CN114648046A - Motion recognition method, device and wearable device - Google Patents

Abstract

The invention provides a motion recognition method, a motion recognition device and wearable equipment, wherein the method comprises the steps of receiving motion data acquired by an acceleration sensor and a gyroscope sensor; then identifying a target motion type corresponding to the motion data; and controlling to start a sensor group associated with the target motion type when the motion data in the preset time period all meet the preset motion intensity requirement associated with the target motion type. Firstly, motion data are collected only by adopting an acceleration sensor and a gyroscope sensor, after a corresponding target motion type is identified, if the motion data in a preset time period meet the requirements of preset motion intensity and motion duration associated with the target motion type, then a sensor group associated with the target motion type is controlled to be started to monitor real motion, so that motion identification by adopting all sensors associated with motion is avoided, and power consumption for identifying the motion of a user is reduced.

Description

Motion recognition method, device and wearable device

technical field

The present invention belongs to the field of smart wearable technology, and more particularly, relates to a motion recognition method, device and wearable device.

Background technique

At present, the smart wearable devices on the market support the automatic motion recognition function, which uses the sensors on the wearable device to identify the user's activity type in real time, such as walking, running, swimming, cycling, etc. Reminds and records the user's movement.

The principle of this solution is that, for common sports such as walking/running/riding/swimming, the waveform of the electrical signal or optical signal in the wearable device sensor will show certain characteristics and regularities, and the wearable device will walk/run/ Learning and recording the regular characteristics of sensor waveforms such as riding/swimming (target movement). When the user is exercising, the wearable device monitors the sensor signal in real time and compares it with the learned target motion waveform characteristics, thereby identifying whether the user is performing a certain target motion.

The existing motion recognition mode is: all motion-related sensors of the wearable device are turned on and use high sampling frequency for motion recognition. , accompanying children, chasing bus/subway, etc. This sporadic walking will frequently trigger the sensor to enter the acquisition state, resulting in high power consumption.

SUMMARY OF THE INVENTION

The purpose of the embodiments of the present invention is to provide a motion recognition method, device and wearable device, so as to solve the technical problem of high power consumption when the wearable device recognizes the user's motion in the prior art.

In a first aspect, an embodiment of the present application provides a motion recognition method, which is applied to a wearable device, including:

Receive motion data collected by accelerometer sensors and gyroscope sensors;

Identify the target motion type corresponding to the motion data;

When the motion data within the preset time period meets the preset motion intensity requirement associated with the target motion type, the sensor group associated with the target motion type is controlled to be turned on.

In a possible implementation manner, identifying the target motion type corresponding to the motion data includes: comparing the motion waveform feature corresponding to the motion data with the motion waveform feature corresponding to the target motion type.

In a possible implementation manner, the sensor group at least includes: one or more of an acceleration sensor, a gyroscope sensor, a PPG sensor, a GPS sensor, and an air pressure sensor.

In a possible implementation manner, when the motion data in the preset time period all meet the preset motion intensity requirement associated with the target motion type, turning on the sensor group associated with the target motion type includes: according to cadence, speed Or the stroke frequency to determine whether the exercise data in the preset time period all meet the preset exercise intensity requirement associated with the target exercise type.

In a possible implementation manner, controlling to turn on the sensor group associated with the target movement type includes: increasing the sampling frequency of the sensor group associated with the target movement type.

In a possible implementation manner, after the step of controlling to enable the sensor group associated with the target motion type, the method further includes: increasing the sampling frequency of the acceleration sensor and the gyro sensor.

In a possible implementation manner, after the step of controlling to turn on the sensor group associated with the target movement type, the method further includes: displaying a reminder message indicating that the user has entered a movement state.

In a possible implementation manner, the method further includes: detecting a first request to suspend the movement corresponding to the target movement type; in response to the first request, reducing the acceleration sensor, the gyro sensor, and the The sampling frequency of the sensor group.

In a possible implementation manner, the method further includes: detecting a second request for ending the movement corresponding to the target movement type; and turning off the sensor group in response to the second request.

In a second aspect, an embodiment of the present application provides a motion recognition device, including a receiving module and a processing module;

The receiving module is used for receiving the motion data collected by the acceleration sensor and the gyroscope sensor;

The processing module is used to identify the motion target type corresponding to the motion data, and the processing module is also used for when the motion data within a preset time period all meet the preset motion intensity requirements associated with the target motion type. , and control to open the sensor group associated with the moving target type.

In a third aspect, embodiments of the present application provide a wearable device, including an acceleration sensor, a gyroscope sensor, a processor, and a memory, where the processor is respectively connected in communication with the acceleration sensor, the gyroscope sensor, and the memory ,in:

The acceleration sensor is used to collect acceleration data when the user moves,

The gyroscope sensor is used for collecting angular velocity data for movement;

the memory is used to store software instructions;

The processor is configured to execute the instructions in the memory to execute the method according to the first aspect.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium for storing one or more computer programs, where the one or more computer programs include instructions, and when the computer program runs on a computer, The instructions are used to execute the steps of the method described in the first aspect above.

In this embodiment of the present application, the motion data collected by the acceleration sensor and the gyroscope sensor can be received; the target motion type corresponding to the motion data is then identified; the motion data within the preset time period all meet the preset motion intensity associated with the target motion type When the motion duration and motion duration are required, the sensor group associated with the target motion type is controlled to be turned on. First, only the acceleration sensor and gyroscope sensor are used to collect motion data. After identifying the corresponding target motion type, if the motion within the preset time period When the data meet the preset exercise intensity and exercise duration requirements associated with the target movement type, the sensor group associated with the target movement type is controlled to be turned on for real movement monitoring, avoiding the use of all movement-related sensors for movement recognition, thereby reducing to identify the power consumption of the user's motion.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only for the present invention. In some embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

FIG. 1 is a schematic block diagram of a wearable device provided by an embodiment of the present invention;

2 is a schematic flowchart of a motion recognition method provided by an embodiment of the present invention;

3 is another schematic flowchart of a motion recognition method provided by an embodiment of the present invention;

4 is another schematic flowchart of a motion recognition method provided by an embodiment of the present invention;

FIG. 5 is a block diagram of a motion recognition apparatus provided by an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. The components of the embodiments of the invention generally described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations.

Thus, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present invention.

It should be noted that relational terms such as the terms "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any relationship between these entities or operations. any such actual relationship or sequence exists. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article, or device that includes the element.

The basic idea of this application is that, when the user is in the daily life, only the acceleration sensor and the gyroscope sensor are used to collect the motion data, and after identifying the target motion type corresponding to the motion data, if it is determined that the target motion type corresponding to the motion data is determined at a preset time When the motion data in the segment meets the preset motion intensity and motion duration requirements associated with the target motion type, it means that the user has entered the real motion state. At this time, the sensor group associated with the target motion type is controlled to be turned on to monitor the real motion to avoid This enables the user to turn on all motion-related sensors to perform motion recognition when the user is in a non-exercise state in daily life, thereby reducing the power consumption for recognizing the user's motion.

The warm-up adequacy detection method provided in this application can be applied to one or more electronic devices carried by the user. The electronic devices may be mobile phones, wearable devices, portable media players, etc. The wearable devices may include but are not limited to smart Watches, smart bracelets, smart wristbands, smart glasses, rings or helmets, etc.

FIG. 1 shows a block diagram of a wearable device provided by an embodiment of the present application. Wearable device 100 may include one or more processors 101, memory 102, communication module 103, sensor module 104, display screen 105, audio module 106, speaker 107, microphone 108, camera module 109, motor 110, buttons 111, indication device 112 , battery 113 , and power management module 114 . These components may communicate via one or more communication buses or signal lines.

The processor 101 is the final execution unit for information processing and program execution, and can run an operating system or an application program to execute various functional applications and data processing of the wearable device 100 . The processor 101 may include one or more processing units, for example, the processor 101 may include a central processing unit (central processing unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU), an image signal processor (Image Signal Processing, ISP), sensor central processor or communication processor (Central Processor, CP), application processor (Application Processor, AP) and so on. In some embodiments, the processor 101 may include one or more interfaces. The interface is used to couple peripheral devices to the processor 101 to transfer instructions or data between the processor 101 and the peripheral devices. In this embodiment of the present application, the processor 101 is further configured to identify the target motion type corresponding to the motion data collected by the acceleration sensor and the gyro sensor, for example, walking/running/riding/swimming. Specifically, the processor 101 compares the motion waveform feature corresponding to the received motion data with the motion waveform feature corresponding to the target motion type, thereby identifying the target motion type corresponding to the motion data, and the processor 101 is also used to determine Whether the exercise data in the preset time period meets the preset exercise intensity requirements associated with the target exercise type, when it is determined that the exercise data all meet the preset exercise intensity requirements associated with the target exercise type in the preset time period, the processor 101 controls to turn on the sensor group associated with the target movement type.

Memory 102 may be used to store computer-executable program code, which includes instructions. The memory 102 may include a stored program area and a stored data area. The storage program area can store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.), and the like. The storage data area can store data created during the use of the wearable device 100, such as the exercise parameters of the user for each exercise, such as the number of steps, stride, pace, heart rate, blood oxygen, blood sugar concentration, energy consumption (calories), etc. . The memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, universal flash storage (UFS), and the like. In this embodiment of the present application, the memory 102 can store sensor waveform regularity characteristic data corresponding to target movements such as walking, running, riding, and swimming.

The communication module 103 can support the wearable device 100 to communicate with the network and the mobile terminal through wireless communication technology. The communication module 103 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. The communication module 103 may include one or more of a cellular mobile communication module, a short-range wireless communication module, a wireless Internet module, and a location information module. The mobile communication module can send or receive wireless signals based on the technical standards of mobile communication, and can use any mobile communication standard or protocol, including but not limited to Global System for Mobile Communications (GSM), Code Division Multiple Access (CDMA), Code Division Multiple Access 2000 (CDMA2000), Wideband CDMA (WCDMA), Time Division Synchronous Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), LTE-A (Long Term Evolution Advanced), etc. The wireless internet module can send or receive wireless signals via communication networks according to wireless internet technologies, including wireless LAN (WLAN), wireless fidelity (Wi-Fi), Wi-Fi Direct, Digital Living Network Alliance (DLNA), wireless broadband ( WiBro), etc. The short-range wireless communication module can transmit or receive wireless signals according to short-range communication technologies, including Bluetooth, Radio Frequency Identification (RFID), Infrared Data Communication (IrDA), Ultra Wide Band (UWB), ZigBee, Near Field Communication (NFC) , Wireless Fidelity (Wi-Fi), Wi-Fi Direct, Wireless USB (Wireless Universal Serial Bus), etc. The location information module can obtain the position of the wearable device based on the Global Navigation Satellite System (GNSS), which can include Global Positioning System (GPS), Global Navigation Satellite System (Glonass), Beidou satellite navigation system and Galileo satellites One or more of the navigation systems.

The sensor module 104 is used to measure physical quantities or detect the operation state of the wearable device 100 . The sensor module 104 may include an acceleration sensor 104A, a gyroscope sensor 104B, an air pressure sensor 104C, a magnetic sensor 104D, a biometric sensor 104E, a proximity sensor 104F, an ambient light sensor 104G, a touch sensor 104H, and the like. The sensor module 104 may also include control circuitry for controlling one or more sensors included in the sensor module 104 .

The acceleration sensor 104A can detect the acceleration of the wearable device 100 in various directions. The magnitude and direction of gravity can be detected when the wearable device 100 is stationary. It can also be used for recognizing the posture of the wearable device 100, and can be used for switching between horizontal and vertical screens, pedometers and other applications. In one embodiment, the acceleration sensor 104A can be combined with the gyroscope sensor 104B to monitor the stride, stride frequency and pace of the user during exercise.

The gyroscope sensor 104B may be used to determine the motion posture of the wearable device 100 . In some embodiments, the angular velocity of wearable device 100 about three axes (ie, x, y, and z axes) may be determined by gyro sensor 104B.

In this embodiment of the present application, the acceleration sensor 104A and the gyroscope sensor 104B are used for the user to perceive various activities of the user in real time when the user is not in motion, and send the detected sensor waveforms to the processor 101 in real time. After the waveform identifies the target movement type, the acceleration sensor 104A and the gyro sensor 104B are combined with each other to monitor the movement intensity of the target movement type. For example, if the target movement type is walking or running, the cadence can be monitored; the target movement type is cycling. If the target exercise type is swimming, the stroke frequency can be monitored.

The air pressure sensor 104C is used to measure air pressure. In some embodiments, the wearable device 100 calculates the altitude from the air pressure value measured by the air pressure sensor 104C to assist in positioning and navigation.

GPS sensor 104D may be used to record user activity traces to determine user location.

The biometric sensor 104E is used to measure the physiological parameters of the user, including but not limited to a photoplethysmography (PPG) sensor, an ECG sensor, an EMG sensor, a blood glucose sensor, and a temperature sensor. For example, the wearable device 100 may measure the user's heart rate, blood oxygen, and blood pressure data through signals from a photoplethysmography sensor and/or an ECG sensor, and identify the user's blood glucose value based on data generated by the blood glucose sensor. In the embodiment of the present application, the PPG sensor is used to detect the user's heart rate. Specifically, after the PPG sensor is turned on, it can continuously detect the signal data related to the user's heart rate and transmit it to the processor 101, and then the processor 101 uses the heart rate algorithm. Calculate the heart rate value. In the embodiment of the present application, the temperature sensor is used to detect the first temperature of the skin of the user's wrist. Specifically, after the temperature sensor is turned on, it can continuously acquire the temperature data of the skin of the user's wrist and transmit it to the processor 101, and then the processor 101 Calculate the temperature value corresponding to the physical meaning from the electrical signal data of the temperature sensor through the temperature algorithm.

Proximity sensor 104F is used to detect the presence of objects near wearable device 100 in the absence of any physical contact. In some embodiments, proximity sensor 104F may include light emitting diodes and light detectors. The light emitting diodes may be infrared light, and the wearable device 100 uses a light detector to detect reflected light from nearby objects. When the reflected light is detected, it may be determined that there is an object near the wearable device 100 . The wearable device 100 can detect its wearing state using the proximity sensor 104F.

The ambient light sensor 104G is used to sense ambient light brightness. In some embodiments, the wearable device 100 can adaptively adjust the brightness of the display screen according to the perceived ambient light brightness to reduce power consumption.

The touch sensor 104H is used to detect touch operations on or near it, and is also referred to as a "touch device". The touch sensor 104H may be disposed on the display screen 105 , and a touch screen is formed by the touch sensor 104H and the display screen 105 .

The display screen 105 is used to display a graphical user interface (User Interface, UI), and the graphical user interface may include graphics, texts, icons, videos, and any combination thereof. The display screen 105 may be a liquid crystal display (Liquid Crystal Display, liquid crystal display), an organic light-emitting diode (Organic Light-Emitting Diode, OLED) display, and the like. When the display screen 105 is a touch display screen, the display screen 105 can collect a touch signal on or above the surface of the display screen 105 and input the touch signal to the processor 101 as a control signal.

The audio module 106, the speaker 107, and the microphone 108 provide audio functions between the user and the wearable device 100, such as listening to music or talking; for example, when the wearable device 100 receives a notification message from the mobile terminal, the processor 101 controls the audio The module 106 outputs a preset audio signal, and the speaker 107 emits a sound to remind the user. The audio module 106 converts the received audio data into electrical signals and sends them to the speaker 107, and the speaker 107 converts the electrical signals into sound; or the microphone 108 converts the sound into electrical signals and sends them to the audio module 106, and then the audio module 108 converts the sound into electrical signals and sends them to the audio module 106. 106 converts the audio electrical signal into audio data.

The camera module 111 is used to capture still images or video. The camera module 111 may include an image sensor, an image signal processor (ISP), and a digital signal processor (DSP). The image sensor converts the optical signal into an electrical signal, the image signal processor converts the electrical signal into a digital image signal, and the digital signal processor converts the digital image signal into a standard format (RGB, YUV) image signal. The image sensor may be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS).

The motor 110 may convert electrical signals into mechanical vibrations to generate vibration effects. The motor 110 can be used for vibration prompting of incoming calls and messages, and can also be used for touch vibration feedback. The keys 109 include a power-on key, a volume key, and the like. The keys 109 may be mechanical keys (physical buttons) or touch keys. The indicator 112 is used to indicate the state of the wearable device 100 , for example, to indicate the charging state, the change of power, and also to indicate messages, missed calls, notifications, and the like. In some embodiments, the wearable device 100 provides vibration feedback after receiving the notification message from the mobile terminal application.

The battery 113 is used to power various components of the wearable device 100 . The power management module 114 is used to manage the charge and discharge of the battery, and monitor parameters such as battery capacity, battery cycle times, battery health status (whether leakage, impedance, voltage, current, and temperature). In some embodiments, the power management module 114 may charge the battery via wired or wireless means.

It should be understood that, in some embodiments, the wearable device 100 may be composed of one or more of the aforementioned components, and the wearable device 100 may include more or less components than shown, or combine certain components, or split certain components, or different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

In one embodiment, please refer to FIG. 2, which provides a flowchart of a motion recognition method. This embodiment is mainly illustrated by applying the method to the wearable device 100 in FIG. 1 . It should be noted that the motion recognition method of this embodiment is not limited to the specific sequence shown in FIG. 2 and the following. It should be understood that, in other embodiments, the sequence of some steps in the motion recognition method of the present application can be mutually changed according to actual needs. exchange, or some of the steps can also be omitted or deleted. The specific flow shown in FIG. 2 will be described in detail below.

Step S201, receiving motion data collected by an acceleration sensor and a gyroscope sensor. Specifically, the acceleration sensor is always turned on and collects the acceleration of the wearable device in all directions, and the gyro sensor is also always turned on and recognizes the posture of the wearable device. For example, the three axes ( That is, the angular velocity of the x, y and z axes), the acceleration sensor and the gyroscope sensor can all send the collected motion data to the processor of the wearable device.

Step S202, identifying the target motion type corresponding to the motion data. Specifically, the processor of the wearable device can receive and judge the target movement type, such as walking, running, cycling, and swimming, according to the acceleration data collected by the acceleration sensor and the angular velocity data collected by the gyro sensor.

Step S203, when the motion data in the preset time period all meet the preset motion intensity requirement associated with the target motion type, control to open the sensor group associated with the target motion type. Specifically, when the user performs any activity that is suspected to be related to the target movement type while in the daily life, the acceleration sensor and the gyroscope sensor collect the movement data of the activity and send it to the processor of the wearable device. After identifying the target motion type corresponding to the motion data, the acceleration sensor and the gyroscope sensor continue to collect motion intensity data and motion duration data associated with the target motion type and send them to the processor in real time. When the processor determines within a preset time period (for example, 3 minutes) after the target exercise type is recognized In the real running state, the processor controls to open the sensor group associated with the target exercise type, and the sensor group starts to monitor all exercise data related to the exercise. For example, the PPG sensor is enabled to collect the user's heart rate data.

In one embodiment, identifying the target motion type corresponding to the motion data includes: comparing the motion waveform feature corresponding to the motion data with the motion waveform corresponding to the target motion type. Specifically, the memory of the wearable device stores the regular characteristic data of the sensor waveform corresponding to the common motion, and the processor can analyze the motion waveform corresponding to the motion data after receiving the motion data, and compare the analyzed motion waveform with the data stored in the memory. Compare with the sensor waveform regular characteristic data corresponding to the target movement type, so as to identify the target movement type.

In one embodiment, the sensor group includes at least one or more of a PPG sensor, a GPS sensor, and an air pressure sensor.

In one embodiment, when the exercise data within a preset time period meets the preset exercise intensity requirement associated with the target exercise type, turning on the sensor group associated with the target exercise type includes: according to stride frequency, speed or stroke frequency It is determined whether the exercise data in the preset time period meets the preset exercise intensity requirement associated with the target exercise type. Specifically, when the identified target exercise type is walking or running, it is determined whether the exercise data in the preset time period meets the preset exercise intensity requirement associated with walking or running according to the cadence, for example, the identified target exercise type When walking, the preset time period is 5 minutes, and the preset exercise intensity is 80 times/min. It is necessary to judge whether the stride frequency within 5 minutes is greater than 80 times/min. If so, it means that the user has entered a real exercise state. The processor controls to turn on the sensor group associated with the target movement type.

Table 1 shows the conditions to be satisfied by various target movement types when judging whether the user enters the real movement state in the embodiment of the present application.

Table 1

target movement type preset exercise intensity preset time period walk Step frequency>m1 (80 times/min) T1 (=5 minutes) Run Step frequency>m2 (140 times/min) T1 (=3 minutes) ride Speed>m3(6km/h) T1 (=1 minute) swim Stroke frequency>m4 (8 times/min) T1 (=1 minute)

In one embodiment, controlling to turn on the sensor group associated with the target motion type includes increasing the sampling frequency of the sensor group associated with the target motion type. Understandably, when the exercise data in the preset time period meets the preset exercise intensity requirements associated with the target exercise type, it means that the user has entered the real exercise state of the target exercise type. Increasing the sampling frequency of the sensor group while the associated sensor group occurs after the user enters the real exercise state, thereby preventing the user from collecting exercise data at a high sampling frequency before entering the real exercise state, and further reducing the power consumption of the sensor group.

After controlling the step of turning on the sensor group associated with the target motion type, the method further includes: increasing the sampling frequency of the acceleration sensor and the gyro sensor.

In one embodiment, after the step of controlling to turn on the sensor group associated with the target exercise type, the method further includes: displaying a reminder message indicating that the user enters the exercise state. It can be understood that the reminder message can remind the user to enter the real exercise state, and remind the user that the wearable device is about to start monitoring various exercise data of the user in the real exercise state, which improves the user experience.

In one embodiment, referring to FIG. 3, the motion recognition method further includes:

Step S301, detecting a first request to suspend the motion corresponding to the target motion type. Specifically, the first request may be a request generated by the user clicking on the touch screen of the wearable device.

Step S302, in response to the first request, reduce the sampling frequency of the acceleration sensor, the gyro sensor and the sensor group. It is understandable that, after the user pauses the exercise, reducing the sampling frequency can reduce the power consumption of the wearable device.

In one embodiment, referring to FIG. 4 , the motion recognition method further includes:

Step S401, detecting a second request to end the movement corresponding to the target movement type;

Step S402, in response to the second request, the sensor group is turned off.

Referring to FIG. 5 , the present application also provides a motion recognition device 500 , which includes a receiving module 501 and a processing module 502 .

The receiving module 501 is used for receiving motion data collected by the acceleration sensor and the gyroscope sensor.

The processing module 502 is used to identify the motion target type corresponding to the motion data, and the processing module 502 is also used to control the activation associated with the motion target type when the motion data in the preset time period meets the preset motion intensity requirement associated with the target motion type. sensor group.

Embodiments of the present application further provide a readable storage medium, where the readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the foregoing method embodiments can be implemented.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may also be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality and possible implementations of apparatuses, methods and computer program products according to various embodiments of the present application. operate. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executables for implementing the specified logical function(s) instruction. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented in dedicated hardware-based systems that perform the specified functions or actions , or can be implemented in a combination of dedicated hardware and computer instructions.

In addition, each functional module in each embodiment of the present application may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

The above are only preferred embodiments of the present application, and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims (12)

1. a motion recognition method, is applied to wearable device, it is characterised in that the method comprises: Receive motion data collected by accelerometer sensors and gyroscope sensors; Identify the target motion type corresponding to the motion data; When the motion data within the preset time period meets the preset motion intensity requirement associated with the target motion type, the sensor group associated with the target motion type is controlled to be turned on. 2. The motion identification method according to claim 1, wherein identifying the target motion type corresponding to the motion data comprises: combining the motion waveform feature corresponding to the motion data with the motion waveform feature corresponding to the target motion type for comparison. 3 . The motion recognition method according to claim 1 , wherein the sensor group at least comprises: one or more of a PPG sensor, a GPS sensor and an air pressure sensor. 4 . 4. The motion recognition method according to claim 1, wherein when the motion data in the preset time period meets the preset motion intensity requirement associated with the target motion type, the sensor associated with the target motion type is turned on. The group includes: determining whether the exercise data within the preset time period meets the preset exercise intensity requirement associated with the target exercise type according to stride frequency, speed or stroke frequency. 5 . The motion recognition method according to claim 1 , wherein controlling the activation of the sensor group associated with the target motion type comprises: increasing the sampling frequency of the sensor group associated with the target motion type. 6 . 6 . The motion recognition method according to claim 1 , wherein after the step of controlling to enable a sensor group associated with the target motion type, the method further comprises: increasing the acceleration sensor and the gyroscope. 7 . The sampling frequency of the sensor. 7. The motion recognition method according to claim 1, wherein after the step of controlling to turn on the sensor group associated with the target motion type, the method further comprises: displaying a reminder message indicating that the user enters a motion state . 8. The motion recognition method of claim 1, wherein the method further comprises: detecting a first request to suspend the movement corresponding to the target movement type; In response to the first request, the sampling frequency of the acceleration sensor, the gyro sensor, and the sensor group is reduced. 9. The motion recognition method of claim 1, wherein the method further comprises: Detecting a second request to end the movement corresponding to the target movement type; In response to the second request, the sensor group is turned off. 10. A motion recognition device, comprising a receiving module and a processing module; The receiving module is used for receiving the motion data collected by the acceleration sensor and the gyroscope sensor; The processing module is used to identify the motion target type corresponding to the motion data, and the processing module is also used to satisfy the preset motion intensity and motion associated with the target motion type for the motion data within a preset time period. When the time period is required, the sensor group associated with the moving target type is controlled to be turned on. 11. A wearable device, comprising an acceleration sensor, a gyroscope sensor, a processor, and a memory, wherein the processor is respectively connected in communication with the acceleration sensor, the gyroscope sensor, and the memory, wherein: The acceleration sensor is used to collect acceleration data when the user moves, The gyroscope sensor is used for collecting angular velocity data for movement; the memory is used to store software instructions; The processor is configured to execute the instructions in the memory to execute the method according to any one of claims 1-7. 12. A computer-readable storage medium for storing one or more computer programs comprising instructions for executing the rights when the computer programs are run on a computer Steps of the motion recognition method described in any one of requirements 1-7.

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