarily, FIG. 6 is a schematic flowchart of a wearing detection method provided by an embodiment of the present application. As shown in Figure 6, the wearing detection method may include the following steps:

S601. The wearable device acquires infrared light data, ambient light data and temperature data within the first time period.

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

For example, the wearable device can perform the steps shown in S601 when detecting that the user turns on the target function; or the wearable device can also periodically perform the steps shown in S601 according to preset instructions. Among them, 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.

S602. The wearable device determines whether the temperature data meets the preset temperature range.

In the embodiment of the present application, when the wearable device determines that the N pieces of temperature data all meet the preset range, the wearable device can perform the steps shown in S603; or when the wearable device determines that at least one temperature data among the N pieces of temperature data When the preset range is not met, the wearable device can perform the steps shown in S606.

It can be understood that when the user wears the wearable device, the wearable device can detect the body temperature data of the human body, so the wearable device can use the temperature data to exclude scenarios when the wearable device is not in contact with the human body. The preset temperature range may be the body temperature range of the human body in a normal environment.

S603. The wearable device determines whether the average ambient light value is greater than the ambient light threshold.

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

It is understandable that the wearable device can obtain ambient light data to exclude the scenario when the wearable device is not normally worn on the user's wrist. For example, wearable devices can exclude scenarios where the wearable device is placed on an object, or the strap is loose, and the ambient light is strong because the wearable device is not close to the human body.

S604. The wearable device determines whether the average infrared light value is greater than the infrared light threshold.

In the embodiment of the present application, when the wearable device determines that the average infrared light value is greater than the infrared light threshold, the wearable device can perform the steps shown in S605; or, when the wearable device determines that the average infrared light value is less than or equal to the infrared light threshold, the wearable device can The wearable device can perform the steps shown in S606. Wherein, the infrared light threshold can be the value when the infrared signal is reflected back to the wearable device through the human body under normal circumstances, and the infrared light average value and the infrared light threshold can both be current values.

It can be understood that when infrared light is irradiated to the human body, the blood or blood vessels in the skin tissue can absorb part of the infrared light, so that the infrared light data reflected back by the human body is different from the infrared light reflected back when the infrared light is irradiated to other objects. There are large differences in light data, so wearable devices can use infrared light data to rule out scenarios where the wearable device is placed on an object.

S605. The wearable device determines that the first-level wearing detection result is that it is in a wearing state.

For example, when the wearable device is determined to be in the wearing state based on the primary wearing detection, the wearable device may continue to perform the secondary wearing detection based on the embodiment corresponding to FIG. 7 .

S606. The wearable device determines that the first-level wearing detection result is in the unworn state.

In a possible implementation, when the wearable device is determined to be in an unworn state based on the primary wearing detection, the wearable device may perform secondary wearing detection based on the embodiment corresponding to FIG. 7 .

In a possible implementation, when the wearable device is determined to be in an unworn state based on the first-level wearing detection, the wearable device can end the wearing detection process and display the interface as shown in c in Figure 1 . At this time, the wearable device does not need to detect heart rate or blood oxygen, etc., thereby reducing the power consumption of the wearable device.

In a possible implementation, when the wearable device is determined to be in a not-worn state, it can also display prompt information or vibrate or ring to indicate that the user is not currently wearing the wearable device.

It can be understood that 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.

Based on this, the wearable device can use infrared light data, ambient light data and temperature data to conduct initial wear detection to rule out that the wearable device is not in contact with the human body, the wearable device is placed on an object, and the wearable device strap is loose, etc. A variety of unsatisfactory wearing scenarios improve the accuracy of the wearing recognition method.

Based on the embodiment corresponding to Figure 5, in a possible implementation, the method for performing wearing detection by the secondary wearing detection module 504 can refer to the embodiment corresponding to Figure 7.

Exemplarily, FIG. 7 is a schematic flowchart of another wearing detection method provided by an embodiment of the present application. As shown in Figure 7, the wearing detection method may include the following steps:

S701. The wearable device acquires green light data, infrared light data, temperature data, and acceleration data within the second time period.

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

In a possible implementation, when the target data includes green light data, the wearable device can be based on the 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 detect at least one of the infrared light data, temperature data, or acceleration data. Execute the steps shown in S702-S706 below to obtain the secondary wearing detection result.

In this embodiment of the present application, the second time period may be 5 seconds. Specifically, the wearable device can obtain M pieces of green light data, M pieces of infrared light data, M pieces of temperature data, and M pieces of acceleration data within 5 seconds.

In a possible implementation, the wearable device can also acquire M gyroscope data while acquiring M pieces of acceleration data. The acceleration data and/or gyroscope data can be used to detect the motion status of the wearable device.

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

In this embodiment of the present application, the data processing method may include: filtering processing, Fourier transform processing, etc. For example, the wearable device can perform band-pass filtering on green light data, infrared light data, temperature data, and acceleration data to filter noise; and perform Fourier filtering on the filtered green light data and infrared light data respectively. Leaf transform is used to obtain green light data in the frequency domain and infrared light data in the frequency domain.

It can be understood that the wearable device can store the green light data in the time domain before Fourier transformation and the infrared light data in the time domain before Fourier transformation, so that the wearable device can store the green light data in the time domain in a Feature extraction is performed on 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).

In a possible implementation, the wearable device can perform feature extraction on at least one of the green light data, infrared light data, temperature data, or acceleration data that has been processed through the steps shown in S702 to obtain at least one Characteristic values corresponding to the data.

In the embodiment of the present application, 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 auto- Correlation coefficient F5, green light frequency domain maximum value F7, mean value of ordinate difference between adjacent green light peaks F9, standard deviation of ordinate difference between adjacent green light peaks F10, and mean value of abscissa difference between adjacent green light peaks The mean F11, the standard deviation of the abscissa difference between adjacent green light peaks F12, the mean of the ordinate of the green light peak F13, or the number of green light time domain peaks F14; the characteristic value (or called the second Characteristic value) 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; characteristic value obtained based on temperature data (or called the third characteristic value) It may include: temperature average value F8; the characteristic value (or fourth characteristic value) obtained based on the acceleration data may include: resultant velocity average value F15, etc.

Among them, the green light frequency domain maximum value F7 can be: the maximum value of the frequency in the frequency domain coordinate; the average value F9 of the ordinate difference between adjacent green light peaks can be: the green light in the time domain coordinate, the two adjacent peaks The average value of the ordinate difference between them; the standard deviation F10 of the ordinate difference between adjacent peaks of green light can be: the standard deviation of the ordinate difference between two adjacent peaks of green light in the time domain coordinate; The average F11 of the abscissa difference between adjacent peaks of green light can be: the average of the abscissa difference between two adjacent peaks of green light in the time domain coordinate; the standard deviation of the abscissa difference of adjacent peaks of green light F12 can be: the standard deviation of the abscissa difference between two adjacent peaks of green light in the time domain coordinate system; the mean F13 of the ordinate of the green light peak value can be: the green light in the time domain coordinate system, the corresponding peak value of each peak The mean value of the ordinate; the number of green light time domain peaks F14 can be: the number of green light peaks in the time domain coordinate system; the combined velocity mean F15 can be: the corresponding acceleration based on the three axes data obtained.

It can be understood that in the feature extraction module 503, the wearable device can simulate the user's heart rate characteristics by obtaining the characteristic value related to green light; by obtaining the characteristic value related to infrared light, the wearable device can distinguish the wearable device during the wear detection process Whether worn by the user or on other objects; by obtaining characteristic values related to temperature, the effect of temperature on heart rate during the wearing detection process is taken into account; by obtaining characteristic values related to acceleration, the effect of movement on heart rate during wearing detection is taken into account The impact is taken into account.

In a possible implementation, the feature value may also include feature values related to gyroscope data, such as gyroscope data mean, etc., so that the wearable device can use feature values related to acceleration and/or features related to gyroscope data. value to achieve accurate identification of multiple motion states of wearable devices.

S704. The wearable device inputs the feature value into the decision tree classification model to obtain the first detection result.

Wherein, the decision tree classification module may be obtained by training based on sample feature data; the first detection result may include: a probability value P1 used to indicate the wearing state, and a probability value 1- used to indicate the unworn state. P1.

S705. The wearable device inputs the feature value into the LR classification model to obtain the second detection result.

Wherein, the LR classification module can also be obtained by training based on sample feature data; the second detection result can include: a probability value P2 used to indicate the wearing state, and a probability value 1- used to indicate the unworn state. P2.

It can be understood that the wearable device can also input the feature value into other machine learning modules for multiple detections, and the module for wear monitoring may not be limited to the above-mentioned decision tree classification model and LR classification model. For example, the wearable device can input characteristic values into at least three or four different models for wear detection, which is not limited in the embodiments of the present application.

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

Among them, 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 unworn state.

Further, when the wearable device is determined to be in a wearing state based on the secondary wearing detection, the wearable device may continue to perform wearing correction based on the embodiment corresponding to FIG. 8 .

When the wearable device is determined to be in the unworn state based on the secondary wearing detection, the wearable device may continue to perform wearing correction based on the embodiment corresponding to FIG. 8 . Alternatively, when the wearable device is determined to be in the unworn state based on the secondary wearing detection, the wearable device can end the wearing detection process and display the interface as shown in c in Figure 1 . At this time, the wearable device does not need to detect heart rate or blood oxygen, etc., thereby reducing the power consumption of the wearable device.

Based on this, wearable devices can distinguish between users wearing wearable devices and other objects wearing wearable devices through feature extraction of green light data, infrared light data, temperature data, and acceleration data, as well as accurate identification of machine learning modules. Furthermore, the impact of temperature on heart rate and the impact of exercise on heart rate during the wear detection process are taken into account, which significantly improves the accuracy of the wear detection method.

Based on the embodiment corresponding to FIG. 5 , in a possible implementation, the method for the wearing state correction module 505 to perform wearing detection can refer to the embodiment corresponding to FIG. 8 .

Exemplarily, FIG. 8 is a schematic flowchart of yet another wearing detection method provided by an embodiment of the present application. As shown in Figure 8, the wearing detection method may include the following steps:

S801. The wearable device determines whether it meets the unworn service.

The unworn service (or called the second target service) may include one or more of the following, for example: charging services, or pop-up wristband services, etc., which are not limited in the embodiments of this application. Specifically, when the wearable device determines that the unworn service is satisfied, the wearable device can perform the steps shown in S807; or, when the wearable device determines that the unworn service is not satisfied, the wearable device can perform the steps shown in S802. .

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

S802. The wearable device determines whether it meets the wearing business.

The wearing service (or first target service) may include one or more of the following, such as: heart rate detection service, blood oxygen detection service, respiratory rate detection service, services corresponding to each exercise mode, or sleep mode Corresponding services, etc. are not limited in the embodiments of this application. Specifically, when the wearable device determines that the wearing service is satisfied, the wearable device may perform the steps shown in S806; or, when the wearable device determines that the wearing service is not satisfied, the wearable device may perform the steps shown in S803.

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

S803. The wearable device determines whether the time period when the combined speed is greater than the combined speed threshold exceeds the time duration threshold.

For example, when the wearable device determines that the duration for which the combined speed is greater than the combined speed threshold exceeds the duration threshold, the wearable device may perform the steps shown in S804. It is understood that the wearable device can be in motion in this scenario.

Alternatively, when the wearable device determines that the combined speed is less than or equal to the combined speed threshold, or the wearable device determines that the duration during which the combined speed is greater than the combined speed threshold does not exceed the duration threshold, the wearable device may perform the steps shown in S805. It is understood that the wearable device can be in a non-moving state in this scenario.

S804. When detecting the interactive service, the wearable device determines that it is in the wearing state.

It can be 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 can further determine whether an interactive service is detected. Among them, the interactive service can be understood as a service that interacts with users. For example, when the wearable device detects any triggering operation of the user on the wearable device, the wearable device may determine that an interactive service is detected, and perform the steps shown in S806.

S805. The wearable device determines the secondary wearing detection result as the final wearing detection result.

In a possible implementation, when the wearable device determines that one or more of the following conditions are met, the wearable device may determine the secondary wearing detection result as the final wearing detection result. For example, when the wearable device determines that: the unworn service is not satisfied (detected based on the steps shown in S801), the unworn service is not satisfied (detected based on the steps shown in S802), and the motion state is not satisfied (detected based on the steps shown in S803). When the interactive service is not detected (detected based on the steps shown in S804), or the interactive service is not detected (detected based on the steps shown in S804), the wearable device may determine the secondary wearing detection result as the final wearing detection result.

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

For example, when the wearable device determines that the final wearing detection result is that the wearer is in a wearing state, the wearable device may continue to use the wearable device to detect human body characteristics such as heart rate, blood oxygen, and/or respiration rate.

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

For example, when the wearable device determines that the final wearing detection result is that the device is not worn, the wearable device may end the wearing detection process and display the interface shown in c in Figure 1 .

It can be understood that in the embodiment of the present application, there is no specific limitation on the execution order of each step in the embodiment corresponding to Figure 8. Certainly.

Based on this, wearable devices can perform business-based detection to improve the stability of the wear detection method. It can be understood that based on the wearing detection processes described in the corresponding embodiments of Figures 6 to 8, the wearable device can achieve the accuracy of wearing detection in various scenarios.

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

As shown in FIG. 9 , the wearing detection device 90 can be used in communication equipment, circuits, hardware components or chips. The wearing detection device includes: a collection unit 901 and a processing unit 902 . The collection unit 901 is used to support the wearing detection device 90 to perform data collection steps, and the processing unit 902 is used to support the wearing detection device 90 to perform data processing steps.

Specifically, the embodiment of the present application provides a wearing detection device 90 and a collection unit 901 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 wearing the wearable device; processing Unit 902 is used to perform feature extraction on the target data to obtain feature values related to the wearing status; the processing unit 902 is also used to input the feature values into the preset model to obtain the first wearing detection result; the first wearing detection result Indicates whether the wearable device shown is being worn.

In a possible implementation, the wearing detection device 90 may also include a communication unit 903. Specifically, the communication unit 903 is used to support the wearing detection device 90 in performing the steps of sending data and receiving data. The communication unit 903 may be an input or output interface, a pin or a circuit, etc.

In a possible embodiment, the wearing detection device 90 may also include: a storage unit 904. The processing unit 902 and the storage unit 904 are connected through lines. The storage unit 904 may include one or more memories, which may be devices 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 through a communication line. The storage unit 904 may also be integrated with the processing unit 902.

The storage unit 904 may store computer execution instructions for methods in the wearable device, so that the processing unit 902 executes the methods in the above embodiments. The storage unit 904 may be a register, cache, 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 type of static storage device that can store static information and instructions, and the storage unit 904 may be independent from the processing unit 902.

Figure 10 is a schematic diagram of the hardware structure of another wearable device provided by an embodiment of the present application. As shown in Figure 10, the wearable device includes a processor 1001, a communication line 1004 and at least one communication interface (exemplary in Figure 10 Taking the communication interface 1003 as an example for explanation).

The processor 1001 can be a general central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more processors used to control the execution of the program of the present application. integrated circuit.

Communication lines 1004 may include circuitry that communicates information between the above-described components.

The communication interface 1003 uses any device such as a transceiver to communicate with other devices or communication networks, such as Ethernet, wireless local area networks (WLAN), etc.

Possibly, the wearable device may also include memory 1002 .

Memory 1002 may be 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 that can store information and instructions. A dynamic storage device can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), disk storage media or other magnetic storage devices, or can be used to carry or store desired program code in the form of instructions or data structures and can be used by a computer Any other medium for access, but not limited to this. The memory may exist independently and be connected to the processor through a communication line 1004 . Memory can also be integrated with the processor.

Among them, the memory 1002 is used to store computer execution instructions for executing the solution of the present application, and the processor 1001 controls the execution. The processor 1001 is used to execute computer execution instructions stored in the memory 1002, thereby implementing the wearing detection method provided by the embodiment of the present application.

Possibly, the computer execution instructions in the embodiments of the present application may also be called application codes, which are not specifically limited in the embodiments of the present application.

In specific implementation, as an embodiment, the processor 1001 may include one or more CPUs, such as CPU0 and CPU1 in FIG. 10 .

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

A computer program product includes one or more computer instructions. When computer program instructions are loaded and executed on a computer, processes or functions according to embodiments of the present application are 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 or transmitted from one computer-readable storage medium to another computer-readable storage medium, e.g., computer instructions may be transmitted from a website, computer, server or data center via a wired link (e.g. Coaxial cable, optical fiber, digital subscriber line (DSL) or wireless (such as infrared, wireless, microwave, etc.) means to transmit to another website site, computer, server or data center. The computer-readable storage medium can be Any available media that a computer can store or is a data storage device such as a server, data center, or other integrated server that includes one or more available media. For example, available media may include magnetic media (eg, floppy disks, hard disks, or tapes), optical media (eg, Digital versatile disc (digital versatile disc, DVD)), or semiconductor media (for example, solid state disk (solid state disk, SSD)), etc.

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

As a possible design, the computer-readable medium may include compact disc read-only memory (CD-ROM), RAM, ROM, EEPROM or other optical disk storage; the computer-readable medium may include a magnetic disk memory or other disk storage device. Furthermore, any connecting wire may also be appropriately referred to as a computer-readable media. For example, if coaxial cable, fiber optic cable, twisted pair, DSL or wireless technologies (such as infrared, radio and microwave) are used to transmit the Software from a website, server or other remote source, then 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 optical disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray disc, where disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Reproduce data.

Combinations of the above should also be included within the scope of computer-readable media. The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, and all of them should be covered. within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

A wearing detection method, characterized in that the method 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 wearing the wearable device;

The wearable device performs feature extraction on the target data to obtain feature values related to the wearing status;

The wearable device inputs the characteristic value into the 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 method according to claim 1, wherein the characteristic value includes: a first characteristic value obtained based on the green light data, and the first characteristic value includes one or more of the following: green light communication component, green light DC component, green light time domain autocorrelation coefficient, green light frequency domain maximum value, the mean of the ordinate difference between adjacent green light peaks, the standard deviation of the ordinate difference between adjacent green light peaks, green light The mean of the abscissa difference between adjacent peaks, the standard deviation of the abscissa difference between adjacent green light peaks, the mean of the ordinate of the green light peak, or the number of green light time domain peaks.
The method according to claim 1 or 2, characterized in that the target data further includes one or more of the following: infrared light data, temperature data or acceleration data.
The method of claim 3, wherein the characteristic value further includes one or more of the following: a second characteristic value obtained based on the infrared light data, a third characteristic obtained based on the temperature data. value, or a fourth characteristic value obtained based on the acceleration data;

Wherein, the second characteristic value includes one or more of the following: infrared light AC component, infrared light DC component, or infrared light time domain autocorrelation coefficient; the third characteristic value includes: temperature average; the third The four eigenvalues include: mean mean speed.
The method according to claim 3 or 4, characterized in that the wearable device performs feature extraction on the target data, including:

In the case where the wearable device determines that the mean value of ambient light data is less than or equal to the first threshold, the temperature data satisfies the preset temperature range, and/or the mean value of the infrared light data is less than or equal to the second threshold, the The wearable device performs feature extraction on the target data.
The method according to any one of claims 1-5, characterized in that 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;

Wherein, the first target task 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 method according to claim 6, characterized in that 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 exercise status, Or a service for monitoring sleep status; the second target service includes one or more of the following: charging service, or a service for indicating ejection of the wristband.
The method according to claim 6 or 7, characterized in that the wearable device determines that the first target service and/or the second target service is not detected, including: the wearable device determines that the first target service is not detected and/or the second target service is not detected. The first target service, the second target service is not detected, and/or it is detected that the wearable device is not in a motion state.
The method according to any one of claims 6-8, characterized in that the method further includes:

When the wearable device determines that the first target task is detected, the wearable device determines that the second wearable device The 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.
The method of claim 9, further comprising:

When the second wearing detection result is that the wearable device is in the wearing state, the wearable device activates the target function.
The method according to any one of claims 1 to 10, characterized in that the preset model includes: a first preset model and a second preset model, and the first preset model and the second preset model The preset model is different. The wearable device inputs the characteristic value into the preset model to obtain the first wearing detection result, including:

The wearable device inputs the characteristic values into the first preset module and the second preset model respectively, and obtains the first detection result corresponding to the first preset model and the second preset model. The second detection result corresponding to the preset model;

The wearable device obtains the first wearing detection result based on the first detection result and the second detection result.
A wearable device, characterized in that it includes: a processor, the processor is coupled to a memory, the memory is used to store a computer program, and when the processor calls the computer program, the wearable device The method as claimed in any one of claims 1 to 11 is carried out.
A computer-readable storage medium, characterized in that it is used to store a computer program, the computer program including instructions for implementing the method according to any one of claims 1 to 11.
A computer program product, characterized in that the computer program product includes computer program code. When the computer program code is run on a computer, it causes the computer to implement the method according to any one of claims 1 to 11. .