Method for recognizing target motion pattern and related device

Abstract

Embodiments of the present application provide a target motion mode recognition method and related equipment, the method comprising: obtaining target information, the target information comprising sound wave information of an environment in which a user is located, motion information of the user, and position information of the user; and determining whether to start a target motion mode according to the target information. By using the embodiments of the present application, the target motion mode can be automatically recognized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent wearing, in particular to a target motion mode recognition method and related equipment. BACKGROUND

[0002] Square dance is a kind of dance with rich rhythm performed by residents spontaneously in open space such as square and courtyard for the purpose of keeping fit, usually accompanied by high-decibel and strong-rhythm music accompaniment. Square dance is a kind of exercise form that is popular among the elderly, has wide mass foundation, is practiced frequently and lasts for a long time, and is of great help to the health of the elderly. There are some simple motion recognition on the current smart bracelet, such as outdoor walking, running, cycling, etc., to help automatically and scientifically record the user's exercise habits.

[0003] In the prior art, a commonly used detection mode for starting or ending exercise is a manual detection mode, which is manually set by the smart bracelet touch screen or the mobile phone app to enter or end the exercise detection mode. This scheme is a pure manual operation with high accuracy and will not record invalid data due to false triggering. Although the manual detection mode is convenient, the user is easy to forget to manually start or end the exercise mode, resulting in no data recording or recording of non-square dance mode data for the whole scene.

[0004] In the prior art, another commonly used detection mode for starting or ending exercise is a voice control start mode, which is controlled by the smart bracelet or the mobile phone app to start or end the exercise detection mode through specific voice information set by the system or the user. This scheme is based on voice recognition technology to match and recognize the detected voice signal of the user, thereby controlling the start or end of the exercise detection mode. The main disadvantage of this scheme is that in a noisy environment such as a square, the input voice signal of the user has strong background noise and poor signal-to-noise ratio, thus resulting in difficulty in matching and recognizing the voice signal and low recognition accuracy, thereby causing poor playability of the voice control function, and the problem of data loss caused by user forgetfulness cannot be solved.

[0005] In summary, the current smart device square dance mode is started or ended by manual or voice control, and the smart device cannot automatically recognize the square dance mode. SUMMARY

[0006] The embodiments of the present application provide a target motion mode recognition method and related equipment, which can automatically recognize the target motion mode.

[0007] In a first aspect, embodiments of the present application provide a target exercise mode recognition method applied to a smart wearable device, the method comprising: obtaining target information, the target information comprising sound wave information of an environment in which a user is located, motion information of the user, and position information of the user; and determining whether to start a target exercise mode according to the target information.

[0008] In the embodiments of the present application, a smart wearable device worn on a user can obtain sound wave information of an environment in which the user is located, motion information of the user, and position information of the user, and then determine whether to start a target exercise mode according to the sound wave information of the environment in which the user is located, the motion information of the user, and the position information of the user. Specifically, the smart wearable device determines whether the environment in which the user is currently located is a background environment for performing a target exercise according to sound wave information of the environment in which the user is currently located, determines whether the user is currently performing a motion of the target exercise according to motion information of the user, and determines whether the current position of the user is a location for performing the target exercise according to position information of the user. If the environment in which the user is currently located is the background environment for performing the target exercise, the user is currently performing the motion of the target exercise, and the current position of the user is the location for performing the target exercise, the smart wearable device starts the target exercise mode. For example, when a user is dancing in a square, the smart wearable device obtains square dance music information of an environment in which the user is located, and determines that the current background environment of the user is a square dance background. The smart wearable device obtains motion information of hands and feet of the user, and determines that the motion of the hands and feet of the user is a square dance motion. The smart wearable device obtains position information of the user, and determines that the user is currently located in a square. Thus, the smart wearable device determines that the user is dancing in the square, and starts a square dance mode to record data of the user dancing in the square. In this way, the smart wearable device can automatically recognize a target exercise mode and automatically start the target exercise mode.

[0009] In a possible implementation, the smart wearable device comprises a sound sensor, a motion sensor, and a positioning module and / or a network module, and the obtaining of the target information comprises: obtaining the motion information of the user by the motion sensor, the motion information comprising at least one of motion intensity, motion frequency, motion speed, and motion amplitude; obtaining the position information of the user by the positioning module and / or the network module; and obtaining the sound wave information of the environment in which the user is located by the sound sensor, the sound wave information comprising at least one of energy of a sound wave, frequency of the sound wave, and peak value of the sound wave.

[0010] In the embodiments of the present application, the smart wearable device comprises a sound sensor, a motion sensor, a positioning module and / or a network module; the smart wearable device can acquire sound wave information of an environment in which a user is located through the sound sensor (for example, a microphone); the smart wearable device can acquire motion information of the user through the motion sensor (for example, an inertial sensor); the smart wearable device can acquire position information of the user through the positioning module (for example, GPS, Beidou, etc.) and / or the network module (for example, Wi-Fi, BT, etc.); thus, it can be ensured that the smart wearable device can acquire sound wave information of an environment in which a user is located, motion information of the user and position information of the user. Moreover, the sound wave information comprises at least one of energy of a sound wave, frequency of a sound wave and peak value of a sound wave, and the motion information comprises at least one of motion intensity, motion frequency, motion rate and motion amplitude; thus, the sound wave information of an environment in which a user is located and the motion information of the user can be multi-dimensional feature information, which is beneficial to correctly judging whether an environment in which a user is currently located is a background environment for performing a target motion and whether the user is currently performing a motion of the target motion.

[0011] In a possible implementation manner, the acquiring, by the sound sensor, of the sound wave information of the environment in which the user is located comprises: preliminarily determining whether to start the target motion mode according to the motion information of the user and the position information of the user; and if it is preliminarily determined to start the target motion mode, acquiring the sound wave information of the environment in which the user is located through the sound sensor.

[0012] In the embodiments of the present application, the smart wearable device first acquires motion information of a user and position information of the user, and then preliminarily judges whether to start a target motion mode according to the motion information of the user and the position information of the user, specifically, preliminarily judges whether the user is performing a target motion according to the motion information of the user and the position information of the user; in the case that it is preliminarily judged that the user is performing a target motion, a sound sensor is started, and sound wave information of an environment in which the user is located is collected through the sound sensor; thus, it is further judged whether the user is performing a target motion according to the collected sound wave information in combination with the motion information of the user and the position information of the user, so that it can be avoided that the sound sensor is always started to collect sound wave information, and power consumption is saved.

[0013] In a possible implementation manner, the acquiring, by the positioning module and / or the network module, of the position information of the user comprises: determining the position information of the user through the positioning module and map information; and / or scanning a Wi-Fi hotspot name of a position in which the user is located through the network module, and comparing the Wi-Fi hotspot name of the position in which the user is located with a pre-stored Wi-Fi hotspot name to determine the position information of the user.

[0014] In this embodiment, the user's location information can be determined in several ways. First, the smart wearable device obtains the user's location through a positioning module, and then determines the user's current location information based on the user's location and map information. Second, the smart wearable device has multiple pre-stored Wi-Fi hotspot names. Since Wi-Fi hotspots have service ranges, meaning they can only be scanned by electronic devices within a specific location range, the smart wearable device can scan for Wi-Fi hotspot names at the current location and compare the scanned Wi-Fi hotspot names with the pre-stored Wi-Fi hotspot names. If a matching Wi-Fi hotspot name is found, it means that the smart wearable device is currently within the service range of the Wi-Fi hotspot corresponding to the matching pre-stored Wi-Fi hotspot name, thereby obtaining the user's location information.

[0015] In one possible implementation, determining whether to activate the target motion mode based on the target information includes: determining a first probability based on the sound wave information of the user's environment, wherein if the first probability is greater than a first preset threshold, the user's environment is a target motion environment; determining a second probability based on the user's action information, wherein if the second probability is greater than a second preset threshold, the user's action is a target motion action; determining a third probability based on the user's location information, wherein if the third probability is greater than a third preset threshold, the user's location is a target motion location; and determining a fourth probability of activating the target motion mode based on the first probability, the second probability, and the third probability, wherein if the fourth probability is greater than a fourth preset threshold, the target motion mode is activated.

[0016] In this embodiment, the smart wearable device determines a first probability by judging the probability that the user's current environment is a background environment for target movement based on the sound wave information of the user's current environment. If the first probability is greater than a first preset threshold, the user's environment can be considered as the target movement environment (i.e., the background environment for target movement). The smart wearable device also determines a second probability by judging the probability that the user is currently performing a target movement action based on the user's current body movement information. If the second probability is greater than a second preset threshold, the user's action can be considered as the target movement action (i.e., the target movement action). The smart wearable device further determines the probability that the user's current action is a target movement action based on the user's current position. The system determines the probability that the user's current location is the location for the target movement based on the given information, thus establishing a third probability. If the third probability is greater than a third preset threshold, the user's location can be considered the target movement location. After determining the probability that the user's current environment is the background environment for the target movement, the probability that the user is currently performing the target movement action, and the probability that the user's current location is the target movement location, a fourth probability for activating the target movement mode can be determined through majority voting. If the fourth probability is greater than a fourth preset threshold, the smart wearable device activates the target movement mode, thereby achieving automatic activation of the target movement mode.

[0017] In one possible implementation, after determining that the target motion mode is turned on, the method further includes: reminding the user that the smart wearable device has turned on the target motion mode via voice broadcast or vibration; receiving a confirmation instruction from the user, wherein if the confirmation instruction is to confirm that the target motion mode is not turned on, then the target motion mode is turned off.

[0018] In this embodiment, to ensure the accuracy of automatically identifying the target movement mode, after determining that the target movement mode is enabled, the smart wearable device will also remind the user that the target movement mode has been enabled via voice broadcast or vibration. If the user agrees to enable it, the smart wearable device will continue to enable the target movement mode; if the user does not agree to enable it, the smart wearable device will disable the target movement mode. For example, in square dance mode recognition, if the determination result is square dance movement mode, the smart wearable device will automatically determine that the square dance movement mode is enabled and remind the user that the square dance movement mode is enabled via voice broadcast or vibration, requesting the user to confirm again; if the determination result is not square dance movement mode, the smart wearable device will not enable the square dance movement mode.

[0019] In one possible implementation, after the target motion mode is activated, the method further includes: acquiring target information every preset time interval, and determining whether to activate the target motion mode based on the acquired target information; if it is determined that the target motion mode is activated, remaining silent and maintaining the current state; otherwise, reminding the user that the target motion mode is about to be deactivated through voice broadcast or vibration, and requesting the user's confirmation.

[0020] In this embodiment, the smart wearable device can not only automatically activate the target exercise mode, but also automatically deactivate it. Specifically, it acquires target information every preset time interval and determines whether to activate the target exercise mode based on the acquired information. If the target exercise mode is activated, it remains silent and maintains the current state; otherwise, it reminds the user via voice announcement or vibration that the target exercise mode is about to be deactivated and requests the user to confirm again. For example, when a user is square dancing, the smart wearable device checks and judges at regular time intervals. If the judgment result is that the user is still square dancing, it remains silent and maintains the current state; if the judgment result is that the user is no longer square dancing, it reminds the user via voice announcement or vibration that the square dancing exercise mode is about to be deactivated and requests the user to confirm whether to deactivate it.

[0021] Secondly, embodiments of this application provide a target motion pattern recognition device applied to a smart wearable device. The device includes: an acquisition unit for acquiring target information, the target information including sound wave information of the user's environment, the user's action information, and the user's location information; and a determination unit for determining whether to activate the target motion pattern based on the target information.

[0022] In one possible implementation, the smart wearable device includes a sound sensor, a motion sensor, and a positioning module and / or a network module. The acquisition unit is specifically used to: acquire the user's motion information through the motion sensor, the motion information including at least one of the following: motion intensity, motion frequency, motion rate, and motion amplitude; acquire the user's location information through the positioning module and / or the network module; and acquire sound wave information of the user's environment through the sound sensor, the sound wave information including at least one of the following: sound wave energy, sound wave frequency, and sound wave peak value.

[0023] In one possible implementation, the acquisition unit is specifically used to: preliminarily determine whether to activate the target motion mode based on the user's action information and the user's location information; if it is preliminarily determined that the target motion mode should be activated, then acquire the sound wave information of the user's environment through the sound sensor.

[0024] In one possible implementation, the acquisition unit is specifically used to: determine the user's location information through the positioning module and map information; and / or scan the Wi-Fi hotspot names at the user's location through the network module, and compare the Wi-Fi hotspot names at the user's location with pre-stored Wi-Fi hotspot names to determine the user's location information.

[0025] In one possible implementation, the determining unit is specifically configured to: determine a first probability based on the sound wave information of the user's environment, wherein if the first probability is greater than a first preset threshold, the user's environment is a target motion environment; determine a second probability based on the user's action information, wherein if the second probability is greater than a second preset threshold, the user's action is a target motion action; determine a third probability based on the user's location information, wherein if the third probability is greater than a third preset threshold, the user's location is a target motion location; and determine a fourth probability of activating the target motion mode based on the first probability, the second probability, and the third probability, wherein if the fourth probability is greater than a fourth preset threshold, the target motion mode is activated.

[0026] In one possible implementation, the determining unit is further configured to: after determining that the target motion mode is turned on, remind the user that the smart wearable device has turned on the target motion mode by voice broadcast or vibration; receive the user's confirmation instruction, wherein if the confirmation instruction is to confirm that the target motion mode is not turned on, then the target motion mode is turned off.

[0027] In one possible implementation, the acquisition unit is further configured to: acquire target information once every preset time interval after the target motion mode is activated; the determination unit is further configured to: determine whether to activate the target motion mode based on the acquired target information; if it is determined to activate the target motion mode, remain silent and maintain the current state; otherwise, remind the user that the target motion mode is about to be deactivated through voice broadcast or vibration, and request the user's confirmation.

[0028] Thirdly, embodiments of this application provide a smart wearable device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing steps in the method as described in any one of the first aspects above.

[0029] Fourthly, embodiments of this application provide a chip, including: a processor, configured to call and run a computer program from a memory, causing a device on which the chip is mounted to perform the method as described in any one of the first aspects above.

[0030] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform the method as described in any one of the first aspects above.

[0031] In a sixth aspect, embodiments of this application provide a computer program that causes a computer to perform the method as described in any one of the first aspects above. Attached Figure Description

[0032] FIG. 1A This is a schematic diagram of the structure of the smart wearable device provided in the embodiments of this application.

[0033] FIG. 1B This is a schematic diagram of the software structure of the smart wearable device provided in the embodiments of this application.

[0034] FIG. 1C This is a schematic diagram of the physical components involved in the embodiments of this application.

[0035] FIG. 2 This is a flowchart illustrating a method for identifying target motion patterns provided in an embodiment of this application.

[0036] FIG. 3 This is a schematic diagram of a square dance mode recognition process provided in an embodiment of this application.

[0037] FIG. 4 This is a schematic diagram of a user action recognition process provided in an embodiment of this application.

[0038] FIG. 5 This is a schematic diagram of the structure of a target motion pattern recognition device provided in an embodiment of this application. Detailed Implementation

[0039] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0040] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0041] In this specification, the term "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0042] First, the smart wearable device provided in the following embodiments of this application will be introduced.

[0043] Please see FIG. 1A , FIG. 1A This is a schematic diagram of the structure of the smart wearable device 100 provided in the embodiments of this application.

[0044] The smart wearable device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, a headphone jack 170D, a sensor module 180, buttons 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, a barometric pressure sensor 180C, a magnetic sensor 180D, an accelerometer sensor 180E, a distance sensor 180F, a proximity sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, etc.

[0045] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the smart wearable device 100. In other embodiments of this application, the smart wearable device 100 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0046] Processor 110 may include one or more processing units, such as: application processor (AP), modem processor, graphics processing unit (GPU), image signal processor (ISP), controller, memory, video codec, digital signal processor (DSP), baseband processor, and / or neural network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0047] The controller can serve as the central nervous system and command center of the smart wearable device 100. The controller can generate operation control signals based on instruction opcodes and timing signals to control the fetching and execution of instructions.

[0048] The processor 110 may also include a memory for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. This memory can store instructions or data that the processor 110 has just used or that are used repeatedly. If the processor 110 needs to use the instruction or data again, it can retrieve it directly from the memory. This avoids repeated accesses, reduces the waiting time of the processor 110, and thus improves the efficiency of the system.

[0049] In some embodiments, the processor 110 may include one or more interfaces. Interfaces may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0050] It is understood that the interface connection relationships between the modules illustrated in the embodiments of this application are merely illustrative and do not constitute a structural limitation on the smart wearable device 100. In other embodiments of this application, the smart wearable device 100 may also employ different interface connection methods or combinations of multiple interface connection methods as described in the above embodiments.

[0051] The charging management module 140 receives charging input from a charger. The charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 receives charging input from the wired charger via the USB interface 130. In some wireless charging embodiments, the charging management module 140 receives wireless charging input via the wireless charging coil of the smart wearable device 100. While charging the battery 142, the charging management module 140 can also supply power to the smart wearable device via the power management module 141.

[0052] The power management module 141 connects the battery 142, the charging management module 140, and the processor 110. The power management module 141 receives input from the battery 142 and / or the charging management module 140, providing power to the processor 110, internal memory 121, external memory, display screen 194, camera 193, and wireless communication module 160, etc. The power management module 141 can also monitor parameters such as battery capacity, battery cycle count, and battery health status (leakage current, impedance). In some other embodiments, the power management module 141 may also be located within the processor 110. In other embodiments, the power management module 141 and the charging management module 140 may be located in the same device.

[0053] The wireless communication function of the smart wearable device 100 can be realized through antenna 1, antenna 2, mobile communication module 150, wireless communication module 160, modem processor and baseband processor.

[0054] Antennas 1 and 2 are used to transmit and receive electromagnetic wave signals. Each antenna in the smart wearable device 100 can be used to cover one or more communication frequency bands. Different antennas can also be reused to improve antenna utilization. For example, antenna 1 can be reused as a diversity antenna for a wireless local area network. In some other embodiments, the antennas can be used in conjunction with a tuning switch.

[0055] The mobile communication module 150 can provide wireless communication solutions, including 2G / 3G / 4G / 5G, for use in the smart wearable device 100. 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 via antenna 1, and perform filtering, amplification, and other processing on the received electromagnetic waves before transmitting them to a modem processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modem processor and convert it into electromagnetic waves for radiation via antenna 1. In some embodiments, at least some functional modules of the mobile communication module 150 may be housed in the processor 110. In some embodiments, at least some functional modules of the mobile communication module 150 and at least some modules of the processor 110 may be housed in the same device.

[0056] The modem processor may include a modulator and a demodulator. In some embodiments, the modem processor may be a separate device. In other embodiments, the modem processor may be independent of the processor 110 and may be housed in the same device as the mobile communication module 150 or other functional modules.

[0057] The wireless communication module 160 can provide solutions for wireless communication applications on the smart wearable device 100, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies. The wireless communication module 160 can be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via antenna 2, performs frequency modulation and filtering of the electromagnetic wave signals, and sends the processed signal to processor 110. The wireless communication module 160 can also receive signals to be transmitted from processor 110, perform frequency modulation and amplification, and convert them into electromagnetic waves for radiation via antenna 2.

[0058] In some embodiments, antenna 1 of the smart wearable device 100 is coupled to mobile communication module 150, and antenna 2 is coupled to wireless communication module 160, enabling the smart wearable device 100 to communicate with networks and other devices via wireless communication technology. The wireless communication technology may include Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technologies, etc. The GNSS may include the Global Positioning System (GPS), the Global Navigation Satellite System (GLONASS), the BeiDou Navigation Satellite System (BDS), the Quasi-Zenith Satellite System (QZSS), and / or satellite-based augmentation systems (SBAS).

[0059] The smart wearable device 100 implements display functions through a GPU, a display screen 194, and an application processor. The GPU is a microprocessor for image processing, connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations and for graphics rendering. The processor 110 may include one or more GPUs, which execute program instructions to generate or modify display information.

[0060] Display screen 194 is used to display images, videos, etc. Display screen 194 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), a miniature LED, a microLED, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the smart wearable device 100 may include one or N displays 194, where N is a positive integer greater than 1.

[0061] The smart wearable device 100 can achieve shooting functions through ISP, camera 193, video codec, GPU, display 194 and application processor.

[0062] The ISP (Image Signal Processor) is used to process data fed back from the camera 193. For example, when taking a picture, the shutter is opened, and light is transmitted through the lens to the camera's photosensitive element. The light signal is converted into an electrical signal, and the camera's photosensitive element transmits the electrical signal to the ISP for processing, transforming it into an image visible to the naked eye. The ISP can also perform algorithmic optimization of image noise, brightness, and skin tone. The ISP can also optimize parameters such as exposure and color temperature of the shooting scene. In some embodiments, the ISP can be set in the camera 193.

[0063] Camera 193 is used to capture still images or videos. An object is projected onto a photosensitive element by generating an optical image through the lens. The photosensitive element can be a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the light signal into an electrical signal, which is then passed to an ISP for conversion into a digital image signal. The ISP outputs the digital image signal to a DSP for processing. The DSP converts the digital image signal into image signals in standard RGB, YUV, or other formats. In some embodiments, the smart wearable device 100 may include one or N cameras 193, where N is a positive integer greater than 1.

[0064] Digital signal processors (DSPs) are used to process digital signals. Besides digital image signals, they can also process other digital signals. For example, when the smart wearable device 100 selects a frequency, the DSP performs Fourier transforms on the frequency energy.

[0065] Video codecs are used to compress or decompress digital video. The smart wearable device 100 may support one or more video codecs. Thus, the smart wearable device 100 can play or record videos in various encoding formats, such as Moving Picture Experts Group (MPEG) 1, MPEG 2, MPEG 3, MPEG 4, etc.

[0066] NPU stands for Neural Network (NN) Computing Processor. By borrowing the structure of biological neural networks, such as the transmission patterns between neurons in the human brain, it can rapidly process input information and continuously learn on its own. NPUs can enable intelligent wearable devices to perform applications such as image recognition, facial recognition, speech recognition, and text understanding.

[0067] The external storage interface 120 can be used to connect an external storage card, such as a Micro SD card, to expand the storage capacity of the smart wearable device 100. The external storage card communicates with the processor 110 through the external storage interface 120 to perform data storage functions. For example, music, video, and other files can be saved on the external storage card.

[0068] Internal memory 121 can be used to store computer executable program code, which includes instructions. Processor 110 executes various functional applications and data processing of the smart wearable device 100 by running the instructions stored in internal memory 121. Internal memory 121 may include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback, image playback, etc.), etc. The data storage area may store data created during the use of the smart wearable device 100 (such as audio data, phonebook, etc.). Furthermore, internal memory 121 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0069] The smart wearable device 100 can realize audio functions, such as music playback and recording, through an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, a headphone jack 170D, and an application processor.

[0070] The audio module 170 is used to convert digital audio information into analog audio signals for output, and also to convert analog audio input into digital audio signals. The audio module 170 can also be used for encoding and decoding audio signals. In some embodiments, the audio module 170 may be located in the processor 110, or some functional modules of the audio module 170 may be located in the processor 110.

[0071] The speaker 170A, also known as a "loudspeaker," is used to convert audio electrical signals into sound signals. The smart wearable device 100 can listen to music or make hands-free calls through the speaker 170A.

[0072] The receiver 170B, also known as the "earpiece," is used to convert audio electrical signals into sound signals. When the smart wearable device 100 answers a phone call or voice message, it can do so by bringing the receiver 170B close to the user's ear.

[0073] Microphone 170C, also known as a "microphone" or "voice transducer," is used to convert sound signals into electrical signals. When making a phone call or sending a voice message, the user can speak by bringing their mouth close to microphone 170C, inputting the sound signal into microphone 170C. Smart wearable device 100 may have at least one microphone 170C. In some embodiments, smart wearable device 100 may have two microphones 170C, which, in addition to collecting sound signals, can also perform noise reduction. In other embodiments, smart wearable device 100 may also have three, four, or more microphones 170C, enabling sound signal collection, noise reduction, sound source identification, and directional recording, among other functions.

[0074] The 170D headphone jack is used to connect wired headphones. The 170D headphone jack can be a USB 130 interface or a 3.5mm Open Mobile Terminal Platform (OMTP) standard interface, a CTIA (Cellular Telecommunications Industry Association of the USA) standard interface.

[0075] Pressure sensor 180A is used to sense pressure signals and can convert the pressure signals into electrical signals. In some embodiments, pressure sensor 180A can be disposed on display screen 194. There are many types of pressure sensors 180A, such as resistive pressure sensors, inductive pressure sensors, and capacitive pressure sensors. A capacitive pressure sensor may include at least two parallel plates with conductive material. When force is applied to pressure sensor 180A, the capacitance between the electrodes changes. Smart wearable device 100 determines the pressure intensity based on the change in capacitance. When a touch operation is applied to display screen 194, smart wearable device 100 detects the intensity of the touch operation based on pressure sensor 180A. Smart wearable device 100 can also calculate the touch position based on the detection signal from pressure sensor 180A. In some embodiments, touch operations applied to the same touch position but with different touch operation intensities can correspond to different operation commands. For example, when a touch operation with an intensity less than a first pressure threshold is applied to the SMS application icon, a command to view SMS messages is executed. When a touch operation with a strength greater than or equal to the first pressure threshold is applied to the SMS application icon, the instruction to create a new SMS message is executed.

[0076] The gyroscope sensor 180B can be used to determine the motion posture of the smart wearable device 100.

[0077] The 180C barometric pressure sensor is used to measure barometric pressure.

[0078] The magnetic sensor 180D includes a Hall sensor.

[0079] The 180E accelerometer can detect the magnitude of acceleration of a smart wearable device 100 in various directions (typically three axes). When the smart wearable device 100 is stationary, it can detect the magnitude and direction of gravity. It can also be used to identify the posture of the smart wearable device, and can be applied to applications such as landscape / portrait switching and pedometers.

[0080] Distance sensor 180F is used to measure distance.

[0081] The proximity light sensor 180G may include, for example, a light-emitting diode (LED) and a light detector, such as a photodiode.

[0082] The 180L ambient light sensor is used to detect ambient light intensity.

[0083] The fingerprint sensor 180H is used to collect fingerprints. The smart wearable device 100 can use the characteristics of the collected fingerprints to achieve fingerprint unlocking, accessing app locks, taking photos with fingerprints, answering calls with fingerprints, etc.

[0084] Temperature sensor 180J is used to detect temperature. In some embodiments, smart wearable device 100 uses the temperature detected by temperature sensor 180J to execute a temperature processing strategy.

[0085] Touch sensor 180K, also known as a "touch panel," can be located on display screen 194. The touch sensor 180K and display screen 194 together form a touchscreen, also known as a "touchscreen." Touch sensor 180K detects touch operations applied to or near it. The touch sensor can transmit the detected touch operation to the application processor to determine the type of touch event. Visual output related to the touch operation can be provided through display screen 194. In other embodiments, touch sensor 180K may also be located on the surface of the smart wearable device 100, in a different position than display screen 194.

[0086] The bone conduction sensor 180M can acquire vibration signals. In some embodiments, the bone conduction sensor 180M can acquire vibration signals from the vibrating bone segments of the human vocal cords. The bone conduction sensor 180M can also contact the human pulse to receive blood pressure signals. In some embodiments, the bone conduction sensor 180M can also be incorporated into headphones to form bone conduction headphones. The audio module 170 can parse the voice signals from the vibrating bone segments of the vocal cords acquired by the bone conduction sensor 180M to realize voice functionality. The application processor can parse heart rate information from the blood pressure signals acquired by the bone conduction sensor 180M to realize heart rate detection functionality.

[0087] Buttons 190 include a power button, volume buttons, etc. Buttons 190 can be mechanical buttons or touch-sensitive buttons. The smart wearable device 100 can receive button input and generate key signal inputs related to user settings and function control of the smart wearable device 100.

[0088] Motor 191 can generate vibration alerts. Motor 191 can be used for incoming call vibration alerts or for touch vibration feedback. For example, different vibration feedback effects can correspond to different touch operations applied to different applications (such as taking photos, playing audio, etc.). Motor 191 can also correspond to different vibration feedback effects for touch operations applied to different areas of the display screen 194. Different application scenarios (such as time reminders, receiving messages, alarm clocks, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also be customized.

[0089] Indicator 192 can be an indicator light, used to indicate charging status, power changes, or to indicate messages, missed calls, notifications, etc.

[0090] The SIM card interface 195 is used to connect a SIM card. The SIM card can be inserted into or removed from the SIM card interface 195 to make contact with and separate from the smart wearable device 100. The smart wearable device 100 can support one or N SIM card interfaces, where N is a positive integer greater than 1. The SIM card interface 195 can support Nano SIM cards, Micro SIM cards, SIM cards, etc. Multiple cards can be inserted into the same SIM card interface 195 simultaneously. The multiple cards can be of the same or different types. The SIM card interface 195 is also compatible with different types of SIM cards. The SIM card interface 195 is also compatible with external memory cards. The smart wearable device 100 interacts with the network through the SIM card to realize functions such as calls and data communication. In some embodiments, the smart wearable device 100 uses an eSIM, i.e., an embedded SIM card. The eSIM card can be embedded in the smart wearable device 100 and cannot be separated from the smart wearable device 100.

[0091] The software system of the smart wearable device 100 can adopt a layered architecture, event-driven architecture, microkernel architecture, microservice architecture, or cloud architecture. This application embodiment uses the layered architecture Android system as an example to exemplify the software structure of the smart wearable device 100.

[0092] Please see FIG. 1B , FIG. 1B This is a schematic diagram of the software structure of the smart wearable device 100 provided in the embodiments of this application.

[0093] A layered architecture divides software into several layers, each with a clear role and function. Layers communicate with each other through software interfaces. In some embodiments, the Android system is divided into four layers, from top to bottom: the application layer, the application framework layer, the Android runtime and system libraries, and the kernel layer.

[0094] The application layer can include a series of application packages.

[0095] like FIG. 1B As shown, the application package may include applications such as camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, and SMS.

[0096] The application framework layer provides application programming interfaces (APIs) and a programming framework for applications in the application layer. The application framework layer includes some predefined functions.

[0097] like FIG. 1BAs shown, the application framework layer may include a window manager, content provider, view system, phone manager, resource manager, notification manager, event manager, etc.

[0098] The window manager is used to manage windowed applications. It can retrieve screen size, determine the presence of a status bar, lock the screen, and capture screenshots, among other things.

[0099] Content providers store and retrieve data, making that data accessible to applications. This data may include videos, images, audio, made and received phone calls, browsing history and bookmarks, phone books, etc.

[0100] A view system includes visual controls, such as controls for displaying text and controls for displaying images. View systems can be used to build applications. A display interface can consist of one or more views. For example, a display interface including a text notification icon could include views for displaying text and views for displaying images.

[0101] The phone manager is used to provide communication functions for the smart wearable device 100. For example, it manages call status (including connection, hang-up, etc.).

[0102] The file explorer provides applications with various resources, such as localized strings, icons, images, layout files, video files, and more.

[0103] The notification manager allows applications to display notifications in the status bar. These notifications can be used to deliver informational messages and can disappear automatically after a short pause, requiring no user interaction. Examples include notifications of download completion and message alerts. The notification manager can also display notifications as icons or scrolling text in the top status bar, such as notifications from background applications, or as dialog boxes on the screen. Examples include displaying text messages in the status bar, emitting sounds, vibrating smart wearables, and flashing indicator lights.

[0104] The event manager can be used to determine whether the touch coordinates of a user's touch operation are within the first area when the first control mode is enabled. If yes, the event for that touch operation is reported to the application layer; otherwise, no action is taken on the touch operation.

[0105] The Android Runtime consists of core libraries and a virtual machine. The Android runtime is responsible for scheduling and managing the Android system.

[0106] The core library consists of two parts: one part is the functionalities that need to be called by the Java language, and the other part is the Android core library.

[0107] The application layer and application framework layer run in a virtual machine. The virtual machine executes the Java files of the application layer and application framework layer as binary files. The virtual machine is used to perform functions such as object lifecycle management, stack management, thread management, security and exception management, and garbage collection.

[0108] System libraries can include multiple functional modules. For example: surface manager, media libraries, 3D graphics processing libraries (e.g., OpenGL ES), 2D graphics engines (e.g., SGL), etc.

[0109] The Surface Manager is used to manage the display subsystem and provides the blending of 2D and 3D layers for multiple applications.

[0110] The media library supports playback and recording of various common audio and video formats, as well as still image files. It supports multiple audio and video encoding formats, such as MPEG4, H.264, MP3, AAC, AMR, JPG, and PNG.

[0111] The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, compositing, and layer processing.

[0112] A 2D graphics engine is a graphics engine for 2D drawing. The kernel layer is the layer between hardware and software. The kernel layer contains at least display drivers, camera drivers, audio drivers, and sensor drivers.

[0113] The following example, using a scene of capturing a photo, illustrates the workflow of the software and hardware of the smart wearable device 100 when the first control mode is enabled.

[0114] With the first control mode enabled, when touch sensor 180K receives a touch operation, a corresponding hardware interrupt is sent to the kernel layer. The kernel layer processes the touch operation into a raw input event (including touch coordinates, timestamp of the touch operation, etc.). The raw input event is stored in the kernel layer. The application framework layer retrieves the raw input event from the kernel layer, and the event manager determines whether the touch coordinates are within the first area. If so, it identifies the control corresponding to the raw input event. For example, if the touch operation is a single click and the corresponding control is the camera application icon, the camera application calls the application framework layer's interface to launch the camera application, and then calls the kernel layer to launch the camera driver, capturing still images or videos through camera 193. If not, no processing is performed on the raw input event.

[0115] Please see FIG. 1C , FIG. 1CThis is a schematic diagram of the physical components involved in the embodiments of this application. For example... FIG. 1C As shown, the physical components involved in the embodiments of this application include at least: a sensor module (e.g., an accelerometer, a gyroscope and GPS positioning module, a Wi-Fi module, a microphone, etc.), a processor (e.g., an MPU), and an (internal) memory (e.g., a ROM, etc.). The (internal) memory stores information such as motion information, sound wave information, and a library of algorithms for extracting and matching location information, as well as associated storage of the Wi-Fi hotspot name scanned at any location of the smart wearable device and the location information of that arbitrary location.

[0116] Please see FIG. 2 , FIG. 2 This is a flowchart illustrating a method for recognizing a target motion pattern according to an embodiment of this application. The method is applied to a smart wearable device and includes, but is not limited to, the following steps:

[0117] 201. Obtain target information, which includes sound wave information of the user's environment, user action information, and user location information.

[0118] 202. Determine whether to activate the target motion mode based on the target information.

[0119] Specifically, the background environment of the user can be determined by the sound wave information of the user's surroundings, which can be obtained by the microphone data collected by the smart wearable device; the user's action information can be the action information of key parts of the user (such as hands and feet), which can be used to determine the user's current movement behavior; further, the user's location information is determined based on GPS, Wi-Fi, BT, etc.; based on the user's movement behavior, the background environment of the user, the user's location information, etc., a fusion judgment is performed to evaluate the user's current movement behavior, thereby realizing the automatic recognition of the target movement pattern.

[0120] For example, this application describes a smart wearable device that uses an inertial sensor to calculate motion information of key user body parts (such as hands and feet) during outdoor square dancing, and outputs the user's current movement behavior. This inertial sensor can be an inertial sensor within the smart wearable device or a separate inertial sensor worn by the user. In the latter case, the inertial sensor can transmit the collected data to the smart wearable device. The inertial sensor can be a six-axis inertial sensor. Based on data collected by a microphone, the application outputs the user's current background environment, such as the presence of square dancing music. It also determines the user's location information based on GPS, Wi-Fi, BT, etc. Finally, based on the user's movement behavior, background environment, and location information, a fusion judgment is performed to evaluate the user's current movement behavior, thereby achieving automatic recognition of the square dancing mode.

[0121] In this embodiment, the smart wearable device worn by the user can acquire sound wave information of the user's current environment, the user's action information, and the user's location information. Then, based on the sound wave information of the user's environment, the user's action information, and the user's location information, it determines whether to activate the target movement mode. Specifically, the smart wearable device determines whether the user's current environment is a background environment for the target movement based on the sound wave information of the user's current environment, determines whether the user is currently performing the target movement action based on the user's current body action information, and determines whether the user's current location is a location for the target movement based on the user's current location information. If the user's current environment is a background environment for the target movement, the user is currently performing the target movement action, and the user's current location is a location for the target movement, then the smart wearable device activates the target movement mode. For example, when a user is square dancing in a public square, the smart wearable device obtains the square dance music information of the user's environment and determines that the user's current background environment is a square dance background; the smart wearable device obtains the user's hand and foot movement information and determines that the user's hand and foot movements are square dance movements; the smart wearable device obtains the user's location information and determines that the user is currently in a public square; thus, the smart wearable device determines that the user is square dancing, activates the square dance mode, and records the user's square dance data. In this way, the smart wearable device can automatically identify the target movement mode and automatically activate the target movement mode.

[0122] In one possible implementation, the smart wearable device includes a sound sensor, a motion sensor, and a positioning module and / or a network module. The acquisition of target information includes: acquiring the user's motion information via the motion sensor, the motion information including at least one of the following: motion intensity, motion frequency, motion rate, and motion amplitude; acquiring the user's location information via the positioning module and / or the network module; and acquiring sound wave information of the user's environment via the sound sensor, the sound wave information including at least one of the following: sound wave energy, sound wave frequency, and sound wave peak value.

[0123] The sound sensor can be a microphone. Based on the microphone, sound wave signals from different background environments are pre-collected. These background signals include sound wave data from both the background environment of the moving target and the background environment of the non-moving target (indoor and outdoor). Feature extraction is performed on the sound wave signals from different background environments, such as extracting parameters like sound energy, frequency, and peak value. Based on these extracted parameters, a Gaussian mixture model (GMM) for voiceprint is trained, resulting in the GMM. After acquiring the target information, the sound wave information of the user's current environment is input into the GMM, outputting a classification result of the user's current environment. This classification result includes, but is not limited to, the background environment of the moving target and the background environment of the non-moving target.

[0124] Among them, the motion sensor can be an inertial sensor, which extracts the motion features of the user's key parts, specifically extracting parameters such as motion intensity, motion frequency, motion rate, and motion amplitude. For example, if the key part is the foot, parameters such as stride length, step frequency, impact, and motion intensity are extracted. Based on these extracted parameters, combined with a pre-trained machine learning classification model, the user's current motion is determined. The user's current motion includes, but is not limited to, target motion and non-target motion.

[0125] Specifically, the system uses a positioning module and map information to determine the user's current location. Based on this location information and a pre-trained machine learning classification model, it outputs a classification result for the user's current location, which includes at least one of the following: a location where the user is moving, a location where the user is not moving, or an unknown location. Alternatively, the network module can be a Wi-Fi module. This module scans for Wi-Fi hotspot names at the user's location and compares them with pre-stored Wi-Fi hotspot names in a database to determine the user's current location. Based on this location information and a pre-trained machine learning classification model, it outputs a classification result for the user's current location, which also includes at least one of the following: a location where the user is moving, a location where the user is not moving, or an unknown location.

[0126] For example, when the target activity is square dancing and the target activity mode is square dancing, the sound wave energy, frequency, and peak value from the user's environment are input into the Gaussian mixture model, which outputs a classification result of the user's current environment. This classification result includes, but is not limited to, square dancing environments and non-square dancing environments. The user's action intensity, frequency, rate, and amplitude are input into a pre-trained machine learning classification model, which outputs a classification result of the user's current movement. This classification result includes, but is not limited to, square dancing movements and non-square dancing movements. Finally, the user's current location information is input into the pre-trained machine learning classification model, which outputs a classification result of the user's current location. This classification result includes at least one of the following: square area, non-square area, and unknown area.

[0127] In this embodiment, the smart wearable device includes a sound sensor, a motion sensor, and a positioning module and / or a network module. The smart wearable device can acquire sound wave information of the user's environment through the sound sensor (e.g., a microphone); it can acquire the user's motion information through the motion sensor (e.g., an inertial sensor); and it can acquire the user's location information through the positioning module (e.g., GPS, BeiDou, etc.) and / or the network module (e.g., Wi-Fi, BT, etc.). This ensures that the smart wearable device can obtain sound wave information of the user's environment, the user's motion information, and the user's location information. Furthermore, the sound wave information includes at least one of the following: sound wave energy, sound wave frequency, and sound wave peak value; the motion information includes at least one of the following: motion intensity, motion frequency, motion rate, and motion amplitude. Thus, the sound wave information of the user's environment and the user's motion information can be multi-dimensional feature information, which is beneficial for correctly determining whether the user's current environment is a background environment for target motion and whether the user is currently performing a target motion action.

[0128] In one possible implementation, acquiring the sound wave information of the user's environment through the sound sensor includes: initially determining whether to activate the target motion mode based on the user's action information and the user's location information; if it is initially determined that the target motion mode should be activated, then acquiring the sound wave information of the user's environment through the sound sensor.

[0129] For example, when the system determines that the user is in square dancing mode based on their location information and motion data collected by the motion sensor, it wakes up the microphone and provides voice or vibration prompts to notify the user that the smart device is about to start collecting microphone data. The user is asked to confirm, and the microphone will be enabled by default thereafter.

[0130] In this embodiment, the smart wearable device first acquires the user's action information and location information, and then preliminarily determines whether to activate the target motion mode based on the user's action information and location information. Specifically, it preliminarily determines whether the user is performing a target motion based on the user's action information and location information. If it is preliminarily determined that the user is performing a target motion, the sound sensor is activated to collect sound wave information of the user's environment. Then, based on the collected sound wave information combined with the user's action information and location information, it further determines whether the user is performing a target motion. This avoids continuously activating the sound sensor to collect sound wave information, thus saving power consumption.

[0131] In one possible implementation, obtaining the user's location information through the positioning module and / or the network module includes: determining the user's location information through the positioning module and map information; and / or scanning the Wi-Fi hotspot names at the user's location through the network module, and comparing the Wi-Fi hotspot names at the user's location with pre-stored Wi-Fi hotspot names to determine the user's location information.

[0132] In this embodiment, the user's location information can be determined in several ways. First, the smart wearable device obtains the user's location through a positioning module, and then determines the user's current location information based on the user's location and map information. Second, the smart wearable device has multiple pre-stored Wi-Fi hotspot names. Since Wi-Fi hotspots have service ranges, meaning they can only be scanned by electronic devices within a specific location range, the smart wearable device can scan for Wi-Fi hotspot names at the current location and compare the scanned Wi-Fi hotspot names with the pre-stored Wi-Fi hotspot names. If a matching Wi-Fi hotspot name is found, it means that the smart wearable device is currently within the service range of the Wi-Fi hotspot corresponding to the matching pre-stored Wi-Fi hotspot name, thereby obtaining the user's location information.

[0133] In one possible implementation, determining whether to activate the target motion mode based on the target information includes: determining a first probability based on the sound wave information of the user's environment, wherein if the first probability is greater than a first preset threshold, the user's environment is a target motion environment; determining a second probability based on the user's action information, wherein if the second probability is greater than a second preset threshold, the user's action is a target motion action; determining a third probability based on the user's location information, wherein if the third probability is greater than a third preset threshold, the user's location is a target motion location; and determining a fourth probability of activating the target motion mode based on the first probability, the second probability, and the third probability, wherein if the fourth probability is greater than a fourth preset threshold, the target motion mode is activated.

[0134] For example, the process of determining whether to activate the square dance mode based on target information is as follows: First, the similarity between the user's current environment and the square dance environment is determined using a Gaussian mixture model (i.e., the first probability). If the similarity is greater than a first threshold, the user's background environment is considered a square dance environment. Second, the similarity between the user's actions and square dance movements is determined using a pre-trained machine learning classification model (i.e., the second probability). If the similarity is greater than a second threshold, the user's actions are square dance movements. Third, the similarity between the user's location and the square dance location is determined using a pre-trained machine learning classification model (i.e., the third probability). If the similarity is greater than a third threshold, the user's location is the square dance location. Finally, a majority vote is conducted to determine the similarity between the user's movement pattern and the square dance pattern (i.e., the fourth probability). If the similarity is greater than a fourth threshold, the user is in square dance mode, the smart wearable device activates square dance mode, and the user's square dance data is recorded.

[0135] In this embodiment, the smart wearable device determines a first probability by judging the probability that the user's current environment is a background environment for target movement based on the sound wave information of the user's current environment. If the first probability is greater than a first preset threshold, the user's environment can be considered as the target movement environment (i.e., the background environment for target movement). The smart wearable device also determines a second probability by judging the probability that the user is currently performing a target movement action based on the user's current body movement information. If the second probability is greater than a second preset threshold, the user's action can be considered as the target movement action (i.e., the target movement action). The smart wearable device further determines the probability that the user's current action is a target movement action based on the user's current position. The system determines the probability that the user's current location is the location for the target movement based on the given information, thus establishing a third probability. If the third probability is greater than a third preset threshold, the user's location can be considered the target movement location. After determining the probability that the user's current environment is the background environment for the target movement, the probability that the user is currently performing the target movement action, and the probability that the user's current location is the target movement location, a fourth probability for activating the target movement mode can be determined through majority voting. If the fourth probability is greater than a fourth preset threshold, the smart wearable device activates the target movement mode, thereby achieving automatic activation of the target movement mode.

[0136] In one possible implementation, after determining that the target motion mode is turned on, the method further includes: reminding the user that the smart wearable device has turned on the target motion mode via voice broadcast or vibration; receiving a confirmation instruction from the user, wherein if the confirmation instruction is to confirm that the target motion mode is not turned on, then the target motion mode is turned off.

[0137] In this embodiment, to ensure the accuracy of automatically identifying the target movement mode, after determining that the target movement mode is enabled, the smart wearable device will also remind the user that the target movement mode has been enabled via voice broadcast or vibration. If the user agrees to enable it, the smart wearable device will continue to enable the target movement mode; if the user does not agree to enable it, the smart wearable device will disable the target movement mode. For example, in square dance mode recognition, if the determination result is square dance movement mode, the smart wearable device will automatically determine that the square dance movement mode is enabled and remind the user that the square dance movement mode is enabled via voice broadcast or vibration, requesting the user to confirm again; if the determination result is not square dance movement mode, the smart wearable device will not enable the square dance movement mode.

[0138] In one possible implementation, after the target motion mode is activated, the method further includes: acquiring target information every preset time interval, and determining whether to activate the target motion mode based on the acquired target information; if it is determined that the target motion mode is activated, remaining silent and maintaining the current state; otherwise, reminding the user that the target motion mode is about to be deactivated through voice broadcast or vibration, and requesting the user's confirmation.

[0139] In this embodiment, the smart wearable device can not only automatically activate the target exercise mode, but also automatically deactivate it. Specifically, it acquires target information every preset time interval and determines whether to activate the target exercise mode based on the acquired information. If the target exercise mode is activated, it remains silent and maintains the current state; otherwise, it reminds the user via voice announcement or vibration that the target exercise mode is about to be deactivated and requests the user to confirm again. For example, when a user is square dancing, the smart wearable device checks and judges at regular time intervals. If the judgment result is that the user is still square dancing, it remains silent and maintains the current state; if the judgment result is that the user is no longer square dancing, it reminds the user via voice announcement or vibration that the square dancing exercise mode is about to be deactivated and requests the user to confirm whether to deactivate it.

[0140] Please see FIG. 3 , FIG. 3This is a schematic flowchart of a square dance mode recognition method provided in an embodiment of this application. This method is applied to a smart wearable device and, based on sound wave information from the user's environment, the user's current movement information, and the user's location information, performs a decision-level fusion judgment to ultimately evaluate and vote on the user's current movement, thereby enabling the automatic start or end of the square dance mode. The square dance mode recognition method includes, but is not limited to, the following steps:

[0141] 301. Background environment recognition.

[0142] The background environment recognition operation includes the following sub-steps:

[0143] 3011. Microphone data acquisition.

[0144] Specifically, the microphone built into the smart wearable device collects sound wave signals (sound wave information) from the user's current environment.

[0145] 3012. Extraction of sound wave features (sound wave energy, sound wave frequency, and sound wave peak value).

[0146] Specifically, based on the sound wave signals collected by the microphone, feature extraction of the sound wave signals is performed, such as extracting the sound wave energy, sound wave frequency, and sound wave peak value.

[0147] 3013. Background Environment Classification.

[0148] Specifically, after extracting feature parameters such as the energy, frequency, and peak value of the sound waves in the user's current environment, a similarity matching calculation is performed based on these extracted feature parameters and a Gaussian mixture model. When the similarity score is greater than a set first threshold, the user's environment is determined to be a square dance environment; otherwise, the environment is determined to be a non-square dance environment. Specifically, when the user's environment is a square dance environment, the output is the probability P1 (first probability) of being in square dance mode; when the user's environment is a non-square dance environment, the output is the probability P2 of being in non-square dance mode.

[0149] Among them, the Gaussian mixture model training is based on the parameters such as sound wave energy, sound wave frequency, and sound wave peak value extracted from sound wave signals in different background environments. The sound wave signals in different background environments are obtained based on microphone pre-acquisition, including sound wave signals in square dancing environments and non-square dancing environments.

[0150] 302. User action recognition.

[0151] Based on data collected by accelerometers and gyroscopes, the user's action information is estimated in real time through a multi-source data fusion method. The action information includes action intensity, action frequency, action rate, and action amplitude. Based on the detected user action information and the established user action database, machine learning methods are used to classify the user's current action information, thereby outputting the classification result of the user's current action.

[0152] For details on user action recognition, please refer to [link / reference]. FIG. 4 The user action recognition operation includes the following sub-steps:

[0153] 3021. Motion sensor data acquisition (acquiring accelerometer and gyroscope data).

[0154] Specifically, smart wearable devices collect the user's current gyroscope and accelerometer data through inertial sensors.

[0155] 3022. Motion posture estimation (motion intensity, motion frequency, motion rate, motion amplitude).

[0156] Specifically, the gyroscope and accelerometer data collected within a certain sampling period Δt are used to estimate the user's current action information, which includes parameters such as action intensity, action frequency, action rate, and action amplitude. Here, the threshold of the sampling period can be set to Δt = 1 min.

[0157] 3023. User Action Classification.

[0158] This involves collecting users' square dancing movements to establish a user movement database fM(N), where M is the total number of samples and N is the feature dimension of each sample (i.e., movement intensity, frequency, speed, amplitude, etc.), serving as the training set for user movement classification. Based on this training set, a machine learning classification model is trained using machine learning methods. This model then classifies the user's current movement based on their current movement information. The classification results include square dancing movements and non-square dancing movements, and provide the probability of each classification result. For example, the probability of a square dancing movement (i.e., the probability of a square dancing pattern) is P3 (the second probability), and the probability of a non-square dancing movement (i.e., the probability of a non-square dancing pattern) is P4.

[0159] In summary, the embodiments of this application estimate the user's action information in real time based on data collected by accelerometers and gyroscopes through a multi-source data fusion method. The action information includes parameters such as action intensity, action frequency, action rate, and action amplitude. Based on the detected user's current action information and the established user action database, a machine learning method is used to classify the user's current motion action, thereby outputting the classification result of the user's current motion action.

[0160] 303. User location identification.

[0161] The user location identification operation includes the following sub-steps:

[0162] Data is collected by modules such as 3031, GPS, and Wi-Fi.

[0163] Specifically, the GPS module collects location data from the smart wearable device (i.e., the user), such as geographic coordinate information (longitude, latitude, altitude, etc.); the Wi-Fi module collects the names of Wi-Fi hotspots within a certain range of the user's location.

[0164] 3032. Location estimation.

[0165] Among them, the GPS positioning module of the smart wearable device obtains the geographic coordinates (longitude, latitude, altitude, etc.) of the user's current location, and combines it with the map information embedded in the smart wearable device to obtain the user's location information.

[0166] Alternatively, the smart wearable device can scan and obtain all Wi-Fi hotspot names at any location of the user via its Wi-Fi module, and store them in the device's memory. This means associating the user's location with all Wi-Fi hotspot names at that location. When the Wi-Fi module detects a Wi-Fi hotspot name at the user's current location, it can compare this name with the stored names. The location corresponding to the successfully matched stored Wi-Fi hotspot name is the user's current location.

[0167] 3033. User location classification.

[0168] Specifically, based on the detected geographic coordinates of the user's current location and / or the scanned Wi-Fi hotspot names at the current location, combined with the established Wi-Fi hotspot name database and geographic coordinate information database, machine learning methods are used to classify the user's current location. The classification results of the user's current location include square dancing locations and non-square dancing locations, and the probability of the user's location classification is given. For example, the probability of a square dancing location (i.e., the probability of square dancing mode) is P5 (third probability), and the probability of a non-square dancing location (i.e., the probability of non-square dancing mode) is P6.

[0169] 304. Majority vote.

[0170] Specifically, based on the results of background environment recognition, user action recognition, and user location recognition, the user's movement is classified using a majority voting method. The user's movement classification results include either square dancing or non-square dancing movements.

[0171] If the user's exercise category is square dancing, the smart wearable device will automatically activate the square dancing exercise mode and remind the user through voice broadcast or vibration to confirm that the square dancing exercise mode has been activated, and ask the user to confirm again; if the user's exercise category is not square dancing, the smart wearable device will not activate the square dancing exercise mode.

[0172] After that, the smart wearable device checks and judges every certain time interval (e.g., Δt = 1 minute). If the user's exercise classification result is still square dance exercise, the smart wearable device remains silent and maintains the current state; if the user's exercise classification result changes to non-square dance exercise, the smart wearable device will remind the user through voice broadcast or vibration that it is about to turn off the square dance exercise mode and ask the user to confirm again.

[0173] In summary, the smart wearable device in this application embodiment performs decision-level fusion judgment based on the sound wave information of the user's environment, the user's current action information, and the user's location information, and makes a final evaluation and vote on the user's current movement, thereby realizing the automatic start or end of the square dance exercise mode.

[0174] Please see FIG. 5 , FIG. 5 This is a schematic diagram of the structure of a target motion pattern recognition device 500 provided in an embodiment of this application, which is applied to a smart wearable device. The target motion pattern recognition device 500 may include an acquisition unit 501 and a determination unit 502, wherein the detailed description of each unit is as follows:

[0175] The acquisition unit 501 is used to acquire target information, which includes sound wave information of the user's environment, user action information, and user location information.

[0176] The determining unit 502 is used to determine whether to activate the target motion mode based on the target information.

[0177] In one possible implementation, the smart wearable device includes a sound sensor, a motion sensor, and a positioning module and / or a network module. The acquisition unit 501 is specifically used to: acquire the user's motion information through the motion sensor, the motion information including at least one of the following: motion intensity, motion frequency, motion rate, and motion amplitude; acquire the user's location information through the positioning module and / or the network module; and acquire sound wave information of the user's environment through the sound sensor, the sound wave information including at least one of the following: sound wave energy, sound wave frequency, and sound wave peak value.

[0178] In one possible implementation, the acquisition unit 501 is specifically used to: preliminarily determine whether to activate the target motion mode based on the user's action information and the user's location information; if it is preliminarily determined that the target motion mode should be activated, then acquire the sound wave information of the user's environment through the sound sensor.

[0179] In one possible implementation, the acquisition unit 501 is specifically used to: determine the user's location information through the positioning module and map information; and / or scan the Wi-Fi hotspot names at the user's location through the network module, and compare the Wi-Fi hotspot names at the user's location with pre-stored Wi-Fi hotspot names to determine the user's location information.

[0180] In one possible implementation, the determining unit 502 is specifically configured to: determine a first probability based on the sound wave information of the user's environment, wherein if the first probability is greater than a first preset threshold, the user's environment is a target motion environment; determine a second probability based on the user's action information, wherein if the second probability is greater than a second preset threshold, the user's action is a target motion action; determine a third probability based on the user's location information, wherein if the third probability is greater than a third preset threshold, the user's location is a target motion location; and determine a fourth probability of activating the target motion mode based on the first probability, the second probability, and the third probability, wherein if the fourth probability is greater than a fourth preset threshold, the target motion mode is activated.

[0181] In one possible implementation, the determining unit 502 is further configured to: after determining that the target motion mode is turned on, remind the user that the smart wearable device has turned on the target motion mode by voice broadcast or vibration; receive the user's confirmation instruction, wherein if the confirmation instruction is to confirm that the target motion mode is not turned on, then the target motion mode is turned off.

[0182] In one possible implementation, the acquisition unit 501 is further configured to: acquire target information once every preset time interval after the target motion mode is turned on; the determination unit 502 is further configured to: determine whether to turn on the target motion mode based on the target information acquired; if it is determined to turn on the target motion mode, remain silent and maintain the current state; otherwise, remind the user that the target motion mode is about to be turned off through voice broadcast or vibration, and request the user's confirmation.

[0183] It should be noted that the implementation of each unit can also be referenced accordingly. FIG. 2 to FIG. 5 The corresponding description of the illustrated embodiment. Of course, the target motion pattern recognition device 500 provided in this application embodiment includes, but is not limited to, the above-described unit modules. For example, the target motion pattern recognition device 500 may also include a storage unit 503, which may be used to store the program code and data of the target motion pattern recognition device 500.

[0184] exist FIG. 5 The target motion pattern recognition device 500 described herein can acquire sound wave information of the current user's environment, the user's action information, and the user's location information; then, it determines whether to activate the target motion mode based on the sound wave information of the user's environment, the user's action information, and the user's location information; specifically, it determines whether the user's current environment is a background environment for target motion based on the sound wave information of the user's current environment, determines whether the user is currently performing a target motion action based on the user's current body action information, and determines whether the user's current location is a location for target motion based on the user's current location information. If the user's current environment is a background environment for target motion, the user is currently performing a target motion action, and the user's current location is a location for target motion, then the target motion mode is activated. For example, when a user is square dancing in a square, the system obtains information about the square dance music in the user's environment to determine that the user's current background environment is a square dance background; it obtains information about the user's hand and foot movements to determine that the user's hand and foot movements are square dance movements; it obtains the user's location information to determine that the user is currently in a square; thus, it determines that the user is square dancing, activates the square dance mode, and records the user's square dance data. In this way, it can automatically identify the target movement mode and automatically activate the target movement mode.

[0185] This application also provides a chip, which includes at least one processor, a memory, and an interface circuit. The memory, the transceiver, and the at least one processor are interconnected by a circuit. The at least one memory stores a computer program. When the computer program is executed by the processor, the method flow shown in the above embodiment is implemented.

[0186] This application also provides a computer-readable storage medium storing a computer program, which, when run on a smart wearable device, enables the method flow shown in the above embodiments to be implemented.

[0187] This application also provides a computer program that, when run on a smart wearable device, enables the method flow shown in the above embodiments to be implemented.

[0188] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0189] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this specification can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0190] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0191] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0192] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0193] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0194] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or terminal device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0195] The steps in the methods of this application can be adjusted, combined, or deleted according to actual needs. Furthermore, the terminology and explanations in the embodiments of this application can be referred to the corresponding descriptions in other embodiments.

[0196] The modules in the device of this application embodiment can be merged, divided, and deleted according to actual needs.

[0197] The above description and embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for recognizing target motion patterns, characterized in that, Applied to smart wearable devices, the method includes: Acquire target information, which includes sound wave information of the user's environment, user action information, and user location information; Determine whether to activate the target motion mode based on the target information; Determining whether to activate the target motion mode based on the target information includes: A first probability is determined based on the sound wave information of the user's environment, wherein if the first probability is greater than a first preset threshold, the user's environment is a square dancing environment. A second probability is determined based on the user's action information, wherein if the second probability is greater than a second preset threshold, the user's action is a square dance action; A third probability is determined based on the user's location information, wherein if the third probability is greater than a third preset threshold, the user's location is the square dance location; A fourth probability for activating the square dance mode is determined based on the first probability, the second probability, and the third probability. If the fourth probability is greater than a fourth preset threshold, the square dance environment mode is activated.

2. The method according to claim 1, characterized in that, The smart wearable device includes a sound sensor, a motion sensor, and a positioning module and / or a network module. The acquisition of target information includes: The motion sensor acquires the user's motion information, which includes at least one of the following: motion intensity, motion frequency, motion rate, and motion amplitude. The user's location information is obtained through the positioning module and / or the network module; The sound sensor acquires sound wave information about the user's environment, and the sound wave information includes at least one of the following: sound wave energy, sound wave frequency, and sound wave peak value.

3. The method according to claim 2, characterized in that, The step of obtaining the user's location information through the positioning module and / or the network module includes: The user's location information is determined using the positioning module and map information; And / or scan the Wi-Fi hotspot names at the user's location using the network module, and compare the Wi-Fi hotspot names at the user's location with pre-stored Wi-Fi hotspot names to determine the user's location information.

4. A device for recognizing target motion patterns, characterized in that, The device is used in smart wearable devices and includes: The acquisition unit is used to acquire target information, which includes sound wave information of the user's environment, user action information, and user location information. A determining unit is used to determine whether to activate the target motion mode based on the target information; The determining unit is specifically used to determine a first probability based on the sound wave information of the user's environment, wherein if the first probability is greater than a first preset threshold, the user's environment is a square dance environment; to determine a second probability based on the user's action information, wherein if the second probability is greater than a second preset threshold, the user's action is a square dance action; to determine a third probability based on the user's location information, wherein if the third probability is greater than a third preset threshold, the user's location is a square dance location; and to determine a fourth probability of activating the square dance mode based on the first probability, the second probability, and the third probability, wherein if the fourth probability is greater than a fourth preset threshold, the square dance environment mode is activated.

5. The apparatus according to claim 4, characterized in that, The smart wearable device includes a sound sensor, a motion sensor, and a positioning module and / or a network module. The acquisition unit is specifically used for: The motion sensor acquires the user's motion information, which includes at least one of the following: motion intensity, motion frequency, motion rate, and motion amplitude. The user's location information is obtained through the positioning module and / or the network module; The sound sensor acquires sound wave information about the user's environment, and the sound wave information includes at least one of the following: sound wave energy, sound wave frequency, and sound wave peak value.

6. The apparatus according to claim 5, characterized in that, The acquisition unit is specifically used for: The user's location information is determined using the positioning module and map information; And / or scan the Wi-Fi hotspot names at the user's location using the network module, and compare the Wi-Fi hotspot names at the user's location with pre-stored Wi-Fi hotspot names to determine the user's location information.

7. A smart wearable device, characterized in that, The method includes a processor, a memory, a communication interface, and one or more programs, said one or more programs being stored in the memory and configured to be executed by the processor, said programs including instructions for performing the steps of the method as described in any one of claims 1-3.

8. A computer-readable storage medium, characterized in that, It stores a computer program for electronic data interchange, wherein the computer program causes the computer to perform the method as described in any one of claims 1-3.

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